محسن محمدی

Objective: This study aimed to develop and test a structural equation model for predicting marital conflict based on early maladaptive schemas, gender stereotypes, and emotional needs, with the mediating role of coping styles. Methods and Materials: The research adopted a correlational design using structural equation modeling. The statistical population included 1,760 married men and women with marital conflicts who had sought counseling services in Tehran in 2024. Using purposive sampling, 500 participants were selected who had been married for at least six months and voluntarily completed six standardized questionnaires: the Marital Conflict Questionnaire (MCQ), Young Schema Questionnaire-Short Form, Bem Sex Role Inventory (short form), Emotional Needs Questionnaire, Young-Rigg Schema Avoidance Inventory, and Young’s Overcompensation Inventory. Data were analyzed using SmartPLS software through both descriptive and inferential statistics, including path coefficients and indirect effects. Findings: The results showed that disconnection and rejection schema (β = 0.413, p < 0.001), other-directedness (β = 0.214, p < 0.001), avoidance coping (β = 0.217, p < 0.001), and overcompensation coping (β = 0.122, p = 0.010) were significant direct predictors of marital conflict. Emotional needs significantly predicted both avoidance (β = 0.175, p = 0.014) and overcompensation coping (β = 0.246, p < 0.001). Gender stereotypes also played a role, with neutrality negatively predicting marital conflict (β = –0.229, p < 0.001). Mediation analysis confirmed significant indirect effects of impaired autonomy and emotional needs on marital conflict via avoidance coping, and of impaired limits and over-vigilance via overcompensation coping (p < 0.05). However, several indirect paths through gender stereotypes were not statistically significant. Conclusion: The findings support a comprehensive model where early maladaptive schemas, emotional needs, and gender stereotypes predict marital conflict through maladaptive coping styles. These insights can inform schema-based couple therapy by addressing cognitive-emotional vulnerabilities and promoting healthier coping mechanisms in marital relationships.

In the contemporary dynamic business environment, the dependability of process mining algorithms is intricately tied to the quality of event logs, often marred by data challenges stemming from human involvement in business processes. This study introduces a novel approach that amalgamates insights from prior works with unsupervised techniques, specifically Principal Component Analysis (PCA), to elevate the precision and reliability of event log representations. Executed through Python and the pm4py library, the methodology is applied to real event logs. The adoption of Petri nets for process representation aligns with systematic approaches advocated by earlier studies, enhancing transparency and interpretability. Results demonstrate the method’s efficacy through enhanced metrics such as Fitness, Precision, and F-Measure, accompanied by visualizations elucidating the optimal number of principal components. This study offers a comprehensive and practical solution, bridging gaps in existing methodologies, and its integration of multiple strategies, particularly PCA, showcases versatility in optimizing process mining analyses. The consistent improvements observed underscore the method’s potential across diverse business contexts, making it accessible and pertinent for practitioners engaged in real-world business processes. Overall, this research contributes an innovative approach to improve event log quality, thereby advancing the field of process mining with practical implications for organizational decision-making and process optimization.

Enterprises employ information systems to carry out their day-to-day business operations. Organizations implement business policies to enhance their competitive edge through efficient process management. This paper aims to propose a method that combines two approaches: evidence-based policymaking and process mining, to facilitate process reengineering. While numerous evidence-based approaches utilizing process mining techniques have been employed to assess process performance through measurements, these methods often focus on individual process instances. This is in contrast to Business Process Redesign (BPR) assessments, which encompass more comprehensive performance measurements, including overall process performance. This study proposes a method for analyzing process execution time, which includes Cycle time, Lead time, and Activity time. The aim is to support evidence-based policymaking in information systems through the use of process mining. Several key performance indicators (KPIs) have been defined for evidence-based management of business processes to identify process bottlenecks. The results of this paper demonstrate the application of process mining in analyzing the execution time of business processes. Using a real-world dataset, the study identified time-consuming activities and provided key performance indicators (KPIs) to guide process optimization. These findings demonstrate the effectiveness of process mining in identifying bottlenecks and inefficiencies within operational processes, ultimately leading to improved process performance and efficiency.

In today’s data-centric business landscape, characterized by the omnipresence of advanced Business Intelligence and Data Science technologies, the practice of Process Mining takes center stage in Business Process Management. This study addresses the critical challenge of ensuring the quality of event logs, which serve as the foundational data source for Process Mining. Event logs, derived from interactions among process participants and information systems, offer profound insights into the authentic behavior of business processes, reflecting the organizational rules, procedures, norms, and culture. However, the quality of these event logs is often compromised by interactions among various actors and systems. In response, our research introduces a systematic approach that leverages Python and the pm4py library for data analysis. We employ trace filtering techniques and utilize Petri nets for process model representation. This paper proposes a methodology demonstrating a significant improvement in the quality metrics of extracted subprocesses through trace filtering. Comparative analyses between the original logs and filtered logs show enhancements in fitness, precision, generalization, and simplicity, highlighting the practical importance of trace filtering in refining complex process models. These findings offer practical insights for practitioners and researchers involved in process mining and modeling, highlighting the significance of data quality in obtaining precise and dependable business process insights.