London Metropolitan University
An assessment of performance and efficiency of team-based organizations: empirical evidence of English premier league (EPL) using data envelopment analysis (DEA) and naturalistic approach (NA), 2005 to 2016
Abstract
dc:description.abstractThe last two decades have witnessed a progressive growth in professional sports entertainment in Europe and football has gained impetus in most part of the world. Teams with immense economic potentials are found in America, United Kingdom and other European countries, but the English Premier League (EPL) is no doubt the most affluent league with many financially powerful clubs concentrated in the twenty-team league (Deloitte and Touché, 2008). A critique of issues about measuring performance and efficiency of sports resource to justify ranking in the EPL is presented in this study. This thesis addressed the changing focus in football management and many unresolved questions relating to Performance and Efficiency Measurement (PEM) of a team-based organisation like a Football Club (FC). At a certain point, every organisation needs to evaluate its operation in accordance with its specific objectives to assess its performance; measure the efficiency of its operation and determine if there is any need for corrective actions. Performance and efficiency measures are tools that enhance our understanding of processes that lead to the achievement of specific goals. These tools assist in determining how well an organisation is doing, whether goals and objectives are met, whether owners and other stakeholders are satisfied and areas where improvements are necessary.
Degree
thesis:*- Name dc:type.qualificationname
- phd
- Level dc:type.qualificationlevel
- doctoral
- Grantor dc:publisher.institution
- London Metropolitan University
- Year dc:date.issued
- 2019
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Badmus, Shamisudeen Olusesan
Subjects
dc:subject × 1Identifiers
dc:identifier.*- Dc Identifier Grantnumber
- N/A
- OAI identifier oai:identifier
- oai:repository.londonmet.ac.uk:7303