Consumer Perceptions of Dynamic Pricing in the Electronics Industry: A Case in Makassar City
Keywords:
Dynamic pricing, consumer perception, electronics industry, purchasing behavior, Emerging MarketsAbstract
Dynamic pricing has proliferated across global electronics retail, yet consumer responses to this practice remain critically underexplored in emerging economies. This qualitative study examines consumer perceptions of dynamic pricing in Makassar City's electronics retail sector, conducted through 27 in-depth interviews and 7 focus group discussions involving 76 participants, and analyzed using reflexive thematic analysis. Five interconnected themes emerged: (1) fragmented awareness—consumers recognize price fluctuations but misattribute them to external economic forces rather than deliberate retailer strategies; (2) conditional fairness—cost-justified variations are acceptable, whereas opaque algorithmic personalization triggers strong unfairness judgments; (3) emotional ambivalence oscillating between excitement at price decreases and betrayal at unexpected increases; (4) strategic decision paralysis manifesting as purchase postponement and compulsive price monitoring; and (5) systematic trust erosion transforming loyal customers into price-sensitive switchers and generating retaliatory negative word-of-mouth. The study introduces the "ignorance dividend"—temporary retailer advantages derived from consumer unawareness that carry substantial latent backlash risks as digital literacy spreads—and documents a dynamic pricing paradox wherein algorithmic optimization paradoxically contracts rather than expands demand. A digital literacy divide further creates de facto price discrimination, favoring sophisticated consumers while leaving vulnerable populations subject to unrecognized exploitation. Theoretically, this research challenges the universality of Western fairness models, demonstrating that fairness perceptions are fundamentally context-dependent and culturally contingent. Practically, the findings call for transparency-enhancing pricing strategies and regulatory frameworks that address information asymmetries in digitally-mediated commerce, affirming that sustainable competitive advantage derives from trust-based relationships rather than short-term algorithmic exploitation.
Downloads
References
Alderighi, M., Nava, C. R., Calabrese, R., & Salvemini, M. (2022). Consumer perception of price fairness and dynamic pricing: Evidence from Booking.com. Journal of Business Research, 148, 196–206. https://doi.org/10.1016/j.jbusres.2022.03.017
Ashiq, R., & Hussain, A. (2024). Exploring the effects of e-service quality and e-trust on consumers' e-satisfaction and e-loyalty: Insights from online shoppers in Pakistan. Journal of Electronic Business & Digital Economics, 3(2), 117–141. https://doi.org/10.1108/JEBDE-09-2023-0019
Bolton, L. E., Warlop, L., & Alba, J. W. (2003). Consumer perceptions of price (un)fairness. Journal of Consumer Research, 29(4), 474–491. https://doi.org/10.1086/346244
Chand, S. P. (2025). Methods of data collection in qualitative research: Interviews, focus groups, observations, and document analysis. Advances in Educational Research and Evaluation, 6(1), 303–317. https://doi.org/10.25082/AERE.2025.01.001
Chaudhuri, A., & Holbrook, M. B. (2001). The chain of effects from brand trust and brand affect to brand performance: The role of brand loyalty. Journal of Marketing, 65(2), 81–93. https://doi.org/10.1509/jmkg.65.2.81.18255
Chenavaz, R. Y., & De Giovanni, D. (2025). Dynamic pricing, objective and subjective quality, and the price–quality relationship. European Journal of Operational Research. Advance online publication. https://doi.org/10.1016/j.ejor.2025.08.047
Chenavaz, R. Y., & Dimitrov, S. (2025). Artificial intelligence and dynamic pricing: A systematic literature review. Journal of Applied Economics, 28(1), 2466140. https://doi.org/10.1080/15140326.2025.2466140
Cohen, M. C., Miao, S., & Wang, Y. (2025). Dynamic pricing with fairness constraints. Operations Research, 73(6), 3027–3043. https://doi.org/10.1287/opre.2023.0123
Geampana, A., & Perrotta, M. (2025). Using interview excerpts to facilitate focus group discussion. Qualitative Research, 25(2), 508–527. https://doi.org/10.1177/14687941241234283
Hagaman, A., Rhodes, E. C., Aloe, C. F., Hennein, R., Peng, M. L., Deyling, M., Georgescu, M., Nyhan, K., Schwartz, A., Zhou, K., Katague, M., Egger, E., & Spiegelman, D. (2025). How are qualitative methods used in implementation science research? Results from a systematic scoping review. Implementation Research and Practice, 6, 1–23. https://doi.org/10.1177/26334895251367470
Haws, K. L., & Bearden, W. O. (2006). Dynamic pricing and consumer fairness perceptions. Journal of Consumer Research, 33(3), 304–311. https://doi.org/10.1086/508435
Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.
