Mobile Consumer Apps: Big Data Brother is Watching You

Mobile Consumer Apps: Big Data Brother is Watching You The data generated through mobile applications on smart mobile devices repre-sents one of the most interesting and valuable shares of Big Data. This highly per-sonalized and traceable information comes with implications for basically all stake-holders and should lead consumers to rethink their imprudent usage of apps.

Christoph Buck, Christoph Buck, Christoph Buck Chris Horbel, Chris Horbel, Chris Horbel Tim Kessler, Tim Kessler, Tim Kessler Claas Christian Germelmann

26 Marketing Review St. Gallen 1 | 2014

Schwerpunkt | Chancen und Gefahren

The EMC-sponsored ‘Digital Universe’ study by the International Data Cor-poration (IDC) reported that in 2012 2.8 zettabytes (ZB) of data have been generated worldwide; the amount is expected to increase to 40 ZB by 2020. Most of the data (68% in 2012) is generated by consumers, e.g. by sharing information in social networks, providing credit card information for on-line purchases, etc. Increasingly, consumers create information through their use of mobile phones or tablet PCs, in particular by downloading and using mobile applications. However, most of the data generated by consumers is not actively and voluntarily created by them, but captured by recording con-sumers’ activities or created through data aggregation (IDC 2012; OECD 2013).

Data generated through consumers’ use of mobile applications is of par-ticular analytic value because it not only offers insights into consumers’ “digital” lives (e.g. internet browsing preferences), but also into their “real” lives (e.g. location data, interests, satisfaction of needs). While the data generated by a single app contains only a tiny fraction of information about the consumer, the variety of data which can be created is extraordinary. The specific architecture in which apps are embedded allows for a combination and aggregation of these fragmented pieces of data. This link to the individual identity creates a deep and holistic picture of the consumer (see figure 1). As a result, mobile applications generate a particular type of Big Data: highly personalized, rich data about consumers.

Such data is highly valuable as examples of market value for personal data demonstrate. Whereas the price for an address is US$ 0.50 or US$ 3 for the driver’s license number, a combination of address, date of birth, social secu-rity number, credit record and military record has a value of US$ 55 (OECD 2013). Furthermore, aggregated personal data is not just economically val-

Dipl.-Kfm. Christoph Buck M. Sc.Chair of Information Systems Manage-ment, Universität Bayreuth, Universitätsstraße 30, 95447 BayreuthE-mail: [email protected]

Prof. Dr. Chris HorbelDepartment of Environmental and Busi-ness Economics, University of Southern Denmark, Niels Bohrs Vej 9-10, DK-6700 EsbjergE-mail: [email protected]

Prof. Dr. Tim KesslerJunior Professor International Manage-ment of Technology and Industrial Services, Universität Bayreuth, Universitätsstraße 30, 95447 BayreuthE-mail: [email protected]

Prof. Dr. Claas Christian GermelmannChair of Marketing, Universität Bayreuth, Universitätsstraße 30, 95447 BayreuthE-mail: [email protected]

Source: authors´ illustration

Fig. 1 App Data as a Personalized Part of Big Data

Big Data

Data Aggregator

ApAppp

ApAppp

ApAppp

ApAppp



uable, it also benefits the individual. For example, the use of personal data in an innovative way can lead to improved health outcomes and traffic safe-ty (Vodafone 2013; World Economic Forum 2013). Examples of cases in which it has been possible to predict flu outbreaks or traffic jams illustrate that such data might also have social value. In Germany,that such data might also have social value. In Germany,that such data might also have social value. In Germany the navigation spe-cialist TomTom exploits anonymized GPS data from Vodafone users to pro-vide alternative routes, while Google predicts flu outbreaks by evaluating lo-cal search data, a procedure which could become even more accurate by using a specialized drugstore app.

However, these opportunities are not without risks. By generating personal data, consumers give up their privacy and security to some degree and data protection increasingly becomes an issue.

It is questionable whether consumers know that personal data (let alone which data) is used by apps and that often they give away their personal data in exchange for the download of apps. In addition, the type of data provided through app purchase and usage is not always clearly related to the tasks per-formed by an app, which decreases the probability that consumers are aware of this (mis)use of their data. Examples which illustrate such disparity are the apps ‘Mobile Metronom’ (counts the beat of music) and ‘Foodspotting’ (provides restaurant information). These apps provide only a limited range of functions but capture the user’s device number and mobile network op-erator (‘Mobile Metronom’), or even the device number, e-mail addresses and usage data (‘Foodspotting’). In a similar vein, the ‘Barcoo’ app, an EAN code reader, sends device numbers and user statistics to ‘Flurry’, a mobile data aggregator.

