Organization-Cognition Fit: Exploring recruitment and selection through Agent -based Modelling

Gayanga Herath

Research output: ThesisPh.D. thesis

23 Downloads (Pure)


Over the years the area of human resource management (HRM) and especially the organizational recruitment and selection (R&S) process have had a fair share of research interest. Accordingly, R&S is a vital component of any organization as it operationalizes the process of finding a suitable candidate for an available job position. In so doing the R&S process deals with the entire process that takes place in finding an appropriate candidate where, for example, it includes everything from the identification of the requirements for the potential candidate, to the planning and development of the job position, to making it an attractive job offer and most importantly the operationalization of the selection process to identify the most suitable candidate (the temporal stages of these steps may vary). In light of this, throughout the years scholars have introduced various theories and potential solutions for the facilitation of effective R&S. One such widely used and popularized stream of research is the
Person-Environment fit (P-E fit) paradigm. Essentially, P-E fit carries the notion that for an individual to effectively fit in to an environment, there should be a match (referred to as ‘fit’) between both parties either in terms of a shared ground of understanding or by providing what the other is looking for. In following this underlying premise, researchers have introduced a range of measurements such as Person-Person fit, Person-Organization fit, Person-Vocation fit and many more. All things considered, these measures are operationalized to seek compliance at a particular point of time based on either values, goals, vocational aspirations, career opportunities, or job requirements.

In spite of these efforts, research on these measurements have shown that they tend to be insufficient in effectively representing fit between a person and an environment. This insufficiency
can be traced back to the fundamental operationalization of fit, where they are reliant on fitting certain units, processes, or structures at a given point in time, hence rendering them to function as static measures. In so doing, this PhD study attempts to address these issues that are present in currently operationalized fit measures used in organizations. By this means, this study instigates that a possible solution to overcome these issues would be to operationalize an approach that accounts the dynamic nature of organizational life, which is inherently rooted in an unavoidable social oriented cognitive temperament. As an approach to further inquire the bounds of this proposition, the study follows a distributed cognitive perspective to understand the distributed nature of operating in an organizational
environment. As such, the ability in which it provides to understand highly complex dynamic situations that adapts based on aspects such as the environment, other social beings, tools, temporality and spatial positioning; deemed this approach a perfect fit for its inquiry.

Given the study’s cognitive orientation along with the need for an approach that can capture the bounds of dynamic organizational life, this study attempted to transcend the static dimensions
employed in current fit measures. In so doing, the human tendency referred to as ‘docility’—which originated from Herbert Simon’s work and was later adapted in relation to distributed cognition—is considered as an effective measurement that has the potential to map the social organization (referred to as organizational cognition in this study) of individuals in a social environment. Accordingly, this study utilized the essence of distributed cognition and the tendency ‘docility’ to operationalize an approach that can capture the bounds of social organization in a social oriented work environment. Hence, given its organizational and cognition-oriented context and operationalization, this approach was referred to as Organization-Cognition fit (O-C fit) in this study.

Subsequently, in order to test the bounds of the proposed O-C fit approach, the study expressed the importance of utilizing an epistemic tool that has the potential to explore the boundaries
of a highly complexed theoretical proposition (i.e. due to overwhelming interactional qualities of social environments such as organizations). In this manner, the utility of Agent-based simulation modeling (ABM) was particularly promising, given the distinctive advantages it provided over other more conventional research methods in exploring highly complex multifaceted and adaptive systems. In essence, ABM is a particular approach to computational simulation modeling which is generally
centered around observing behavioral outcomes that stem from the simulation of autonomous agents that interact in relation to its environment and other agents.

Accordingly, an Agent-based simulation model was developed from the ground up to encompass the dynamics of R&S in relation to social team learning and problem solving. The ABM model
featured two primary components, namely, (a) a recruitment and selection component to operationalize the proposed O-C fit approach, and (b) a team problem solving component to comparatively analyze the influence of O-C fit on team problem solving and collaborative team performance in general. Through the course of this study the above-mentioned ABM model was expanded to include two facets of O-C fit, thus providing the study to explore the utility of two variants of O-C fit, namely
the supplementary O-C fit and the complementary O-C fit. Where the former represents seeking similarity or in other words congruence in organizational cognition, while the latter represents seeking change in the form of complementing/providing what one party is missing, thus attempting to change the existing level of organizational cognition.

The ABM model developed in this study produced simulated data based on the theoretical bounds that were modelled to explore the utility of O-C fit in relation to team problem solving. By
this means, the analysis of the resultant simulated data was used as the premise to explore and unravel the utility of O-C fit with regards to other various factors that either contribute or contravene effective problem solving. Interestingly, this exploration indicated a number of very noteworthy and resourceful insights into the utility and the viability of the proposed O-C fit approach in relation to attaining effective problem solving and optimum performance. Most importantly, the findings from the overall study indicated that it is simply not sufficient to only factor in the competencies one holds, but rather
the ability in which one can make use of their competencies in a social environment is key to effective problem-solving success. As such the study shows that docility plays a crucial role in facilitating the effective use of competencies, thus allowing individuals to head in the direction of attaining optimum potential. In turn, indicating an effective balance between competence and docility is needed to attain greater problem-solving performance. In this manner, the study also shows that even though docility has a promising influence on problem-solving, yet it cannot be understood and utilized in a vacuum,
but rather the situational conditions that either play into the effective facilitation of docility or on the contrary conditions that impair docility should also be factored in. Thus, highlighting the need to employ the O-C fit approach in a strategic manner which plays into its emphasized strengths as presented in this study
Original languageEnglish
Awarding Institution
  • University of Southern Denmark
  • Secchi, Davide, Supervisor
Date of defence15. Dec 2021
Publication statusPublished - 17. Nov 2021

Note re. dissertation

Print copy of the full thesis is restricted to reference use in the Library.


Dive into the research topics of 'Organization-Cognition Fit: Exploring recruitment and selection through Agent -based Modelling'. Together they form a unique fingerprint.

Cite this