Discrimination grounds and personalised pricing: Consumer perceptions of fairness, norm alignment, legality, and trust in markets

Heidary, Kimia; van der Rest, Jean-Pierre; Custers, B. H. M. Article Discrimination grounds and personalised pricing: Consumer perceptions of fairness, norm alignment, legality, and trust in markets Internet Policy Review Provided in Cooperation with: Alexander von Humboldt Institute for Internet and Society (HIIG), Berlin Suggested Citation: Heidary, Kimia; van der Rest, Jean-Pierre; Custers, B. H. M. (2024) : Discrimination grounds and personalised pricing: Consumer perceptions of fairness, norm alignment, legality, and trust in markets, Internet Policy Review, ISSN 2197-6775, Alexander von Humboldt Institute for Internet and Society, Berlin, Vol. 13, Iss. 4, pp. 1-37, https://doi.org/10.14763/2024.4.1809 This Version is available at: https://hdl.handle.net/10419/312558 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/3.0/de/deed.en Volume 13 | Discrimination grounds and personalised pricing: Consumer perceptions of fairness, norm alignment, legality, and trust in markets Kimia Heidary Leiden University Jean-Pierre van der Rest Leiden University Bart Custers Leiden University DOI: https://doi.org/10.14763/2024.4.1809 Published: 18 October 2024 Received: 10 October 2023 Accepted: 26 March 2024 Competing Interests: The author has declared that no competing interests exist that have influenced the text. Licence: This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 License (Germany) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. https://creativecommons.org/licenses/by/3.0/de/deed.en Copyright remains with the author(s). Citation: Heidary, K. & van der Rest, J.-P. & Custers, B. (2024). Discrimination grounds and personalised pricing: Consumer perceptions of fairness, norm alignment, legality, and trust in markets. Internet Policy Review, 13(4). https://doi.org/10.14763/ 2024.4.1809 Keywords: Personalised pricing, Consumer profiling, Price discrimination, Norms, Antidiscrimination law Abstract: This article explores consumer perceptions of different grounds by which online prices can be personalised. We conducted a survey among Dutch consumers (n = 727) presenting them with 25 segmentation bases, drawing from legally permissible and legally prohibited grounds. We then ranked these bases and accompanying consumer perceptions across five dimensions: fairness, alignment with personal norms, alignment with social norms, perceived legality, and trust in markets. We find that while consumer perceptions generally align with what is currently prohibited in law, there are some “new” grounds, in particular intelligence and physical appearance, that elicit similar negative perceptions as legally prohibited grounds. This raises questions regarding the further regulation of personalised pricing. We discuss pros and cons of updating legislation to better reflect (new) ethical and social norms. Issue 4 1. Introduction Online price discrimination or personalised pricing, the practice of setting prices based on consumer characteristics and behaviour, with the help of AI-based technologies and big data processing, is slowly becoming a reality. The rapidly increasing flow of personal data, in combination with technological developments that make it possible to analyse and value these data, makes it increasingly possible for companies to personalise prices based on what they (think they) know about consumers. These data are observed, derived from behaviour, or provided by consumers themselves and can be information about a person's device type or purchasing behaviour, the type of pages they view, social media data, and even the battery percentage of the device that is used to surf the Internet (OECD, 2018a). Especially when these data are combined, companies can build more sophisticated profiles and experiment with pricing accordingly. Personalising prices and differentiating between different types of consumers can be quite profitable for companies (Odlyzko, 2003; Shiller, 2014). As the practice continues to evolve, consumer profiles and the underlying technology are expected to become more sophisticated (OECD, 2018a). Differentiating between consumers and the price they pay is not a new phenomenon. Examples of charging ships different lighthouse fees can already be found in 13th century England (Odlyzko, 2004). From an economics perspective, such practice is viewed in a neutral way. The term “price discrimination” represents a value free concept, without the normative (often negative) connotations that a common understanding of the word “discrimination” suggests in other disciplines such as law (Steppe, 2017). Price discrimination in an economic sense is not always harmful and can even be beneficial in terms of opening the market to consumers who might not have been able to purchase a product or service under a uniform pricing system (Sauter, 2020). At first glance, economic textbooks present quite innocent and generally accepted ways of distinguishing between consumers. For example, students and the elderly may be charged reduced prices by offering discounts. Loyal customers who are offered loyalty discounts is another well-known example. In addition, prices can differ when products do not have a fixed price, such as at a car dealership or street vendor, where consumers with high negotiating skills may walk away with better deals than consumers who do not know how overpriced the product is or who cannot hide their interest well enough. Many forms of price discrimination are much less accepted. Empirical research and anecdotal evidence indicate that discriminatory pricing often meets with resistance from consumers. They generally view the practice as unfair and deem it to 2 Internet Policy Review 13(4) | 2024 be illegitimate (Poort & Zuiderveen Borgesius, 2019; Turow et al., 2005; Priester et al., 2020). Some examples, although deeply rooted in our daily lives, still raise debates about acceptability and fairness. A typical example includes distinguishing between consumers based on gender, with women often getting the short end of the stick and paying a higher price for products and services: the so-called “pink tax” (Department of Consumer Affairs, 2015). Another example is the selective charging of higher prices, for example for consumers who live in a wealthier neighbourhood or a “fine” for loyal customers (Maxwell & Garbarino, 2010; CMA, 2020). Social norms are an important explanation why some practices are deemed more (un)fair than others. Consumers that perceive a pricing practice to violate a social norm, react negatively towards the company, such as lower purchase intentions (Campbell, 1999) and loss of trust (Garbarino & Lee, 2003). The current European legal framework does not explicitly prohibit online price discrimination, but it does pose boundaries as to the rightful use and processing of data for personalising prices and the transparency that needs to be provided by companies who engage in the practice.1 Several authors have questioned whether the current European legal framework is equipped to address the challenges associated with online price discrimination (Sears, 2021; Barros Vale, 2020; Grochowski et al. 2022). One of these challenges is the discrimination that can