Thus, the present article will try to shed some light on the ‘bright’ and ‘dark’ sides of Big Data generated through mobile applications. We will fo-cus on the particular characteristics of mobile applications and the conse-quences regarding consumer decision making about app purchase and us-age. We will conclude with some implications for the parties involved in the creation and usage of app-generated data.

The Mobile Innovation WaveWith the introduction of the iPhone in 2007, the whole industry and the way we use and integrate mobile devices in our everyday life have changed dra-matically. While some hardware manufacturers like Apple and Samsung rose to unprecedented heights, others like Nokia or Research in Motion are strug-gling in this highly dynamic, competitive environment. Smartphones have replaced standard mobile phones in recent years and tablets are on their way to replacing notebooks and desktop PCs. Along with the changes in mobile device manufacturing a whole new sector has evolved, providing innovative software and mobile applications.

A recent study by Meeker and Lu (2013) notes that global mobile traf-fic accounts for 15% of the total internet traffic in 2013 and that by 2015 more people will access the internet via a mobile device than from a desk-top.

Management SummaryConsumers extensively use mobile ap-plications and thus generate an impor-tant share of Big Data. Such data is of particular value because it does not only offer insights into consumers’ “digital” lives, but also into their “real” lives as the specific architecture in which apps are embedded allows for an aggregation of fragmented pieces of personalized data. This may raise privacy and secu-rity concerns. Therefore, mobile net-work operators and manufacturers of mobile devices should enforce the pro-tection of personal data.

Main Proposition 1Mobile applications generate a particular type of Big Data: highly personalized, rich data about consumers.



Against this backdrop, mobile innovation becomes increasingly impor-tant at all levels. Consumers can hardly imagine managing their everyday life without their smart mobile devices (SMD) anymore (Buck/Eymann 2013). They ask for constantly improved and extended technical features and mobile applications to match their digitized lifestyle and personal needs. These mobile applications increasingly provide comprehensive features that exploit the devices’ technical capabilities to a large extent, in turn triggering the need for continuous technological innovation. Furthermore, consumers’ extensive use of mobile applications and information technology generates huge amounts of data that increase the need for greater bandwidths and

higher bit rates. This ever increasing data volume is paving the way for an-other innovation closely associated with the mobile internet and mobile communication – the business of trading highly personalized and granular consumer data.

The Particular Value of App DataData generated through apps is of high quality and enriches Big Data with verified personalized data sets on the ‘glass consumer’. These data sets emerge from a great variety of apps, each providing access to a very small, but highly detailed portion of personal data. Over 2 million apps are cur-rently provided by the leading app stores and up to 21 billion apps were downloaded worldwide in 2013 (hitec 2013). So there is literally ‘an app for everything’. Through aggregation of the pieces of information generated by the enormous variety of apps, consumers’ (digital) lives, from daily move-ment profiles, purchase histories, and bank account activities to social rela-tionships can be reproduced.

In this article we address general phenomena regarding consumer behav-ior in digital ecosystems. For that reason we primarily discuss operating sys-tem (OS)-integrated apps (excluding web-based apps) without considering platform-related differences.

As figure 2 illustrates, the particular value of app data and their special characteristics – as compared with other types of Big Data – are related to the architecture of the underlying mobile ecosystem. The first tier includes basic consumer data which is required for enrollment within the mobile eco-system. It already generates first-class information to personalize the user profile (Felt et al. 2011). The second tier represents the ecosystem’s ability to track and store the entire data by using the basic OS. Furthermore, the eco-system provider is able to analyze the purchase activities and generate infor-

“The specific architecture in which apps are embedded allows for a combination and aggregation of the fragmented pieces of data.”



