Kharazmi University International Journal of Supply and Operations Management 2383-1359 13 1 2026 02 01 Gamifying Human Behavior: How Gamification Drives Consumer Stickiness in E-Commerce 1 38 2974 10.22034/ijsom.2026.110809.3403 EN Nhan-Thanh Thi Nguyen Ho Chi Minh City University of Foreign Languages – Information Technology, Ho Chi Minh City, Vietnam Tri-Quan Dang Ho Chi Minh City University of Foreign Languages – Information Technology, Ho Chi Minh City, Vietnam 0000-0003-3551-9198 Son-Hoang Dang Ho Chi Minh City University of Foreign Languages – Information Technology, Ho Chi Minh City, Vietnam Luan-Thanh Nguyen Ho Chi Minh City University of Foreign Languages – Information Technology, Ho Chi Minh City, Vietnam 0000-0002-3118-0572 Anh-Ly Quynh Ho Chi Minh City University of Foreign Languages – Information Technology, Ho Chi Minh City, Vietnam Dang Thi Viet Duc Posts and Telecommunications Institute of Technology, Hanoi, Vietnam 0000-0002-4953-4906 Journal Article 2025 05 20 Objective: Despite the growing integration of gamification in digital commerce, its impact on consumer stickiness remains underexplored, particularly in emerging markets. This study develops and empirically tests a framework examining how specific gamification elements in e-commerce platforms—badge upgrades, random rewards, and gamified design—affect consumer stickiness through perceived value (hedonic and utilitarian) and social interaction. The research aims to clarify the mechanisms through which gamification enhances customer loyalty and continued platform engagement in the Vietnamese context. Methods: A questionnaire-based survey was conducted with 310 consumers who had participated in gamified activities on e-commerce platforms in Vietnam. The study integrates Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine linear relationships and Artificial Neural Networks (ANN) to capture nonlinear interactions within the proposed model. This dual-stage analytical approach enhances the robustness and predictive power of the findings. Results: The findings show that gamified design and badge upgrades positively influence both perceived hedonic and utilitarian values, while random rewards significantly affect perceived hedonic value only. Social interaction is significantly influenced by gamified design but not by badge upgrades or random rewards. Perceived value and social interaction, in turn, contribute to consumer stickiness on e-commerce platforms. Conclusion: The study confirms that different gamification elements generate distinct effects on consumer perceptions and stickiness. By highlighting the mediating roles of hedonic and utilitarian values as well as social interaction, the research contributes to the literature on smart e-commerce and gamification. The findings suggest that businesses should strategically design gamification features that simultaneously enhance functional benefits and experiential enjoyment to strengthen long-term customer retention on digital platforms.