occur with the use of AI-based pricing algorithms. Anti-discrimination law and data protection law protect certain “sensitive” characteristics and fundamental choices that cannot be changed, or at least not without high damage to one’s core identity, such as religion, gender, and sexual orientation (Clarke, 2015; Khaitan, 2015). The use of such grounds in personalised pricing would constitute (in)direct discrimination and likely elicit negative reactions from consumers. Even if companies make efforts not to include such sensitive (synthetic) data in their analyses, the technology used can still indirectly lead to the systematic disadvantaging of certain (groups of) consumers (Calders & Žliobaitė, 2013). In other words: “insensitive” (non-legally prohibited) data can show strong associations with sensitive or legally prohibited data (Li, 2022). Moreover, some “newer” grounds or profiles that could be used for personalised pricing, such as intelligence or browsing activity, could also constitute similar immutability and vulnerability or are at least not within a person’s control, which could warrant further regulatory protection (Wachter, 2022). In order to assess to what extent the current legal framework is aligned with social norms and perceptions surrounding personalised pricing, it is imperative to study existing fairness perceptions regarding both legally prohibited and permissible grounds. 1. See in this regard also Article 6(1)(ea) of the Consumer Rights Directive, which introduces an information requirement for companies engaging in personalised pricing. 3 Heidary, van der Rest, Custers The literature tends to pay little attention to the various grounds for discrimination on the basis of which prices can be personalised. There is work that has explored consumer perceptions of personalised pricing (e.g. Turow et al., 2005; Poort & Zuiderveen Borgesius, 2019), as well as some specific non-prohibited grounds (e.g. location, device type, purchase history) for personalised pricing (Priester et al., 2020; Hufnagel et al., 2022). Yet, a more comprehensive review of perceptions regarding discrimination grounds – legally prohibited or not – is missing. While some grounds and the processing thereof are protected under data protection law and anti-discrimination law, the question remains how well the legally protected grounds align with consumer (un)fairness perceptions and the extent to which the EU legal framework can be leveraged to provide future protection against discrimination and/or exploitation on the basis of these grounds – especially given that these new segmentation criteria (i.e. discrimination grounds that are not legally prohibited) could still have (unforeseen) legally discriminatory outcomes. The focus of this article is mostly on the role of anti-discrimination law, as the grounds that we examine flow mostly from this framework. It builds on the existing debate regarding the role that anti-discrimination law can play to address the discrimination that can result from the use of pricing algorithms (Tanna & Dunning, 2023; Wachter, 2022) However, we believe that the findings have implications for other fields of law, particularly regarding enforcement. Therefore, the aim of this research is twofold. First, we provide a comprehensive overview of consumer perceptions regarding known grounds that can be used to personalise prices. Here, we do not only analyse grounds that are already prohibited or declared sensitive by law, but also perceptions regarding “new” grounds (i.e. grounds that have more recently come to play and are not legally prohibited), as they are heavily intertwined (Solove, 2024). Second, using this overview, we examine whether there is a gap between perceptions of discrimination grounds that are prohibited, and those that are not legally prohibited. Our findings provide input for the discussion on the extent to which current legislation offers appropriate safeguards against personalised pricing and which grounds, if any, should be better protected in the future (e.g. Van der Rest et al., 2020). We explored consumer perspectives by conducting a survey among 727 Dutch consumers. The paper is structured as follows. In Section 2, we review the current literature on personalised pricing and the current legal framework. In Section 3, we explain the methodology used in the empirical study. Section 4 presents the survey results and Section 5 discusses the implications of the findings of our study, particularly the extent to which there is a gap between the current legal landscape and the per4 Internet Policy Review 13(4) | 2024 ceptions of consumers, and whether the legal framework should be reconsidered, including avenues for future research. In Section 6, we provide conclusions. 2. Literature review 2.1 Online price discrimination Online price discrimination is generally defined as charging different prices to consumers for the same product, based on inferences drawn from consumer data with the help of pricing algorithms and big data processing (Zuiderveen Borgesius & Poort, 2019). Here, the difference in price cannot be explained by a difference in costs, but rather the information that the company has about its (prospective) clients (Carroll & Coates, 1999). Price differences due to, for example, higher shipping or delivery costs, or due to insured parties posing a higher risk to an insurer, are not considered price discrimination (Preston McAfee, 2008). The reasoning behind price discrimination is that consumers vary in their willingness to pay for products and services, as they value these differently (OECD, 2016). Companies observe such differences in consumers’ often fluctuating preferences and assess the willingness to pay accordingly. These assessments do not have to be accurate to the penny in order to be considered price discrimination (OECD, 2018a). If there is no discernible difference between consumers and their willingness to pay, it is often not profitable for a company to roll out a discriminatory pricing strategy (Stole, 2007). When creating and optimising segments, companies can use a plethora of consumer data. These data include, but are not limited to, observed data (e.g. information about device, purchase history), volunteered data (e.g. name, gender), and inferred data (e.g. income, life phase (OECD, 2018b)). Data originally collected for other purposes can be reused for this (Custers & Bachlechner, 2017). Section 2.2 will map an overview of legally prohibited grounds of discrimination, on which companies could in theory base their analyses. 2.2 Legally prohibited grounds of discrimination The current European legal landscape does not explicitly prohibit personalised pricing; the only direct mention of personalised pricing can be found in Directive 2019/2161,2 on the basis of which companies are required to disclose use of auto2. Directive (EU) 2019/2161 of the European Parliament and of the Council of 27 November 2019 amending Council Directive 93/13/EEC and Directives 98/6/EC, 2005/29/EC and 2011/83/EU of the European Parliament and of the Council as regards the better enforcement and modernisation of Union consumer protection rules. 