can users price real-time contextual information? 1

Cite as: Kamleitner, B., Dickert, S. & Haddadi, H. (2017): Can users price real-time contextual information? Working paper. Paper under consideration at ACM Transactions on Internet Technology (TOIT): Special issue on economics of security and privacy. https://dl.acm.org/citation.cfm?id=J780.

CAN USERS PRICE REAL-TIME CONTEXTUAL INFORMATION?

1

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BERNADETTE KAMLEITNER, WU Vienna University of Economics and Business STEPHAN DICKERT, Queen Mary University of London HAMED HADDADI, Queen Mary University of London

Personal data (PD) markets tend to exclude data subjects because they are considered incapable of consistent PD valuations. But perhaps users simply struggle to comprehend the data they are trading. Assessing real-time contextual information (RTCI) via an app, we aim for a proof of concept and show that in principle users are capable of consistent valuations; but observe this for data about social contexts only. Further explorations suggest that perceptions of data sensitivity and ownership may contribute to users’ ability to consistently price RTCI. Insights facilitate the design of ecosystems that actively include users in data markets. • Security and privacy➝ Human and societal aspects of security and privacy • economics of security and privacy➝ social aspects of security and privacy Additional Key Words and Phrases: endowment effect, app experiment, information sensitivity, psychological ownership, situational influence, WTA, WTP, value of data

INTRODUCTION

Mobile devices enable comprehensive snapshots of every moment of an individual’s life. Call logs, location data [Cheung 2014] and social media apps often provide real time contextual information (RTCI) on-the-go, i.e. information on where individuals are, what they do and with whom they do so. Upon download, many apps request permissions to obtain personal data (PD) including data that allow inferring RTCI, and they are basing their business models on trading these PD [Ducarroz et al. 2016; Hoofnagle and Whittington 2013]. The markets for PD are thriving (e.g., Spiekermann et al. 2015). The asset sold in these markets is something all natural persons are endowed with because it is about individuals. Arguably PD are also individuals’ property [Etzioni 2011; Purtova 2015]. Yet, the players in PD markets are corporate. They consist of businesses harvesting PD from the individual and of businesses buying, aggregating, and commercialising PD, often in bulk. These players determine the monetary value of PD in a market place that excludes those eventually providing the data. Individuals are, if at all compensated (e.g., by getting access to a mobile app), not usually paid for their PD. Harvesting and trading user data without user involvement is the norm. Two streams of research suggest that this practice may, in fact, be necessary. One stream of research centers on the notion of privacy calculus. It presumes that users engage in a tradeoff between the costs to their privacy and the services obtained so that whatever compensation they agree to adequately reflects their valuation of PD [Dinev and Hart 2006; Xu et al. 2009]. In essence, this stream assumes that users are already part of the data market and that the market reflects users’ own data valuation. Rather than assuming that data volunteering equals adequate compensation, a second stream of research suggests that users are not even capable of becoming actors in the data market. To actively participate in a market and enable negotiations, actors need to be able to consistently value the good being traded. A comprehensive body of evidence suggests that this is where individuals struggle. Valuations by users show substantial variations across value elicitation methods and contexts [Acquisti et al. 2015; Acquisti et al. 2013; Grossklags and Acquisti 2007; Rose 2006]

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This work was supported by the EPSRC Pump-Priming grant number EP/J501360/1 at Queen Mary University of London awarded to Hamed Haddadi and Bernadette Kamleitner. Author’s addresses: Bernadette Kamleitner, Institute for Marketing & Consumer Research, WU Vienna, Welthandelsplatz 1, 1020 Vienna, Austria; Stephan Dickert & Hamed Haddadi, Queen Mary University of London E1 4NS, London, UK. Permission to make digital or hardcopies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies show this notice on the first page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credits permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may be requested from Publications Dept., ACM, Inc., 2 Penn Plaza, Suite 701, New York, NY 10121-0701 USA, fax +1 (212) 869-0481, or [email protected]. © 2016 ACM ACM Transactions on Internet Technology. Special issue on economics of security and privacy.

