Research the Effectiveness of Management Decision Making and Evaluation in the University

Abstract

The qualification work presents methodological and practical recommendations for effectiveness of management decision making and evaluation, conducts a theoretical and methodical foundations of management decision making and evaluation in the university, analyzes of effectiveness of management decision making and evaluation, identifies, and substantiates ways to improve the management decision making and evaluation in the university. This study underscores the importance of comprehensive evaluation standards that align with the university’s educational mission and societal expectations, with a detailed focus on both quantitative metrics and qualitative assessments. Key findings reveal a steady increase in student enrollment, up by 2700 undergraduates from 2021 to 2023, reflecting the institution's growth and appeal. Faculty development also shows positive trends, with the number of senior-titled teachers rising by 45 within the same period, enhancing the academic strength of the university. Financial analysis highlights a rise in expenditure, from 150000 to 169000 thousand Yuan over three years, indicating the university's expanding investment in areas like teaching resources, infrastructure, and research. The study integrates PEST and SWOT analyses to evaluate the external and internal factors impacting Yantai Nanshan University, identifying both opportunities, such as international collaborations, and threats, like economic downturns and competition. To address these, the thesis proposes a dynamic, multidimensional evaluation model tailored to departmental needs, with a projected implementation cost of 100 thousand Yuan. The study highlights the influence of quality control mechanisms in decision-making. For example, initiatives aimed at enhancing teaching quality surpassed expectations, with student satisfaction rates reaching 92%, exceeding the target of 90%, and graduation rates climbing to 96%, resulting in a 101% completion rate relative to objectives. Resource utilization also improved, achieving a 106% completion rate, highlighting successful allocation strategies. Recommendations focus on improving the financial returns of education, optimizing resource allocation, and employing advanced technologies like AI for data analysis. The author highlights the influence of quality control mechanisms in decision-making. For example, initiatives aimed at enhancing teaching quality surpassed expectations, with student satisfaction rates reaching 92%, exceeding the target of 90%, and graduation rates climbing to 96%, resulting in a 101% completion rate relative to objectives. Resource utilization also improved, achieving a 106% completion rate, highlighting successful allocation strategies.