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mation on the users’ interests and lifestyles. The third tier refers to the abil-ity of a 3rd-party publisher to use specific data of the consumers’ app usage. The information can be recorded and personalized by the 3rd-party pub-lisher, by downlinking to the OS. OS providers narrow the access to very critical data such as device IDs by a method called sandboxing (e.g., within the OS version iOS 7). Nevertheless, they allow fundamental insights into consumers’ behavior (Enck 2011). Finally, a comprehensive user profile is generated by 4th-party aggregators like Google, Flurry, and other advertising networks. Regarding this profile, Flurry’s stated goal is ‘advertise’ and ‘mon-etize’ (Flurry 2013). Furthermore, additional privacy leaks (e.g., uncoded passwords) may also be a result of using apps, leading to unwanted exposure of personal data.

The Context of App Purchase DecisionsEach purchase decision and the way in which consumers use products and services and thus create value for themselves depends on the context in which this process takes place (Vargo et al. 2011). In the case of app purchas-es, some of the main context factors are the system entry via SMDs, the pur-chase channel and the underlying mobile ecosystem.

SMDs are extraordinarily personalized gadgets to which consumers have a personal relationship. In order to function as personalized devices, SMDs have to be registered in the consumer’s personal account of the underlying ecosystem. This ecosystem is the central hub for consumers’ and publishers’ mobile digital activities. From the consumer’s perspective, the integrated app store is the only way to get access to mobile apps. These apps are provided by 3rd-party publishers but have to follow the strict standards of the ecosys-

Main Proposition 2The particular value of app data is related to the four-tier architecture of the underlying mobile ecosystem with the tiers ‘basic data to obtain sys-tem access’, ‘general usage data via OS’, ‘specific usage data via 3rd-party app publish-er’, and ‘aggregated data via 4th-party aggregator’.

Source: authors´ illustration

Fig. 2 Four-tier Architecture of Data Aggregation in Mobile Ecosystems

Aggregated Data via 4th-Party Aggregatore.g. household spending tracked by GPS, tour, time, and shops, ...

Specific Usage Data via 3rd-Party App Publishere.g. banking accesses, household spending, ...

General Usage Data via OSe.g. OS-usage, purchase history, primary interests (lifestyle), ...

Basic Data to Obtain System Accesse.g. e-mail, phone number, device ID, payment information, ...

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tem’s platform. These standards include rigid design templates and security reviews which lead to a homogeneous product presentation. Moreover, the entire purchase transaction, including the search process, download, and payment handling, takes place in a standardized environment.

Low Involvement and Habitualized Buying DecisionsThe technical context in which app buying processes take place stimulates consumer buying decision styles which correspond to the so-called privacy paradox, i.e. the observation that in digital markets consumers articulate their need for privacy, but act in the opposite way (Acquisti/Gross 2006).

The specific technical and contextual features of apps suggest that the typ-ical buying process is accompanied by low levels of involvement and cogni-tive control. Consequently, most app purchases can be characterized as lim-ited (e.g., buying another game app from a software house that is already stored in a consumer’s evoked set), habitual (buying habitually from the same app store), or impulsive (buying an app as a reaction to external stimuli such as a recommendation or a sudden realization of a specific demand) (Wein-berg 1995). Such buying processes are characterized by low levels of infor-mation search.

Consequently, in app purchase situations, the probability that consumers actively search for information about the data an app requires from users is rather low. They would need some motivation and the capability to conduct this information search (Bettman/Park, 1980). However, this motivation can be expected to be particularly low when consumers perceive the risk relat-ed to the app and the buying process to be low (Grazioli/Jarvenpaa 2000). Therefore, consumers only rarely use opportunities to search for informa-tion on the privacy and security of apps. mediaTest digital, for instance, pro-vides an app directory which evaluates mobile applications regarding their level of security (hitec 2013).

Trust Relationships as Behavioral ModeratorsApart from risk perception, the concept of trust becomes relevant for un-derstanding why consumers readily give away personal data without con-sidering related privacy issues. In this context, two forms of trust may be dis-tinguished: organizational trust and system trust.

Organizational trust can be defined as the future-oriented tendency of an individual to rely on a behavior of an organization despite its uncertainty (Gröppel-Klein/Germelmann 2009). Consumers trust that app publishers and platforms will not misuse the information they share. Such trust rela-tionships can be intensified by the integration of assurances in the presen-tation of the apps (Kim/Benbasat 2010). However, negative feedback by con-sumers concerning the privacy policy of certain app publishers can be det-rimental to consumers’ trust in those providers. Branded platforms like Google Play or Apple iTunes can increase consumers’ trust by, for instance, establishing app approval processes (Grazioli/Jarvenpaa 2000). Although such processes may not rule out data misuse, consumers, especially in low-

Main Proposition 3The way consumers use apps and give away their personal data via apps depends on con-text factors like the system en-try via SMDs, the purchase channel and the underlying mobile ecosystem.



involvement buying situations, perceive such regulations as indicating trust-worthiness of the app publishers when it comes to privacy issues.