5 Heidary, van der Rest, Custers mated personalised pricing to consumers. Interestingly, companies are not required to disclose the parameters used for personalised pricing, only that the price has been personalised. Nevertheless, there are two concrete starting points for protected or sensitive grounds for price discrimination. These can be found in antidiscrimination law and data protection law. We will zoom in on the Dutch constitution and its enumeration of discrimination grounds, as it was recently updated to include new grounds of discrimination and concurrently, these were the grounds that we presented to survey respondents. The Charter of Fundamental Rights of the European Union (CFEU), which entered into force on 1 December 2009, enshrines various fundamental rights, freedoms, and principles. The principle of equal treatment is protected in Article 21 CFEU, which states that any discrimination shall be prohibited and provides a non-exhaustive enumeration of fourteen discrimination grounds, more than any national constitution of European member States. The discrimination grounds mentioned include sex, race, genetic features, religion, disability, and age. The rationale behind these protected characteristics is to protect both immutable characteristics (i.e. characteristics that were not chosen and cannot be changed, or at least not without high damage to one’s core identity), such as gender and ethnicity, and fundamental choices, such as religion (Clarke, 2015; Khaitan, 2015). Although the scope of the CFEU is formally restricted to EU institutions when implementing EU law (Article 51 CFEU), the CJEU has recognized horizontal effects in recent jurisprudence (Muir, 2019).3 The Dutch national constitution provides a similar non-exhaustive enumeration, although with less examples listed. Article 1 of the national constitution (Grondwet) states that all persons in the Netherlands shall be treated equally in equal circumstances. Discrimination on the grounds of religion, belief, political opinion, race or sex, or any other grounds whatsoever shall not be permitted. As of 2023, the Dutch constitution includes two new grounds of discrimination: disability and sexual orientation (Corder, 2023). The Equal Treatment Act (AWGB), which dates from 1994 and constitutes secondary legislation, further elaborates on Article 1 of the constitution. The AWGB prohibits making a distinction on the basis of an exhaustive enumeration of sensitive criteria. The most fundamental objective of this Act is the protection of human dignity, more specifically to promote equal participation in society, without being subjected to discrimination or exclusion on the basis of personal characteristics.4 The exhaustive enumeration in Article 1 pro3. See for example Case C-414/16 Vera Egenberger v Evangelisches Werk für Diakonie und Entwicklung eV, §81. 6 Internet Policy Review 13(4) | 2024 hibits discrimination on the basis of religion, belief, political opinion, race, gender, nationality, sexual orientation, and marital status. In later subordinate legislation some additional grounds were added, including disability or chronic illness, age, and type of employment contract.5 It is important to note that throughout European constitutions, there exists a clear lack of harmonisation of discrimination grounds and whether an exhaustive or non-exhaustive enumeration is used (Custers, 2023). For instance, wealth and social status are a protected characteristic in 11 out of 27 Member States but are not mentioned in Article 21 CFEU. In addition to anti-discrimination law, data protection law also provides legal boundaries to the use of certain grounds in personalising pricing. Although the GDPR does not directly deal with bias and discrimination that can flow from the use of AI-based pricing algorithms, it is an important instrument in addressing these concerns as it provides both ex post legal remedies and ex ante measure aimed at preventing unfair processing of personal data (Ivanova, 2020; Li, 2022). A key objective of the GDPR is to protect fundamental rights of data subjects, among which are the right to privacy and to non-discrimination.6 In accordance with Article 5(1)(a) and (b), personal data shall be processed fairly, lawfully, and transparently. Although “fairness” is an elusive concept, it at least means that data processing should not create detrimental effects and should not discriminate or exploit consumer vulnerabilities (Clifford & Ausloos, 2018; Tanna & Dunning, 2023). Articles 9 and 10 GDPR prohibit the processing of “sensitive” personal data, i.e. trade union membership, genetic and biometric data, health data, and criminal convictions, given that the exemptions of Article 9(2) do not apply. This ties in closely with transparency, as without transparency it is highly difficult to identify breaches or (non-)compliance with the legal framework (Tanna & Dunning, 2023). Article 22 GDPR, the right not to be subjected to automated decision-making, could be an important instrument to address decisions that flow from pricing algorithms. However, there is currently disagreement on the application of Article 22 GDPR to personalised pricing, which weakens its potential remedial role (Zuiderveen Borgesius & Poort, 2019; Wong, 2020). Furthermore, affinity-based personalised pricing, where consumers are grouped according to their inferred preferences rather than their personal data, may circumvent the legal frameworks of data protection law 4. Preamble AWGB. 5. Wet gelijke behandeling op grond van handicap of chronische ziekte [Equal Treatment (Disability and Chronic Illness) Act] (WGBH/CZ), 2003; Wet gelijke behandeling op grond van leeftijd bij arbeid [Equal Treatment on the Grounds of Age at Work Act] (WGBL), 2004; Wet onderscheid arbeidsduur [Distinction in Working Hours Act] (WOA), 1996; and Wet onderscheid bepaalde en onbepaalde tijd [Distinction in Definite Time Act] (WOBOT), 2002. 6. See Article 7, 8, and 21 CFEU. 7 Heidary, van der Rest, Custers and anti-discrimination law (Wachter, 2020; Li, 2022). 2.3 Reconsidering price discrimination grounds Apart from grounds that are legally protected, there are many segmentation bases conceivable (e.g. loyalty status, intelligence/education level, income) which are not protected in anti-discrimination or data protection law (See, Baker, 2001; Tannock, 2008; Maxwell & Garbarino, 2010). Grounds that companies have started to use, for example, include physical appearance (Hern, 2020), and battery level (Natelhoff, 2023). While these grounds are permissible, they can lead to consumer backlash; when consumers deem them unfair and exploitative, there can be a sudden and strong reaction against it in social media. However, little attention has been paid to the (legal) implications of the plethora of (new) segmentation grounds on which personalised pricing can be based online, beyond a call to broaden the consumer backlash and corporate social responsibility (CSR) literature to include personalised pricing (Van der Rest et al., 2022), and a study that explored why companies are reluctant to use personalised pricing online (Heidary et al., 2022). Given a lack of harmonisation of protected grounds across national constitutions, the risk of unintended or indirect discrimination, and consumer unfairness perceptions, a reconsideration of the current European legal framework nonetheless seems warranted. First, the level of protection