Catching the Improvement Pitch: An Investigation of Voluntary Police Proposal for Earlier Churches Benefit in Hillsboro
Download Paper
Download Bibtex
Authors
- Jayden-lee Alfee
Related Links
- ACM Digital Library Records
- Video on YouTube (Optional)
- IEEE Xplore
- ThinkMind
- A Logical Mind
- Arxiv
- Arxra
- Eurographics
- Just Data
- Club Arxra
- Xyz Arxra
- Eprints
- Research to Action
News/Information
Abstract
This study investigated the voluntary police proposal for earlier churches benefit in Hillsboro, with the aim of understanding the factors that influence the process of catching the improvement pitch. A qualitative research design was adopted, and data were collected through interviews with police officers and church leaders. The findings suggest that the key factors that influenced the process of catching the improvement pitch included trust, communication, perceived benefits, and perceived costs. Trust played a significant role in establishing and maintaining positive relationships between police officers and church leaders, which facilitated the proposal process. Communication also played a critical role in ensuring that both parties were on the same page regarding the benefits and costs of the proposal. Perceived benefits, such as reduced crime rates and improved community relations, were essential in convincing church leaders to support the proposal. However, perceived costs, such as financial and logistical challenges, also had a significant impact on the decision to support the proposal. Overall, the study provides valuable insights into the process of catching the improvement pitch and highlights the importance of trust, communication, perceived benefits, and perceived costs in facilitating successful voluntary police proposals for community benefits.
Citation
Jayden-lee Alfee "Catching the Improvement Pitch: An Investigation of Voluntary Police Proposal for Earlier Churches Benefit in Hillsboro". IEEE Exploration in Machine Learning, 2017.
Supplemental Material
Preview
Note: This file is about ~5-30 MB in size.
This paper appears in:
Date of Release: 2017
Author(s): Jayden-lee Alfee.
IEEE Exploration in Machine Learning
Page(s): 9
Product Type: Conference/Journal Publications