Cite as: Kamleitner, B., Dickert, S. & Haddadi, H. (2017): Can users price real-time contextual information? Working paper. Paper under consideration at ACM Transactions on Internet Technology (TOIT): Special issue on economics of security and privacy. https://dl.acm.org/citation.cfm?id=J780.

suggesting that users are either unable to price their own PD or do not value it highly [Beresford et al. 2012]. So while coming to very different conclusions in terms of users’ roles and capabilities, this stream of research also provides arguments that support the practice of excluding users from having an active share in data markets. Here, we re-inquire into this second stream of evidence and question whether users are in fact generally incapable of pricing their PD at a level of consistency that enables them to trade PD like other market goods. We take a novel perspective and suggest that part of the reason why users struggle to consistently value PD may be because they have no clear notion of what it is they evaluate. Often users are asked to agree to pass on a vast set of data points encompassing data about uncertain future events. This is, for example, the case when providing permission to track location data. What users are asked to share or price is a comprehensive and ambiguous data bundle; such as all locations a person may be in at numerous points in time for an unspecified period. Anticipating and in particular comprehending such data bundles and their scope is at best cognitively challenging and at the worst impossible. Our premise is that the inability to value a boundless bundle does not also implicate an inability to value a subset of the bundle’s components [Greenleaf et al. 2016; Hsee et al. 2013]. Consequently, we aim for a general proof of concept and investigate whether users may be able to consistently value well-specified, meaningful pieces of PD. Specifically we look at RTCI, i.e. in-situ information comprising what people do, where, and with whom. Because RTCI focuses on the very moment they experience, users should have a nuanced and concrete understanding of it [Bhatia and Walasek 2016; Liberman et al. 2007]. This should help for valuation. To assess RTCI, we developed a realtime, longitudinal mobile survey application. To assess consistency of valuations, we manipulated the way valuations were elicited following the endowment effect paradigm [Grossklags and Acquisti 2007; Knetsch and Sinden 1984]. Comparing valuations of RTCI across elicitation modes, we show, for the first time, that the premise of users’ inability to consistently value PD does not universally hold. In doing so we add substantive arguments to a growing body of endeavors that aim to include users in PD markets [Chaudhry et al. 2015; Crabtree and Mortier 2015]. In addition and building on the fact that RTCI is as heterogeneous as the situations people experience, we explore whether there are situations in which valuations of RTCI are particularly consistent. This facilitates a more nuanced understanding of when users’ capability of pricing RTCI is particularly high and contributes to a growing body of research documenting contextual malleability of the perception of privacy and PD [Acquisti, et al. 2015]. Finally we aim to enhance current theorizing about why people struggle to consistently value PD. We explore perceptions of data sensitivity and ownership and identify novel patterns that point towards the power of contextual and social interdependence. We conclude that individuals’ struggle to value PD is pervasive but not universal, and that the key to including users in markets for PD may reside in ensuring that people better understand what data are collected and who they might concern.

CONCEPTUAL BACKGROUND Perspectives on the purported inability to value PD

The premise that users cannot valuate their PD in an unbiased way is the backbone of several business models. Many online business models rely on additional revenue streams generated through users providing their data. Rarely is attention drawn to the fact that the data requested also act as compensation. For example, when downloading a mobile app most users will not know which of the permissions they are asked to grant are needed purely for the app to work properly, which permissions may be needed to harvest data of commercial value, and which permissions may be needed for both, or which permissions may have no real purpose at all [Au et al. 2012]. The starting point for any discussion about users’ ability to valuate data is, thus, a market in which the seller may often not even fully be aware of her role. By virtue of their very design (e.g. asking individuals to put a price on data or to decide between options that differ in terms of PD requests and price), studies on users’ capabilities of data valuation ensured that awareness of PD as part of the actual transaction was somewhat heightened. They nonetheless find that users are unable to consistently price their PD [Acquisti, et al. 2013] and in turn often trade them for small conveniences or gains [e.g., $1 off a DVD; Beresford, et al. 2012; Preibusch et al. 2013]. ACM Transactions on Internet Technology. Special issue on economics of security and privacy.

