## 1 Introduction
In contrast to previous studies, the current study used a comprehensive methodology that combines the assessment of a single author with expert evaluations, resulting in a more rigorous evaluation of decision quality. Moreover, the utilization of machine learning techniques, as described in [17] establishes a robust framework for assessing the quality of decisions in the field of engineering design. This approach will improve the credibility of these assessments using a single evaluator; a Naive Bayes machine learning model is utilized to make predictions about the Quality of Decisions (QODs) by analyzing textual decision statements. The current study utilizes a comprehensive sampling strategy to further improve the generalizability of its results, a methodological approach that is consistent with modern advances in research design [18]. The paper entitled \"Sampling in Software Engineering Research: A Critical Review and Guidelines\" offers a comprehensive evaluation of the current state of sampling in research of high quality within the field of software engineering. Additionally, the article provides suggestions that are relevant and applicable beyond the domain of software engineering [19]. The study applies purposeful sampling procedures [20] to assure the relevance and generalizability of its findings across different research settings. Moreover, a comprehensive overview of the current state of sampling in the recent literature [21], which offers supplementary recommendations for enhancing sample methodologies, utilizes qualitative research methods to strengthen its research design [22]. The implementation of a comprehensive approach to sampling in this study aligns with the investigation of the influence of personality factors and domain-specific knowledge on decision quality. Moreover, this approach is consistent with current standards in research design. This study builds upon prior research that has examined the influence of personality traits and decision-making on decision quality in various domains [23], focusing on the impact of decision-making and problem-solving quality in engineering design contexts [24], and how certain personality traits may play a role in this process [25]. Our research focuses on the integration of decision-making processes and personality traits, highlighting the significance of decision-makers internal features as strong indicators of decision quality within the domain of engineering design. This paper's research gap is the lack of examination of how people behave during the engineering design process, particularly when provided with difficult choices. Even though it is acknowledged that decision-making plays a crucial role in enhancing organizational performance, decision-making is typically carried out by individuals using their time and knowledge. This article emphasizes the need for more research that examines the \"people dimension\" of engineering design and investigates how dispositional features like personality traits and domain expertise affect decision-making confidence and quality. Organizations can perform more effectively and make better decisions by having a deeper grasp of these behavioral concerns. The significance of age and how it affects decision-making is a significant component that has not been evaluated in this study. Due to variations in individual and professional exposure, age differences can have a substantial influence on confidence and decision quality. By assessing the impact of age on decision-making, future studies can close this knowledge gap and offer a more thorough grasp of the variables affecting engineering decisions. Furthermore, previous research frequently uses homogeneous participant groups, which limits the findings' generalizability in a variety of scholarly and cultural contexts. The methodology employed in this study addresses these challenges and demonstrates its feasibility and importance in advancing the field.