Understanding purchase decision for remanufactured laptops: A utility-based analysis of consumer preferences in an emerging economy
DOI:
https://doi.org/10.30656/jsmi.v10i1.11890Keywords:
Latent class analysis, Maxdiff, Purchase decision , Remanufactured productsAbstract
This study investigates the determinants of consumers’ purchase decisions for remanufactured laptops in Indonesia, where a limited understanding of remanufacturing continues to hinder adoption of the circular economy. Rather than examining purchase intention, the study model’s purchase decision as a trade-off-based decision process reflects consumers’ decision-making of remanufactured products. A MaxDiff (Best–Worst Scaling Case 1) discrete choice experiment was conducted involving 434 Indonesian undergraduate students. Preferences were modeled within a random utility theory framework using multinomial logit estimation via maximum likelihood, and consumer heterogeneity was subsequently examined through latent class analysis, revealing four distinct preference segments. The results show that the dominant driver of purchase decision is functionality expectation, followed by perceived consumer effectiveness and perceived safety, indicating that consumers prioritize performance assurance and risk reduction over environmental motivations. Environmental consciousness and social influence have weaker effects, indicating an attitude–behavior gap for sustainable consumption. Furthermore, the latent class analysis identified four heterogeneous consumer segments with varying valuation logics. This suggests practical implications for future strategies to increase the purchase decision of remanufactured electronics in emerging economies through knowledge creation about the customer perceptions, where a greater emphasis on functional reliability, certification, and transparent quality communication may prove more effective than traditional environmental appeals.
Downloads
References
[1] L. Kvasničková Stanislavská et al., “Sustainability reports: Differences between developing and developed countries,” Front. Environ. Sci., vol. 11, Mar. 2023, doi: https://doi.org/10.3389/fenvs.2023.1085936.
[2] R. Mahadeva, E. N. Ganji, and S. Shah, “Sustainable Consumer Behaviours through Comparisons of Developed and Developing Nations,” Int. J. Environ. Eng. Dev., vol. 2, pp. 106–125, May 2024, doi: https://doi.org/10.37394/232033.2024.2.10.
[3] H. Soesanto, M. S. Maarif, S. Anwar, and Y. Yurianto, “Design of sustainable electronic waste management business process transformation towards circular economy transition in Jakarta, Indonesia,” IOP Conf. Ser. Earth Environ. Sci., vol. 1266, no. 1, p. 012038, Dec. 2023, doi: https://doi.org/10.1088/1755-1315/1266/1/012038.
[4] K. Chinen, M. Matsumoto, and A. Chinen, “A Synergy Effect of Consumer Orientation and Disruptive Information on Choice in Remanufactured Products,” Sustainability, vol. 15, no. 22, p. 15831, Nov. 2023, doi: https://doi.org/10.3390/su152215831.
[5] T. Yang, C. Li, and Z. Bian, “Recycling Strategies in a Collector-Led Remanufacturing Supply Chain under Blockchain and Uncertain Demand,” Processes, vol. 11, no. 5, p. 1426, May 2023, doi: https://doi.org/10.3390/pr11051426.
[6] N. Kamarudin, N. Z. Nizam, and M. H. Bakri, “Consumer Value Considerations: Impact of Remanufactured Products in The Supply Chain Industry,” Int. J. Acad. Res. Bus. Soc. Sci., vol. 14, no. 5, May 2024, doi: https://doi.org/10.6007/IJARBSS/v14-i5/21412.
[7] H. Abdulla, J. D. Abbey, A. S. Atalay, and M. G. Meloy, “Show, don’t tell: Education and physical exposure effects in remanufactured product markets,” J. Oper. Manag., vol. 70, no. 2, pp. 243–256, Mar. 2024, doi: https://doi.org/10.1002/joom.1248.