provided against discrimination varies across member states in terms of the number of grounds listed, but also the wording of the enumeration – some Member States provide exhaustive enumerations of grounds, while others provide a non-exhaustive enumeration, or even no enumeration at all (Custers, 2023). Although the Dutch constitution was recently updated and two grounds were added to reflect developments in society, it takes much time and effort to update national constitutions to reflect such changes. This raises questions regarding the robustness of existing legal frameworks in relation to developments in the technology surrounding personalised pricing. There has been quite some scepticism regarding whether national constitutions can (and should) keep up with societal developments (Gerards, 2016). This point has also been raised for anti-discrimination law in general (Tanna & Dunning, 2023). This raises the question whether perhaps other modes of regulation are more suitable to deal with the challenges associated with personalised pricing, such as the constantly changing grounds on which a personalised price can be based. Second, there is a risk of unintended or indirect discrimination. In the case of direct discrimination, a legally prohibited ground is decisive for the unequal treat8 Internet Policy Review 13(4) | 2024 GROUND UNFAIRNESS PERSONAL NORM SOCIAL NORM LEGALITY TRUST IN MARKET LOYALTY STATUS 3.82 3.82 4.12 4.14 4.33 MARITAL STATUS 2.96 2.92 3.16 5.00 5.08 NATIONALITY 2.41 2.36 2.76 5.45 5.30 PHOTO/APPEARANCE 2.71 2.50 3.06 5.35 5.27 POLITICAL VIEWS 2.63 2.48 2.90 5.38 5.35 PURCHASE HISTORY 2.99 2.94 3.54 4.69 4.99 RACE/ETHNICITY 2.44 2.28 2.42 5.30 5.07 RELIGION 2.27 2.17 2.33 5.36 5.26 SEXUAL ORIENTATION 2.66 2.52 2.83 5.19 5.12 SOCIAL MEDIA DATA 2.88 2.83 3.48 4.83 4.82 SOCIOECONOMIC STATUS 3.27 3.23 3.74 4.44 4.70 STUDENT STATUS 3.49 3.55 3.80 4.09 4.16 First, fairness perceptions of the discrimination grounds revealed that participants ranked all grounds as unfair (i.e. all under 4 on the 7-point scale). Out of all grounds, the use of loyalty status for personalised pricing was deemed the least unfair (M = 3.82, SD = 1.70), with location (M = 3.57, SD = 1.69) and student status (M = 3.49, SD = 1.77) in second and third place, respectively. Religion was deemed the most unfair ground (M = 2.27, SD = 1.50), with nationality (M = 2.41, SD = 1.60) and gender (M = 2.41, SD = 1.60) in shared second place and ethnicity (M = 2.44, SD = 1.70) in third place. For the complete ranking, see Figure 2. 15 Heidary, van der Rest, Custers FIGURE 2: Perceived fairness of discrimination grounds (N = 727). Second, when asked about the alignment of the discrimination grounds with personal norms, a response similar to perceived fairness was observed. That is, none of the grounds aligned with personal norms (i.e. all under 4 on the 7-point scale). The grounds that aligned the least with participants’ personal norms were religion (M = 2.17, SD = 1.55), gender (M = 2.25, SD = 1.59), and ethnicity (M = 2.28, SD = 1.57). Loyalty status (M = 3.82, SD = 1.74), student status, and location reportedly misaligned the least with personal norms. For the complete ranking, see Figure 3. 16 Internet Policy Review 13(4) | 2024 FIGURE 3: Perceived alignment with personal norms of discrimination grounds (N = 727). Third, the grounds were also ranked in terms of the (perceived) alignment with existing social norms. Here we however observed a small positive difference with personal norms, t(579) = -7.47, p < .001, D = .31. The scores for social norms were consistently higher for each ground than for personal norms. The grounds were perceived to align less with personal norms than with social norms. In other words, for all grounds participants assumed higher acceptance in society than their own acceptance. The grounds that were perceived to align the least with societal norms, were religion (M = 2.33, SD = 1.45), ethnicity (M = 2.42, SD = 1.53), and gender (M = 2.73, SD = 1.78). Loyalty status (M = 4.12, SD = 1.74), location (M = 4.03, SD = 1.60), and student status (M = 3.8, SD = 1.82) were perceived to align more with societal norms but still scored under 4 on the 7-point scale; see Figure 4 for the complete ranking. 17 Heidary, van der Rest, Custers FIGURE 4: Perceived alignment with social norms of discrimination grounds (N = 727). Fourth, we asked participants to what extent each ground should be legally prohibited. The three grounds that were deemed the most illegitimate, were nationality (M = 5.45, SD = 1.60), health data (M = 5.4, SD = 1.59), and intelligence (M = 5.39, SD = 1.72). Student status (M = 4.09, SD = 1.89), loyalty status (M = 4.14, SD = 1.83), and location data (M = 4.31, SD = 1.70) were deemed the least illegitimate, albeit they still were ranked over 4 on the 7-point scale. Figure 5 shows the complete ranking. 18 Internet Policy Review 13(4) | 2024 FIGURE 5: Perceived illegitimacy of discrimination grounds (N = 727). Fifth and last, participants had to indicate to what extent the use of a certain ground would diminish their trust in the market. The use of political views (M = 5.35, SD = 1.54), nationality (M = 5.3, SD = 1.69), and intelligence (M = 5.29, SD = 1.75) were the top three grounds that would lead to a loss of trust. Student status (M = 4.16, SD = 1.81), loyalty status (M = 4.33, SD = 1.73), and location (M = 4.35, SD = 1.58) came out as the three grounds that would lead to less loss in trust, albeit still all grounds scored over 4 on the 7-point scale, hence all leading to less trust in markets. For the complete ranking, see Figure 6. 19 Heidary, van der Rest, Custers FIGURE 6: Reported loss of trust in the market in case of use of discrimination grounds (N = 727). We compared the legally prohibited grounds to the grounds that are not (yet) legally prohibited. We created two categories, bundling all perceptions for each of the five dimensions and conducted a Paired Samples test to compare the average scores of the two categories. For fairness, personal norms, and social norms, the mean value of all legal grounds was significantly higher than that of the “non-legal” grounds. Legal grounds were perceived as more unfair than non-legal grounds (M = -.43, SD = 1.09), less in alignment with personal norms (M = -.45, SD = 1.10) and social norms (M = -.60, SD = 1.16). Additionally, overall, legal grounds were deemed more illegitimate (i.e. less permissible) than “non-legal grounds” (M = .46, SD = 1.21) and were reported to lead to a higher loss in trust (M = .32, SD = 1.16). In addition to categorising grounds based on their current legality, we also distinguished between immutable and (technically) mutable grounds. We selected three unambiguous grounds for each category. We considered ethnicity, sexual orientation, and intelligence to be immutable, and browser type, battery percentage, and device type to be mutable. As for the immutable grounds, ethnicity and sexual orientation are legally protected discrimination grounds, whereas intelligence is not. We found significant differences (p < .001) between the groups: immutable grounds were considered more unfair than mutable grounds (M = 2.53 vs. M = 2.82), less aligned with personal norms (M = 2.40 vs. M = 2.72), and less aligned with social norms (M = 2.68 vs. M = 3.10). Moreover, immutable grounds were deemed to be more illegitimate than mutable grounds (M = 5.30 vs. M = 4.93) and 20 Internet Policy Review 13(4) | 2024 would diminish participants’ trust in the market more (M = 5.16 vs. M = 4.93). 