Posts filed under ‘Measuring HR’

Why are women better leaders than men?

What makes a good leader? This is a hotly discussed question and also when the topic falls upon differences in the effectiveness between male and female leaders. Emotions have so far driven these conversations, but we are now beginning to get some data to support a more informed debate.

Personally, when it comes to these types of conversations I like to let the data speak; what do we know about the difference in leadership effectiveness between men and women? The data I will share first come from 360-evaluations, which we have collected in collaboration with our partner Zenger Folkman, who are also writing extensively about this. 360-evaluations measure the judgment of a leader’s leader, peers, direct reports and other relevant input givers. We ask these individuals to rate each leader’s effectiveness by evaluating how well he or she performs on 16 competencies, which have proven to be the most important to overall leadership effectiveness. Also it is worth bearing in mind, that 360-surveys have the highest predictor for success within an organisation compared with other assessment forms.

In a specific survey of 7,280 leaders from a mix of public and private organisations across the world give some really interesting insights into this area; in some ways it confirms things we assumed about men and women leaders in the workplace but also holds some surprises.

The main conclusion is that female leaders score significantly higher than their male counterparts on 360-degree evaluations and that this gap widens the more senor the leaders are. This is shown in the below table.

Men vs Women

Interestingly, looking deeper into the data, of the 16 competencies, which the leaders are measured upon, women leaders score significantly higher on 12 of them (mostly on Takes Initiative, Practice Self-Development, Displays High Integrity & Honesty as well as Drives For Results) and only significantly lower on one (Develops Strategic Perspective). So the typical stereotypes, which would have us believe that women leaders excel at the “soft” competencies are not completely true; women actually do really well on many of the so-called hard competencies as well.

Another analysis of the data provides an interesting insight; the relative effectiveness of women leaders appears to improve over time. At the beginning of their respective careers there appear to be little difference between men and women, but over time this women are perceived in an increasingly positive way and more effecting than their male counterparts. This continues until they reach their 60’s, when the gap begins to narrow.

Our data is just one out of many backing up this conclusion. One study, led by Professor Øyvind Martinsen, head of Leadership and Organizational Behavior at the BI Norwegian Business School, assessed in a study the personality and characteristics of nearly 3,000 managers. In nearly all areas, the study concluded that women were better leaders than their male colleagues. Women outperformed men in four of the five categories: initiative and clear communication; openness and ability to innovate; sociability and supportiveness; and methodical management and goal-setting. This corresponds well with our own findings. Interestingly, men did better than women at dealing with work-related stress and they had higher levels of emotional stability.

When we look into the split between male and female leaders, it is unfortunately still the case that women represent a relatively small part of the overall leadership population – especially at top level. Why is that the case  if they are better than their male counterparts? Certainly, discrimination is a potential explanation but frankly, I don’t really think the gap can be explained by conscious discrimination. If we are looking at that direction, I think it has more to do with unconscious bias but again, I think this is only one (small) element of the explanation. Let’s look more into that in another blog post.

Why might it be true that women are better than men when it comes to leadership? I think there might be many avenues of answers to this. Let me offer four different ones;

  1. It might be that women receive higher scores on 360’s because of bias. These bias could come from believing that women are better at ‘people issues’, that they (the women) must be good if they have made it into leadership or other cognitive bias.
  2. Another explanation is that because so few women are selected to leadership positions, only the very best are selected – a natural selection bias if you will – which will mean that women are not better than men as leaders but because the quality selected means that the female appear better. Had there been an equal amount of men and women the scores would have been the same.
  3. Another reason might be that women has to be better leaders than men to be selected as leaders thus making them better. This suggests that there are two levels of entrance into leadership; one for men and one for women.  This was explained by a group of women in a Harvard Business Review article with statements such as “We need to work harder than men to prove ourselves.”, “We feel the constant pressure to never make a mistake, and to continually prove our value to the organization.”
  4. A fourth reason could lie in an underlying intelligence of succeeding as leader i.e. being good in emotional intelligence. There is plenty of evidence to suggest that women score higher than men generally on emotional intelligence competencies. Perhaps traditional cognitive intelligences which men traditionally has performed well in account for less in terms of leadership effectiveness today.

The above suggestions are just speculations with limited theory and data to support. But I would be very interested in your possible answers to this.

