Essays on ‘disorganization’ in contemporary organizations.

Twentieth century management thought consisted of assuming ‘order’ as a necessary condition for increasing employee productivity. However, from mid- century a number of studies started to indicate that assuming ‘order’ as a necessary condition for productivity is misguided. More recent studies have shown that ‘order’ may be largely detrimental to productivity. These findings have prompted researchers to look deeper into organizational ‘order’ and ‘disorder’. In this work the term disorder now has been replaced with the broader concept of ‘disorganization’. In its various incarnations (i.e. chaos, disorder, mess, entropy), disorganization has been explored in many biological, cultural, social, legal, physical, information and political systems. Disorganization is universally encountered within all organizations but has received relatively little attention from academics and practitioners in the management field. This is due to ambiguities in the concept, strongly held management beliefs (i.e. assuming order is good), and a general negative perception of disorganization. These issues have led to major shortcomings and confusion among academics in advancing research directed towards understanding disorganization. This research attempts to address these issues in depth and explores the usefulness of disorganization in contemporary organizations. The research herein is a systematic study of disorganization in order to achieve three specific objectives: a) Provide a theoretical clarification of disorganization and its benefits, b) Develop an understanding of the causes, characteristics, and effects of disorganization, c) Understand the implications of disorganization for academic research and management practice. In order to achieve these objectives novel techniques for theory building and experimental simulation design have been utilized. The research relies on agent-based simulations and conventional data analysis techniques. This work explores disorganization operating within organizations and how it affects its individuals and teams and falls under organizational behavior and presents three primary contributions in terms of theory, method and empirical evidence

The Effects of Disorganization on Goals and Problem Solving

This chapter presents an agent-based simulation of the ability of employees to solve problems. The primary aim of the chapter is to discern the difference in problem solving under two structural conditions. One has rigid structural constraints imposed on the agents while the other has very little structural constraints (called “disorganization” in this work). The simulation further utilizes organizational goals as a basis for motivation and studies the effects of disorganization on goals and motivation. Results from the simulation show that, under the condition of a more disorganized environment, the number of problems solved is relatively higher than under the condition of a less disorganized and more structured environment

Effects of Disorganization on Team problem solving and motivation – An agent-based modeling approach

This paper aims at simulating how “disorganization” affects team problem solving and motivation. The prime objective is to determine how team problem solving varies between an organized and disorganized environment. Using agent-based modeling, we use a real world data set from 226 volunteers at five different types of non-profit organizations in Southwest England in order to define some attributes of the agents. We introduce the concepts of natural, structural and functional disorganization while operationalizing natural and functional disorganization. The simulations show that “disorganization” is more conducive for problem solving efficiency than “organization” given enough flexibility (range) to search and acquire resources. Our findings further demonstrate that teams with resources above their hierarchical level (access to better quality resources) tend to perform better than teams that have only limited access to resources. Our nuanced categories of “(dis-)organization” allow us to compare between various structural limitations, thus generating insights for improving the way managers’ structure teams for better problem-solving. Keywords: agent-based modeling, disorganization, team performance, public service motivatio

Organization vs Disorganization: A Computational Model of Goals, Motivation and Problem Solving

This paper presents an agent-based simulation of “disorganization” and its effect on problem solving. The Prime objective of the simulation is to determine the variance in problem solving under two settings. The first has inflexible structural limitations enacted on the agents (organization) while the other has very little structural limitations (disorganization). The simulation also employs goals as a foundation for the motivation with which agents solve problems. Findings of the simulation show that the “organization” setting guarantees a minimal number of problems are solved independent of how much simulated employees interact with each other. Instead, the “disorganization” setting seems to be more efficient when agents extend the range of social interaction, in or outside their working group, department, or division

Team Problem Solving and Motivation under Disorganization – An agent-based modeling approach.

Purpose This paper aims at simulating on how “disorganization” affects team problem solving. The prime objective is to determine how team problem solving varies between an organized and disorganized environment also considering motivational aspects. Design/methodology/approach Using agent-based modeling, the authors use a real-world data set from 226 volunteers at five different types of non-profit organizations in Southwest England to define some attributes of the agents. The authors introduce the concepts of natural, structural and functional disorganization while operationalizing natural and functional disorganization. Findings The simulations show that “disorganization” is more conducive for problem solving efficiency than “organization” given enough flexibility (range) to search and acquire resources. The findings further demonstrate that teams with resources above their hierarchical level (access to better quality resources) tend to perform better than teams that have only limited access to resources. Originality/value The nuanced categories of “(dis-)organization” allow us to compare between various structural limitations, thus generating insights for improving the way managers structure teams for better problem solving

On the quest for defining organisational plasticity: a community modelling experiment

Purpose: This viewpoint article is concerned with an attempt to advance organisational plasticity (OP) modelling concepts by using a novel community modelling framework (PhiloLab) from the social simulation community to drive the process of idea generation. In addition, the authors want to feed back their experience with PhiloLab as they believe that this way of idea generation could also be of interest to the wider evidence-based human resource management (EBHRM) community. Design/methodology/approach: The authors used some workshop sessions to brainstorm new conceptual ideas in a structured and efficient way with a multidisciplinary group of 14 (mainly academic) participants using PhiloLab. This is a tool from the social simulation community, which stimulates and formally supports discussions about philosophical questions of future societal models by means of developing conceptual agent-based simulation models. This was followed by an analysis of the qualitative data gathered during the PhiloLab sessions, feeding into the definition of a set of primary axioms of a plastic organisation. Findings: The PhiloLab experiment helped with defining a set of primary axioms of a plastic organisation, which are presented in this viewpoint article. The results indicated that the problem was rather complex, but it also showed good potential for an agent-based simulation model to tackle some of the key issues related to OP. The experiment also showed that PhiloLab was very useful in terms of knowledge and idea gathering. Originality/value: Through information gathering and open debates on how to create an agent-based simulation model of a plastic organisation, the authors could identify some of the characteristics of OP and start structuring some of the parameters for a computational simulation. With the outcome of the PhiloLab experiment, the authors are paving the way towards future exploratory computational simulation studies of OP