Transactions on Data Analysis in Social Science2821-19363320210901The Impact of E-Service Quality on Customer Satisfaction and Trust in Online Purchases12713523045210.47176/TDASS.2021.127ENF.SouriManagement Group, Faculty of Management and Marketing, American Liberty University, California State, Los AngelesJournal Article20210503Background and Objective: With the increasing growth of e-commerce worldwide, attracting and retaining customers has become crucial for companies. The utilization of e-services enables companies and organizations to gain a sustainable competitive advantage, providing unique opportunities for businesses. Previous studies have indicated that the quality of e-services can significantly impact customer trust and satisfaction. Therefore, this study aims to examine the influence of e-service quality on customer satisfaction and trust in online purchases. Research Methodology: The statistical population of this research includes 4200 purchasers of clothing, bags, shoes, food, and beverages throughout Iran. Due to no significant differences in the type of online purchasing methods among customers, a simple random sampling method was employed, and the sample size was determined to be 352 individuals. The required data were collected through the standard questionnaire developed by Rita et al. (2019). The collected data were analyzed using SPSS version 23, employing correlation and regression tests. Findings: The results of the research analysis revealed a significant positive relationship between e-service quality and website design and customer service. Additionally, a positive and significant correlation was found between e-service quality, privacy security, and service fulfillment. Other findings indicated the impact of e-service quality on customer satisfaction and trust. Conclusion: Based on the obtained results, it can be inferred that companies aiming to enhance profitability should invest in improving customer satisfaction and loyalty through e-services. Focusing on enhancing the quality of e-services is recommended to achieve this goal.https://www.transoscience.ir/article_230452_0f6f1e2dd18a274a00383ae2d94f80f9.pdfTransactions on Data Analysis in Social Science2821-19363320210901Application of Positive Matrix Factorization (PMF) and Multivariate Statistical Techniques in Identifying and Managing Sources of Heavy Metal Pollutants in Sediments13614923045310.47176/TDASS.2021.136ENF.HedayatzadehPh.D. Student in Environmental Science, Environmental Group, Faculty of Natural Resources and Environment, Malayer University, Malayer, IranN.HassanzadehAssociate Professor, Watershed Management Group, Faculty of Natural Resources and Environment, Malayer University, Malayer, IranN.BahramifarAssistant Professor, Environmental Group, Faculty of Natural Resources and Environment, Malayer University, Malayer, IranA. Ildoromi4A. Ildoromi4Associate Professor, Watershed Management Group, Faculty of Natural Resources and Environment, Malayer University, Malayer, IranJournal Article20210517Understanding and identifying various sources of water pollution and the processes affecting them are essential for achieving a comprehensive description of the quality of essential water resources. To this end, implementing a suitable network for monitoring water quality is crucial. Therefore, this study aims to examine the effectiveness of the Positive Matrix Factorization (PMF) model compared to multivariate statistical techniques. Additionally, the combined application of the PMF model with these methods in determining the contribution and management of heavy metal pollutants in aquatic sediments is investigated. Multivariate statistical analysis methods have proven effective in preparing and interpreting data on the quality of aquatic environments and determining the information available in them. However, they have some limitations. Thus, in this research, the application of Positive Matrix Factorization for sediment quality data, especially concerning heavy metals, is compared with multivariate statistical methods. The study also evaluates the status and extent of using this model in various environmental studies worldwide in recent years. Positive Matrix Factorization allows considering uncertain data and provides a positive constraint, leading to an environmentally interpretable result. The results of examining the applications of the PMF model in determining the contribution of various pollutants, including heavy metals, in different environmental sectors over the past two decades, indicate a significant increase in its usage in recent years compared to the past. Recent study results suggest that while Positive Matrix Factorization leads to a stronger understanding of the sources of pollution in the studied system compared to multivariate statistical methods, the combined application of the PMF model with other multivariate statistical methods for determining pollutant