Cite as: Kamleitner, B., Dickert, S. & Haddadi, H. (2017): Can users price real-time contextual information? Working paper. Paper under consideration at ACM Transactions on Internet Technology (TOIT): Special issue on economics of security and privacy. https://dl.acm.org/citation.cfm?id=J780.

The implications are potentially far reaching [Acquisti et al. 2016]. Data have no expiry date and they gain in information value as more data become available, thus, allowing for their aggregation into commercially but also socially viable insights [Chaudhry, et al. 2015; Conitzer et al. 2012; Kostkova et al. 2016]. How much value eventually becomes extracted from what data is a dynamic parameter. This makes it hard to determine how fair or unfair a users’ de facto compensation (e.g., usage of an app) eventually turns out to be. One current practice aggravating the potential issue is that of obtaining wholesale permissions for classes of data (e.g., location data). This practice implies that additional data can be harvested from the same person at (nearly) no extra cost [Rifkin 2014] and it entails three peculiarities that may compromise users’ ability to understand what they are giving permission to. First much PD is traded in large bundles. Users are generally asked to permit access to all of a class of data rather than to individual data points. This likely holds implications for people’s ability to consistently value data. A large body of research suggests that people generally struggle to comprehend anything that comes in larger scopes and that with increasing scope they become increasingly insensitive to variations in scope [Loureiro et al. 2013; Urminsky and Kivetz 2011]. At least in part this is because they lose track of what this bundle actually contains. As a consequence people value the whole differently than they would evaluate the sum of its parts were they valued in isolation. Given the scope of personal data, as well as the fact that it has yet to accrue, failure to consistently valuate such data bundles appears logical. In fact, Carrascal et al. [2013] show that users assign similar values to individual pieces of PD as they do for entire bundles of PD. However, if the bundle cannot be valued that does not necessarily mean that the things in it cannot be valued either. One proven way to increase evaluability is to single out units of information (Hsee et al. 2013). For example, when asked to volunteer their location information people are likely thinking of specific locations rather than of the fact that their every move implies a change in location. It is likely impossible to anticipate all the numerous coordinates this will eventually entail. Asking people to evaluate each location separately and sequentially would very likely lead to more nuanced, well-specified and, therefore, likely also more reliable valuations than asking for the faceless commodity of location data. Second, PD are collected in units that may hold little meaning for the individual. People may face the challenge of valuing something that is hard to grasp and that they never thought about. Research on people’s ability to value non-market goods—which, like data, are often hard to grasp— has shown that identifying single units plus embedding them in a bigger meaningful context helps curb exaggerated valuations [Hausman 2012; Kahneman and Knetsch 1992; Sælensminde 2003]. Location data, for instance, would come in the form of geographical coordinates. It is only once we give a name to this location and once we imbue it with a context that location data becomes meaningful. For example, “51,30’N,0,7’W” may mean little to most but “being in a shop in London doing the daily grocery shopping” does. The integration of data points into meaningful units eventually provides value for users and companies alike [Ackoff 1989]. Third, PD that traces user parameters, such as location data, also entail issues of uncertainty. These stem from the fact that many of the data harvested arise in and capture a future that is unknown at the time users are asked to permit access to data about it. Dynamically accruing PD entail issues of scope, of a lack of embeddedness and of uncertainty. If we want to discern whether people are truly incapable of consistently valuing PD, it appears necessary to present and assess them in a manner that strips away these issues and turns PD into something people can emotionally and intellectually grasp. Following this logic, we ask what happens when people are asked to value well-specified PD holistically and in real-time. Specifically, we look at a class of PD that becomes more important as the amount of sensors in human surroundings increases [Cukier and Mayer-Schoenberger 2013; Sundmaeker et al. 2010]. We look at what we call real-time contextual information (RTCI), i.e. PD comprising situational snapshots of a person’s live including what she does where and with whom. This is also important in providing privacy feedback in location settings [Patil et al. 2015; Patil et al. 2014]. RQ1: Are users generally unable to consistently value RTCI?

