Abstract
This dissertation examines the ethical acceptability of personalised pricing strategies and investigates consumer responses upon discovering such practices. Employing a systematic literature synthesis methodology, this study analyses contemporary peer-reviewed research spanning business ethics, consumer psychology, and regulatory policy domains. The findings reveal that ethical acceptability remains contingent upon the normative framework applied: whilst egalitarian perspectives may justify personalised pricing under specific conditions, utilitarian and prioritarian frameworks typically deem it ethically inferior to uniform pricing when accounting for perceived unfairness and privacy aversion. Empirical evidence demonstrates predominantly negative consumer responses, characterised by diminished perceptions of price fairness, reduced purchase intentions, heightened privacy concerns, and adversarial behaviours including negative word-of-mouth and platform abandonment. Limited acceptance emerges when prices are framed as discounts, derived from purchase history rather than sensitive data, or when participatory mechanisms exist. The research concludes that whilst personalised pricing possesses theoretical ethical defensibility under narrow conditions, its practical implementation generates substantial consumer backlash that undermines trust and loyalty. These findings carry significant implications for marketing practitioners, policymakers, and regulatory bodies seeking to balance commercial innovation with consumer protection.
Introduction
The digital transformation of commerce has fundamentally altered the relationship between sellers and consumers, enabling unprecedented capabilities for price differentiation based on individual characteristics, behaviours, and circumstances. Personalised pricing, whereby firms charge different prices to different consumers for identical products or services based on algorithmically-derived assessments of willingness to pay, has emerged as a particularly contentious manifestation of this technological evolution. This practice represents a significant departure from traditional uniform pricing models and raises profound questions concerning fairness, privacy, and the ethical boundaries of commercial activity in digital marketplaces.
The academic and practical significance of examining personalised pricing cannot be overstated. From a commercial perspective, personalised pricing offers firms the theoretical opportunity to maximise revenue extraction and potentially expand market access to price-sensitive consumers. However, these potential benefits exist in tension with fundamental principles of market fairness and consumer autonomy that underpin trust in commercial relationships. The opacity of algorithmic pricing systems, combined with substantial information asymmetries between firms and consumers, creates conditions that many scholars and policymakers regard as potentially exploitative (Seele et al., 2019).
The contemporary relevance of this topic has intensified considerably as technological capabilities have advanced. Machine learning algorithms, combined with vast repositories of consumer data harvested through digital interactions, have rendered personalised pricing increasingly feasible and sophisticated. Major online retailers, travel booking platforms, and insurance providers have implemented or experimented with various forms of price personalisation, often without explicit consumer knowledge or consent. This proliferation has attracted regulatory attention across multiple jurisdictions, with the European Union’s General Data Protection Regulation and various national consumer protection frameworks addressing aspects of automated decision-making and price discrimination (Borgesius and Poort, 2017).
Understanding how consumers respond when they discover personalised pricing practices carries substantial practical importance for firms contemplating such strategies. Consumer reactions determine not merely immediate purchase behaviour but also longer-term outcomes including brand loyalty, advocacy behaviour, and willingness to share personal data. The reputational risks associated with consumer backlash may significantly offset any revenue gains from price optimisation, rendering the commercial case for personalised pricing considerably more complex than initial analyses might suggest.
This dissertation addresses the dual questions of ethical acceptability and consumer response through a comprehensive synthesis of contemporary research evidence. By integrating insights from business ethics, consumer psychology, marketing science, and regulatory scholarship, this study aims to provide a nuanced understanding of personalised pricing that informs both academic discourse and practical decision-making.
Aim and objectives
The overarching aim of this dissertation is to critically evaluate the ethical acceptability of personalised pricing strategies and to systematically analyse consumer responses when such practices are discovered or disclosed.
To achieve this aim, the following specific objectives guide the investigation:
1. To examine personalised pricing through multiple ethical frameworks, including utilitarian, prioritarian, and egalitarian perspectives, identifying conditions under which such practices may be considered morally acceptable or objectionable.