[8] X. Zhang, G. Zhou, J. Cao, and J. Lu, “A remanufacturing supply chain network with differentiated new and remanufactured products considering consumer preference, production capacity constraint and government regulation,” PLoS One, vol. 18, no. 8, p. e0289349, Aug. 2023, doi: https://doi.org/10.1371/journal.pone.0289349.
[9] A. Sharma et al., “Reshaping Industry: Adoption of Sustainable Techniques providing Remanufacturing Solutions in High-Tech industries,” E3S Web Conf., vol. 453, p. 01028, Nov. 2023, doi: https://doi.org/10.1051/e3sconf/202345301028.
[10] P. Centoamore and L. F. R. Pinto, “Remanufacturing Assessment of Machine Tools under a Circular Economy Perspective: A Resource Conservation Initiative,” Sustainability, vol. 16, no. 8, p. 3109, Apr. 2024, doi: https://doi.org/10.3390/su16083109.
[11] S. Sahoo and S. K. Jakhar, “Industry 4.0 deployment for circular economy performance—Understanding the role of green procurement and remanufacturing activities,” Bus. Strateg. Environ., vol. 33, no. 2, pp. 1144–1160, Feb. 2024, doi: https://doi.org/10.1002/bse.3542.
[12] Statista, “Laptop Sales in Malaysia, Thailand, and Philippines - Market Data,” 2023. [Online]. Available: https://www.statista.com/outlook/cmo/consumer-electronics/computing/laptops/malaysia
[13] Statista, “Laptop Market in Indonesia - Statistics & Facts,” 2024, Jakarta. [Online]. Available: https://www.statista.com/outlook/cmo/consumer-electronics/computing/laptops/indonesia
[14] V. K. Purushothaman, S. Guna Segar, Y. Muniandy, A. V. Subbarayalu, S. Prabaharan, and P. Rubavathi Marimuthu, “Comparison of neck muscle strength, range of motion, and craniovertebral angle among Malaysian young adults using different electronic devices,” Electron. J. Gen. Med., vol. 20, no. 4, p. em499, Jul. 2023, doi: https://doi.org/10.29333/ejgm/13185.
[15] G. T. Nguyen, T. T. K. Lam, and N. T. H. Huynh, “Assessment of E-Waste Management and Potential for Laptop Reuse and Recycling,” Civ. Eng. J., vol. 9, no. 6, pp. 1471–1481, Jun. 2023, doi: https://doi.org/10.28991/CEJ-2023-09-06-013.
[16] L. Fraccascia, G. Ceccarelli, and R. M. Dangelico, “Green products from industrial symbiosis: Are consumers ready for them?,” Technol. Forecast. Soc. Change, vol. 189, p. 122395, Apr. 2023, doi: https://doi.org/10.1016/j.techfore.2023.122395.
[17] Y. Liu, M. Li, H. Feng, and N. Feng, “Cross‐licensing or not: The optimal choices of competing ICT firms in a duopoly market,” Manag. Decis. Econ., vol. 46, no. 1, pp. 67–87, Jan. 2025, doi: https://doi.org/10.1002/mde.4352.
[18] S. Hadi, “The Influence of Awareness of Consequences, Internal Locus of Control, and External Locus of Control on Pro-Environmental Behavior in Students,” Int. J. Curr. Sci. Res. Rev., vol. 07, no. 08, Aug. 2024, doi: https://doi.org/10.47191/ijcsrr/V7-i8-82.
[19] M. K. Uddin, “Environmental education for sustainable development in Bangladesh and its challenges,” Sustain. Dev., vol. 32, no. 1, pp. 1137–1151, Feb. 2024, doi: https://doi.org/10.1002/sd.2728.
[20] W. Shang, R. Zhu, W. Liu, and Q. Liu, “Understanding the Influences on Green Purchase Intention with Moderation by Sustainability Awareness,” Sustainability, vol. 16, no. 11, p. 4688, May 2024, doi: https://doi.org/10.3390/su16114688.