Contextual malleability

From waking and brushing one’s teeth in the morning, to working in the office, to going to the gym, to meeting friends, to relaxing at home, the situations we experience are numerous and ever

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changing. RTCI reflects the manifold situations each person dynamically finds herself in. As a consequence, RTCI is as heterogeneous as the situations people experience. A growing amount of evidence in the domain of privacy perceptions [Acquisti, et al. 2015; Martin and Shilton 2016; Zafeiropoulou et al. 2013] suggests that the value people assign to privacy and therefore also to PD is contextually malleable. In some situations people seem more privacy concerned than in others. Even the mere presence of specific cues such as a happy user interface [Kehr et al. 2015] or a hint that others disclosed information [Acquisti et al. 2011], suffices to alter privacy concerns in otherwise unchanged situations. The valuation and the consistency of valuations of RTCI about different contexts is, thus, likely to see variations (note, however, that Carrascal, et al. [2013] find no variations in real-time valuations across web-contexts). Provided there are variations, insights about them offer hints as to what makes situations hard to evaluate and how different classes of PD (e.g., activity and location) interact in bringing about privacy concerns and valuations. In this research we thus also explore variations in valuation consistency of RTCI across situational contexts. RQ2: Does the consistency of valuations of RTCI vary across contexts? Select drivers of (in)consistent valuations of PD

If there are contextual variations in the ability to value RTCI, one may ask what drives them. Addressing this question may help identify strategies to increase consistency of valuations because it unveils proxies for the conditions under which user-inclusive data markets could work. The last set of questions we explore in this paper, thus, relates to potential factors that may cause (in)consistent valuations of PD. Specifically, we explore the effect of perceptions of data sensitivity and ownership. Both perceptions have been shown to account for variations in valuation consistency [Baron and Spranca 1997; Reb and Connolly 2007], including in the context of data [Bauer et al. 2012]. Perceived data sensitivity

Sensitivity has been suggested as a needed boundary to the monetization of information [e.g., Nissenbaum 2004] but also as a proxy for the value of PD [Hirschprung et al. 2016; Kosa et al. 2011; Preibusch 2013]. Indeed, there is evidence that people are unwilling to make trade-offs for sensitive issues and goods [Baron and Spranca 1997; McGraw et al. 2003] or as Boyce et al. [1992] put it: for goods with intrinsic value. If asked to value a good, people appear to use the degree to which this good is perceived as sensitive as a cue. Notably, sensitivity tends to have differentially strong effects on valuations depending on how these valuations are elicited. Past evidence indicates that the perception of a good as sensitive primarily affects the price people demand when selling this good [Anderson et al. 2000; McGraw and Tetlock 2005]. It has less effect on the price people are willing to pay for the same good. To summarize, estimations of sensitivity vary and they co-vary with the valuation of data [Hirschprung, et al. 2016]. It thus appears plausible that perceived sensitivity relates to the extent to which people are capable of providing consistent valuations. RQ3: Do user’s sensitivity perceptions of RTCI relate to the extent to which they are capable of consistently valuing RTCI? Perceived data ownership

Another factor potentially driving valuations is the extent to which people feel that the data belongs to them. The perception of something as “mine” has also been termed “psychological ownership” [Pierce et al. 2003]. It is a phenomenon that stretches beyond physical goods and legal rights [Kamleitner and Dickert 2015]. For example, Kirk et al. [2015] show that psychological ownership determines whether people recommend an investment and Lee and Chen [2011] find that it predicts engagement in virtual worlds. There is no doubt that the experience of ownership is powerful [for reviews see Kamleitner 2014; Pierce and Jussila 2011]. Of relevance here is that ownership has been identified as a robust predictor of monetary valuations [Kamleitner and Feuchtl 2015; Reb and Connolly 2007; Shu and Peck 2011], including in the context of PD [Spiekermann and Korunovska 2016], and of information sharing. Both Kim et al. [2016] and Raban and Rafaeli [2007] find this in the context of social networks. Perceptions of ownership are thus likely to contribute to valuations of data and the willingness to trade them. ACM Transactions on Internet Technology. Special issue on economics of security and privacy.