So what does this mean? First of all, most organisations find it hard to attract and/or develop great leaders. I believe leadership is a fundamental element in any successful organisation. Great leaders creates great business results. So from a pure business perspective, organisations should attract qualified leaders from the largest possible pool of talent which includes of course all women. Secondly, whatever practice or cultural element is creating this situation, you should know that this is holding back your organisation. Work on your culture to eliminate any processes, behaviours and reasons for this.

A final note: More companies are implementing unconscious bias training in recruitment as a mean to hire more female leaders. The argument goes that it is due to unconscious bias that women are not selected as leaders and with proper training men will become aware of these bias an adjust accordingly. Having spent some time trying to find evidence to support the effectiveness of this kind of training – and being unsuccessful at that – I would suggest that other methods should be used instead. It is unclear what exact bias the training is targeted, how the training will sustain such a radical change and how the effect is measured.

03/05/2018 at 12:22 1 comment

Six must-have competencies in a world-class analytics team

Succeeding with workforce analytics is difficult. It requires a mix of skills not found in one person only, and you should not assume, that you can do it on your own. We are all decent at most things but really only good in a few. You should therefore assemble a team, which has a multiple of superheroes each with a superpower of their own.

I described this in a previous post, where I suggested six competencies a superhero analytics team should have:

  1. Strong data management skills
  2. Captivating storyteller
  3. Understand the business
  4. Ability to visualize your results
  5. Strong psychological skills
  6. Excellent statistics and numbers skills

But what happens if just one of those skills are not present? Can’t we manage anyway? My answer is no. If just one of the skills is missing from the team, six outcomes are possible – each with a disastrous outcome – as shown in the figure below:

Superhero Analytics Team competencies

In essence, if you:

  1. have no good data, you will not be able to perform analytics. It is as the old saying goes: crap in – crap out. If you do not have good data, it is sometimes better not to do analytics.
  2. lack of storytelling abilities, the message will nog. As Tom Davenport describes: “Narrative is the way we simplify and make sense of a complex world” and it is the way messages are most effectively conveyed and the best way to get people to change (which is the ultimate goal of analytics).
  3. have no business acumen will mean that your team will perform excellent analytics on the wrong issues. Workforce Analytics should help decision making on vital must win battles for your organization. Understanding the business is vital to understand what those must win battles are.
  4. are not able to do visualizations you will bore your audience. Data and numbers are boring (and I am a numbers guy), but data and numbers effectively conveyed through visualization
  5. lack psychological skills you will misunderstand your findings, be unable to convert your information to knowledge and be subject to important challenges such as bias, cognitive dissonance, imposter syndrome etc.
  6. have poor numbers and statistics abilities, your analysis will just be plain poor. You can get really far with simple regression-, factor- and t-test analysis skills but at other times, you will need skills in more advanced statistics when the data set become really big or you are looking for more predictive analysis.

Analytics require a lot of skills and abilities – superpowers if you like. The best way to ensure that you have the right ones to deliver on your task is to assemble the best team. An analytics superhero team.

06/06/2016 at 11:16 4 comments

Cost and value – the difference that makes a successful workforce analytics function

CostValue

My second take-away from the workforce analytics case-studies and conferences I have heard, attended and experience over the last year is what I call the confusion of cost savings and value creation.  While the good news is that we are starting to deliver, my warning would be, that we should be careful not to deliver on the wrong things – or more important; on the least value added things. Let me elaborate.

At most conferences and in most reports by leading consultants, we are being presented with a maturity model, which illustrates activities from the least mature to the most. It goes something like this; first there is some descriptive methods, such as reporting and trend analysis, then maturity increases and the methods goes on to being predictive and prescriptive and finally the maturity goes on to machine learning or something like this. One example of such as maturity model is from IBM shown below (but frankly they all look very similar).

WorkforceAnalyticsMaturity

I fully agree with the idea of maturity and that prescriptive analysis is better than descriptive. It is also a good way to illustrate this maturity journey albeit they could be a little more operational in terms of assessing level of maturity and suggested next step depending upon current level. However there is one dimension missing from this picture: the focus of the analysis itself. All good at being mature of the methods but we must also assess maturity on the object of our analysis.

In rough terms: If it the focus is on cost savings elements then the potential shareholder creation will always be limited (it will be the net present value of the cost savings minus the investment). If the focus is directly on creating customer value/business value then the potential shareholder creation will be great.

