Imagine yourself looking for a job in a few years from now. Your résumé probably doesn’t do the trick anymore, your business card is pointless and there are hardly any employment agencies left. And then, at the same time, imagine a recruiter having to deal with these changes. It may sound like the future, but it’s already happening since LinkedIn reached her tipping period around 2009. LinkedIn is being used for recruitment purposes more and more, but the quality of the average applicant is actually decreasing with respect to traditional recruitment.
The question I ask myself: what will LinkedIn-based recruitment look like in a few years from now?
Direction to work towards
The answer to this question, of course, remains unknown, because we aren’t able to scientifically report on the practice of looking into a crystal ball. But we are able to make this future less vague and better grounded on a scientific foundation. How? By building scenarios: descriptions of possible future states of being. This method provides organizations with a grip to adapt to a sudden change of the game: scenarios give us a direction to work towards.
Driving forces for the future
In order to build scenarios on the future for LinkedIn-based recruitment, it is necessary to determine driving forces which influence the future to that extend that they can be seen as fundamental for LinkedIn-based recruitment. Based on the trend of e-HRM and the sudden emergence of big data, recruitment is progressively being quantified using big data, which enables organizations to judge candidates’ effectiveness and quality in a sound and evidence-based fashion (see also Sullivan’s article about the HR department of 2020).
On the other hand, the popularity of LinkedIn and the leading position LinkedIn obtained, is its right to exist in the first place. Any disruption from this leading position would soon lead to a decrease in the self-life of LinkedIn. This proposes the following to driving forces:
Recruitment metrics are quantifiable measurements used to make better informed decisions in order to, in the long term, receive the best return on investment. Yet a major lack of HR is the ability to make data-driven decisions, the availability of proper metrics and analytic models to assess effectiveness. It is argued that HR, and explicitly recruitment for this matter, will probably be more data driven and more metric in the future. This trend is upcoming right now; see for instance KPMG’s data-driven HR posts and developments.
Because of these constraints and future emphasis, the first driving force is the extent to which LinkedIn implements data-driven functionalities that enable recruitment to make better informed decisions. A greater extent to which this data is implemented can, evidently, be regarded as a benefit for the future scenario, as it helps to make a qualified hire and solidifies the reputation of HR, especially with respect to talent acquisition. LinkedIn now is in a situation of low recruitment metrics, as not the full potential with respect to data analysis is released. LinkedIn measures everything, but as interviews showed, recruitment still welcomes features with regard to predictive analytics and a better suggested talent-pipeline.
Recruitment disruption, the second driving force, is basically the difference between LinkedIn being in a situation in which it is in a monopoly or in competition. A low rate of disruption would mean that LinkedIn is in a situation of monopoly in the midst of other insignificant social network sites. Other social network sites simply lack the scale and power to disrupt LinkedIn. A high rate of disruption would mean that LinkedIn is in a situation of competition with other large social network sites that lend themselves for recruitment purposes. An example of such disruption within social network sites is MySpace which was disrupted by Facebook around 2008, or the Dutch social network site Hyves that starved in isolation around 2012 – again; because of the enormous disruptive force of Facebook.
A greater extent to which disruption has taken place will, evidently, be a disadvantage for LinkedIn on the future scenario. The smaller the disruption, the more monopolistic the future for LinkedIn is assumed. LinkedIn is now the most popular social network site for recruitment purposes, and is still growing with a huge potential still to be reached. LinkedIn now is therefore in a situation of low disruption.
Combining driving forces
With the two driving forces determined, the future scenarios are built on the combination of both. This becomes clearer when we take these two determinants into a matrix shape and see the four distinctive combinations that are a result of this combination.
Scenario-matrix adapted from Haar, R.J. (2015, p. 4)
Our current situation is a situation, in which there is a low implementation of recruitment metrics and a low rate of disruption as well. The first scenario is one in which there is a low implementation of recruitment metrics as well, but LinkedIn is disrupted. The second scenario is one in which LinkedIn implemented recruitment metrics, but is disrupted as well. The third scenario is the situation in which LinkedIn implemented recruitment metrics and is not disrupted. Let us take a swift walk through each scenario.
The first scenario
Because the implementation of recruitment metrics is low and the rate of disruption is high at the same time, there are probably other recruitment methods used that are more feasible than LinkedIn. In this situation, finding a job is difficult, but finding a proper candidate probably even more. As LinkedIn will not be used that much anymore in this situation, this is most likely to mean an end-phase for LinkedIn. Recruitment will happen with advertisements again, visiting events and business cards will be part of the game again. This is what I consider the worst-case scenario.
The second scenario
Because the implementation of recruitment metrics is high but the rate of disruption is high at the same time, LinkedIn is in competition on a high level in this scenario. LinkedIn will need to innovate at the pace of its competitors and fight to keep its head above water. In this scenario, finding a job and a candidate is easier than in the first scenario, but it is exhaustive. Both jobseeker and recruiter will have to be and stay active on all different social network sites to meet each other, therefore the usage of LinkedIn will be moderate in this scenario. Different functionalities will emerge at a high pace, but in order to use each one effectively, recruitment needs to expand its budget and the jobseeker is left staying up late to keep all his different profiles and connections updated.
The third scenario
Because the implementation of recruitment metrics is high and the rate of disruption is low, LinkedIn is free to innovate at its own pace. In this scenario, finding a job or a candidate is by far the easiest. The jobseeker has to keep its profile and connections updated solely on LinkedIn, and the recruiter can specialize on just LinkedIn as well. HR can scale down, perform better and gain a huge deal of efficiency. LinkedIn will probably introduce some analytics feature in this scenario, enabling organizations to make better informed decisions and quantitative measurements on effectiveness and predictive analytics. Because this situation is the most cost-effective and efficient, I consider this to be the ideal scenario.
Forewarned is forearmed
What the future will look like eventually remains, of course, uncertain. But with these three scenarios organizations are enabled to tackle the future better and adapt towards a clear direction. Looking further into the future, a fruitful implementation of recruitment metrics might eventually mean a narrowing down on the pool of applicants and a benefit over traditional recruitment in terms of the quality of hire.
This research was executed as a bachelor thesis over the second quartile of 2015 at the University of Twente, the Netherlands, by Robbin-Jan Haar. The paper, forecasting on LinkedIn-based recruitment in the audit sector: a scenario study, is available as a full text file for free via this link. If there are any questions remaining or if you like to contact me about this research, feel free to send me an invitation and we’ll get in touch!