Cite as: Kamleitner, B., Dickert, S. & Haddadi, H. (2017): Can users price real-time contextual information? Working paper. Paper under consideration at ACM Transactions on Internet Technology (TOIT): Special issue on economics of security and privacy. https://dl.acm.org/citation.cfm?id=J780.

Notably, the perception of ownership acts as a predictor in particular if and when users consider themselves as the main or only owner of a good [cf. Kamleitner and Rabinovich 2010; Raban and Rafaeli 2007]. When it comes to PD, and in particular to RTCI, this last insight may be important. RTCI captures a person’s situation and people are rarely truly alone. Much of our waking hours are spent either with others or in a manner that can be observed by others. For example, any time somebody is leaving their house it is likely that they will be seen by other people. While being an instance of PD, RTCI is often accessible to other human beings and therefore also information they are privy to. This raises the possibility that the perception of ownership of RTCI but also the degree to which people use considerations of ownership when valuing RTCI varies across situations. RQ4: Does the perception of RTCI as owned relate to the extent to which people are capable of consistently valuing RTCI?

Assessing consistency: contrasting willingness to accept (WTA) with willingness to protect (WTP)

To capture people’s ability to value RTCI we draw on a paradigm used in economics and psychology: the endowment effect (Thaler, 1990 or Acquisti et al. 2013 in the context of data). Applying this paradigm allows highlighting the degree of consistency of valuations by contrasting the minimum amount requested to sell an object (willingness to accept, WTA) with the maximum amount paid to obtain or (in the context of PD) protect the same object (willingness to pay/protect, WTP). The endowment effect is the consistent observation that the WTA/WTP ratio tends to be greater than 1. This signals a general unwillingness to part with objects and a wide-spread struggle to consistently value goods. Endowment effects are a robust phenomenon and have been found for both tangible and intangible goods [e.g., Ashby et al. 2012; Horowitz and McConnell 2002; Johnson et al. 2007], including personal data [Acquisti, et al. 2013; Grossklags and Acquisti 2007; Hui and Png 2006]. While the mere presence of the effect is insufficient to question people’s valuation ability, its scope is. Goods that individuals find easy to price, i.e. ordinary private market goods such as mugs or shoes, exhibit WTA/WTP ratios of around 2 to 3. In contrast, goods that individuals struggle to value, i.e. non-market goods such as health and safety, exhibit WTA/WTP ratios that are considerably higher [Horowitz and McConnell 2002; Nataf and Wallsten 2013]. The observed strength of the endowment effect hence serves as a proxy for whether individuals are capable of somewhat consistently valuing and in turn trading a good in a market. Ranging from a ratio of 5.47 for information on how participants spent money on a gift card [Acquisti et al. 2009] to more than 100 for information on the number of past sexual partners [Grossklags and Acquisti 2007], the WTA/WTP ratios observed for PD thus far are in fact on the high side, suggesting users inability to value PD. Nonetheless, given all the afore-mentioned arguments the evidence to date appears insufficient to allow for a final verdict. To the best of our knowledge there is no study that employed the endowment effect paradigm to dynamic and wellspecified PD such as RTCI. While it is hard to anticipate the actual size of the effect for RTCI, we expect it to be lower than that observed in prior studies on PD. The endowment effect emerges by contrasting two different value elicitation methods, WTA and WTP. An interesting question asks which of these two elicitations it is that brings the effect about, both or only one of them? Existing research consistently shows that it is in particular when asked to determine a selling price that people’s valuations become volatile [Anderson, et al. 2000; Kahneman et al. 1991]. WTP is, for example, also more insensitive to scope [Olsen et al. 2004]. This has at least two reasons. First and in contrast to WTP, WTA is not restricted to the funds available to a person. If a person does not want give up a piece of data, the most she could offer to protect it is whatever she has available. Asking prices face no such restrictions. Second and more importantly, the elicitation of WTA shifts the cognitive frame [Morewedge and Giblin 2015] such that an individual is put into the position of a seller. This implies holding property rights and accordingly also moral responsibility for the fate of the good [Boyce, et al. 1992]. Studies suggest that the amount people request for a good is strongly influenced by its perceived sensitivity and moral value [Boyce, et al. 1992; Hirschprung, et al. 2016] and its perceived ownership [Chatterjee et al. 2013; Morewedge et al. 2009; Shu and Peck 2011]. We thus expect to observe in particular variations of WTA, both across contexts and in response to variations in perceived sensitivity and ownership.

