JAQM Volume 17, Summer-Fall-Winter - December 30, 2022

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Contents

Research on the effectiveness of the management of the direction / public service of taxes and local taxes on the reform of the public administration
Dorina SANDU, Andrei Teofil PARVAN

In the last three decades, we have witnessed a series of reforms regarding the management of the public sector and the efficiency of its activity to respond as adequately as possible to the needs or demands of citizens. The governments, through the reform proposals, wanted to improve the efficiency and effectiveness of the public sector, the implementation of solid and stable administrative practices, the development of organizational management and strategic management regarding fiscal activity, fiscal compliance, collection of taxes and fees, risks regarding non-compliance with budgetary obligations. Through this paper, we wanted to analyze the tax reform and public administration reform, the perception of fiscal-budgetary policy by taxpayers, the relationship between local tax administration and taxpayer, the quality of the management of the tax institution reflected in the relationship between managing civil servant and executive civil servant, but also in the relationship with the citizen, the satisfaction and motivation of work in the case of civil servants, the impact of computerization on taxpayers and the activity of civil servants. Through this paper, we wanted to analyze the tax reform and public administration reform, the perception of fiscal-budgetary policy by taxpayers, the relationship between local tax administration and taxpayer, the quality of the management of the tax institution reflected in the relationship between the manager civil servant and the executive civil servant, but also in the relationship with the citizen, the satisfaction and motivation of employees in the case of civil servants, the impact of digitization on taxpayers and the activity of civil servants.

Strength of factors in 3³ factorial designs using Bayesian Analysis
R. VIJAYARAGUNATHAN, M.R. SRINIVASAN, T. MENINI

The study proposes to consider factorial design at three levels and identify all significant factors based on its inherent strength. The methodology considers full, fractional, and reduced factorial designs with three factors each at three levels, to examine the effectiveness of factors in these models through simulation and employing real data. By identifying and quantifying the Bayes factors through simulated datasets, the true strength of the main/interaction effects in these three designs were discovered. Finally, the study concludes that reduced factorial design produces better results than traditional one-third fractional factorial designs when there are no other constraints to adding more factors to the model for analysis.

Validating a Deep Learning Model: The Nexus of Self-Regulation Strategies and Student Well-Being
Andrei Teofil PARVAN, Loredana MANASIA

This study was principally focused on verifying the suitability of the Deep Learning Strategies Questionnaire for Romanian academic environments and examining the interrelations among deep learning strategies, self-efficacy, subjective well-being, and academic performance. Utilizing a correlational-cross-sectional approach, the research involved 130 university students from various Romanian institutions. Data gathering was conducted via an extensive multidimensional questionnaire, which assessed components such as deep learning strategies, perceived self-efficacy, subjective well-being, and academic performance indicators. The methodological process included extensive collaboration with several higher education institutions for participant recruitment. The data analysis was carried out using JASP version 0.18.1, which combined descriptive and inferential statistical approaches with structural equation modeling. The research aimed to endorse a theoretical model that interconnects deep learning self-regulation strategies with elements like student well-being, perceived self-efficacy, and their collective influence on academic achievement. Notably, the exploratory factor analysis revealed the presence of five distinct factors, an enhancement from the four factors identified in the original model, providing a more comprehensive understanding of deep learning strategies. Furthermore, the hierarchical model related to deep learning strategies exhibited strong congruence. The study's instruments demonstrated robust reliability and validity, as evidenced by internal consistency metrics ranging from acceptable to high levels. This substantiates the efficacy of these scales in evaluating a broad range of learning strategies in an educational setting.

In a time marked by significant technological progress, the capital market environment is experiencing swift and transformative changes. Central to this transformation is the integration of algorithmic trading, driven by advanced algorithmic robots (algobots) and powered by state-of- the-art artificial intelligence (AI). This article offers an extensive examination of the substantial influence of algorithms and AI on making financial decisions, illuminating the numerous benefits, associated risks, and broader consequences for enhancing investment results. We embarked on an endeavor to create and evaluate a groundbreaking algorithmic trading bot referred to as "The Dirty Little Robot."