Recruitment lessons to learn for 2017

Written by
James Meachin

11 Jan 2017

11 Jan 2017 • by James Meachin

Lesson one dont select for group think

For managers there is often a temptation to recruit people who are ‘a good fit’. In particular, people who are similar to themselves. A recent example is the selection of the executive committee for Donald Trump’s transition team, where four of the 16 are members of his family. 

In some respects this makes sense as there will be greater cohesion, ease of communication (due to shared assumptions and knowledge), and increased speed of decision making. However, this also increases the risk of ‘group think’ where an overly cohesive team reaches consensus too readily, fails to critically evaluate ideas, and misses perspectives that are not shared by the group. 

It is tempting for managers and leaders who are under pressure and needing to make progress to take a similar approach. However, in the long-run, there is greater value in selecting high-competence, team members with diverse skills, backgrounds and perspectives, who are able to add value because they complement their team through the differences that they bring.

Lesson two leading through uncertainty

2016 has been one of the least predictable years in living memory. We have been deeply unsettled by Brexit and the US presidential elections, or even Leicester City winning the Premier League at odds of 5,000/1. This uncertainty is mirrored in business, where organisations need to select leaders who can deal with uncertainty effectively. We know that when under pressure, some leaders feel an overwhelming need to take greater control, which reduces their time and capacity to ‘look up and out’ as well as disempowering their management teams. Linked to this, some leaders become risk averse and delay decision making, wanting to keep their options open until the picture becomes clearer. This is echoed in criticism of Government inactivity following the Brexit vote. 

Organisations need to select leaders who are well equipped to deal with uncertainty. Key traits to assess are: 

•    The ability to understand and simplify complexity, developing a clear long-term view that others can follow. 

•    The willingness to set clear priorities and to make decisions, as opposed to delaying decisions or trying to do everything. 

•    A high levels of self-awareness. The strongest leaders have the ability to reflect on what they are thinking and feeling. This ‘meta-cognition’ gives leaders more choice in their responses and enables them to adapt and learn more quickly than their peers.

Lesson three focus on why AND what

The role of big data in recruitment has continued to gain traction this year, with numerous examples of how organisations use large volumes of data about their applicants and staff to identify characteristics that predict success. For example, an organisation found that people who were members of one or two social networks stayed in their job for longer than those who belonged to four or more social networks. This approach focuses on ‘what’ – identifying what correlates with job performance and measuring that. 

However, because big data doesn’t explain ‘why’ a point of data matters, its effectiveness is unpredictable over time and across populations. Evolving technology and social attitudes mean that patterns of social network use are liable to change. Likewise demographics change; those future candidates who are still in their teens may well have different online habits compared to people who are only four or five years older.

There is also the law of unintended consequences. As researchers have found, having a city centre address as opposed to a suburban address in Detroit distinguished thieves from non-thieves, but it also tended to distinguish between white and BME groups. If we only focus on ‘what’ predicts performance without asking ‘why’, recruitment processes will produce disappointing results. The key is to understand the qualities that link to successful performance – are your best performers more driven? More agile? Better influencers? Using big data – or any data – to measure these traits and you can be more certain that the best people, whoever they are, will be selected.