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What is your data strategy?
Fortieth International Conference on Information Systems, Munich 2019 1
What is Your Data Strategy? The Strategic
Interactions in Data-Driven Advertising
Short Paper
Xin Zhang
City University of Hong Kong
University of Science and Technology
of China
zx01@mail.ustc.edu.cn
Ran (Alan) Zhang
City University of Hong Kong
Kowloon, Hong Kong
Alan.Ran.Zhang@cityu.edu.hk
Wei Thoo Yue
City University of Hong Kong
Kowloon, Hong Kong
Wei.T.Yue@cityu.edu.hk
Yugang Yu
University of Science and Technology
of China
Hefei, China
ygyu@ustc.edu.cn
Abstract
The increasing demand of using data to generate insights has spawned the new data broker
industry. Data brokers are playing an increasingly critical role in the data-driven advertising
ecosystem. Motivated by this new phenomenon, we build an analytical model to analyze the
interplay between the data broker’s data quality decision and the firm’s decisions on ads delivery
choice and product pricing. We find that when the firm’s ads delivery capability vis-à-vis that of
the data broker is either low or high, the data broker may offer high quality data to the firm.
Interestingly, under some circumstances, the data broker may offer low quality data to the firm,
incentivizing the latter to employ the former’s ads delivery service. This result suggests that data
quality can be a strategic tool leveraged by the data broker to affect firm’s ads delivery choice.
We discuss implications and provide future extensions.
Keywords: data broker, data supply, data-driven advertising, pricing
Introduction
With the prevalence of digital advertising and the availability of consumer data, data-driven advertising has
received growing attention. The widespread use of data in advertising has created vast demand for
consumer data, which in turn has spawned the data broker industry. Some of the major data brokers include
Acxiom, Neilson and MediaMath. The data broker accumulates data from various sources such as social
media platform (e.g., Facebook) and further process them by using advanced data analytics techniques
(Montes et al. 2018). A data broker thus is able to create detailed consumer profiles (e.g., consumer
segmentation, behavior pattern, and prediction of actions), and offer data of different enhanced quality
values to firms (Bimpikis et al. 2019). It is reported that the data broker industry has generated $156 billion
(USD) annually (Montes et al. 2018).
Firms have economic incentives to purchase data from the data broker in order to improve their ads delivery
efficiencies by increasing the probability of delivering and showing ads to the relevant consumers (Bourreau
What is your data strategy?
Fortieth International Conference on Information Systems, Munich 2019 2
et al. 2017; Kim 2017). This also benefits consumers by reducing their search cost. For example, Nike can
purchase information from the data broker about some consumers who are interested in sports shoes.
Based on the consumer information, Nike is able to deliver the ads to relevant consumers. Depending on
the data quality offered by the data broker, the precision of matching between ads and consumers varies. A
higher quality level of data that contains more precise information about consumer characteristics and
preferences is more informative and can thus increase the matching precision. As a result, firms are typically
willing to pay a higher price for high quality data (Bimpikis et al. 2019).
In addition to offering processed data, data broker can also deliver ads on behalf of the firm. For example,
MediaMath, a well-known ad-tech companies, not only sells consumer data, but also provides ads delivery
service for firms. Other data brokers perform as ad networks (e.g., DoubleClick) on the digital ads delivery.
As data brokers typically handle ads delivery more often than firms, they are in a better position in
developing a higher ads delivery capability than firms. A firm may employ data broker’s ads delivery service
or deliver ads by its own, depending on the tradeoff between the gain in the matching efficiency (e.g.,
matching precision) and the delivery cost.
Despite the pervasiveness of data-driven advertising, the economic mechanism behind the process is still
poorly understood. In fact, the interactions among a data broker, a firm, and consumers are complicated in
nature. For example, the decision of data broker on the offering of data quality and delivery service fee could
influence the firm’s decision on ads delivery service choice and the product price, which in turn influences
consumers’ utilities and purchasing decisions. At the heart of the decisions lie the critical data-related
strategic decisions by the different parties, which are increasingly relevant in the data economy, and
motivate us to we study the following questions. (1) Should the data broker always offer high quality data
to the firm? (2) What are the optimal decisions for the firm on the ads delivery and the product price in
response to the data broker’s decisions? (3) What is the economic impact on the market coverage and
consumers’ purchasing decisions based on the strategic interaction between the data broker and the firm?
To explore the above questions, we build a game-theoretical model to investigate the strategic interaction
between the data broker and the firm. Specifically, we analyze the interplay of the data broker’ data quality
decision and the firm’s product price and ads delivery decisions. We consider a sequential game. First, the
data broker decides to offer to the firm with either high or low quality data. Second, the firm decides its
product price and whether to employ data broker’s ads delivery service based on the data quality they
received. Such strategic decisions by the data broker and the firm would impact the matching benefit
accrued to consumers, thus affect consumers’ purchase decisions. We analyze the market outcomes and the
profits of each stakeholder.
