A number of people suggest there is no difference between academic and organistional research and that conclusions from academia can be easily transported to organisations. I argue that this is untrue for the following reasons:

* In the academic world, we optimise the experimental environment to achieve MAXMINCON: MAXimise variance due to the independent variable(s), MINimise error variance, CONtrol nuisance variables and extraneous variance. But the real world is not some controllable experiment where one can reconfigure the client’s organisation to achieve MAXMINCON. Circumstances change half-way through an exercise (economy, new CEO, etc. – all these impact measurements and interventions).

* The sample sizes available in the real world are often too small to deliver the required power to draw the heady conclusions available in academic research (where we keep on building the sample size until it is large enough)

* Error variances are seldom normally distributed in the real world and are related. Most techniques learned in academic programmes are based on a General Linear Model which when applied in the real world lead to inflated alpha and beta errors – or in English, are often not valid. Different analytical techniques are required in the real world in order to deliver ‘evidence’.

* Conclusions gleaned from academic research are valid under the same controlled environment; making claims that they can be generalised to real world organisational environments is irresponsible. For example, if one finds in academic research that a given competency framework results in higher performance in an experimental group relative to a control group, one cannot simply sell this framework to every organisation claiming it will work as well. Workforces, cultures, regions and buiness units differ.

* In the academic world, even small effect sizes are acceptable as long as the result is significant (say p < .05). Say you find a correlation of r = .20 (p <.01) between engagement and performance. In the academic world, this is a publishable win even though only 4% of the variance in performance is explained by engagement. In the real world, are you really going to suggest to head of HR they should invest in an expensive engagement programme – knowing that engagement accounts for only 4% of performance? I often see this being done in the real world and it is at worst unethical and at best shows non-understanding of the difference between significance and effect size.

Bottom line: One needs to be extremely circumspect about claiming to deliver evidence-based I/O services in the real world.

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They say that when jackals start worrying about the welfare of sheep, you know there’s an election coming. So I guess I was naive to ask a recruitment agent whether he was interested in a joint venture selling my system for accurately identifying the right employee for the job.

The system is simple enough (except for a bit of statistical analysis): you psychometrically test a sample of employees from the role in question. Then you work out which personality traits, competencies and values separate the high from the low performers. New applicants applying for the role then do the same test and you simply select those who look more like your high performers.

This approach gets it right about 70% – 85% of the time which is a hell of a lot better than flipping a coin which is what businesses are effectively doing by using off-the-shelf psychometrics and untrained interviewers. But the proof of the pudding is that clients typically report 15% – 30 performance improvements. Whether this is our system or simply because it makes people pay more attention to new personnel is an interesting debate, but the point is – it gets results!

But perhaps asking a recruiter to partner to sell it was naive. As he pointed out: “Max, your system might reject applicants that I put forward”.

“Well, doh, yes” I said, “Isn’t that the idea? We reject the candidates who look like low performers and progress the ones who look like high performers”.

“But that’s bad business for me because then I need to go back and try to find them a high potential candidate. That’s more work for me”.

“So are you saying you’re happy to take the money from the client even if you aren’t providing them with high performers?”

And that was the end of our conversation.

The bottom line is that good science is effective, but it won’t win the support of intermediaries who make money whether or not they perform.

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Sergey Brin – founder of Google – learned that he could be significantly predisposed to Parkinson’s Disease. When asked whether ignorance might have been better, his response is that knowledge is better than uncertainty since he can now make adjustments to his life to reduce the risk and fund further research. I suspect it is this kind of thinking that puts Google where it is today; it’s all about Knowledge.

Yet, many organisations prefer to either live in ignorance about the risks in their organisations or rely on subjective gut-feel rather than objective measurement to assess it.

Of course the advantage of measuring risk (operational or strategic) is that the act of measurement itself often points towards the very thing that needs to be adjusted to reduce it.

Yet many businesspeople are frightened of measurement. Why? Because it’s benefits are uncertain? Because they are afraid of their inadequacies being exposed? Because they’re afraid of looking foolish because they don’t understand the measurement techniques?

The interesting thing is that both Brin and Gates are inherently measurers, and in both cases, it’s paid when they bother to do it.