5. Discussion The aim of this research was to provide a comprehensive overview of consumer perceptions regarding grounds used for personalised pricing, and to examine whether there is a gap between perceptions of grounds that are prohibited and not prohibited. Where previous research focused on general (un)fairness perceptions surrounding personalised pricing (Turow et al., 2009; Poort & Zuiderveen Borgesius, 2019) or zoomed in on specific non-legally prohibited grounds (Priester et al., 2020; Hufnagel et al., 2022), our research set out to map an overview of perceptions of various grounds across several dimensions. By combining both types of grounds (legally and not legally prohibited), it is possible to assess the extent to which current legislation aligns with societal perceptions and whether grounds that are (not) yet legally prohibited, evoke similar responses as legally prohibited grounds. Our findings provide valuable input for the discussion on the extent to which current legislation offers – or should offer – appropriate safeguards against the challenges associated with personalised pricing. We find quite some overlap between consumer perceptions of unfairness (“illegitimacy”) and illegality. The grounds that participants in our survey consider most unfair are also the grounds that most need protection by law, a finding largely consistent with what is already established in anti-discrimination and data protection law. Moreover, from the grounds that are not prohibited, intelligence and physical appearance scored high on unfairness and were reported to align the least with personal and social norms. Personalising prices on browser type and battery percentage also scored relatively high in terms of unfairness and misalignment with personal and social norms. Perceptions of (social) norm misalignment could have far-reaching negative consequences for companies and the digital market. For the latter, we included the dimension “trust in market” to assess the extent to which the use of a ground would lead to (self-reported) loss of trust among participants. We found that the use of grounds that were deemed the least aligned with social norms and the most illegitimate were accompanied with a higher self-reported loss of trust in the digital market. “Newer” grounds that scored high on norm misalignment, such as intelligence and appearance, showed a similar correlation. 21 Heidary, van der Rest, Custers 5.1 Policy implications From a policy perspective, it is interesting to zoom in on the grounds that are not yet legally prohibited, but that were perceived as unfair and in violation of personal and social norms: intelligence, appearance, battery percentage, and browser type. One could argue that these grounds could potentially be included in legislation as prohibited grounds – especially intelligence and appearance. There are at least two arguments for this. One is the viewpoint that the law is a codification of social norms (Basu, 2002). The law is not a static system, but changes over time, reflecting changes in norms and perceptions in society. If social norms and perceptions change, in this case because new technologies enable new grounds for (price) discrimination, this can be sufficient reason to change the legislation accordingly. Legally prohibiting discrimination grounds that are considered unfair by people would mean a further alignment between the legal system and social norms and perceptions. Basically, this is a fairness argument, which applies the strongest to inherently immutable personal characteristics. A second argument to consider changing the law has a more economic perspective: a lack of protection of grounds for price discrimination that are considered unfair would also reportedly lead to a relatively high loss of trust in the market (Cross, 2005). Hence, apart from the unfairness at an individual level, there is a larger economic effect that may provide an argument for the legislator to step in and offer protection through regulation. When reconsidering the grounds that need protection, the question is which grounds to include. The current lack of harmonisation of grounds in EU Member States show that there is not a shared understanding of fairness, or at least no agreement on which grounds require protection (Custers, 2023). Although the reported loss of trust and unfairness perceptions associated with newer grounds could form a justification to protect these grounds – or at least reassess the current legal framework – the rapid pace in which the technology is developing makes it difficult to predict to what extent legal regulation could keep up with these developments. New grounds could emerge that are also perceived as unfair, but companies could also find ways to circumvent prohibited grounds by using proxies or engaging in indirect discrimination, both of which are proven to be difficult to detect and enforce (Zuiderveen Borgesius, 2018). Therefore, adding several new prohibited discrimination grounds to the legal framework does not seem to be the way forward according to some authors (Solove, 2024). 22 Internet Policy Review 13(4) | 2024 Out of all the non-legally prohibited grounds, pricing based on intelligence and appearance received the most negative reactions. Intelligence and appearance are both (technically) immutable, meaning that they are unchangeable, or at least not changeable without significant effort. Discrimination based on intelligence or on appearance is not legally prohibited10 and happens on a daily basis (Tannock, 2008; Liu, 2017). Photos are not only considered biometric data,11 but readily reveal all kinds of physical attributes, such as race, religion, health, and ethnicity. While intelligence is for a large part innate, environmental factors such as a high socioeconomic status can contribute to developing intellectual skills, and the other way around: more intelligent individuals tend to achieve higher level of education, occupational status, income, and even better health outcomes (Deckers et al., 2017; Bosma et al., 2007). However, since data are so heavily intertwined, it would be difficult to substantiate why exactly these two grounds would need to be added to the existing legal framework (Solove, 2024). Alternatively, instead of providing justification for adding new prohibited grounds, our findings provide insight into the degree of harm that might be inflicted when specific grounds are used for price personalisation. In our findings, socioeconomic status comes forward as a relatively acceptable – or less unacceptable – ground to base prices on. Socioeconomic status and its supposed protection are a point of discussion among EU Member States (Ganty & Benito Sanchez, 2021). Socioeconomic status is not (yet) legally protected in Dutch constitutional law, partly because there is still no consensus regarding whether it is an inherent or immutable characteristic (Tweede Kamer, 2020).12 In the context of personalised pricing, the effect of this pricing strategy on consumer welfare is ambiguous (Elegido, 2011). In some cases, it could be beneficial to distinguish between consumers and their socioeconomic position, as it might allow certain consumers access to a product or service that they – because of their socioeconomic position – would not have been able to afford otherwise. However, the vulnerabilities that come with being part of a certain socioeconomic class (e.g. low level of digital literacy), could very well be exploited by companies (Strycharz & Duivenvoorde, 2021). Furthermore, socioeconomic status and social class are heavily intertwined with sensitive data such as political preferences and ethnicity (Gandy, 2009). In line with Solove (2024), we propose that a case-by-case analysis is needed, focusing on how certain data are used in personalised pricing, rather than the type of data. If such analyses 10. Apart from Belgium, France and Serbia, where discrimination on the basis of physical appearance is prohibited. 