sources results in a more accurate and comprehensive analysis of pollutants, including heavy metals.https://www.transoscience.ir/article_230453_9f9d35b3033530aef1fc2cb99c186b9b.pdfTransactions on Data Analysis in Social Science2821-19363320210901Modeling the Assessment of Supply Chain Agility in Urban Search and Rescue Organizations: A Case Study of Tehran Fire Department15015823045810.47176/TDASS.2021.150ENH.Amin TahmasbiAssistant Professor, Department of Industrial Engineering, Faculty of Engineering, Shahrekord University, IranS. J.Razavi NasabPh.D. Student in Industrial Management - Production and Operations, Islamic Azad University, Bandar Anzali International Branch, IranR.Narimani NasabPh.D. Student, Islamic Azad University, Bandar Anzali International Branch, IranJournal Article20210605Agility refers to the ability to respond to unforeseen changes for proactive decision-making based on adaptability. Organizational agility consists of responsiveness, competence, flexibility, and speed components. Today, the quality of providing urban search and rescue services depends on their agility in unforeseen circumstances, requiring attention to these capabilities and capacities. In recent years, minimal efforts have been made to manage and design agile supply chain operations in crisis-oriented organizations, especially firefighting departments in the country. This article focuses on the agility of the supply chain in the fire department, utilizing a novel approach based on Adaptive Neuro-Fuzzy Inference System (ANFIS) in two dimensions: agility capabilities (flexibility, competence, cost, responsiveness, and speed) and agility enablers (collaborative relationships, process integration, information integration, stakeholder sensitivity). Ambiguity and complexity in the characteristics of agility, especially qualitative indicators, and the use of variables derived from experts' experiential knowledge highlight the necessity of using fuzzy logic to analyze the model's component information. By comparing the values obtained from the designed ANFIS in two dimensions of agility with the agility factor matrix, the agility position in the studied organization is located in the B region (potential agility). This assessment informs managers about the gap analysis between the current and desirable levels of agility, indicating that the organization is relatively well-equipped in terms of agility infrastructure, and in the near future, a higher level of agility can be predicted for its supply chain. Additionally, this research designs a dynamic model based on state-space equations and transformation functions to observe and investigate the dynamic behavior of supply chain agility over time. This model allows the organization to predict agility levels for future periods. In the presence of a gap between the current and desirable states, investments can be made to increase agility levels.https://www.transoscience.ir/article_230458_6ba80d65348e7c4dd01206b8a29b897d.pdfTransactions on Data Analysis in Social Science2821-19363320210901Application of Multivariate Receptor Models in Identification, Quantification, and Management of Common Pollutant Sources in Various Environmental Sectors15916823046010.47176/TDASS.2021.159ENF.HedayatzadehPh.D. Student in Environmental Science, Environmental Group, Faculty of Natural Resources and Environment, Malayer University, Malayer, IranA.IldermiAssociate Professor, Watershed Management Department, Faculty of Natural Resources and Environment, Malayer University, Malayer, Iran.N.HassanzadehAssistant Professor, Environmental Science Department, Faculty of Natural Resources and Environment, University of Malayer, Malayer, IranJournal Article20210405Identification and allocation of various pollutant sources are crucial tools for effective prevention, control of pollution, and management of different environmental sectors. In this regard, methods used for determining, allocating, and managing pollutant emission sources in the environment can be categorized into source identification and quantitative source allocation methods. Source identification methods have limitations, including the inability to process missing data and detect values below the detection limit commonly observed in environmental data. Additionally, these methods cannot quantitatively determine the contribution of natural and human sources of pollutants. Conversely, the second type of methods is capable of quantitatively identifying and determining the share of pollutant sources, among which receptor models are particularly notable. In terms of source allocation, receptor models are mathematical computational approaches that can identify and quantify the contribution of sources based on the chemical and physical characteristics of pollutants in sources and receptors. Given the practical importance of receptor models in quantifying the contribution of various pollutant sources in the