System trust refers to the ‘trust in the functioning of bureaucratic sanc-tions and safeguards, especially the legal system’ (Lewis/Weigert 1985). Con-sumers feel safe since they perceive the context as well regulated by third parties (e.g., data protection acts or watchdogs). There may even be a ‘license agreement fallacy’ at work: when asked to accept a license agreement, con-sumers with low levels of involvement might be lured into thinking: “There is something legal going on, so I am protected by the legal system, and thus I and my private data are safe.”

Higher levels of trust have shown to be leading to lower levels of risk per-ception (Kim/Benbasat 2010). Kim and Benbasat (2010) found that consum-ers who feel deceived by inaccurate information on a webpage rely on assur-ance signals to reduce the risk they perceive. Transferring this reaction to the context of apps and data collection, one could argue that high levels of perceived data insecurity would not lead consumers to less data sharing, but first and foremost to more reliance on assurance signals to regain trust and to decrease the perceived risk. Given the low levels of ‘app literacy’, this ef-fect might create a false sense of security in consumers even in the presence of the strong feeling that they are sharing private and highly personalized data.

ConclusionData generated through consumers’ purchases and usage of mobile applica-tions is becoming an increasingly important part of Big Data. Not only may the share of data generated through SMDs be expected to increase even fur-ther, but this type of data also has very special characteristics which make it very valuable for businesses, governments, NGOs, the society, and the con-sumer himself.

An enormous variety of apps, each of them ready to perform a very spe-cific task for the user, is provided in app stores. By purchasing and using an app, consumers give some part of their data to the app publisher. Even this partial information is valuable, because it is verified and can be directly re-lated to the individual. However, the combination of many of these tiny pieces of information generated through various apps creates a rich and holistic picture that turns the user into a ‘glass consumer’.

Quite obviously, such use of consumers’ personal data can raise privacy and consequently corporate security concerns. Even if consumers were mo-tivated and capable of searching for information about the data they share with the publishers of the apps they are using, they would get to know only

Main Proposition 4Consumers’ risk perceptions concerning their personal data are inversely related to their trust in the organizations (app publisher and platforms) and their trust in the system that is guaranteed, for instance, by regulations and watchdog groups.

“In digital markets consumers articulate their need for privacy, but act in the opposite way.”



part of the story. Consumers should be aware that today their phone knows more about them than their mom does. But, who is ‘the phone’? App data is collected and aggregated at various levels and by all kinds of players. In fact, consumers do not provide their data in a one-to-one transaction to the app publisher for the permission to use the app, but in a multilateral transaction (e.g., by continuous usage) to many diverse actors such as operating system providers, app store platforms, data aggregators, etc. As a consequence, the consumer is not able to reconstruct the usage of any of the data.

Our findings suggest that in view of the Big Data phenomenon all the par-ties involved need to adjust their behavior. Consumers urgently need to be educated about the value of their personal data. Mobile network operators should enforce the protection of personal data in order to hold their ground in a highly competitive market that is not only about the price, but also about the service provided, thus retaining consumers’ trust in the system. Compa-nies manufacturing smart mobile devices should build in hardware and soft-ware features that help to protect the consumer’s personal data.

ReferencesAcquisti, A./Gross, R. (2006): Imagined Communities: Awareness, Information Shar-ing, and Privacy on the Facebook, PET 2006, http://bit.ly/1glqtlz, accessed 28.11.2013.

Bettman, J. R./Park, C. W. (1980): Effects of Prior Knowledge and Experience and Phase of the Choice Process on Consumer Decision Processes: A Protocol Analysis, in: Journal of Consumer Research, 7, 3, pp. 234-248.

Buck, C./Eymann, T. (2013): Das Privacy Paradox bei mobilen Applikationen: Kon-textuale Besonderheiten mobiler Applikationen, in: Horbach, M. (Hrsg.): Informa-tik 2013. Informatik angepasst an Mensch, Organisation und Umwelt, Köllen, pp. 1985-2000.