In fact, I will propose, that there is more value added in doing predictive analysis on a business matters than doing prescriptive analysis on an HR matter.

To be clear, let me come with a few examples. If you are analyzing sickness, employee turnover, recruitment effectiveness or training effectiveness, you are really at the cost savings end of the spectrum. There is no harm (at all) in coming up with evidence based suggestions to reducing employee turnover. Indeed for many companies there are significant money to save in doing that. It is however still cost savings and it won’t get you a seat at the table. So do some of that, but don’t put all your efforts there.

WorkforceAnalyticsValueMaturity

At the other end of the spectrum, you are adding workforce data to customer/profit/sales/other business data. Here the examples are less generic as they are (should be) tailored to each company’s specific strategy and situation. A few I have witnessed/been part of: Finding which service behavior adds the most impact to customer experience/satisfaction, and which training programs are most effective in embedding this behavior. In this example, the workforce data leads straight on to more sales and higher profits. Another example; how does change load (employees’ load of change relative to ability to handle change) impact strategy execution.

These two specific examples had a heavy use of non-workforce data as part of the analysis. In fact, you can test your value maturity on the cost/value axis by testing how much business data you have compared with how much workforce data. If you only work with workforce data, you are probably focusing on cost savings rather than value creation.

Some will sometime argue that “Attracting talent is always business critical and therefore what we do is value creating”. That may be true in some cases but they are missing the point. Indirect value creation is important but less straight forward to prove. In most cases they misunderstand HR processes with business matter.

I therefore suggest that we add a dimension to our maturity models. Perhaps some large consultancy company can show how this may look?

26/04/2016 at 12:48 Leave a comment

How you create a Superhero analytics team

Analytics superhero

Analytics is not easy. Or to be clear; getting the most business value from your data is not easy. There is so much to get right before you can unlock the hidden gems which are unquestionable lying deep inside your databases.

Just consider the journey: First you must work on something which is highly important and valuable to the business. Then you must use all available data to make analysis which gives new insights and knowledge upon which decisions can be made. Finally, and most importantly, you must convince the decision makers to make these decisions based upon your analysis and to do that you must present it right, show them the value, consequences and risk of failure. Only then will your work bear any fruit.

But be careful – don’t assume that decision makers will believe you capable of doing all of that on your own. This reminds me when my daughter was younger, I one day tried to convince her, that I was a superhero. She verged on believing me but when I told her that I could do anything, she said “Now I know you are not telling the truth, dad. Everybody knows that superheros only have one superpower”.

I was not able to fool my daughter and neither will you be able to convince your head of HR or your CEO, that you possess all workforce analytics abilities at expert level.

I therefore propose that you assemble a team for you analytics which has a multiple of superheroes each with a superpower of their own. Specifically I suggest six competencies (in random order);

Analytics HR Team Skills

1. Excellent statistics and numbers skills

There is no getting around that good analytics requires excellent statistics and numbers skills. You can get far with doing simple regression-, factor- and t-test analysis but at other times, you will need skills in more advanced statistics when the data set become really big or you are looking for more predictive analysis.

2. Strong data management skills

Let’s be frank; you will get nowhere in your analytics journey if your data is not clean, good and have a strong governance structure around it. Those and many other data management issues are essential for good analytics. For some it is mundane work, for others it is a passion. If it is the former for you, get somebody on board for whom it is a passion. It is that important.

3. Captivating storyteller

Analytics – even predictive – will only add value if a decision is made on the back of it. It sounds trivial, but data does not speak for itself and to move a decision maker into making a decision you must create a compelling story around it. Sounds easy? For some it is for others it is not. Find somebody who does this well. It will make a big difference to the value of your analysis.

4. Ability to visualize your results

Studies on ex. cognitive load show that if you give a decision makers too much data, they will either not make any decision or make the wrong one. Visualization techniques is a powerful tool to present complex data in a simple and easy-to-understand way. This is not about making your pie charts 3D. It is a whole different category and an art more than a skill.

5. Strong psychological skills

There are so many reasons why I feel that strong psychological skills may be the most essential of all six skills. Just to name two reasons here; it is partly because you will understand how to make more impact with you data if you understand terms such as cognitive dissonance, bias, over-conficence etc. And also because your data has not meaning if you don’t understand how to convert information to knowledge which in essence requires a deep understanding of psychology.