[21] Y. Chen, J. Wang, and Y. Yu, “A Study on Consumers’ Willingness to Pay for Remanufactured Products: A Study Based on Hierarchical Regression Method,” Front. Psychol., vol. 10, Sep. 2019, doi: https://doi.org/10.3389/fpsyg.2019.02044.
[22] M. Alyahya, G. Agag, M. Aliedan, Z. H. Abdelmoety, and M. M. Daher, “A sustainable step forward: Understanding factors affecting customers’ behaviour to purchase remanufactured products,” J. Retail. Consum. Serv., vol. 70, p. 103172, Jan. 2023, doi: https://doi.org/10.1016/j.jretconser.2022.103172.
[23] D. Singhal, S. Tripathy, and S. K. Jena, “Acceptance of remanufactured products in the circular economy: an empirical study in India,” Manag. Decis., vol. 57, no. 4, pp. 953–970, Apr. 2019, doi: https://doi.org/10.1108/MD-06-2018-0686.
[24] Z. Muranko, D. Andrews, I. Chaer, and E. J. Newton, “Circular economy and behaviour change: Using persuasive communication to encourage pro-circular behaviours towards the purchase of remanufactured refrigeration equipment,” J. Clean. Prod., vol. 222, pp. 499–510, Jun. 2019, doi: https://doi.org/10.1016/j.jclepro.2019.02.219.
[25] D. Kabel, S. Ahlstedt, M. Elg, and E. Sundin, “Consumer purchase intention of remanufactured EEE products – A study on robotic lawn mowers in Sweden,” Procedia CIRP, vol. 90, pp. 79–84, 2020, doi: https://doi.org/10.1016/j.procir.2020.01.091.
[26] S. Wang, J. Wang, F. Yang, J. Li, and J. Song, “Determinants of consumers’ remanufactured products purchase intentions: Evidence from China,” Int. J. Prod. Res., vol. 58, no. 8, pp. 2368–2383, Apr. 2020, doi: https://doi.org/10.1080/00207543.2019.1630767.
[27] A. T. H. Kuah and P. Wang, “Circular economy and consumer acceptance: An exploratory study in East and Southeast Asia,” J. Clean. Prod., vol. 247, p. 119097, Feb. 2020, doi: https://doi.org/10.1016/j.jclepro.2019.119097.
[28] R. Pisitsankkhakarn and S. Vassanadumrongdee, “Enhancing purchase intention in circular economy: An empirical evidence of remanufactured automotive product in Thailand,” Resour. Conserv. Recycl., vol. 156, p. 104702, May 2020, doi: https://doi.org/10.1016/j.resconrec.2020.104702.
[29] A. Wewer, P. Bilge, and F. Dietrich, “Examination of the attitude and assessment of new, used and overhauled products and the influence on the purchase decision- a survey,” Procedia CIRP, vol. 90, pp. 121–126, 2020, doi: https://doi.org/10.1016/j.procir.2020.01.092.
[30] Y. Wang, Q. Zhu, H. Krikke, and B. Hazen, “How product and process knowledge enable consumer switching to remanufactured laptop computers in circular economy,” Technol. Forecast. Soc. Change, vol. 161, p. 120275, Dec. 2020, doi: https://doi.org/10.1016/j.techfore.2020.120275.
[31] A. D. Hunka, M. Linder, and S. Habibi, “Determinants of consumer demand for circular economy products. A case for reuse and remanufacturing for sustainable development,” Bus. Strateg. Environ., vol. 30, no. 1, pp. 535–550, Jan. 2021, doi: https://doi.org/10.1002/bse.2636.
[32] G. A. Abbasi, K. Q. Chee Keong, K. M. Kumar, and M. Iranmanesh, “Asymmetrical modelling to understand purchase intention towards remanufactured products in the circular economy and a closed-loop supply chain: An empirical study in Malaysia,” J. Clean. Prod., vol. 359, p. 132137, Jul. 2022, doi: https://doi.org/10.1016/j.jclepro.2022.132137.