Kharbat, F. F., & Ngah, A. H. (2025). Using thematic analysis in qualitative research. Research and Practice in Technology Enhanced Learning, 20(022), 1–18. https://doi.org/10.58459/rptel.2025.20022
Kopalle, P. K., Pauwels, K., Akella, L. Y., & Gangwar, M. (2023). Dynamic pricing: Definition, implications for managers, and future research directions. Journal of Retailing, 99(4), 580–593. https://doi.org/10.1016/j.jretai.2023.11.003
Li, Y., Hu, N., Jia, Y., & Hou, M. (2021). Understanding information asymmetry in online trading through the dual lens of reciprocity and social ties. Information & Management, 58(8), 103541. https://doi.org/10.1016/j.im.2021.103541
Lim, W. M. (2025). What is qualitative research? An overview and guidelines. Journal of Innovation & Knowledge, 10(1), 100568. https://doi.org/10.1016/j.jik.2024.100568
Lu, J., Zhang, Y., Park, E., & Song, M. (2025). Would you shop on a platform that discriminates against users? A study on the impact of algorithmic price discrimination on consumer purchase intention. Telematics and Informatics, 101, 102306. https://doi.org/10.1016/j.tele.2025.102306
Malc, D., Mumel, D., & Pisnik, A. (2016). Exploring price fairness perceptions and their influence on consumer behavior. Journal of Business Research, 69(9), 3693–3697. https://doi.org/10.1016/j.jbusres.2016.03.031
Naeem, M., Ozuem, W., Howell, K., & Ranfagni, S. (2023). A step-by-step process of thematic analysis to develop a conceptual model in qualitative research. International Journal of Qualitative Methods, 22, 1–18. https://doi.org/10.1177/16094069231205789
Nguyen, T. T. N. (2025). Perceptions of AI-driven dynamic pricing strategies and their financial impact on Vietnamese tourism companies. Journal of Information Systems Engineering and Management, 10(3s), 145–163. https://doi.org/10.52783/jisem.v10i3s.367
Priester, A., Robbert, T., & Roth, S. (2020). A special price just for you: Effects of personalized dynamic pricing on consumer fairness perceptions. Journal of Revenue and Pricing Management, 19(2), 99–112. https://doi.org/10.1057/s41272-019-00224-3
Qian, J. (2025). A study on the effect of pricing strategy and perceived value on purchase intention. Advances in Economics, Management, and Political Sciences. https://doi.org/10.54254/2754-1169/2025.bl26133
Quintus, M., Mayr, K., Hofer, K. M., & Chiu, Y. T. (2024). Managing consumer trust in e-commerce: Evidence from advanced versus emerging markets. International Journal of Retail & Distribution Management, 52(10–11), 1038–1056. https://doi.org/10.1108/IJRDM-10-2023-0609
Sahabuddin, R., Arif, H. M., Lestari, W., Alviolin, E., & Dzaky, M. N. (2024). Information transparency as a mediator in the relationship between digital marketing ethics and consumer trust in e-commerce. Maximal Journal: Jurnal Ilmiah Bidang Sosial, Ekonomi, Budaya Dan Pendidikan, 2(1), 27-37.
Senali, M. G., Iranmanesh, M., Ghobakhloo, M., Foroughi, B., Asadi, S., & Rejeb, A. (2024). Determinants of trust and purchase intention in social commerce: Perceived price fairness and trust disposition as moderators. Electronic Commerce Research and Applications, 64, 101370. https://doi.org/10.1016/j.elerap.2024.101370
Taylor, C. R. (2025). A slap in the face! Why artificial intelligence should not be used to price-discriminate against loyal customers. International Journal of Advertising, 44(3), 393–395. https://doi.org/10.1080/02650487.2025.2465148
UNCTAD. (2020). Consumer trust in the digital economy: The case for online dispute resolution (Research Paper No. 72). United Nations Conference on Trade and Development.
Victor, V., Karakunnel, J. J., Loganathan, S., & Meyer, D. F. (2024). From loyalty to liability: How AI-driven price discrimination erodes customer trust. International Journal of Advertising, 44(3), 495–503. https://doi.org/10.1080/02650487.2025.2465148
Wang, J., Zhou, Z., Cao, S., Liu, L., Ren, J., & Morrison, A. M. (2025). Who sets prices better? The impact of pricing agents on consumer negative word-of-mouth when applying price discrimination. Tourism Management, 106, 105003. https://doi.org/10.1016/j.tourman.2024.105003
Wattoo, M. U., Du, J., Shahzad, F., & Kousar, S. (2025). Shaping e-commerce experiences: Unraveling the impact of service quality on youth customer behavior in a developing nation. SAGE Open, 15(1), 1–17. https://doi.org/10.1177/21582440241311786
Wutich, A., Beresford, M., & Bernard, H. R. (2024). Sample sizes for 10 types of qualitative data analysis: An integrative review, empirical guidance, and next steps. International Journal of Qualitative Methods, 23, 1–29. https://doi.org/10.1177/16094069241296206
Xia, L., & Monroe, K. B. (2010). Is a good deal always fair? Examining the concepts of transaction value and price fairness. Journal of Economic Psychology, 31(6), 884–894. https://doi.org/10.1016/j.joep.2010.07.001
Xia, L., Monroe, K. B., & Cox, J. L. (2004). The price is unfair! A conceptual framework of price fairness perceptions. Journal of Marketing, 68(4), 1–15. https://doi.org/10.1509/jmkg.68.4.1.42733
YouGov. (2024). Fair or unfair? Consumer opinion on dynamic pricing in 2024. YouGov Surveys. https://business.yougov.com/content/49709-fair-unfair-consumer-opinion-on-dynamic-pricing-2024
Zhang, X., & Cheng, X. (2024). I lose vs. I earn: Consumer perceived price fairness toward algorithmic (vs. human) price discrimination. Proceedings of the CHI Conference on Human Factors in Computing Systems, 1–17. https://doi.org/10.1145/3613904.3642280
Zhao, H., Yao, X., Liu, Z., & Yang, Q. (2021). Impact of pricing and product information on consumer buying behavior with customer satisfaction in a mediating role. Frontiers in Psychology, 12, 720151. https://doi.org/10.3389/fpsyg.2021.720151