2. To synthesise empirical evidence concerning consumer perceptions of fairness in personalised pricing contexts, including emotional responses and attributional processes.
3. To analyse documented behavioural responses to personalised pricing discovery, encompassing purchase intentions, loyalty outcomes, and strategic consumer behaviours.
4. To evaluate the role of privacy concerns in shaping consumer responses to data-driven pricing practices.
5. To assess the effectiveness of disclosure mechanisms and identify factors that may moderate negative consumer responses to personalised pricing.
6. To develop evidence-based recommendations for practitioners and policymakers regarding the implementation and regulation of personalised pricing strategies.
Methodology
This dissertation employs a systematic literature synthesis methodology to address the research questions. Literature synthesis represents an appropriate methodological approach when the research objective involves integrating findings from multiple existing studies to develop comprehensive understanding of a phenomenon, rather than generating new primary data. This approach enables rigorous aggregation of evidence across diverse research contexts, methodological approaches, and disciplinary perspectives.
The literature search strategy encompassed peer-reviewed academic journals across multiple relevant disciplines, including business ethics, consumer behaviour, marketing science, information systems, and legal studies. Primary databases interrogated included Web of Science, Scopus, and Google Scholar, supplemented by targeted searches of specialist journals in pricing, ethics, and consumer psychology. Search terms included combinations of “personalised pricing,” “personalized pricing,” “price discrimination,” “dynamic pricing,” “algorithmic pricing,” “consumer response,” “fairness perceptions,” “price fairness,” and “ethical pricing.”
Inclusion criteria specified peer-reviewed empirical studies and theoretical analyses published in English within the past decade, ensuring contemporary relevance given the rapidly evolving technological and regulatory landscape. Sources were evaluated for methodological rigour, with preference given to studies employing robust experimental designs, validated measurement instruments, and appropriate analytical techniques. Both quantitative and qualitative research contributions were incorporated to capture the full spectrum of evidence regarding consumer responses.
The analytical approach involved systematic extraction of key findings concerning ethical evaluations, consumer fairness perceptions, emotional responses, behavioural intentions, and actual behaviours. These findings were then synthesised thematically, with attention to convergent and divergent evidence across studies. Where possible, moderating factors influencing consumer responses were identified to develop a nuanced understanding of contextual influences on reactions to personalised pricing.
The synthesis was supplemented by examination of relevant policy documents from governmental and intergovernmental organisations to contextualise findings within contemporary regulatory frameworks. This multi-source approach ensures comprehensive coverage of the topic whilst maintaining rigorous quality standards appropriate for academic publication.
Literature review
Conceptualising personalised pricing
Personalised pricing, also termed individual price discrimination or first-degree price discrimination in economic theory, involves charging consumers different prices for identical goods or services based on estimated individual willingness to pay. This practice differs from traditional market segmentation, which applies uniform prices to defined consumer groups, by targeting price differentiation at the individual level. The feasibility of such granular pricing has dramatically increased with advances in data collection, storage, and algorithmic processing capabilities.
Seele et al. (2019) provide a comprehensive mapping of the ethicality of algorithmic pricing, distinguishing personalised pricing from dynamic pricing, which adjusts prices based on temporal factors such as demand fluctuations rather than individual characteristics. Their review identifies transparency, fairness, consent, and privacy as central ethical dimensions requiring consideration when evaluating personalised pricing practices. The authors emphasise that algorithmic opacity creates particular ethical challenges, as consumers and regulators may be unable to understand or scrutinise the bases upon which prices are determined.
The legal and regulatory context of personalised pricing varies across jurisdictions but generally permits price discrimination absent specific prohibitions. Botta and Wiedemann (2019) examine personalised pricing through the lens of EU competition law, analysing whether such practices constitute exploitative abuse of dominance. They argue that whilst blanket prohibition would be disproportionate, case-by-case assessment is necessary to identify circumstances where personalised pricing becomes exploitative. Similarly, Borgesius and Poort (2017) examine the intersection of personalised pricing with EU data privacy law, noting that the General Data Protection Regulation imposes constraints on automated decision-making that may limit certain forms of data-driven pricing.