http://www.ijsom.com/article_2974_6788076842014c83cedadbe6b0ba0314.pdf

Kharazmi University International Journal of Supply and Operations Management 2383-1359 13 1 2026 02 01 Optimizing Mill Bolt Production Efficiency in a Metal Mechanical Firm via Digital Twin Technology and Lean Methodologies 39 60 2975 10.22034/ijsom.2026.110872.3452 EN Sigmund Junco Industrial Engineering Program, Faculty of Engineering, Universidad Peruana de Ciencias Aplicadas UPC, Lima, Perú 0009-0006-1609-6731 Gabriela Lozano Industrial Engineering Program, Faculty of Engineering, Universidad Peruana de Ciencias Aplicadas UPC, Lima, Perú 0009-0009-4252-2134 Rosa Salas-Castro Industrial Engineering Program, Faculty of Engineering, Universidad Peruana de Ciencias Aplicadas UPC, Lima, Perú 0000-0002-8297-1104 Ron Mesia Marketing & Logistics, Florida International University, Miami, FL, USA 0000-0003-3571-5105 Journal Article 2025 08 22 Objective: This study evaluates the impact of integrating Digital Twin technology with Lean methodologies on the operational efficiency of a firm in the Peruvian metal‑mechanic sector, focusing on a mill‑bolt production line. The company currently experiences substantial operational challenges, including high variability in production times, an inadequate facility layout, and limited technological integration. These deficiencies contribute to a low overall efficiency level of 44.04%. The purpose of this research is to demonstrate how the combined application of Digital Twin–based modeling and Lean process improvement strategies can enhance system performance, reduce operational inefficiencies, and strengthen organizational productivity. Methods: This study employs an applied research approach using a quasi‑experimental design. Data was collected through informal conversations with production operators and the review of historical production records. The methodological process was structured in two phases. In the first phase, model validation was conducted through pilot experimentation focused on Lean methodologies. In the second phase, the proposed enhancements were evaluated and validated through computational simulations, enabling a controlled assessment of their impact on system performance. Results: The findings indicate an increase in operational efficiency from 44.04% to 61.66%, demonstrating the effectiveness of integrating Digital Twin technology with Lean methodologies. These results support the significance of the combined model in enhancing system performance and reducing operational inefficiencies Conclusion: The results of this study underscore the substantial impact that a comprehensive, integrated intervention can have on the operational efficiency of metal‑mechanic production environments. The research highlights the critical value of uniting traditional process‑improvement approaches with emerging digital tools. This integration not only enhances decision‑making and process control but also strengthens the organization’s capacity for continuous improvement and long‑term competitiveness.

http://www.ijsom.com/article_2975_2ef3e50fd7c1091dda165f25be7f64fd.pdf

Kharazmi University International Journal of Supply and Operations Management 2383-1359 13 1 2026 02 01 Evaluating Blockchain Integration In Intelligent Logistics Ecosystems: A Comparative MCDM Approach 61 79 2976 10.22034/ijsom.2026.110886.3464 EN Zineb Kamal Idrissi Hassan First University, ENSA, LAMSAD Laboratory, Berrechid, Morocco Mohamed Lachgar Cadi Ayyad University, Higher Normal School, L2IS Laboratory, Marrakech, Morocco Hamid Hrimech Hassan First University, ENSA, LAMSAD Laboratory, Berrechid, Morocco Journal Article 2025 09 04 Objective: Supply chain management in dynamic environments requires advanced digital technologies to enhance transparency, security, and operational efficiency. Blockchain technology has emerged as a promising solution for improving traceability and trust in intelligent logistics ecosystems. The objective of this study is to evaluate and compare blockchain platform alternatives using a structured multi-criteria decision-making framework in order to support technology selection in modern logistics systems. Methods: This research applies a comparative multi-criteria decision-making approach integrating Analytic Hierarchy Process (AHP), Fuzzy Analytic Hierarchy Process (FAHP), AHP-TOPSIS, and Fuzzy AHP-Fuzzy TOPSIS methods. A hierarchical evaluation model was developed. Expert judgments and literature-based criteria were used to determine weights and assess the relative performance of blockchain platform alternatives. Results: The evaluation results demonstrate consistent rankings across both crisp and fuzzy decision models. Sensitivity analysis further confirms the robustness of the ranking results under different weighting scenarios. Conclusion: The findings highlight the importance of scalability, interoperability, and transparency when selecting blockchain platforms for intelligent logistics ecosystems. The proposed framework provides decision-makers with a systematic evaluation tool that can support strategic technology adoption and improve decision quality in supply chain digital transformation initiatives.

