Cooperating for the Rehabilitation of Government Equipment: The Warwick Companies Interest in Carrying out Ordinance Collection Stages at the Infield Pennant Station

Authors

Troy Korrin

Abstract

This study explores the cooperation of private companies and government agencies in the rehabilitation of government equipment, specifically focusing on the role of Warwick Companies in carrying out ordinance collection stages at the Infield Pennant Station. The research aims to investigate the motivations and benefits of private companies engaging in such collaborative efforts with the government, and to identify the challenges and limitations that may arise during the process. A mixed-methods approach was utilized, including interviews with Warwick Companies executives, government officials, and relevant stakeholders, as well as a review of relevant literature and documents. The findings reveal that Warwick Companies was driven by a combination of altruistic motives, such as contributing to the public good, and business-related motives, such as enhancing its reputation and creating new business opportunities. The cooperation was found to have several benefits for both parties, including improved efficiency and cost-effectiveness, increased capacity and resources, and enhanced trust and collaboration. However, the study also identified several challenges, such as conflicting interests, bureaucratic obstacles, and communication barriers, which highlight the importance of careful planning, effective communication, and mutual understanding in such collaborative efforts. The study contributes to the literature on public-private partnerships and provides practical insights for both private companies and government agencies seeking to engage in similar collaborations.

Citation

Troy Korrin "Cooperating for the Rehabilitation of Government Equipment: The Warwick Companies Interest in Carrying out Ordinance Collection Stages at the Infield Pennant Station". IEEE Exploration in Machine Learning, 2022.

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This paper appears in:
Date of Release: 2022
Author(s): Troy Korrin.
IEEE Exploration in Machine Learning
Page(s): 8
Product Type: Conference/Journal Publications