A Data Driven Assessment of Cognitive Fit in Recruitment

Abstract

The recruitment and selection (R&S) process is generally considered a highly vital component of an organization (Searle, 2009). This is due to the fact that successful R&S has been closely linked to positive organizational outcomes in the literature (Billsberry, 2007). One such stream of literature that has received some attention over the last decades is person-environment fit, also known as P-E fit (Edwards, 2008; Werbel, and Gilliland, 1999; Kristof, 1996). Essentially, P-E fit carries the notion that an individual should match aspects of the organizational environment in which they work or are to become part of (Edwards, 2008). Accordingly, since its conception, a number of fit measures have been introduced ranging from person-supervisor/person fit, person-job fit, person-organization fit, person-group fit, and others (Kristof-brown, Zimmerman, and Johnson, 2005).

                      Over the years a number of criticism have surfaced. For example, such measures have been found to (a) be too static in nature (Boon & Biron 2016), (b) be unable to effectively contribute and explain employee performance (Edwards, 2008; 1991), and (c) inadequate when used in combination with other fit measures (Edwards and Billsberry, 2010). In general, such measures assume that it is sufficient to match various units (e.g., beliefs, values, job requirements) between two parties in order to find the adequate fit of an individual to a physical or social organizational element. However, this notion does not hold well when the dynamic aspects of change are considered (Suddaby & Foster,2017).

In this study, we argue that a cognitive approach could perhaps help with the concerns above. This is because cognition may serve as a way to identify prospective workers who can quickly grasp team dynamics and easily integrate into a work environment. When cognition in organizations is defined from a distributed cognitive viewpoint (Cowley and Vallee-Tourangeau, 2017; Secchi & Cowley, 2020) in combination with the behavioural disposition of docility (Secchi & Bardone 2009), then it is relatively easier to highlight the social interactive dynamics that identify ‘fit’. The term docility here refers to “the tendency to depend on suggestions, perceptions, comments, and to gather information from other individuals on the one hand, and to ‘provide’ information on the other” (Secchi & Bardone 2009, p.8). The concept of distributed cognition carries the notion that cognition is distributed from within to one’s surrounding environment (Cowley and Vallee-Tourangeau, 2017). The idea here is that individuals collaborate and share information with their surrounding environment (team members, for example) hence they ‘socially organize’. Secchi and Cowley (2020, p.34) define this phenomenon, called social organizing, as the process where “people act as parts of organizations and connect ‘intelligence’ with various social dynamics”.

In order to explore the utility of using such an approach, in this study we propose a system to capture and identify an individual’s social organizing (i.e. by utilizing docility as a measurement), thus used as the premise to ‘fit’ an individual into an environment. Moreover, we focus our attention on teams as they are fundamental working units for most organizations. In doing so we intend to conduct a two-way survey where we target (a) the current members of an existing team and (b) the job candidates that are applying to be hired by those teams. Once we have collected the required data from both parties, we then intend to use the collected data to calibrate an existing organization-cognition fit (O-C fit) agent-based simulation model (developed by Herath & Secchi, 2021). The work by Herath and Secchi (2021) in combination with the O-C fit model is considered as a theoretical prelude to this study and therefore follows a similar cognitive foundation.

The goal of the current study is to explore the utility of such a cognitive approach by using empirical data, in an attempt to investigate if such an approach would yield in better employee performance. This would lead to something called ‘verification’ of the agent-based model (Secchi, 2021). Ultimately, we seek to provide valuable implications to HRM practitioners and contribute to the understanding of cognitive fit in the organizational fit literature and beyond.