11. Recital 51 GDPR. 12. The ground is currently protected in 11 Member States. 23 Heidary, van der Rest, Custers would reveal the need to include new discrimination grounds in legislation, this does not necessarily need to be done in formal legislation, such as international treaties, national constitutions, or equal treatment acts. The open-endedness of the listings of discrimination grounds (i.e. non-exhaustive listings that use phrases like “or other characteristics”) in many legal instruments allows judges and courts to qualify new grounds as illegal in particular contexts. Furthermore, company behaviour that exploits consumer characteristics can possibly be deemed as unfair under competition and consumer law (Sauter, 2020; Li et al., 2023; Duivenvoorde, 2023). Nevertheless, online price discrimination remains difficult to detect due to the difficulties in isolating the grounds that the price difference was based on. Considering how the current European legal framework could be leveraged to address changes in the grounds that could be used for online price discrimination is therefore only one part of solving the puzzle. Increased transparency about the underlying mechanisms of a personalised price will further our knowledge about the current state of the art and the (personal) information that can be used to this end. The individual access and information rights in the GDPR could aid in overcoming consumers’ evidentiary deficiencies (Article 13-15 GDPR; Hacker, 2018). Clearing up the current confusion surrounding the applicability of Article 22 GDPR would be another step towards providing a more robust protection against online price discrimination (Wong, 2020). Furthermore, data protection law can provide for ex ante measures to implement more transparency about the grounds used in data processing (Li, 2022). 5.2 Theoretical implications Our findings are consistent with previous findings and observations that personalised pricing is generally viewed by the public as unfair and illegitimate (Poort & Zuiderveen Borgesius, 2019; Turow et al., 2005, Priester et al., 2020). The differences that we find between grounds are also in line with previous studies that focused on specific non-legally prohibited grounds. For instance, in line with Hufnagel et al. (2022), we find that personalised pricing based on location is deemed as less unfair than device type. Furthermore, in line with Priester et al., (2021), we also find that location is deemed as less unfair than purchase history. The grounds that we investigated all rank under the second half of the seven-point scale of fairness, personal norms, and social norms inasmuch that no ground is considered fair for personalised pricing – only less unfair. A similar trend is observed for the perceived illegitimacy and the effect that the use of the discrimination grounds has for consumers’ trust in markets. In sum, this confirms that the current perception of online price discrimination among consumers is still negatively loaded. Con24 Internet Policy Review 13(4) | 2024 Hufnagel, G., Schwaiger, M., & Weritz, L. (2022). Seeking the perfect price: Consumer responses to personalized price discrimination in e-commerce. Journal of Business Research, 143, 346–365. http s://doi.org/10.1016/j.jbusres.2021.10.002 Ivanova, Y. (2020). The data protection impact assessment as a tool to enforce non-discriminatory AI. In L. Antunes, M. Naldi, G. F. Italiano, K. Rannenberg, & P. Drogkaris (Eds.), Privacy Technologies and Policy (Vol. 12121, pp. 3–24). Springer. https://doi.org/10.1007/978-3-030-55196-4_1 Khaitan, T. (2015). A theory of discrimination law. Oxford University Press. https://doi.org/10.1093/ac prof:oso/9780199656967.001.0001 Larson, J., Mattu, S., & Angwin, J. (2015). Unintended consequences of geographic targeting [Project report]. ProPublica. https://static.propublica.org/projects/princetonLi, Z. (2022). Affinity-based algorithmic pricing: A dilemma for EU data protection law. Computer Law & Security Review, 46. https://doi.org/10.1016/j.clsr.2022.105705 Liu, X. (2017). Discrimination and lookism. In K. Lippert-Rasmussen (Ed.), The Routledge handbook of the ethics of discrimination (pp. 276–288). Routledge. https://doi.org/10.4324/9781315681634 Malgieri, G. (2023). In/acceptable marketing and consumers’ privacy expectations: Four tests from EU data protection law. Journal of Consumer Marketing, 40(2), 209–223. https://doi.org/10.1108/JC M-03-2021-4571 Maxwell, S. (2002). Rule-based price fairness and its effect on willingness to purchase. Journal of Economic Psychology, 23(2), 191–212. https://doi.org/10.1016/S0167-4870(02)00063-6 Maxwell, S., & Garbarino, E. (2010). The identification of social norms of price discrimination on the internet. Journal of Product & Brand Management, 19(3), 218–224. https://doi.org/10.1108/1061042 1011046193 Mikians, J., Gyarmati, L., Erramilli, V., & Laoutaris, N. (2012). Detecting price and search discrimination on the internet. Proceedings of the 11th ACM Workshop on Hot Topics in Networks, 79–84. https://doi.org/10.1145/2390231.2390245 Miller, A. A. (2014). What do we worry about when we worry about price discrimination? The law and ethics of using personal information for pricing. Journal of Technology Law & Policy, 19(1), 41–104. https://scholarship.law.ufl.edu/jtlp/vol19/iss1/2 Muir, E. (2019). The horizontal effects of charter rights given expression to in EU legislation, from Mangold to Bauer. Review of European Administrative Law, 12(2), 185–215. https://doi.org/10.7590/1 87479819X15840066091312 Natelhoff, Y. (2023, April 10). Différents tarifs pour une même course: Uber ne met pas tout le monde sur un même pied d’égalité [Different fares for the same journey: Uber does not put everyone on the same footing]. Dernière Heure. https://www.dhnet.be/conso/auto-moto/2023/04/1 0/differents-tarifs-pour-une-meme-course-uber-ne-met-pas-tout-le-monde-sur-un-meme-pied-deg alite-CE3CGQL3EVH6HAJN33NRKM36Q4/ Odlyzko, A. (2003). Privacy, economics, and price discrimination on the Internet. Proceedings of the 5th International Conference on Electronic Commerce, 355–366. https://doi.org/10.1145/948005.9480 51 Odlyzko, A. (2004). The evolution of price discrimination in transportation and its implications for the internet. Review of Network Economics, 3(3). https://doi.org/10.2202/1446-9022.1055 31 Heidary, van der Rest, Custers Organisation