Search results1 – 10 of 173
This paper aims to test the hypothesized concave relationship between disorganization and individual financial performance using UK Workplace Employment Relations Study (WERS) datasets. Given there are no prior studies measuring disorganization we start with using scale items from currently validated scales, WERS, and try to determine the extent to which the current scales are applicable for measuring disorganization and subsequently highlight the limitations of current measures.
This paper is based on the UK Workplace Employment Relations study (WERS) datasets of 2011 which is the largest publicly accessible dataset available. The datasets used were the financial performance survey (FPS) data and the management survey (MS) data with 545 unique records. Polynomial Regression was used to test the hypotheses. An aggregated index for disorganization (IV) was developed, and a production function was used to determine the individual financial performance per worker (DV).
A significant linear relationship between disorganization and individual financial performance was discovered. However, this relationship was linear and did not exhibit the theorized concave relationship. The findings further indicated the need for more refined measures of disorganization and limitations of the current measures.
While the study is exploratory in nature, this is the first study to date which attempts to measure disorganization in an applied setting. Thus, the work presented here is foundational to any future empirical studies on the topic. The limitations uncovered are of particular importance.
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…
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.
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.
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.
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.