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MOBILE APP STUDY

To examine the extent to which individuals exhibit biases in valuations of their private data, a mobile Android app was built, made available in the Google playstore, and brought to participants’ attention via a media campaign and word of mouth. All participants had the chance to enter a raffle for vouchers of a big online store. Upon installation an initial questionnaire assessed participants’ profile (including demographics). The mobile app then popped up at random times (between 8am and 9pm) twice a day for a maximum of ten days. Sample screen shots of the app can be found in the appendix. Each time it asked for self-reports of current activity, social context, and location. Using the format of a mobile app it was important to ensure that these self-reports were easy to make while also providing sufficient situational detail. In a multistep-procedure that preceded app development we, thus, identified and classified a large range of activity and location contexts. To do so we conducted a diary study with 10 volunteers. All participants were personally handed a specially designed diary in which they documented all situations (activity, location, persons present) they found themselves over the course of an entire week. Participants were instructed to document their exact activity for every single waking hour within that time frame. This procedure allowed us to capture a wide range of activities that people would normally engage in. All volunteers were compensated for their time with online shopping vouchers. In a next step participants reports were grouped and coded and enriched by activity categories known from prior evidence on human activities [Kahneman et al. 2004]. Finally, two of the authors and a research assistant iteratively condensed and classified the resulting activities, locations and social contexts into larger sets of categories. Eventually we identified 36 types of activities which were grouped in 7 main categories of activity, 12 location types which were grouped into 3 main categories of location, and 6 types of companions participants interacted with (see Appendix B for the full list of categories). In the app participants could then first select a main category which opened up a new set of sub-options. Usability and comprehensiveness of this classification process was eventually verified by 3 volunteers who agreed to test the app and its user-friendliness for some days before it was launched. After asking participants to self-report their current situation, the app asked for the £-value people attach to this RTCI information. In order to gauge potential endowment effects in a between subject variation participants were randomly assigned to different versions of the app. In this paper we compare WTP and WTA using different scenarios. In the WTP condition valuations were elicited by asking “What is the maximum amount that you would be willing to pay to restrict others from accessing this information?”. In the WTA condition valuations were elicited by asking “What is the minimum amount that somebody would have to pay you to get access to this information?”. Participants next reported on the potential processes affecting valuations. In a single item they reported the extent to which they felt they personally and individually own the data in question (“To which extent to you feel this is just YOUR information?” from ‘not at all’ to ‘very much’ on 100 grid points). Next they reported the extent to which they perceived information about this situation as sensitive. Because specific components of the situation may be differentially sensitive we asked for the perceived sensitivity of location, social company and activity data with one item each on a slider scale (100 grid points). All items loaded on one factor that explained 86 per cent of variance. Items were thus averaged into a scale (Cronbach’s alpha=.91). In addition to the variables of interest to this paper we assessed mood and the perceived value of these data for specific stakeholders. Sample and data preparation

Ninety-five participants (mean age = 29 years, 15% female, 48% full-time employees) provided a price for their information at least once. Overall, 775 valuations were obtained. As generally found in PI valuations [e.g., Bauer, et al. 2012] the distribution of values provided was positively skewed (M = £24 , Median = £3) necessitating non-parametric statistical procedures for most of the analyses. To facilitate interpretation, valuations were grouped into five meaningful categories. The first category comprises responses indicating that participants consider this information ‘free’. The second category comprises valuations of up to £5, we named this category ‘low’ valuations. The ‘medium’ and ‘high’ valuation categories contained price indications of up to £10 and £50, respectively. The final category ‘no sale’ comprises all prices in excess of £50. These valuations are so high as to indicate little actual willingness to give the data away. Notably, substantive results do not change if category boundaries are shifted. Each situation our participants encounter is unique. To capture the holistic nature of a situation and to avoid issues related to low cell sizes, distinct situation profiles were inferred by means of a

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two-step cluster analysis across specific activities, locations and company. This yielded five interpretable situation clusters (Table 1). Table I. Description of situation clusters based on dominant RTCI per cluster.