[33] L. Milios and M. Matsumoto, “Consumer Perception of Remanufactured Automotive Parts and Policy Implications for Transitioning to a Circular Economy in Sweden,” Sustainability, vol. 11, no. 22, p. 6264, Nov. 2019, doi: https://doi.org/10.3390/su11226264.
[34] Y. Chen, J. Wang, and X. Jia, “Refurbished or Remanufactured?—An Experimental Study on Consumer Choice Behavior,” Front. Psychol., vol. 11, May 2020, doi: https://doi.org/10.3389/fpsyg.2020.00781.
[35] R. Aydin and M. Mansour, “Investigating sustainable consumer preferences for remanufactured electronic products,” J. Eng. Res., vol. 11, no. 1, p. 100008, Mar. 2023, doi: https://doi.org/10.1016/j.jer.2023.100008.
[36] T. Van Nguyen, L. Zhou, A. Y. L. Chong, B. Li, and X. Pu, “Predicting customer demand for remanufactured products: A data-mining approach,” Eur. J. Oper. Res., vol. 281, no. 3, pp. 543–558, Mar. 2020, doi: https://doi.org/10.1016/j.ejor.2019.08.015.
[37] Y. Zhang, N. M. Hassan, and A. A. Sheikh, “Unboxing the dilemma associated with online shopping and purchase behavior for remanufactured products: A smart strategy for waste management,” J. Environ. Manage., vol. 351, p. 119790, Feb. 2024, doi: https://doi.org/10.1016/j.jenvman.2023.119790.
[38] R. H. W. Boyer, A. D. Hunka, M. Linder, K. A. Whalen, and S. Habibi, “Product Labels for the Circular Economy: Are Customers Willing to Pay for Circular?,” Sustain. Prod. Consum., vol. 27, pp. 61–71, Jul. 2021, doi: https://doi.org/10.1016/j.spc.2020.10.010.
[39] M. Zhai, X. Wang, and X. Zhao, “The importance of online customer reviews characteristics on remanufactured product sales: Evidence from the mobile phone market on Amazon.com,” J. Retail. Consum. Serv., vol. 77, p. 103677, Mar. 2024, doi: https://doi.org/10.1016/j.jretconser.2023.103677.
[40] J. Quariguasi Frota Neto and M. Dutordoir, “Mapping the market for remanufacturing: An application of ‘Big Data’ analytics,” Int. J. Prod. Econ., vol. 230, p. 107807, Dec. 2020, doi: https://doi.org/10.1016/j.ijpe.2020.107807.
[41] D. Singhal, S. K. Jena, and S. Tripathy, “Factors influencing the purchase intention of consumers towards remanufactured products: a systematic review and meta-analysis,” Int. J. Prod. Res., vol. 57, no. 23, pp. 7289–7299, Dec. 2019, doi: https://doi.org/10.1080/00207543.2019.1598590.
[42] Y. Wang and B. T. Hazen, “Consumer product knowledge and intention to purchase remanufactured products,” Int. J. Prod. Econ., vol. 181, pp. 460–469, Nov. 2016, doi: https://doi.org/10.1016/j.ijpe.2015.08.031.
[43] J. A. Sivasothey, S. F. Yeo, and C. L. Tan, “Assessing the Applicability and Reliability of the SOR Theory in the Healthcare Sector: A Comprehensive Review of Recent Research,” paperASIA, vol. 40, no. 1(b), pp. 1–12, Jan. 2024, doi: https://doi.org/10.59953/paperasia.v40i1(b).55.
[44] A. Lotem, M. A. Fishman, and L. Stone, “From reciprocity to unconditional altruism through signalling benefits,” Proc. R. Soc. London. Ser. B Biol. Sci., vol. 270, no. 1511, pp. 199–205, Jan. 2003, doi: https://doi.org/10.1098/rspb.2002.2225.
[45] A. Novisma and E. Iskandar, “The study of millennial farmers behavior in agricultural production,” IOP Conf. Ser. Earth Environ. Sci., vol. 1183, no. 1, p. 012112, May 2023, doi: https://doi.org/10.1088/1755-1315/1183/1/012112.
