Presenting a Warranty Decision Model from the Perspective of Warranty Providers and Buyers Using an Agent-Based Approach
Keywords:
Warranty decisions, providers and buyers, agent, based model, riskAbstract
Objective: This study aimed to present a warranty decision model from the perspective of warranty providers and buyers using an agent-based approach and to analyze the effects of market conditions, buyers’ risk preferences, and firms’ performance improvement on warranty purchase and sales patterns.
Methodology: This quantitative and applied study employed a simulation-based modeling strategy. Agent-based modeling was used to analyze the interactions between warranty providers and buyers. The simulated market consisted of 30 warranty-providing firms and 200 buyers. Buyers were modeled according to three risk-preference conditions: risk-seeking, risk-averse, and mixed. Firms were differentiated based on product quality, product price, warranty period, and the percentage of parts covered by warranty. The model was implemented in NetLogo, and several scenarios were examined, including a normal market, a monopolistic market, and a market condition involving firms’ performance improvement.
Findings: The simulation results indicated that, under normal market conditions and when risk-seeking and risk-averse buyers were equally distributed, firms with different warranty policies, product quality levels, prices, warranty periods, and covered parts achieved relatively similar sales outcomes. In this condition, buyers’ preferences did not strongly shift the market toward a specific group of firms. In the monopolistic market scenario, success was concentrated among a limited number of warranty providers that demonstrated superior performance in at least two key factors, such as high product quality, low price, longer warranty period, or broader warranty coverage. The findings also showed that as more firms improved their performance, warranty providers increasingly interacted with risk-averse buyers. This pattern was mainly explained by the greater improvement in product quality compared with other performance factors, which increased the willingness of risk-averse buyers to purchase and even pay higher prices. Conversely, when all buyers were risk-seeking, firms offering lower-priced products, even with comparatively lower quality, performed more successfully in the market.
Conclusion: The agent-based model demonstrated that warranty decisions emerge from the dynamic interaction among firm characteristics, market structure, and buyers’ risk preferences. Therefore, warranty providers should design their warranty policies according to market conditions and customer behavioral patterns to improve sales performance and maintain competitive advantage.
Downloads
References
Afsahi, M. (2017). Integrated price optimization, basic warranty policies, and warranty extension considering out-of-warranty products. Journal of Quality Engineering and Management, 8(1), 63-74.
Aggrawal, D., Anand, A., Singh, O., & Singh, J. (2014). Profit maximization by virtue of price and warranty length optimization. Journal of High Technology Management Research, 25, 1-8.
Batat, W. (2024). Phygital Customer Experience in the Metaverse: A Study of Consumer Sensory Perception of Sight, Touch, Sound, Scent, and Taste. Journal of Retailing and Consumer Services, 78, 103786. https://doi.org/10.1016/j.jretconser.2024.103786
Caruana, A., & Vella, J. (2024). Communal-brand connection and service loyalty: the mediation effect of corporate reputation and customer satisfaction among retail banking customers. International Journal of Bank Marketing, 42(7), 1472-1491. https://doi.org/10.1108/IJBM-01-2024-0012
Chauhan, S., Akhtar, A., & Gupta, A. (2022). Customer experience in digital banking: A review and future research directions. International Journal of Quality and Service Sciences, 83(2), 104-120. https://doi.org/10.1108/IJQSS-02-2021-0027
Fazli, S., & Jamati Tafti, R. (2017). Determining warranty policy using associative rules in data mining: Case study of an electronic equipment manufacturing company. Production and Operations Management, 9(2), 37-56.
Hasanvand, R., Islamobolchi, A., Ghabadi Lemoqi, T., & Sahat, S. (2022). Designing a customer service customization model in the banking industry using grounded theory (Case study: Bank Mellat). Dynamic Management and Business Analysis, 4(1), 1-16. https://doi.org/10.61838/dmbaj.388
Hijazi, R. (2022). Mobile banking service quality and customer value co-creation intention: A moderated mediated model. International Journal of Bank Marketing, 40(7), 1501-1525. https://doi.org/10.1108/IJBM-01-2022-0004
Huang, Y. S., Huang, C. D., & Ho, J. W. (2017). A customized two-dimensional extended warranty with preventive maintenance. European Journal of Operational Research, 257(3), 971-978.
Mohaghar, A., Hashemi Petrodi, H., & Talaei, H. (2016). The dynamic modeling in supply chain of new product based on dynamic systems. Journal of Industrial Management Perspective, 9-36.
Nasralahi, M., & Asgharizadeh, E. (2015). Estimating manufacturer and buyer warranty costs based on a new PRW warranty policy. Industrial Management, 8(1), 97-112.
Nasralhi, M. (2016). PRW warranty pricing model with risk-averse manufacturers. Scientific-Research Quarterly Journal of Industrial Management Study, 15(45), 75-96.
Nasralhi, M., & Asgharizadeh, A. (2015). Warranty pricing model with risk-averse buyers. Management Research in Iran, 1(20), 131-154.
Park, M., & Pham, H. (2016). Cost models for age replacement policies and block replacement policies under warranty. Applied Mathematical Modelling, 40(9), 5689-5702.
Ranjan, J., & Kadam, S. (2025). Analysis of Customer satisfaction, Service Quality and Scope of Knowledge Sharing in Retail Branch Banking of Small and Medium Enterprises in India. https://ijmec.org.in/index.php/ijmec/article/view/139
Srinivasan, R., Manivannan, S., Ethiraj, N., Prasanna Devi, S., & Vinu Kiran, S. (2016). Modelling an optimized warranty analysis methodology for fleet industry using data mining clustering methodologies. Procedia Computer Science, 87, 240-245.
Tsai, C. M., Huang, Y.-W., & Lou, K. R. (2025). The Effects of Service Quality, Brand Loyalty, and Customer Satisfaction on Repurchase Intention: An Empirical Case Study of Online Travel Agents in Taiwan. Asia Pacific Viewpoint, 66(1), 73-84. https://doi.org/10.1111/apv.12438
Turunen, A. (2025). Co-creating and validating customer value in Higher Education IT Services using Service Design. EPiC Series in Computing,
Yang, D., He, Z., & He, S. (2016). Warranty claims forecasting based on a general imperfect repair mode considering usage rate. Reliability Engineering & System Safety, 145, 147-154.
Ye, Z. S., Murthy, D. P., Xie, M., & Tang, L. C. (2016). Optimal burn-in for repairable products sold with a two-dimensional warranty. IIE Transactions, 45(2), 164-176.
Zahrawi, A., Khashashneh, S., & Althunibat, A. (2025). The impact of customer knowledge management on innovation capability and business performance: Evidence from Jordanian financial services. International Journal of Innovative Research and Scientific Studies. https://doi.org/10.53894/ijirss.v8i3.7401
Downloads
Published
Submitted
Revised
Accepted
Issue
Section
License
Copyright (c) 2025 Alireza Jafari, Ezzatollah Asgharizadeh (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