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Dr. Max Blumberg :

Sergey Brin – founder of Google – learned that he could be significantly predisposed to Parkinson’s Disease. When asked whether ignorance might have been better, his response is that knowledge is better than uncertainty since he can now make adjustments to his life to reduce the risk and fund further research. I suspect it is this kind of thinking that puts Google where it is today; it’s all about Knowledge.

Yet, many organisations prefer to either live in ignorance about the risks in their organisations or rely on subjective gut-feel rather than objective measurement to assess it.

Of course the advantage of measuring risk (operational or strategic) is that the act of measurement itself often points towards the very thing that needs to be adjusted to reduce it.

Yet many businesspeople are frightened of measurement. Why? Because it’s benefits are uncertain? Because they are afraid of their inadequacies being exposed? Because they’re afraid of looking foolish because they don’t understand the measurement techniques?

The interesting thing is that both Brin and Gates are inherently measurers, and in both cases, it’s paid when they bother to do it.

Dr. Max Blumberg :

The term silo has a well-known negative connotation suggesting uncordinated activity to the detriment of the organization. HR people are particularly keen on silo-busting because it inhibits implementation of best HR practices.

But HR functions can also fall pray to silo-creation if the various specialists functions do not work closely together and lead to unintended consequences. For example in many organizations, employees are rewarded for their individual effort and receive some kind of bonus if the organization as a whole performs well.

However, an employees’ locus of control usually extends to their own work and to that of their team. Unless they are a board-member or the CEO, they can seldom influence the organization as a whole and therefore company-wide bonuses (like share-schemes) are flawed from the start.

A more pragmatic approach is therefore for reward and performance-appraisal specialists to de-silo and work together to create rewards that are a function of individual and immediate team performance.

Such systemic effects can occur between all HR functions (employee development, relations, succession planning, and so on) and will be addressed in future posts.

Dr. Max Blumberg :

Shareholders invest in companies. Companies invest in assets. Shareholders receive the returns generated by these assets.

These assets can be viewed as the well-known “factors of production” required to create wealth. The basis for wealth until the industrial revolution was land. The revolution changed the focus to capital goods and technology which served us well until about the 1980s.

We are now climbing steadily into the knowledge economy and the focus is shifting rapidly towards people as the creators of organizational wealth. It is also argued that talented people are becoming relatively more scarce than capital or technology. (More on this in future posts).

So whereas before, it made sense to consider “return on capital employed” where that capital was land, financial investments, or capital goods, there is an growing need to measure “return on people employed”. As Lowell Bryan of McKinsey puts it, profit per employee.

But whereas it is relatively easy to assign profitability to individual capital goods (machinery), funds, and technology, it is not as easy to determine the profitability generated by each individual in an organization, critical though this knowledge is. Without it, we might be under-investing or over-investing in people. Human capital measurement is therefore critical for maximizing earnings.

Currently, the best way of measuring return on people investments is to do it on a profit centre by profit centre basis. That is, determine how much was invested in people within a given profit centres, and then try to determine how of the profit was due to investments in people as opposed to investments in other intangible assets, or in capital and technology.

In the coming weeks, this blog will examine approaches for teasing apart how much of the profit can be attributed to people as opposed to other assets.

As an aside, investments in people are not really classed as investments at all. They are expensed, probably because organizations cannot “own” people in the same way they own other assets. Organizations rent people and the profits generated by individual is the return on rent paid. Furthermore, it is argued that the disparity between a company’s market value and its book value is due to its intangible assets, one of which are the people that it “rents”.

More on this later.

Dr. Max Blumberg :

A fascinating article on The Manager talks about how to manage salespeople born around 1986 – 2000. In fact, the article content is useful advice for any older person who must manage younger people.

As with most articles about Generation Y, the key message (for me at any rate) is about their loyalty and how much harder it will be to retain them. What are the secrets to retaining them? For one, they are “information” generation to whom the Internet is second nature. Therefore, one way to manage them would be to allow them to reach their own conclusions by letting them seek the information they need. Older team members, on the other hand, are more used to being “told” what to do. In fact, this is another area where the old could profitably learn from the young.

Enjoy the article.