Lack of diversity compels many workers to move on, to the detriment of the organisation’s performance, innovation, and employee trust. Behavioural science insights can help guide diversity and inclusion (D&I) initiatives, but not all interventions work. Daniel Jolles suggests three steps to leverage data that can better equip companies to create the diverse, multi-generational and inclusive workplaces of the future.
Rick*, a software engineer, tells me that he left his job in advertising because he felt excluded. “They would get together for lunch or campaign events, and they just didn’t ask me. Maybe they thought I didn’t like that kind of stuff.” Rick was in his 60s at the time, thirty to forty years older than his tech colleagues. It wasn’t just social activities; he felt a subtle exclusion from tech discussions and training plans. “There was an assumption that because of my age, I wasn’t learning.” Feeling the company was failing to deliver the inclusive culture he desired, Rick moved on. As I listen to Rick’s story, I ask myself, ‘how can leaders uncover and prevent stories of age-exclusion like this?’
An inclusive workplace is crucial to attracting and retaining the best talent, with D&I initiatives increasingly recognised for improving performance, innovation, and employee trust. Yet, workplace ageism remains widespread (WHO, 2021), and D&I initiatives often ignore ‘age’ altogether. One third of UK workers are aged 50+ (ONS, 2022). Therefore, leaders must leverage data that helps create diverse, multi-generational, inclusive workforces. Here are three steps to get there:
1. The diversity you have
Too often, businesses talk about improving diversity but fail to define or consider the type of diversity being improved. First, compare representation data with external benchmarks to shape explicit diversity goals. Not all businesses have an age-diversity problem, but it is suspected many do (including leading tech firms). Here, increasing age diversity might be a goal. Next, compare ‘who applies’ with ‘who gets hired’. This can identify shortcomings in attracting diverse applicants or biases in hiring and selection processes.
2. The diversity you want
Behavioural science provides promising interventions to boost the diversity of ‘who applies’ and ‘who gets the job’. Applicant diversity can be unintentionally hampered by job advertisement language, inflexible work arrangements, and over-reliance on employee referrals or universities for recruitment. Altering this can make roles more visible and attractive to diverse candidates. When it comes to ‘who gets hired’, biases influencing selection diversity are harder to correct. Older candidates for technology roles are falsely perceived as inflexible, slow, and outdated in their skills. These are hard stereotypes to overcome and limit chances of success against younger candidates with similar experience. Interventions such as blind CV screening, longer candidate shortlists and changes to sorting and scoring of applications have potential to reduce bias and boost diversity.
Sadly, not all behavioural science interventions translate seamlessly from the lab into workplaces or across dimensions of diversity. For example, hiring for multiple roles at once may help employers choose more gender-diverse teams compared to hiring for roles in isolation (Chang et al., 2020), but it fails to increase age-diversity (Jolles et al., 2022). This points to an uncomfortable truth that not all diversity is created equal, and not all interventions work. To confirm which interventions deliver diversity, test and collect data on successes (and failures).
3. The inclusion we all need
Finally, inclusion data informs leaders if diverse representation is thriving. It is not surprising that employees are motivated to work for inclusive companies. High-quality relationships, free from stereotypes between people of different ages means higher job satisfaction for all employees. The same is true for gender and race/ethnic inclusion. Inclusion means different things to each organisation and employee, and this changes over time (Adamson et al., 2022). It is more than just ‘who quits’. Inclusion data requires asking employees how they feel and perceive inclusion and identifies groups that might feel undervalued.
This is a continual process of monitoring and improvement with surveys, training packages and workshops serving as popular interventions. Yet many of these are not tested, leaving inadequate data on their effectiveness. For example, unconscious bias training may raise awareness but there is little evidence it changes employee behaviours (Herbert, 2021). Handled poorly, initiatives can be perceived as ‘tokenistic’, ‘box-ticking’ or even ‘punitive’, potentially heightening employee differences and biases. Handle these initiatives carefully to uncover important data that sets the foundation for difficult, meaningful conversations about barriers to inclusion.
For a new age of multigenerational diversity and inclusion, leverage existing data to understand ‘diversity’ within your organisation. Then collect new data to measure the success of steps taken towards increasing diversity, and to ensure your organisation has the ‘inclusion’ to make this diversity thrive.