Ethical frameworks and their application
The ethical acceptability of personalised pricing fundamentally depends upon the normative framework employed for evaluation. Mazrekaj et al. (2024) provide particularly valuable insight through simulation studies that model the welfare implications of personalised pricing under different ethical theories whilst incorporating realistic assumptions about consumer psychology.
From a utilitarian perspective, which prioritises maximisation of aggregate welfare, personalised pricing might initially appear beneficial if it enables some consumers to access products at lower prices than uniform pricing would permit. However, when perceived unfairness and surveillance or privacy aversion are incorporated into welfare calculations, personalised pricing frequently performs worse than uniform pricing. Consumers experience genuine disutility from perceived unfairness, and this negative welfare effect may outweigh gains from improved market access for price-sensitive consumers.
Prioritarian frameworks, which assign greater moral weight to benefits accruing to worse-off individuals, similarly tend to evaluate personalised pricing negatively once realistic psychological factors are incorporated. Whilst personalised pricing could theoretically benefit low-income consumers through lower prices, the evidence suggests that disadvantaged consumers often pay higher prices under such schemes, particularly when price discrimination is based on geographic location or device characteristics that correlate with socioeconomic status.
Interestingly, egalitarian and leximin frameworks, which prioritise equal outcomes over total welfare, may evaluate personalised pricing more favourably under specific conditions. If personalised pricing reduces price variance across consumers relative to uniform pricing, it may align with egalitarian objectives despite potentially reducing aggregate welfare. However, this theoretical possibility depends upon implementation details that may not characterise real-world personalised pricing systems.
Broader ethical concerns transcend specific normative frameworks. Power asymmetries between firms and consumers, wherein firms possess vastly superior information about both pricing algorithms and consumer characteristics, create conditions many ethicists regard as fundamentally problematic. Extraction of consumer surplus through sophisticated algorithmic targeting may leave consumers systematically worse off than under uniform pricing, transferring welfare from consumers to firms without corresponding efficiency gains.
Consumer perceptions of price fairness
Empirical research consistently demonstrates that consumers perceive personalised pricing as unfair, with negative perceptions extending even to those who receive lower prices. Hufnagel, Schwaiger and Weritz (2022) examined consumer responses to personalised price discrimination in e-commerce contexts, finding that personalised pricing sharply reduces perceived price fairness across consumer segments. Importantly, consumers who pay less than others do not experience corresponding gratitude; rather, they exhibit suspicion regarding the legitimacy of pricing practices and concern about future price vulnerability.
Ohlwein and Bruno (2025) specifically examine fairness perceptions of algorithmic personalised pricing, characterising consumer assessments as falling between “fair game” and “foul play.” Their findings indicate that algorithmic determination of personalised prices does not ameliorate fairness concerns; indeed, algorithm aversion may exacerbate negative perceptions compared to human-determined price differentiation. The opacity of algorithmic pricing systems appears particularly problematic, as consumers cannot evaluate the reasonableness of the criteria employed.
Richards, Liaukonyte and Streletskaya (2019) provide nuanced evidence regarding framing effects on fairness perceptions. Their research demonstrates that personalised pricing framed as discounts from a reference price generates less negative response than pricing framed as surcharges above a baseline. This finding suggests that firms employing personalised pricing may partially mitigate fairness concerns through careful presentation, although such framing strategies raise additional ethical questions regarding manipulation and transparency.
The role of negative moral emotions in driving fairness perceptions deserves particular attention. Julienne et al. (2021) document that consumers experiencing personalised pricing exhibit anger, frustration, and moral indignation that extend beyond rational self-interest. These emotional responses appear to reflect deeply held intuitions about distributive justice and commercial ethics that algorithmic efficiency arguments fail to address.
