The role of consultative leadership on administrative development

IF 0.9 4区计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Intelligent Data Analysis Pub Date : 2023-11-30 DOI:10.3233/ida-237448

Ayas Mohammed Rasheed Omar, Khairi Ali Auso

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引用次数: 0

Abstract

Consultative leadership is a democratic style that deliberately incorporates employees into organizational management and decision-making to increase employees’ feelings of ownership and align their objectives with company objectives. As a result, during their everyday tasks, leaders constantly utilize “consultation management” for their staff. As examples, consider how to coordinate reports, communicate key ideas, and use a variety of flexible promotion strategies. This study investigates the role of consultive leadership on administrative development in developing countries. For this reason, this study has applied a questionnaire to take the respondents’ opinions in the Iraqi ministry of interior affairs. Using the Likert scale has provided quantitative value for the qualitative study. For this reason, questionnaires were provided, and this study’s results showed a positive correlation between consultative leadership and administrative development. As a result, the organization’s leader has more chances to administer the organization successfully than a manager or an unofficial leader who lacks status power.

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协商式领导对行政发展的作用

协商式领导是一种民主作风，它有意识地将员工纳入组织管理和决策，以增强员工的主人翁意识，并使他们的目标与公司目标保持一致。因此，在日常工作中，领导者会不断对员工进行 "协商式管理"。举例来说，可以考虑如何协调报告、沟通关键想法以及使用各种灵活的晋升策略。本研究探讨了协商式领导对发展中国家行政发展的作用。为此，本研究在伊拉克内政部采用问卷调查的方式来了解受访者的意见。利克特量表为定性研究提供了定量价值。因此，本研究提供了调查问卷，研究结果表明协商式领导与行政发展之间存在正相关关系。因此，与缺乏地位权力的经理或非官方领导相比，组织领导更有机会成功管理组织。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Intelligent Data Analysis 工程技术-计算机：人工智能

CiteScore

2.20

自引率

5.90%

发文量

审稿时长

3.3 months

期刊介绍： Intelligent Data Analysis provides a forum for the examination of issues related to the research and applications of Artificial Intelligence techniques in data analysis across a variety of disciplines. These techniques include (but are not limited to): all areas of data visualization, data pre-processing (fusion, editing, transformation, filtering, sampling), data engineering, database mining techniques, tools and applications, use of domain knowledge in data analysis, big data applications, evolutionary algorithms, machine learning, neural nets, fuzzy logic, statistical pattern recognition, knowledge filtering, and post-processing. In particular, papers are preferred that discuss development of new AI related data analysis architectures, methodologies, and techniques and their applications to various domains.