for Economic Co-operation and Development. (2015). Data-driven innovation: Big data for growth and well-being. OECD Publishing. https://doi.org/10.1787/9789264229358-en Organisation for Economic Co-operation and Development. (2016). Price Discrimination – Background note by the Secretariat (Report No. DAF/COMP(2016)15). https://one.oecd.org/document/ DAF/COMP(2016)15/en/pdf Organisation for Economic Co-operation and Development. (2018b). Personalised pricing in the digital era – Note by the Netherlands (Report No. DAF/COMP/WD(2018)124). https://one.oecd.org/do cument/DAF/COMP/WD(2018)124/en/pdf Organisation for Economic Co-operation and Development. (2018a). Personalised pricing in the digital era – Background note by the Secretariat (Report No. DAF/COMP(2018)13). https://one.oecd.or g/document/DAF/COMP(2018)13/en/pdf Poort, J., & Zuiderveen Borgesius, F. J. (2019). Does everyone have a price? Understanding people’s attitude towards online and offline price discrimination. Internet Policy Review, 8(1). https://doi.org/ 10.14763/2019.1.1383 Preston McAfee, R. (2008). Price discrimination. In W. D. Collins & J. Angland (Eds.), Issues in competition law and policy 465 (pp. 465–484). ABA Section of Antitrust Law. 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 Rosencrance, L. (2000, September 13). Customer outrage prompts Amazon to change price-testing policy. Computerworld. https://www.computerworld.com/article/2597088/customer-outrage-prompt s-amazonto-change-price-testing-policy.html Sauter, W. (2020). A duty of care to prevent online exploitation of consumers? Digital dominance and special responsibility in EU competition law. Journal of Antitrust Enforcement, 8(2), 406–427. htt ps://doi.org/10.1093/jaenfo/jnz023 Sears, A. M. (2021). The limits of online price discrimination in Europe. Science and Technology Law Review, 21(1), 1-42 Pages. https://doi.org/10.7916/STLR.V21I1.5761 Shiller, B. (2014). First degree price discrimination using big data (Working Paper No. 58). https://Econ Papers.repec.org/RePEc:brd:wpaper:58 Solove, D. J. (2024). Data is what data does: Regulating use, harm, and risk instead of sensitive data. Northwestern University Law Review, 118(4), 1081–1138. https://doi.org/10.2139/ssrn.4322198 Steppe, R. (2017). Online price discrimination and personal data: A General Data Protection Regulation perspective. Computer Law & Security Review, 33(6), 768–785. https://doi.org/10.1016/j.c lsr.2017.05.008 Stole, L. A. (2007). Price discrimination and competition. In M. Armstrong & R. Porter (Eds.), Handbook of industrial organization (Vol. 3, pp. 2221–2299). Elsevier. https://doi.org/10.1016/S157 3-448X(06)03034-2 Strycharz, J., & Duivenvoorde, B. (2021). The exploitation of vulnerability through personalised marketing communication: Are consumers protected? Internet Policy Review, 10(4), 1–27. https://do i.org/10.14763/2021.4.1585 Tanna, M., & Dunning, W. (2023). Bias and discrimination in the use of AI in the financial sector. In 32 Internet Policy Review 13(4) | 2024 N. Remolina & A. Gurrea-Martinez (Eds.), Artificial intelligence in finance (pp. 320–349). Edward Elgar Publishing. https://doi.org/10.4337/9781803926179.00025 Tannock, S. (2008). The problem of education-based discrimination. British Journal of Sociology of Education, 29(5), 439–449. https://www.jstor.org/stable/40375368 Turow, J., Feldman, L., & Meltzer, K. (2005). Open to exploitation: America’s shoppers online and offline. (Departmental Papers (ASC)) [Working paper]. https://repository.upenn.edu/handle/20.500.1 4332/2000 Tweede Kamer der Staten-Generaal [ House of Representatives of the Netherlands]. (2020). Het Voorstel van wet van de leden Bergkamp, Özütok en Van den Hul houdende verklaring dat er grond bestaat een voorstel in overweging te nemen tot verandering in de Grondwet, strekkende tot toevoeging van handicap en seksuele gerichtheid als non-discriminatiegrond (32411) [Proposal for a law by the members Bergkamp, Özütok and Van den Hul declaring that there are grounds to consider a proposal to amend the Constitution to add disability and sexual orientation as grounds for non-discrimination (32411)] (No. Proposal). Government of the Netherlands. https://www.eerstekamer.nl/behandeling/2 0200623/voortzetting_behandeling/docume Van Bekkum, M., & Zuiderveen Borgesius, F. (2023). Using sensitive data to prevent discrimination by artificial intelligence. Does the GDPR need a new exception? Computer Law & Security Review, 48, 1–12. https://doi.org/10.1016/j.clsr.2022.105770 Van der Rest, J.-P. I., Sears, A. M., Miao, L., & Wang, L. (2020). A note on the future of personalized pricing: Cause for concern. Journal of Revenue and Pricing Management, 19(2), 113–118. https://doi.o rg/10.1057/s41272-020-00234-6 Van der Rest, J.-P., Sears, A. M., Kuokkanen, H., & Heidary, K. (2022). Algorithmic pricing in hospitality and tourism: Call for research on ethics, consumer backlash and CSR. Journal of Hospitality and Tourism Insights, 5(4), 771–781. https://doi.org/10.1108/JHTI-08-2021-0216 Wachter, S. (2020). Affinity profiling and discrimination by association in online behavioural advertising. Berkeley Technology Law Journal, 35(2), 367–430. https://doi.org/10.15779/Z38JS9H82M Wachter, S. (2022). The theory of artificial immutability: Protecting algorithmic groups under antidiscrimination law (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2205.01166 Wong, B. (2020). Online personalised pricing as prohibited automated decision-making under Article 22 GDPR: A sceptical view. Information & Communications Technology Law, 30(2), 193–207. ht tps://doi.org/10.1080/13600834.2020.1860460 Zuiderveen Borgesius, F. (2018). Discrimination, artifiical intelligence and algorithmic decision-making [Study]. Council of Europe. https://rm.coe.int/discrimination-artificial-intelligence-and-algorithmicdecision-making/1680925d73 Zuiderveen Borgesius, F., & Poort, J. (2017). Online price discrimination and EU data privacy law. Journal of Consumer Policy, 40(3), 347–366. https://doi.org/10.1007/s10603-017-9354-z 33 Heidary, van der Rest, Custers Appendices Appendix 1: Explanation of discrimination grounds NOTE: The underlined grounds are legally prohibited either in the European legal framework or in the Dutch constitution. The grounds in italics are legally prohibited in some other national constitutions in the EU. GROUND ORIGIN REFERENCE/FIELD OF LAW ARTICLE EXAMPLES USED IN SURVEY AGE Law Anti-discrimination law 21 CFEU; Article 1 Grondwet; “Young”/”old", age category, etc. BATTERY PERCENTAGE Literature OECD 2018a; Natelhoff, 2023 The battery percentage of the device on which the price is shown. BROWSER TYPE Literature Mikians et al., 2012; OECD 2018a Google Chrome or Safari, cookies, incognito mode, etc. BROWSING ACTIVITY Literature OECD 2018a Browsing history, origin page, time spent on webpages, etc. CRIMINAL RECORD/ OFFENCES Law Data protection law Article 9 GDPR Criminal record, criminal offences, allegations, etc. DEVICE TYPE Literature OECD 2018; Hufnagel et al., 2022 Mobile, PC or tablet, Apple or Windows, etc. GENDER/SEX Law Anti-discrimination law 21 CFEU; Article 1 Grondwet; Article 1 AWGB Female, male, transgender, non-binary, etc. HEALTH DATA Law Data protection law Article 9 GDPR Medical record, information about smoking, blood