Behavioural responses to personalised pricing discovery
Consumer behavioural responses to discovering personalised pricing are predominantly negative and manifest across multiple dimensions. Research documents effects on purchase intentions, loyalty behaviours, retaliatory actions, and strategic consumer responses.
Regarding purchase behaviour, Hufnagel, Schwaiger and Weritz (2022) find that personalised pricing reduces both immediate purchase intentions and longer-term repurchase intentions. Consumers report unwillingness to complete transactions when they believe they may be charged unfair prices, even when unable to determine whether their specific price is above or below average. This behavioural inhibition suggests that mere awareness of personalised pricing practices, rather than confirmed disadvantageous pricing, suffices to deter purchases.
Revenge and complaint intentions increase substantially upon personalised pricing discovery. Farkas, Victor and Nathan (2021) document heightened complaint behaviour directed at firms and increased negative word-of-mouth communication to other consumers. Such retaliatory responses carry significant reputational implications for firms, potentially generating negative publicity that damages brand equity beyond any immediate revenue effects.
Strategic consumer behaviours represent particularly important responses to personalised pricing awareness. Gerlick and Liozu (2020) examine how consumers attempt to game pricing systems once aware of personalisation, engaging in behaviours such as clearing cookies, using virtual private networks to obscure location, creating multiple accounts, and strategically timing purchases. These strategic responses impose costs on firms through increased system complexity and reduced predictive accuracy whilst simultaneously reducing consumer welfare through transaction costs and time expenditure.
Yan et al. (2025) employ a cognition-affect-behaviour framework to analyse consumer reactions to big data-based personalised pricing. Their findings confirm that cognitive assessments of unfairness generate affective responses that subsequently drive behavioural outcomes including reduced loyalty and platform discontinuation. This sequential process suggests that interventions addressing cognitive fairness assessments may have downstream effects on emotions and behaviours.
Privacy concerns and data sensitivity
Privacy concerns constitute a distinct dimension of consumer response to personalised pricing, operating alongside fairness perceptions but with independent effects. The use of personal data, particularly sensitive data categories, to determine individualised prices raises privacy objections that many consumers regard as equally or more important than fairness considerations.
Farkas, Victor and Nathan (2021) document that awareness of personal data use in pricing algorithms significantly depresses consumer loyalty and increases required discount levels for acceptable personalised pricing. Consumers expect compensation for the perceived privacy intrusion inherent in data-driven pricing, although the magnitude of required compensation typically exceeds any benefits firms might offer.
Pizzi et al. (2022) provide particularly valuable evidence regarding differential sensitivity to data types in personalised pricing contexts. Their research compares consumer responses to pricing based on biometric data versus behavioural data, finding substantially stronger negative reactions to biometric data use. This finding suggests that consumer privacy concerns are not uniform but rather reflect intuitions about data sensitivity that firms must navigate carefully.
The relationship between privacy concerns and fairness perceptions appears to be mutually reinforcing. Yan et al. (2025) find that privacy concerns heighten perceptions of unfairness, whilst unfairness perceptions amplify privacy-related distress. This bidirectional relationship suggests that addressing either dimension in isolation may prove insufficient to generate consumer acceptance.
Disclosure effects and potential moderators
Regulatory attention has focused partly on disclosure as a potential remedy for personalised pricing concerns, yet evidence regarding disclosure effectiveness is largely discouraging. Julienne et al. (2021) examined the effects of online disclosure about personalised pricing on consumers, finding that simple disclosures improve understanding only slightly and do not reliably protect consumers or change purchasing behaviour. Following disclosure, consumers report that personalised pricing is unfair and often express support for prohibition.
Borgesius and Poort (2017) reach similar conclusions regarding disclosure limitations, noting that transparency requirements alone appear insufficient to address the power asymmetries and potential harms associated with personalised pricing. Their analysis suggests that effective regulation may require substantive constraints on permissible pricing practices rather than merely procedural transparency requirements.