http://www.ijsom.com/article_2976_a209ca7b50dcaab2db7c2d4d1223d4d5.pdf

Kharazmi University International Journal of Supply and Operations Management 2383-1359 13 1 2026 02 01 Robust multi-objective optimization for debris removal during the response phase of unpredictable natural disasters under uncertainty 80 108 2973 10.22034/ijsom.2025.110728.3343 EN Roya Soltani Department of Industrial Engineering, Faculty of Engineering, Khatam University, Tehran, Iran Ali Nazari Science and Culture University Journal Article 2025 03 09 Objective: This study aims to address post-earthquake emergency response challenges by emphasizing the critical role of timely debris removal operations in ensuring rapid accessibility for the rescue team thereby reducing casualties, and mitigating the operational risks faced by rescuers in post-disaster environments under uncertain conditions. The objective is to develop a decision-making approach to determine the visiting order of critical nodes, the travel path between consecutive critical nodes, and the blocked edges to be cleared during debris removal operations, whose effectiveness remains stable across all plausible realizations of uncertain parameters while dealing with multiple objectives. Methods: To deal with uncertainty, a robust routing mathematical model is presented to help debris removal teams to find suitable routes subject to three objective functions including minimizing debris removal team’s travelling time plus debris removal operations time, minimizing the risk of rescuers in critical regions and maximizing the total benefit gained by accessing to damaged and critical regions of the city thereby reducing the loss of lives. To solve the proposed multi-objective model while simultaneously handling the uncertainty of parameters, a robust multi-objective optimization approach with augmented epsilon constraint is proposed in this paper. To test the efficiency of the proposed model of this study, real data taken from Rudbar-Manjil devastating Earthquake (20 June 1990, Iran) is used as a case study. The results identified the most effective routes and operational sequences for debris removal teams under uncertainty, with a fuzzy decision-making method selecting the preferred Pareto-optimal solution. Results: The analysis determined the optimal visiting sequence of critical nodes for debris removal operations. For each pair of consecutive critical nodes, the most efficient routes were identified for the debris removal teams. Additionally, the specific road segments on which debris clearance should be performed were mapped and prioritized. Sensitivity analysis confirmed the robustness of the proposed model across different budgets of uncertainties.Conclusion: This research provides a practical framework for optimizing debris removal operations under real-world uncertainties and supporting robust decision-making, which can improve the efficiency of disaster response and inform planning for future emergency management scenarios. The findings indicate that the model is versatile and can be adapted to other disaster scenarios by adjusting geographical parameters, resource constraints, and uncertainty modeling.

http://www.ijsom.com/article_2973_e37b08dd3015330dcbb5d6663667b8b8.pdf

Kharazmi University International Journal of Supply and Operations Management 2383-1359 13 1 2026 02 01 A Two-Stage Optimization Model for P2P Market Design considering Role of Retailer and Demand Response Programs 109 132 2971 10.22034/ijsom.2025.110833.3422 EN Malihe Masoumi Department of Industrial Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran 0000-0001-9541-5345 Amir Saman Kheirkhah Department of Industrial Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran Journal Article 2025 06 20 Objective: With the increasing penetration of distributed energy resources (DERs), peer-to-peer (P2P) energy trading has emerged as a promising mechanism to enhance renewable energy utilization and market efficiency. This study aims to design a P2P electricity market for grid-connected microgrids that coordinates local trading with retail and wholesale markets while accounting for geographical distance and demand response programs. Methods: A two-stage optimization framework is proposed. In the first stage, a mixed-integer linear programming (MILP) model determines the optimal neighborhood set of prosumers by maximizing renewable energy consumption and minimizing the geographical distance between trading peers. In the second stage, a mixed-integer nonlinear programming (MINLP) model is developed to optimize energy exchanges, battery storage, and pricing decisions, with the objectives of maximizing retailer profit and minimizing prosumer costs. The model incorporates time-based and incentive-based demand response programs and is validated using real residential data from Iran. Results: The numerical results show that limiting P2P transactions to geographically closer peers improve local renewable energy utilization. Sensitivity analysis on time-based DR programs indicates that the optimal pricing mechanism applies real-time pricing (RTP) to both the retail and P2P markets. Conclusion: The proposed two-stage P2P optimization framework enhances renewable energy utilization by prioritizing local trading and RTP-based pricing. Results indicate that applying real-time pricing in both retail and P2P markets increases renewable energy share and economic efficiency, while providing actionable insights for sustainable microgrid and P2P market design.