6. Understand the business

A final skill which I find is most often not present as much as it should is the simple but powerful skill of actually understanding the business.  This requires you to fully understand what is the customer value proposition is, what the strategy is (the must win battles), key differentiating factors, financial situation and more. I mean really understand the business.

Analytics require a lot of skills and abilities – superpowers if you like. The best way to ensure that you have the right ones to deliver on your task is to assemble the best team. An analytics superhero team.

02/06/2014 at 11:44 6 comments

Is HR evidence based? Seriously, are you kidding me?

Is HR Evidence Based - Are you kidding me

The hype surrounding Workforce analytics, metrics and Big Data in HR has really increased over the last 12 months. Every conference, article, blog and strategic initiative is filled with buzz words around data and fact-based HR. So much so that Workforce analytics now is in danger of overselling itself. To outsiders it may appear that HR is becoming more evidence-based in its approach. Unfortunately this is not the case.

When I completed my master in psychology 10 years ago I read a book called “Evidence-Based Practices in Mental Health” by Norcross, Beutler and Levant. It is a great book, which argues for a more evidence-based approach for psychology. Because to be frank, it really isn’t that evidence based. Take an example. If a person has mild depression there are many potential approaches to take. Lets take just a few; therapy (cognitive), medication (ex. Prozac), therapy (behavioral), meditation, physical exercise, therapy (psychodynamic), mindfulness therapy, self-help books and many more. You would think – and hope – that the advise and subsequent treatment this person would get would be based upon evidence. What works. For example, there is a lot of  evidence which suggests that everything else being equal that cognitive behavioral therapy is significantly more effective than both medication and psychodynamic therapy for treating mild depression on its own. But if a patient happens to stumble upon a therapist who focus primarily on psychodynamic therapy – that’s what the patient will get. Psychology is a lot of things, but evidence based it is not.

With HR it is the same. The options we chose and our design of solutions are not based upon evidence but instead on intuition, personal preferences and habits. And often not the right ones.  This is very problematic and with HR there is the added problem that we still don’t even know what works (in medicine and health care some evidence is available). What works and what works best are two questions w cannot answer.

Why is HR not more evidence based? I think there are four reasons;

  1. We don’t share data. There is too little data and evidence out there. Some is being produced but very little shared. A study from 2006 published in American Psychologist, showed that almost three-quarters of researchers who had published a paper in a high-impact psychology journal had not shared their data. This is not just an issue at universities. When I see Google and other leading companies in Workforce Analytics talk about great findings they never share their data. At conference when speakers talk about their great internal studies they never share data. And frankly it is not that sensitive. It really isn’t. I hope they don’t share because they wrongly believe their data is sensitive rather than the studies are really not that good. We must produce better evidence.
  2. Lack of the right competencies. Working with EB-HR mean that you have to understand what evidence is and how to get it. Many in HR wrongly believe that evidence means 100% certainty or proof of something. That is not correct. Evidence is always about probabilities and assumptions. Always. Even in natural science. Also, it is also not just about quantitative data but also includes more fluffy things such as qualitative data (my favorite) and experience. But too few Being able to design and implement a executive leadership program using an evidence based approach is something too few in HR can do.
  3. Lack of the right mindset. As with EB-mental health, many in HR don’t really know why this is important. “We have a talent program, it works, people are happy about it and talents are staying at the company, why should we used another approach to our program?”. While it may sound tempting to think like this – and most in HR do – it is missing the point completely. HR must – as any other support function or organization – be as effective and efficient as possible. The only way it is possible to tell if HR is that is to measure and use evidence to improve. The only way!
  4. We can get away with not being evidence based. Our primary stakeholders (managers, employees and shareholders) do not demand us to be evidence based. We can many times get away with presenting a talent management program with little or no proof that this is the best way to develop talents.

BUT it is not all bad. I think there are many small movements which suggests that the interest is evidence-based HR is growing, our knowledge of what works in HR is also improving, we have the technology to help us find facts, our basic data is better and there are more people with a broader mindset entering HR. Perhaps things will change. But for now please don’t pretend that HR is evidence-based, because it is not.

