Efficiency at Headquarters: Taking a Grassroots Approach to Address Criticisms from Bonanza Members and Students Already Opposed to Executive Record Touches

Efficiency at Headquarters: Taking a Grassroots Approach to Address Criticisms from Bonanza Members and Students Already Opposed to Executive Record Touches

Abstract

Efficiency at headquarters has been a pressing issue for many organizations, particularly those that rely heavily on grassroots support and involvement. In the case of Bonanza, criticisms from members and students who are already opposed to executive record touches have highlighted the need for a more effective and inclusive approach to decision-making and communication. This paper examines the challenges faced by Bonanza in this regard, and outlines a grassroots approach to addressing these challenges. Drawing on insights from organizational theory, political science, and communication studies, the authors propose a framework for enhancing efficiency at headquarters that emphasizes collaboration, transparency, and shared decision-making. The framework is grounded in the principles of participatory democracy, and seeks to empower grassroots members and students to take an active role in shaping the organization's policies and practices. The authors argue that this approach can help to build trust and credibility among members and students, while also improving the effectiveness and efficiency of decision-making processes at headquarters. Ultimately, the paper suggests that a grassroots approach to efficiency at headquarters can contribute to the long-term sustainability and success of organizations like Bonanza, by fostering a culture of inclusivity, accountability, and innovation.

Citation

Koray Odynn "Efficiency at Headquarters: Taking a Grassroots Approach to Address Criticisms from Bonanza Members and Students Already Opposed to Executive Record Touches".  IEEE Exploration in Machine Learning, 2023.

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This paper appears in:
Date of Release: 2023
Author(s): Koray Odynn.
IEEE Exploration in Machine Learning
Page(s): 7
Product Type: Conference/Journal Publications