This is a subject I am interested in at the moment. It is one of the key current questions for organisations. It is also quite a hard one to answer. The concept of measuring inclusion interests me for both its intellectual interest but also the value that measuring inclusion could add to organisations. It is said in many managerial tomes that if you do not measure something, it will not happen. Therefore, measuring inclusion to show benefits of activities undertaken would very likely create traction for future projects in this area. And we have evidence that positive inclusion correlates with increases in motivation and engagement of an organisation’s people leading to greater productivity, retention and associated positive benefits.

As a basis for this blog, I looked for a robust definition of both Diversity and Inclusion. Considering the volume written on the subject, it was surprisingly hard to find one for the workplace.

The CIPD defines Diversity as “about recognising difference, but not actively leveraging it to drive organisational success. It’s acknowledging the benefit of having a range of perspectives in decision-making and the workforce being representative of the organisation’s customers”.

Profiles in Diversity Journal defines Inclusion as “…ibringing together and harnessing diverse forces and resources in a way that is beneficial. Inclusion puts the concept and practice of diversity into action by creating an environment of involvement, respect, and connection—where the richness of ideas, backgrounds, and perspectives are harnessed to create business value.”

So, what’s the issue about measuring Inclusion?

Well, at the heart of it, it is difficult to directly measure inclusion compared to diversity. As a result, most organisations concentrate on measuring diversity as it more easily produces a number based on robust data. For example, finding the answers to questions such as:

  • What is the breakdown of the organisation’s workforce by protected characteristics?
  • How has this breakdown changed over time?
  • How does this mix of protected characteristics compare to local/target/customer populations?

As you can see, this data is fairly easy to acquire through HR records and can be analysed to create demographic outputs that leadership can decide to take action on.

However, inclusion has been harder to measure in this direct manner. You might decide to ask your workforce how “included they feel on a scale of 1 to 10” but this causes some key issues:

For example, how do you define inclusion to your people so they can respond to the question?

Secondly, self-scoring on such an interpretive topic is very subjective to the individual. What does “inclusion of 5” look like compared to “7”?

Finally, how is obtaining this result actually useful to the organisation? OK, let me elaborate. Imagine we have a score for inclusion of 6.8 out of 10. We can compare this to historical figures or maybe a benchmark to see if the figure has gone up or down, but what do you do to improve your result? Any answer to this question at this stage is merely conjecture, or the “gut feel”, of the decision makers.

Are there alternatives to a subjective, direct Inclusion measure?

Covey, Huling & McChesney advocate measuring “lead” variables and not “lag” variables in their work on executing change. Using an analogy to explain, imagine you are dieting. The “lag” measure is when you weigh yourself. This is a result based on the “output of dieting”. However, the “lead variables” you could measure might be “how many calories you plan to consume” or “how much exercise you are doing this week”. If you focus on affecting the “lead” measures you can affect the output of dieting more directly and would probably have a quicker impact.

Taking this back to measuring Inclusion, an organisation could identify the activities that drive inclusion and could focus on these (your lead measures) rather than obtaining a subjecting output “score” as your way of measuring inclusion. Identifying these behaviours or activities would have a causal effect on how inclusive people feel. This may then be a better set of measures than one overarching “lag” score of inclusion.

However, this is where I feel current research is thin on the ground.

If these variables or activities had been robustly identified through academic research then we could apply this to our organisations. Without this however, we have to identify these for our own organisations. And, arguably the behavioural drivers for inclusion might vary between organisational cultures, i.e. feeling that you belong when zookeeping, might be different to a sense of belonging in financial services, as a person’s intrinsic motivators might be different.

In essence, scientific theory for the drivers of workplace inclusion is still emerging.

So how can we identify these for our own organisation?

First, what resources might we be able to utilise? Doyin Atewologun, Rob Briner and Tinu Cornish recently wrote in HR Magazine of the 4 sources of D&I evidence. They summarised the four sources of evidence-based D&I management as:

  1. Scientific findings from behavioural sciences and HR literature
  2. HR/D&I practitioners professional opinion
  3. The values, concerns and experience of stakeholders from within and without the organisation
  4. Data or information available from or collected by the organisation

So, using academia findings that are available and professional practitioners’ experiences can give you some “scientific robustness” for your investigation. And then it is using this to gather quality information from your stakeholders and your workforce to give you the data you need to understand the “lead” behaviours and activities that will make a positive change to inclusion.

And here’s the good news…

The good news is that technology is emerging that uses machine learning and NLP (natural language processing) to make “point 4” above manageable. It is now possible using platforms such as Qlearsite, to ask open questions to your people to understand what inclusion means to them and what they want to change. Machine learning can now read thousands of open comments and understand the emotion in each sentence. This can then give great insight to consultants/practitioners who can apply their skill to the behavioural science to create a strategy that will develop the “lead” behaviours that will raise inclusion for all. What’s more this technology can take the qualitative responses from individuals and can create QUANTATIVE summaries of the level of sentiment (positive or negative) around inclusion topics. Inclusion can now be properly “measured” thanks to new technology.

In summary

Finally, technology is here that can turn the theories into real action. It is now possible to measure inclusion. It is an exciting time for the topics of diversity, inclusion and belonging. Real impact can now be made in these areas. Science, practitioner experience and technology are coming together to enable us to be able to usefully measure inclusion with tangible actions based on individual observation and feedback.

If you would like to learn more about Qlearsite, please get in touch.