11/10/2013 at 12:09 12 comments

Don’t go to a job interview at 11am

If you could choose the time of day to go to a job interview what time would you pick? Before you answer, let me just warn you; the time you pick may impact your chance of getting the job.

My advice is that you pick a time early in the morning or right after the lunch break.

Let me elaborate…

Consider the following research study by Shai Danziger. He studied the results of 1,112 parole board hearings in Israeli prisons over a ten month period (see the study here). The results are illustrated in the figure below:

Job1

The vertical axis shows the percentage of cases where the judges granted parole. The horizontal axis shows the time which the cases were heard during the day. The dotted lines show when the judges went away for a morning snack and their lunch break.

The graph shows clearly that the odds that the prisoners will be successfully paroled start off fairly high at around 65% and quickly plummets to close to zero just before the first break. After the judges have returned from break, the odds abruptly climb back up to 65% before continuing on their downward slide.

In other words; the time in the day when the case is heard is very important to the outcome. Indeed, Danziger found that the three prisoners seen at the start of each “session” were more likely to be paroled than the three who were seen at the end. That’s true regardless of the length of their sentence, whether they had been incarcerated before and regardless of their gender, ethnicity or the severity of their crime.

Danziger explains the judges’ behavior in this way: All repetitive decision-making tasks drain our mental resources. After a while we start to suffer from “choice overload” and we then opt for the easiest choice. For example, shoppers who have already made several decisions are more likely to go for the default offer, whether they’re buying a suit or a car. And when it comes to parole hearings, the default choice is to deny the prisoner’s request.

There is no reason to suspect that recruitment experts are different from judges in this respect. We are all human beings and we are all subject to biases and imperfections AND it affects our decision making. We may believe that when we interview candidates for a job, we view them objectively and fair. In reality, we are influenced by irrelevant things like our moods and as this study suggests, our breakfasts.

So if you are going for a job interview, see if you can move it to 9am. It will enhance your chances.

26/04/2013 at 09:30 1 comment

There is no best Talent Management KPI

Imagine your CEO asks you to come up with one KPI he can track to evaluate if your talent management program is successful. Which talent management KPI will you choose?

The example may be hypothetical, but not unrealistic.  I know HR executives who have five or seven KPI’s which they discuss with their CEO once a month. One may be on recruitment and one may be on talent management.  So if you had to choose one for talent management, which one would it be?

When I talk about one KPI, I don’t mean to say that I think the success of a talent management program can or should be measured by one KPI. Instead I think such a program should be measured by 3-5 KPI’s.  No more than that – measuring HR should be simple – but also no fewer than that.  But I have experience HR executives who are forced to pick one.

I do believe that you can find one which is the best for you. That is the good news. The bad news is that unfortunately there is not a single generic KPI you can just copy-and-paste. It simply doesn’t work like that. BUT there is a KPI which is best for you.

Just as an aside, if you are looking for the five best generic talent management KPI’s, you can find them here.

Since I cannot offer you the one best talent management KPI, let me instead offer you the process through which you can find it. It is a fairly generic process and you can therefore use it on all types of programs. But will all generic processes; the value is not in the process design itself but in how you conduct the process and what content you bring to it.

It is a four step process:

  1. Identify what problem the talent management program is trying to solve. This is the purpose of the program. Although all programs are talent management programs, they are trying to achieve different things. Some focus on attracting talent, some on retention of talent and some on development and deployment of talent. There is no right or wrong, but which is more important to you?
  2. Imagine that you have implemented your program successfully and it has achieved its purpose, how do you know? What objective, tangible, measurable things have changed? Is it behavioral, attitude or more financial things which have changed? Which one matters most?
  3. Identify what data you have – or can get – to track the program. Most organizations suffer from bad data, wrong data or simply difficult-to-obtain-data. Ignore all of that data. Find the few data that you really need and focus your effort on that. Don’t sweat the small stuff.
  4. Define the KPI clearly. It must track the ultimate purpose of the program as well as being easy to monitor and understand. Formulating a KPI is not difficult, but you should follow these best practice steps when formulating it. Most organizations use KPI’s extensively but for most they don’t do what they are meant to do – help you make HR better. They use bad KPI’s

If you follow this simple process you are likely to come up with the one KPI which you can show to your CEO. He (she) will thank you for it.

03/04/2013 at 15:20 2 comments

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