[46] N. Y. Kim and S. Y. Jeong, “Perioperative patient safety management activities: A modified theory of planned behavior,” PLoS One, vol. 16, no. 6, p. e0252648, Jun. 2021, doi: https://doi.org/10.1371/journal.pone.0252648.
[47] C. Ye, A. A. Ma’rof, H. Abdullah, H. H. Hamsan, L. Zhang, and Q. We, “Research Status and Development Trend of the Theory of Planned Behavior: A Visual Analysis Based between 2012-2022,” Int. J. Acad. Res. Bus. Soc. Sci., vol. 12, no. 12, Dec. 2022, doi: https://doi.org/10.6007/IJARBSS/v12-i12/15747.
[48] G. Antonides and L. Hovestadt, “Product Attributes, Evaluability, and Consumer Satisfaction,” Sustainability, vol. 13, no. 22, p. 12393, Nov. 2021, doi: https://doi.org/10.3390/su132212393.
[49] O. Ben Akpoyomare, L. P. K. Adeosun, and R. A. Ganiyu, “The Influence of Product Attributes on Consumer Purchase Decision in the Nigerian Food and Beverages Industry: A Study of Lagos Metropolis,” Am. J. Bus. Manag., vol. 2, no. 1, p. 196, May 2013, doi: https://doi.org/10.11634/216796061706211.
[50] M. Zhou, W. M. Thayer, and J. F. P. Bridges, “Using Latent Class Analysis to Model Preference Heterogeneity in Health: A Systematic Review,” Pharmacoeconomics, vol. 36, no. 2, pp. 175–187, Feb. 2018, doi: https://doi.org/10.1007/s40273-017-0575-4.
[51] L. Dougherty, N. Bellows, and C. Dadi, “Creating Reproductive Health Behavioral Profiles for Women of Reproductive Age in Niger Using Cross-Sectional Survey Data: A Latent Class Analysis,” Int. J. Public Health, vol. 68, Jan. 2023, doi: https://doi.org/10.3389/ijph.2023.1605247.
[52] T. S. Wallner, J. M. B. Haslbeck, L. Magnier, and R. Mugge, “A network analysis of factors influencing the purchase intentions for refurbished electronics,” Sustain. Prod. Consum., vol. 46, pp. 617–628, May 2024, doi: https://doi.org/10.1016/j.spc.2024.03.009.
[53] S. Budiono, J. T. Purba, and G. P. Adirinekso, “Measurement of Purchase Intention through Brand Awareness, Perceived Quality, Brand Loyalty: An Experience from Indonesia,” in Proceedings of the International Conference on Industrial Engineering and Operations Management, Michigan, USA: IEOM Society International, Aug. 2021. doi: https://doi.org/10.46254/IN01.20210084.
[54] S. Aguilar and V. Kreinovich, “Why Best-Worst Method Works Well,” in 2022 IEEE 11th International Conference on Intelligent Systems (IS), IEEE, Oct. 2022, pp. 1–4. doi: https://doi.org/10.1109/IS57118.2022.10019717.
[55] A. L. R. Schuster, N. L. Crossnohere, N. B. Campoamor, I. L. Hollin, and J. F. P. Bridges, “The rise of best-worst scaling for prioritization: A transdisciplinary literature review,” J. Choice Model., vol. 50, p. 100466, Mar. 2024, doi: https://doi.org/10.1016/j.jocm.2023.100466.
[56] A. C. Mühlbacher, A. Kaczynski, P. Zweifel, and F. R. Johnson, “Experimental measurement of preferences in health and healthcare using best-worst scaling: an overview,” Health Econ. Rev., vol. 6, no. 1, p. 2, Dec. 2016, doi: https://doi.org/10.1186/s13561-015-0079-x.