Cluster1 Name

Social Time

Cluster2 Family Matters

Cluster3

Cluster4

Cluster5

Home and Alone

Out and About

Work Time

Typical Location

no dominant location

home

home

public space

work place

Typical Activity

visiting/ study/eat

eat/ drink

watch TV/ read

get somewhere

work

friends

family

alone

alone

alone/ colleagues

158

132

175

98

145

Typical Company na a Note:

67 observations could not be classified into any of these clusters.

Fifty participants provided five or more valuations but only 17 participants provided all requested valuations. To ensure that there are no general learning effects over time, a chi² test was conducted to contrast the distribution across valuation categories between the first valuation and later valuations. There was no difference (Chi²(df=4) = 2.80, ns). A Spearman correlation likewise shows that valuations do not relate to the amount of times a person has already responded, r = -.01. This indicates the absence of learning effects. It is nonetheless possible that frequent responders bias results. We, thus, not only provide overall statistics but also separate analyses that look at only those instances in which a given person first encounters a specific situation, corresponding to a cross-sectional analysis. This allows eliminating potential person bias and also minimizes biases that could stem from motivated adjustments over time. Note that we only report on the results based on the reduced sample where this yields a difference in results and that the reduced sample size is too small for Chi² tests across situations. Results and discussion Overall endowment effect

We first investigate results across situations before drilling into specific situation profiles. Overall, and as shown in Table 2 there is evidence for an endowment effect. A Mann-Whitney UTest suggests a significant difference in mean ranks across conditions. Contrasting the median valuations suggests an average WTA to WTP ratio of 5 across all observations and of 2.75 if we look at first valuations per situation only. This is higher than that observed for most market goods but still lower than what is often found for non-market goods such as health. Within the context of prior studies on the valuation of data, it is similar to the ratio reported by Acquisti, et al. [2013] and therefore at the bottom end of ratios thus far observed for PD. The use of valuation categories allows us to examine whether what we observe is a simple overall upward shift in the WTA condition. If people are incapable of valuing RTCI consistently this is what we would expect. A Chi²-test confirms a difference in the frequency of valuation categories across WTA and WTP conditions, Χ²(1) = 32.5, p < .001. This difference does, however, not reflect a general upward shift of valuations in the WTA condition. Rather the overall endowment effect appears to result primarily from differences in the general propensity to part with information. Fewer people in the WTA condition sell their info for free and more report ‘no sell’ than expected. Conversely, in the WTP condition more people than expected offer the info for free and fewer report no sell. By and large those that are happy to trade RTCI are happy to do so at similar values across WTP and WTA conditions. This first insight suggests that rather than just leading to shifts in valuation per se, the elicitation frames influence whether the information is at all a good considered worth buying or selling. It appears that what is inconsistent is the propensity to trade RTCI not their valuation.

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Cite as: Kamleitner, B., Dickert, S. & Haddadi, H. (2017): Can users price real-time contextual information? Working paper. Paper under consideration at ACM Transactions on Internet Technology (TOIT): Special issue on economics of security and privacy. https://dl.acm.org/citation.cfm?id=J780. Table II. Valuations across elicitation methods

Frequency of Valuation Categories

Elicitatio n Method

Median

free £0

low £0.01-5

medium £5.01-10

high £10.01-50

no sale £>50

WTA

5.0 (5.5)

18%

40%

26

23%

15%

WTP

1.0 (2.0)

28%

42%

1400%

12%

40%

Z-value (U-Test)

-5.75** (-4.26)

Chi² = 32.49** Note. **…p