environment, the aim of this study is to introduce and compare different types of receptor models for determining pollutant emission sources in the environment. The study also highlights recent research in this area. Therefore, by presenting these models, the current research can serve as a guide and a valuable resource for the identification and quantitative determination of various pollutant sources in the environment, consequently providing a basis for environmental management and improvement.https://www.transoscience.ir/article_230460_801d1eaba4562d473599290277d4d1e3.pdfTransactions on Data Analysis in Social Science2821-19363320210901Tourism in Gilan Province with a Multi-Criteria Decision-Making Approach using Topsis16917423046110.47176/TDASS.2021.169ENH.Amin TahmasbiAssistant Professor, Department of Industries, Faculty of Engineering, East University, Gilan, IranY.ShahrabadiBusiness Management Department, Faculty of Engineering, Fouman University, Tehran, IranA.SalahshourBusiness Management Department, Faculty of Engineering, Fouman University, Tehran, IranJournal Article20210604Tea, as a globally consumed aromatic beverage, ranks second only to water in terms of consumption, reflecting its significant cultural, social, and economic roles. In Iran, tea occupies a central position in daily life, symbolizing hospitality and social cohesion. The Gilan Province represents the primary region for tea cultivation in the country, benefiting from optimal climatic conditions, fertile soils, and abundant rainfall, which collectively ensure the production of high-quality tea. Considering the cultural importance of tea, its widespread popularity, and the touristic attractiveness of Gilan, tea tourism emerges as a promising avenue to integrate agricultural heritage with recreational and cultural experiences. This study aims to evaluate and optimize potential tea tourism destinations within Gilan Province by analyzing the major tea-cultivating cities. The research employs the multi-criteria decision-making (MCDM) approach, specifically the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), to systematically rank cities based on relevant indicators. The analysis identifies cultivation area, number of hotels, presence of tea factories, and average annual tourist influx as the most critical factors influencing the suitability of locations for tea tourism. Results indicate that Rasht offers the most favorable conditions, combining infrastructural readiness, cultural richness, and extensive tea production, thereby positioning it as the optimal hub for promoting and developing tea tourism in the region.https://www.transoscience.ir/article_230461_9aec36cf8845971fcb4b78acd2244b6a.pdfTransactions on Data Analysis in Social Science2821-19363320210901Development and Ranking of Construction Project Performance Based on Value Engineering and Fuzzy VIKOR17518223046210.47176/TDASS.2021.175ENA.AziziAssistant Professor, Faculty of Engineering, Department of Industrial Engineering, Islamic Azad University, Science and Research Branch, Tehran, IranS.MousaviDepartment of Industrial Engineering, Faculty of Engineering, Islamic Azad University, Science and Research Branch, Tehran, IranJournal Article20210616Reducing project cost and duration while maintaining or enhancing quality is a fundamental objective in construction project management and one of the core missions of value engineering, which aims to optimize the functionality of systems while minimizing unnecessary expenditures. This study proposes a comprehensive performance improvement framework that integrates value engineering with the fuzzy Analytic Hierarchy Process (FAHP) and fuzzy VIKOR methodology to enhance the efficiency and effectiveness of construction projects. In the initial stage, the value engineering approach was employed to identify key performance improvement criteria through the evaluation of expert opinions, ensuring that all critical factors influencing project success were considered. In the subsequent stage, the relative importance of these criteria was quantified using fuzzy network analysis, allowing for more precise and nuanced weighting under conditions of uncertainty. Finally, the various scenarios developed during the creativity phase of the value engineering process were systematically ranked using the fuzzy VIKOR method to determine the optimal solution. The results indicate that implementing the scenario recommended by the value engineering team, as opposed to the original project design, can achieve a significant 16.3% reduction in overall project cost and a 26.42% decrease in project duration. These findings demonstrate that the integrated approach of value engineering combined with fuzzy multi-criteria decision-making provides a practical and effective strategy for improving project performance in construction management.https://www.transoscience.ir/article_230462_c235e32b3ac422e3fea6c819c519923b.pdf