Possibly the most difficult and complex task a manager will have is to motivate others. Motivation is the in-person psychological process that gets people to start a particular activity, put effort into it and sustain that effort over time. Given the multitude of activities that a person does in any period, motivation also describes how a person allocates resources to some activities and not to others. There’s a clear idea therefore about the person choosing one activity over another and choosing the effort to expend on each.

Leadership (and by inference, management) is about influencing a follower towards the leader’s point of view. And mostly that point of view describes the activity to be done, the goals to be achieved and the rewards available if the follower does as the leader bids.

The Need to Predict

Now, all of this is perhaps simple, were it not for the need on the part of the manager to predict how staff members will react to this influencing. Staff react through their behavior (in engaging with the activity), and by their beliefs and attitudes. Beliefs and attitudes determine how much effort will be applied over time. Without prediction, the manager would be working in what engineers term ‘open loop’, without benefit of feedback to tell progress.

And how staff react is determined by their personal characteristics. To predict an outcome, a manager must understand the way in which a person, based on their individual characteristics, will respond and tap one or more to achieve the required end.

At this point many engineering managers switch off. They trained as engineers and not as psychologists. But in reality of course, engineers understand more about this sort of closed loop systems operation than most.

Engineers and Systems

To an engineer, a system is a set of components interconnected for a purpose. The system does something; it exhibits behaviour.

Systems have inputs and outputs. Engineers refer to the relationship between inputs and outputs as the transfer function. In engineering this gives rise to the IPO system under which inputs (I), processes (P) (or transfer functions) and outputs (O) are defined, potentially mathematically.

Systems are rarely left as open loop. Most have some sort of goal seeking arrangement. A desirable output or performance level is defined and a sample of the output is fed back to a comparator at the input. If the performance level is not achieved, the transfer function or the input is modified so that the loop adjusts and the performance is regained once more.

Because engineers design their systems, they tend to know what these components do, how they work as discrete parts and how they behave when faced with an input. But for non-designers, the system is a black box with the inference made about what’s inside from its transfer function and from observation of the relationship between inputs and outputs. To make this inference, the system has to be characterised.

People and Systems

You might have noticed the parallels. Behaviour of people and systems are both defined by characteristics. To make a prediction about how a system will behave, the engineer works with system characteristics. To predict how people will behave, the manager needs to know the people characteristics.

Now all would be simple if the analogy stopped there. But people are complex systems. Their propensity to be motivated depends on a complex set of human characteristics that are highly context specific.

Human Characteristics

Human motivation depends on human needs. Needs are things like the need to achieve, the need for affinity and the need for power. Someone who needs power would likely respond well to a promotion whilst someone with high achievement needs would not be happy about being given a routine job in which to plod along.

Personal motivation also depends on our motives. Implicit motives are things like our values and identity whilst explicit motives are things like our desires to progress at work. Both interact with the person’s perceived abilities and volitional control to help us overcome anomalies such as when we want to do something but perceive we haven’t the ability.

Then there’s more esoteric characteristics like self-efficacy – the competence and confidence we possess to overcome challenges. And even more esoteric ideas like locus of control describing our attitude to blame and ability to act. And more basic characteristics like the skills and the knowledge we bring to the job. Most of us also understand the central role that our general mental ability and our personality have in defining who we are and what we are able to do.

The Case for Psychometrics

All these personal characteristics work to define our ability to be motivated. All can be characterised or metricated. And that metrication is the science of psychometrics. Psychometrics is the measurement of characteristics like knowledge, skills, attitudes, personality traits and intelligence and even practical characteristics like density of friendship networks.

Typically human characteristics are measured with the person’s consent. Current research is also looking at the degree to which one can determine a person’s characteristics from commonly available digital records of human behavior. We all leave long trails though the Internet. And a team from Cambridge University have demonstrated that our human characteristics can be inferred from those trails[1]. They demonstrated accurate automatic determination of characteristics like intelligence, conscientiousness (and other personality attributes) and age. They also predicted much more sensitive characteristics like gender, sexual orientation and whether the person used drugs. If developed further, this perhaps represents the ultimate in black box analysis.

Inferring Performance

So with system characteristics, we can infer the performance of a system under differing and varying conditions. Psychometrics gives the manager the ability to characterise each member of staff. These characteristics give the manager the ability to predict staff behavior, attitudes and beliefs given any employment scenarios.

There are of course two significant contextual differences between human and engineering system that makes prediction of human behavior more difficult.

Complexities

The first is that humans work in teams. And behaviours, attitudes and beliefs are influenced by group norms and interpersonal relationships. The second is that humans change. Motivation is a dynamic process that changes over time. The motivational force that drives what people do depends on an aggregation of all different motivational forces from various work incidents and from subconscious motives and reactions to events. So whilst the manager may want to set goals and control progress in the long term, much happens day by day.

Are we therefore to conclude that because of this added complexity, psychometrics should be abandoned and managers should return to crude heuristics? Certainly not. In the Enlightenment, we replaced tradition with reason. And from there we now predict the presence of the Higgs Boson. Our science has come a long way. Initially of course, society thought that all that there was to know would soon be known. In fact we’ve come to understand that there is an ever-expanding knowledge to be had. Psychometrics is not perfect – but it’s better than crude rules and open loop management systems.

The Case for Using Psychometrics

Managers should therefore employ psychometrics and strive to predict how their staff will behave in differing work scenarios. It may well be that predictions will be inaccurate from time to time but from these predictions, managers will gain an increased understanding of the management task. They’ll be able to expand their use of human characteristics from motivation to recruitment, change management and technology implementation.

More significantly, they’ll be able to close the management loop, set goals, get action and celebrate the resulting rewards.

1. Kosinski, M., Stillwell, D., & Graepel, T. (2013). Private traits and attributes are predictable from digital records of human behavior. PNAS Proceedings Of The National Academy Of Sciences Of The United States Of America, 110(15), 5802-5805.