http://www.ijsom.com/article_2971_af44c4c56f385c43f2529f9b1b018f6a.pdf

Kharazmi University International Journal of Supply and Operations Management 2383-1359 13 1 2026 02 01 An End-to-End CRISP-DM Machine Learning Pipeline for Forecasting Demand in FMCG Chain Stores 133 156 2972 10.22034/ijsom.2026.110857.3442 EN Amir Mohammad Khani Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran 0000-0001-8798-2956 Arman Rezasoltani Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran 0009-0003-6960-6713 Maghsoud Amiri Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabatabai University, Tehran, Iran 0000-0002-0650-2584 Ali Husseinzadeh Kashan Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran 0000-0002-6004-6882 Journal Article 2025 08 05 Objective: Accurate forecasting of customer demand is necessary to optimize the efficiency of a supply chain, maximize profits through reduced inventory costs, and increase customer satisfaction. This research presents a new machine learning methodology based on the CRISP-DM for customer order forecasting that is both interpretive and interpretable and validates it with a real-world application from the Ofogh Kourosh Company, which offers the largest number of physical retail locations in Iran. Methods: The dataset analyzed for this research contained 844,275 sales transactions from 40 separate physical locations. Six advanced ensemble machine learning models were developed to forecast customer order demand. A beneficial factor of this research was the ability to automate hyperparameter tuning of the six predictive models using the Optuna framework. The performance of the predictive models was then evaluated using MAE, RMSE, MSE, and R² metrics. Results: Based on R² score, LightGBM was the most accurate predictive model with an R² score of 0.536. Feature importance analysis from LightGBM demonstrated that the three factors that would most determine customer order demand were the percentage of discount, price, and store location. Conclusion: This research contributes both theoretically and practically to the development of a forecast model that is regionally, culturally, and contextually relevant within the Iranian retail marketplace. Compared to the literature, this study uses actual transactional data with ML models to narrow the theory-practice gap. Future research should emanate from this development, incorporating external influences such as climate, advertising, and macroeconomic influences for even greater forecast accuracy

http://www.ijsom.com/article_2972_0f21f0349462cacdc5796990d37760ae.pdf

Kharazmi University International Journal of Supply and Operations Management 2383-1359 13 1 2026 02 01 An Economic Order Quantity model for deteriorating items with trade credit financing for Quadratic demand 157 173 2977 10.22034/ijsom.2026.110773.3379 EN Prashant Kumar Department of Applied Sciences and Humanities, Invertis University, Bareilly, India Rakesh Prakash Tripathi Department of Applied Sciences and Humanities, KNIT, Sultanpur (UP) India 228118 Kamlesh Kumar Dubey Department of Applied Sciences and Humanities, Invertis University, Bareilly, India Sachin Mishra Department of Applied Sciences and Humanities, Invertis University, Bareilly, India Journal Article 2025 04 22 Objective: Researchers established an EOQ model for degrading goods with trade credit policy under invariant, stock-linked, exponential, and linearly time-dependent demand. The analysis imposed a condition for quadratic time-dependent demand. The mathematical model is developed to obtain total profit by considering two different cases. One common observation is that the demands for the goods that are on display in the supermarket fluctuate. In this study demand is measured to be quadratic time-sensitive. The EOQ is generally applied to locate the most favorable order quantity in order to maximize the total supply cost. Methods: The EOQ model considers that the total order for an article is received into inventory at one specified time which is when the EOQ model assumes that the products are produced. Results: There are numerous costs acquired in the existent practice such as ordering cost, sales revenue, carrying cost, interest earned, and interest charged, etc. Conclusion: The implementation of the sensitivity test and an optimal solution helps to confirm how the mathematical model will generate total profit in two different ways. The EOQ method is used to identify the order quantity that maximizes total supply costs in the order’s viewpoints. This should assist with future management of degraded products under a trade credit scheme, as well as advance the accuracy and reliability of making Inventory-related decisions due to demand fluctuations.

http://www.ijsom.com/article_2977_2bd2e3373dce441c6c3bfadd1daa953e.pdf