[57] J. K. Vermunt, “Latent class analysis,” in International Encyclopedia of Education(Fourth Edition), Elsevier, 2023, pp. 637–645. doi: https://doi.org/10.1016/B978-0-12-818630-5.10075-2.
[58] M. Visser and S. Depaoli, “A Guide to Detecting and Modeling Local Dependence in Latent Class Analysis Models,” Struct. Equ. Model. A Multidiscip. J., vol. 29, no. 6, pp. 971–982, Nov. 2022, doi: https://doi.org/10.1080/10705511.2022.2033622.
[59] K. Nylund-Gibson, A. C. Garber, J. Singh, M. R. Witkow, A. Nishina, and A. Bellmore, “The Utility of Latent Class Analysis to Understand Heterogeneity in Youth Coping Strategies: A Methodological Introduction,” Behav. Disord., vol. 48, no. 2, pp. 106–120, Feb. 2023, doi: https://doi.org/10.1177/01987429211067214.
[60] M. Ye, R. Sakiyama, and M. Abdulsalam, “Investigating Heterogeneous Media Multitasking Behavior: A Latent Class Analysis Approach,” Int. J. Mark. Stud., vol. 14, no. 2, p. 98, Sep. 2022, doi: https://doi.org/10.5539/ijms.v14n2p98.
[61] I. Pavlić, K. Vojvodić, and B. Puh, “Consumer Segmentation in Food Retailing in Croatia: A Latent Class Analysis,” Market-Tržište, vol. 32, no. SI, pp. 9–29, Dec. 2020, doi: https://doi.org/10.22598/mt/2020.32.spec-issue.9.
[62] S. Kumar, A. Dabgotra, and D. Mukherjee, “Latent class analysis of multigroup heterogeneity in propensity for academic dishonesty,” J. Math. Sociol., vol. 48, no. 1, pp. 81–99, Jan. 2024, doi: https://doi.org/10.1080/0022250X.2023.2179999.
[63] P. P. Biemer, Latent Class Analysis of Survey Error. Wiley, 2010. doi: https://doi.org/10.1002/9780470891155.
[64] K. L. Nylund, T. Asparouhov, and B. O. Muthén, “Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study,” Struct. Equ. Model. A Multidiscip. J., vol. 14, no. 4, pp. 535–569, Oct. 2007, doi: https://doi.org/10.1080/10705510701575396.
[65] G. Celeux and G. Soromenho, “An entropy criterion for assessing the number of clusters in a mixture model,” J. Classif., vol. 13, no. 2, pp. 195–212, Sep. 1996, doi: https://doi.org/10.1007/BF01246098.
[66] K. Takahashi, H. Takamura, and M. Okumura, “Estimation of Class Membership Probabilities by Using Multiple Classification Scores,” J. Nat. Lang. Process., vol. 15, no. 2, pp. 3–38, 2008, doi: https://doi.org/10.5715/jnlp.15.2_3.
[67] R. A. Cribbie, L. Fiksenbaum, H. J. Keselman, and R. R. Wilcox, “Effect of non‐normality on test statistics for one‐way independent groups designs,” Br. J. Math. Stat. Psychol., vol. 65, no. 1, pp. 56–73, Feb. 2012, doi: https://doi.org/10.1111/j.2044-8317.2011.02014.x.
[68] O. Dag, A. Dolgun, and M. Konar, Naime, “onewaytests: An R Package for One-Way Tests in Independent Groups Designs,” R J., vol. 10, no. 1, p. 175, 2018, doi: https://doi.org/10.32614/RJ-2018-022.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Komang Nickita Sari, Maria Anityasari, Tatbita Titin Suhariyanto, Rindi Kusumawardani

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
All articles in Jurnal Sistem dan Manajemen Industri can be disseminated provided they include the identity of the article and the source of the article Jurnal Sistem dan Manajemen Industri. The publisher is not responsible for the contents of the article. The content of the article is the sole responsibility of the author
Jurnal Sistem dan Manajemen Industri is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.