Enck, W. (2011): Defending Users against Smartphone Apps: Techniques and Future Directions, in: Proceedings of the International Conference on Information Systems (ICIS), pp. 49-70.

Felt, A. P./Finifter, M./Chin, E./Hanna, S./Wagner, D. (2011): A Survey of Mobile Malware in the Wild, in: Proceedings of the 1st Workshop on Security and Privacy in Smartphones and Mobile Devices, pp. 1-12.

Flurry (2013): http://www.flurry.com, accessed 28.11.2013.

Grazioli, S./Jarvenpaa, S. L. (2000): Perils of Internet Fraud: An Empirical Investiga-tion of Deception and Trust with Experienced Internet Consumers, in: IEEE Trans-actions on Systems, Man & Cybernetics: Part A, 30, 44, pp. 395-410.

* a Gröppel-Klein, A./Germelmann, C. C. (2009): Medienberichte und Vertrau-ensverlust von Spendern in Krisen von Spendenorganisationen, in: Gröppel-Klein, A./Germelmann, C.C. (Hrsg.): Medien im Marketing: Optionen der Unternehmen-skommunikation, Wiesbaden, pp. 343-368 (ID:1824998).

hitec (2013): Apps – Eine Offenbarung, http://bit.ly/1cxY1cW, accessed 28.11.2013.

IDC (2012): The Digital Universe in 2020: Big Data, Bigger Digital Shadows, and Biggest Growth in the Far East, http://bit.ly/1btXo2P, accessed 28.11.2013.

Kim, D./Benbasat, I. (2010): Designs for Effective Implementation of Trust Assur-ances in Internet Stores, in: Communications of the ACM, 53, 2, pp. 121-126.

Lewis, J. D./Weigert, A. (1985): Trust as a Social Reality, in: Social Forces, 63, 4, pp. 967-985.

Lessons Learned• Although consumers increasingly give up their privacy and the protection of data becomes an urgent issue, they only rarely use opportunities to search for information on the security of apps.• The Big Data phenomenon makes it necessary to educate consumers about the value of their personal data.• The specific architecture in which apps are embedded allows for a combi-nation and aggregation of highly frag-mented pieces of data, allowing for fun-damental insights into consumers’ be-havior.• High levels of perceived data insecu-rity do not lead consumers to less data sharing, but first and foremost to more reliance on assurance signals to regain trust and to decrease the perceived risk.



Meeker, M./Lu, L. (2013): 2013 Internet Trends, http://www.kpcb.com/insights/2013-internet-trends, accessed 28.11.2013.

OECD (2013): Exploring the Economics of Personal Data: A Survey of Methodolo-gies for Measuring Monetary Value, OECD Digital Economy Papers (220), http://bit.ly/1cxY9ZG, accessed 28.11.2013.

Vargo, S. L./Lusch, R. F./Horbel, C./Wieland, H. (2011): Alternative Logics for Service(s): From Hybrid Systems to Service Ecosystems, in: Spath, D./Ganz, W. (Eds.): Taking the Pulse of Economic Development: Service Trends, München, pp. 123-135.

Vodafone (2013): Echtzeit-Verkehrsinfos von TomTom und Vodafone, http://bit.ly/1bsEvd4, accessed 28.11.2013.

Weinberg, P. (1995): Emotional Aspects of Decision Behaviour: A Comparison of Explanation Concepts, in: Hansen, F. (Ed.): European Advances in Consumer Re-search, Provo, pp. 246-250.

World Economic Forum (2013): Unlocking the Value of Personal Data: From Col-lection to Usage, http://bit.ly/1cxYeNh, accessed 28.11.2013.

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Von der Verlagsredaktion empfohlenEckert, C., Schneider, C.: Smart Mobile Apps: Enabler oder Risiko?, in: Verclas, S., Linnhoff-Popien, C.: Smart mobile Apps, Heidelberg/Berlin, 2012, S.193- 207, www.springerprofessional/ 3355448

Vatrapu, R.: Understanding Social Business, in: Akhilesh, K.B.; Emerging Dimensions of Technology Management, India, 2013, S. 147-158, www.springerprofessional.de/4012084





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Mobile Consumer Apps: Big Data Brother is Watching You