Some research identifies conditions under which consumer acceptance of personalised pricing may increase, although acceptance remains limited even under favourable conditions. Richards, Liaukonyte and Streletskaya (2019) find that discount framing generates less negative response than surcharge framing, suggesting presentation effects on acceptability. Similarly, Priester, Robbert and Roth (2020) demonstrate that personalised dynamic pricing based on purchase history generates less negative reaction than pricing based on location or device characteristics.
Participatory mechanisms may also enhance acceptance. J and Gotmare (2021) find that when consumers can participate in or negotiate pricing through interactive processes, resistance to personalised pricing diminishes. This finding suggests that perceived procedural fairness may partially compensate for distributive fairness concerns, although such participatory systems differ substantially from algorithmic personalised pricing in practice.
Discussion
The synthesis of evidence presented in this dissertation reveals a fundamental tension between theoretical ethical defensibility of personalised pricing under specific frameworks and conditions, and the empirically observed reality of predominantly negative consumer responses. This tension carries significant implications for commercial practice, regulatory policy, and ethical theory development.
Addressing the first objective concerning ethical framework analysis, the evidence demonstrates that ethical acceptability of personalised pricing cannot be determined universally but depends critically upon the normative framework applied and the assumptions incorporated. The simulation evidence from Mazrekaj et al. (2024) proves particularly instructive in demonstrating that once realistic psychological factors, specifically perceived unfairness and privacy aversion, are incorporated into welfare calculations, personalised pricing typically fails to outperform uniform pricing under utilitarian and prioritarian standards. This finding challenges simplistic economic arguments that personalised pricing necessarily improves efficiency or welfare.
The potential ethical defensibility under egalitarian frameworks represents an interesting theoretical result but one with limited practical significance. Real-world personalised pricing systems rarely operate to reduce price inequality; more commonly, they extract maximum willingness to pay from each consumer, potentially exacerbating rather than ameliorating distributional inequity. The theoretical possibility that personalised pricing could serve egalitarian objectives thus remains largely unrealised in practice.
Regarding the second and third objectives concerning fairness perceptions and behavioural responses, the evidence is remarkably consistent across diverse research contexts, methodologies, and consumer populations. Personalised pricing generates perceptions of unfairness that persist even among consumers receiving favourable prices, suggesting that fairness concerns transcend narrow self-interest. The absence of gratitude among advantaged consumers indicates that personalised pricing fails to generate positive reciprocity that might offset negative reactions among disadvantaged consumers.
The behavioural consequences documented in the literature pose substantial challenges for firms considering personalised pricing strategies. Reduced purchase intentions, diminished loyalty, increased complaint behaviour, negative word-of-mouth, and platform abandonment collectively represent a pattern of consumer response that may substantially erode any revenue benefits from price optimisation. The strategic behaviours consumers adopt to game pricing systems impose additional costs through increased system complexity and reduced algorithmic effectiveness.
The fourth objective concerning privacy concerns reveals that data-driven pricing raises distinct objections beyond fairness considerations. The evidence suggests that privacy concerns and fairness perceptions operate as related but distinguishable constructs with independent effects on consumer responses. The differential sensitivity to data types documented by Pizzi et al. (2022) indicates that firms cannot treat all personal data as equivalent for pricing purposes; biometric and other sensitive data categories generate particularly strong negative reactions.
The fifth objective examining disclosure effects yields conclusions that challenge regulatory approaches emphasising transparency as the primary remedy for personalised pricing concerns. The evidence that disclosure improves understanding only marginally whilst failing to protect consumers or change behaviour suggests that transparency alone is insufficient. Indeed, disclosure may primarily serve to document consumer opposition rather than enabling effective consumer choice, raising questions about whether current regulatory frameworks adequately address the harms associated with personalised pricing.
The identification of potential moderators, including discount framing, data source effects, and participatory mechanisms, offers some guidance for firms seeking to implement personalised pricing with reduced consumer backlash. However, these moderating effects operate at the margins; they reduce rather than eliminate negative responses. Moreover, reliance on framing effects to mitigate consumer concerns raises additional ethical questions about manipulation and transparency that deserve careful consideration.