pressure, etc. IDEOLOGY/ PHILOSOPHY Law Anti-discrimination law 21 CFEU; Article 1 Grondwet; Article 1 AWGB Personal convictions about society, humanity, etc. IMPAIRMENT/ DISABILITY Law Anti-discrimination law 21 CFEU; Article 1 Grondwet Mental or physical disability, chronic illness, etc. INCOME/WEALTH Law Solove, 2024 ‘Rich’/‘poor’, high or low income, etc. INTELLIGENCE Literature Tannock, 2008 IQ-score, highest level of education obtained, etc. LOCATION Literature Larson, Mattu & Angwin, 2015; OECD, 2018a; Priester et al., 2020 The country or place in which one resides or is located. LOYALTY STATUS Literature Maxwell & Garbarino, 2010; OECD, 2018a; CMA, 2020 Loyal customer, new customer, bulk customers, etc. MARITAL STATUS Law Anti-discrimination law Article 1 AWGB Married, civil partnership, unmarried, etc. 34 Internet Policy Review 13(4) | 2024 GROUND ORIGIN REFERENCE/FIELD OF LAW ARTICLE EXAMPLES USED IN SURVEY NATIONALITY Law Anti-discrimination law 21 CFEU; Article 1 AWGB Place of birth, citizenship, etc. PHOTO/ APPEARANCE Literature Hern, 2020; Solove, 2024 Conformity with beauty ideals, physical appearance, etc. POLITICAL VIEWS Law Anti-discrimination law; Data protection law 21 CFEU; Article 1 Grondwet; Article 1 AWGB; Article 9 GDPR “Left”/“right”, liberal/ conservative, etc. PURCHASE HISTORY Literature OECD 2018a; Earlier purchases, type of purchases, amount spent, etc. RACE/ ETHNICITY Law Anti-discrimination law; Data protection law 21 CFEU; Article 1 Grondwet; Article 9 GDPR Skin colour, ancestry, etc. RELIGION Law Anti-discrimination law; Data protection law 21 CFEU; Article 1 Grondwet; Article 1 AWGB; Article 9 GDPR Christian, Muslim, Jew, Atheist, etc. SEXUAL ORIENTATION Law Anti-discrimination law; Data protection law 21 CFEU; Article 1 Grondwet; Article 1 AWGB; Article 9 GDPR Heterosexual, homosexual, bisexual, etc. SOCIAL MEDIA DATA Literature OECD 2018a; Likes, comments, interactions, etc. SOCIOECONOMIC STATUS Law Solove, 2024; Ganty & Benito Sanchez, 2021 Position on social scale, i.e. through education or job position. STUDENT STATUS Literature Carroll & Coates, 1999 Enrollment in (higher) educational institution. Appendix 2: Survey instrument Introduction Thank you for your time and participation in this survey. Your answers contribute to research on personalising online prices. Your participation is highly appreciated. Completing the questionnaire takes approximately 5-6 minutes. Please note that it is easiest to fill in this survey on a computer or laptop. We kindly request that you answer all questions truthfully. Your data and answers will be treated confidentially. Only the researchers have access to your data. Participation is completely voluntary, and you can stop at any time during the survey. It is also possible to request the deletion of the data provided afterwards by contacting the researchers. For questions or comments, please contact Kimia Heidary at [email protected]. By clicking the 'I agree' button, you indicate that you have read the above information, that you are aware that participation is voluntary and that you agree to your data being used for research purposes. If you do not want to participate, you can stop now by closing this page. Thank you again for your cooperation. ▢ I agree to the terms and conditions and would like to participate in the questionnaire. Section 1 – Demographic information 1. What is your age? 2. What gender do you identify with most? ▢ Male ▢ Female ▢ Non-binary/Other ▢ Prefer not to say 3. In which country do you reside? [dropdown menu with 206 countries] 4. What is the highest level of formal education you have attained? ▢ Primary school ▢ High school 35 Heidary, van der Rest, Custers ▢ Higher professional education (HBO) ▢ Bachelor’s Degree ▢ Master’s Degree ▢ Doctorate ▢ Other, namely: …. 5. What is your gross annual income? ▢ Less than €20,000 ▢ €20,000 – €49,999 ▢ €50,000 – €74,999 ▢ €75,000 – €99,999 ▢ More than €100,000 ▢ Prefer not to say Section 2 – Scenario Please read the following text carefully before clicking 'next': 'Companies are increasingly experimenting with their prices online. This also applies to offering personalised prices: charging different prices to different consumers based on their data, such as personal characteristics or online behavior. This may result in a higher or lower price for certain groups or consumers.' On the next page we present you with a number of grounds on the basis of which companies could personalise the price. We are curious about your opinion on this. Click 'next' to continue to the questions. Please note that once you click on this, you will not be able to return to the previous page [Participants were randomly assigned to one out of five groups and being shown 5 (out of 25) grounds] Section 3 – Questions related to grounds [All answers on a scale of 1 (‘strongly disagree’) to 7 (‘strongly agree’): 1 = strongly disagree, 2 = disagree, 3 = somewhat disagree, 4 = neither agree nor disagree, 5 = somewhat agree, 6 = agree, 7 = strongly agree] 6. Statement: ‘The use of this ground for setting prices is fair.’ [Indicate answer for each ground] 7. Statement: ‘Personally, I find it acceptable to base prices on this ground.’ [Indicate answer for each ground] 8. Statement: ‘In society, it is considered acceptable to base prices on this ground.’ [Indicate answer for each ground] 9. Statement: ‘The use of this ground for setting prices should be legally prohibited.’ [Indicate answer for each ground] 10. Statement: ‘The use of this ground for setting prices would diminish my trust in the market.’ [Indicate answer for each ground] Section 4 – General questions 11. Please indicate to what extent you agree or disagree with the following statements: [Answers on a scale of 1 (‘strongly disagree’) to 7 (‘strongly agree’)] - It is acceptable if an online store charges different prices for consumers based on personal data. - It is fair if an online store charges different prices for consumers based on personal data. - It is reasonable if an online store charges different prices for consumers based on personal data. Attention check 12. It is very important for the quality of the research that you pay close attention when completing this questionnaire. Please indicate neutral for this statement. ▢ Disagree ▢ Somewhat disagree ▢ Neutral ▢ Somewhat agree ▢ Agree 13. Please indicate to what extent you agree or disagree with the following statement: ‘I find it acceptable to charge people with a higher willingness to pay a higher price.’ [Answers on a scale of 1 (‘strongly disagree’) to 7 (‘strongly agree’)] 14. How often have you made an online purchase on average in the last 6 months? Choose the answer that comes closest. ▢ Daily ▢ Weekly ▢ Monthly ▢ Less than monthly ▢ Never 15. What is your average daily (non-work-related) internet use in hours? ▢ Less than one hour ▢ 1 to 2 hours ▢ 2 to 4 hours ▢ 4 to 6 hours 36 Internet Policy Review 13(4) | 2024 ▢ More than 6 hours 16. How much money have you spent in total on online purchases in the past 6 months? ▢ €0 ▢ €1 – €50 ▢ €51 – €100 ▢ €101 – €300 ▢ €301 – €500 ▢ €501–€1000 ▢ More than €1000 30. Do you have any comments or would you like to share something that was not covered in the survey? [open question] in cooperation withPublished by 37 Heidary, van der Rest, Custers