The implications of these findings extend across multiple domains. For commercial practitioners, the evidence suggests that the business case for personalised pricing is considerably weaker than initial revenue optimisation analyses might indicate. The reputational risks, customer relationship damage, and strategic consumer responses documented in the literature may substantially offset price optimisation gains. Firms considering personalised pricing must weigh potential revenue benefits against substantial risks to customer trust and loyalty.
For policymakers and regulators, the evidence supports active regulatory engagement with personalised pricing rather than reliance on market self-correction. The failure of disclosure to generate effective consumer protection suggests that substantive constraints on permissible practices may be necessary. The case-by-case assessment approach advocated by Botta and Wiedemann (2019) and others appears well-suited to a domain where context significantly influences ethical evaluation.
For ethical theory development, the research highlights the importance of incorporating realistic psychological factors into normative analysis. Welfare calculations that ignore perceived unfairness and privacy aversion produce misleading conclusions about the desirability of pricing practices. This finding suggests that business ethics scholarship should attend more carefully to the psychological evidence regarding how commercial practices affect human welfare.
Conclusions
This dissertation has systematically examined the ethical acceptability of personalised pricing and consumer responses upon discovery of such practices, achieving the stated aim through comprehensive literature synthesis and analysis.
The first objective, concerning ethical framework analysis, has been achieved through examination of utilitarian, prioritarian, and egalitarian perspectives. The evidence demonstrates that personalised pricing can be ethically defended only under narrow conditions and specific frameworks, primarily egalitarian approaches prioritising equal outcomes. Under utilitarian and prioritarian standards that incorporate realistic psychological factors, personalised pricing typically performs worse than uniform pricing.
The second and third objectives, concerning fairness perceptions and behavioural responses, have been comprehensively addressed. The evidence consistently demonstrates that consumers perceive personalised pricing as unfair and respond with reduced purchase intentions, diminished loyalty, retaliatory behaviours, and strategic attempts to game pricing systems. These responses persist even among consumers receiving favourable prices, indicating that fairness concerns transcend narrow self-interest.
The fourth objective regarding privacy concerns has been achieved through analysis of differential sensitivity to data types and the relationship between privacy concerns and fairness perceptions. Privacy objections constitute a distinct dimension of consumer response with independent effects on loyalty and acceptance, particularly pronounced for sensitive data categories.
The fifth objective examining disclosure effects has demonstrated the limited effectiveness of transparency requirements in protecting consumers or generating acceptance. Simple disclosures improve understanding only marginally whilst documenting rather than remedying consumer opposition.
The sixth objective concerning practical recommendations can be addressed through the following observations. For commercial practitioners, personalised pricing carries substantial risks that may outweigh potential revenue benefits. Where personalised pricing is implemented, discount framing, reliance on purchase history rather than sensitive data, and participatory mechanisms may partially mitigate negative responses. For policymakers, the evidence supports active regulatory engagement beyond mere transparency requirements, with case-by-case assessment of specific practices rather than blanket prohibition or permission.
The significance of these findings lies in the fundamental tension they reveal between commercial optimisation objectives and consumer welfare and trust. Personalised pricing represents a domain where technological capability has outpaced ethical consensus and regulatory frameworks, creating conditions that require careful navigation by all stakeholders.
Future research should address several limitations and gaps in current understanding. Longitudinal studies examining how consumer responses evolve as personalised pricing becomes more prevalent would illuminate adaptation effects. Cross-cultural research would identify whether findings predominantly from Western contexts generalise to other cultural settings. Investigation of industry-specific factors would enable more nuanced guidance for particular sectors. Finally, research examining the long-term consequences of personalised pricing for market trust and consumer-firm relationships would inform strategic decision-making by firms and regulatory intervention by policymakers.
In conclusion, whilst personalised pricing possesses theoretical ethical defensibility under specific frameworks and conditions, its practical implementation generates substantial consumer backlash that undermines trust, loyalty, and commercial relationships. The normative complexity and empirical evidence of negative consumer response together suggest that firms and regulators should approach personalised pricing with considerable caution, prioritising consumer welfare and market trust over short-term revenue optimisation.
References
Borgesius, F. and Poort, J. (2017) ‘Online price discrimination and EU data privacy law’, *Journal of Consumer Policy*, 40(3), pp. 347-366. https://doi.org/10.1007/s10603-017-9354-z
Botta, M. and Wiedemann, K. (2019) ‘To discriminate or not to discriminate? Personalised pricing in online markets as exploitative abuse of dominance’, *European Journal of Law and Economics*, 50(3), pp. 381-404. https://doi.org/10.1007/s10657-019-09636-3
Farkas, M., Victor, V. and Nathan, R. (2021) ‘Consumer response towards personalised pricing strategies in online marketing’, *International Journal of Technology Marketing*, 1(1), pp. 1. https://doi.org/10.1504/ijtmkt.2021.10038457
Gerlick, J. and Liozu, S. (2020) ‘Ethical and legal considerations of artificial intelligence and algorithmic decision-making in personalized pricing’, *Journal of Revenue and Pricing Management*, 19(2), pp. 85-98. https://doi.org/10.1057/s41272-019-00225-2
Hufnagel, G., Schwaiger, M. and Weritz, L. (2022) ‘Seeking the perfect price: Consumer responses to personalized price discrimination in e-commerce’, *Journal of Business Research*, 143, pp. 346-365. https://doi.org/10.1016/j.jbusres.2021.10.002
J, J. and Gotmare, P. (2021) ‘Impact of consumer behavior pertaining to personalization of price in an e-commerce context’, *Eurasian Journal of Business and Economics*, 14(28), pp. 105-122. https://doi.org/10.17015/ejbe.2021.028.06
Julienne, H., Barjaková, M., Robertson, D. and Lunn, P. (2021) *The effects of online disclosure about personalised pricing on consumers*. Paris: OECD Publishing. https://doi.org/10.1787/1ce1de63-en
Mazrekaj, D., Verhagen, M., Kumar, A. and Muzio, D. (2024) ‘Does price personalization ethically outperform unitary pricing? A thought experiment and a simulation study’, *Journal of Business Ethics*, 199(1), pp. 207-227. https://doi.org/10.1007/s10551-024-05828-3
Ohlwein, M. and Bruno, P. (2025) ‘Algorithms of (un)fairness – Is personalized pricing fair game or foul play?’, *International Journal of Market Research*, 67(2), pp. 363-374. https://doi.org/10.1177/14707853251338579
Pizzi, G., Vannucci, V., Shukla, Y. and Aiello, G. (2022) ‘Privacy concerns and justice perceptions with the disclosure of biometric versus behavioral data for personalized pricing’, *Journal of Business Research*, 148, pp. 431-442. https://doi.org/10.1016/j.jbusres.2022.04.072
Priester, A., Robbert, T. and 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), pp. 99-112. https://doi.org/10.1057/s41272-019-00224-3
Richards, T., Liaukonyte, J. and Streletskaya, N. (2019) ‘Personalized pricing and price fairness’, *International Journal of Industrial Organization*, 44, pp. 138-153. https://doi.org/10.1016/j.ijindorg.2015.11.004
Seele, P., Dierksmeier, C., Hofstetter, R. and Schultz, M. (2019) ‘Mapping the ethicality of algorithmic pricing: A review of dynamic and personalized pricing’, *Journal of Business Ethics*, 170(4), pp. 697-719. https://doi.org/10.1007/s10551-019-04371-w
Yan, H., Wei, Y., Xiong, H. and Wang, L. (2025) ‘How do consumers react to big data-based personalised pricing? A cognition–affect–behaviour perspective’, *Journal of Psychology in Africa*, 34(6), pp. 667-676. https://doi.org/10.1080/14330237.2024.2425405