Our initial results show that when the firm’s ads delivery capability vis-à-vis that of the data broker is either
low or high, the data broker may offer high quality data to the firm. An unexpected yet interesting result is
that, when the firm’s relative capability of ads delivery is in the intermediate range, it may be optimal for
the data broker to offer low quality data rather than high quality data to the firm. The intuition is that by
offering low quality data, the data broker can induce the firm to employ its ads delivery service so as to gain
more matching benefits, thus the data broker may gain more profit. The above results highlight the
importance that the data quality can serve as a strategic tool to affect the firm’s ads delivery decisions. In
addition, this study sheds light on the interactions between data broker and firms by showing that our
conventional wisdom that the data broker always provides high quality data may not be true.
The remainder of this paper is organized as follows. First, we review the related literature. Then, we present
our analytical model. Next, we analyze our model and show the results. Lastly, we conclude the paper by
discussing the impactions and future directions.
Literature Review
Our study is broadly related to the literature on information disclosure and particularly related to the
literature on data broker and online data markets and on online advertising. We review each stream of
literature in the following.
Data Broker. As data has become the heart of the digital economy, there is a growing literature on online
data markets (Bergemann and Bonatti 2019; Haberer and Schnurr 2018; Wohlfarth 2019), with a particular
focus on data brokers’ strategies. Several studies investigate the information selling strategy of the data
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Fortieth International Conference on Information Systems, Munich 2019 3
broker to competitive firms (Bimpikis et al. 2019; Bounie et al. 2018; Kastl et al. 2018; Montes et al. 2018).
For example, Bounie et al. (2018) and Montes et al. (2018) consider the scenario wherein the data broker’s
information enables competing firms to conduct price discrimination on consumers; Kastl et al. (2018) and
Bimpikis et al. (2019) find that the data broker’s information serves as a signal that helps reduce the demand
uncertainty or cost uncertainty. Overall, the above studies scrutinize how the data broker’s strategy, i.e.,
exclusive vs. non-exclusive selling, would affect the competition between firms and the data broker’s
revenue. Different from these studies, we focus on the interactions between the data broker and the firm in
the context of online advertising ecosystem, where the data can be used for improving the matching benefit
of consumers. We contribute to this strand of literature by investigating and prescribing the optimal
strategies for the data broker’s data quality decision and the firm’s ads delivery and product price decisions.
Online Advertising. Our study is also related to the large strand of literature on online targeted
advertising (Asdemir et al. 2012; Goldfarb and Tucker 2011; Levin and Milgrom 2010). Close to our context,
several studies focus on the behavior targeting, where consumers’ personal data can be used to deliver
tailored ads (Anand and Shachar 2009; Bergemann and Bonatti 2011; Chen and Stallaert 2014; Ozcelik and
Varnali 2019; Zhao and Xue 2012). In particular, Bergemann and Bonatti (2011) suggests that consumers
benefit from the improved targeting of advertising, because the quality of the match between ads and
consumers increases. Chen and Stallaert (2014) examine the welfare effects of behavioral targeting on
publishers’ and advertisers’ payoffs. Krämer et al. (2018) analyze the strategic effects of social logins (e.g.,
Log in with Facebook) in the online advertising ecosystem. They derive the market condition under which
social login would be offered by Facebook and adopted by other smaller publishers. Our work differs from
this strand of research in that we include in our model the data broker, who has played a critical role in
online advertising. We analyze the data broker’s strategy on data offering and ads delivery, two essential
activities in the era of digital economy.
Information Disclosure. In a broader context, this research is related to literature on information
disclosure. Several studies analyze the level of disclosure from firms’ perspectives (Casadesus-Masanell and
Hervas-Drane 2015; Gopal et al. 2018; Kontaxis et al. 2012). In these studies, firms derive revenue from
disclosing information but such disclosure also leads to disutility for consumers due to privacy concern.
Some other studies discuss the disclosure of personal data from consumers’ perspectives, where consumers
can determine whether and how much information they want to disclose to firms (Kim 2017; Koh et al.
2017). We complement this stream of literature by considering the scenario wherein the planform discloses
consumer data to the data broker who performs data analytics to further enhance the value of the data. We
investigate the interaction between the data broker and the firm, and its impact on the market coverage and
consumers’ utilities.
Model
There are three different players in our model: a data broker, a firm and a mass of consumers. We now
describe the strategic decisions of each of the players involved, and their possible actions.
Data broker. The data broker collects consumer data from multiple sources, such as a platform like
Facebook. The data broker plays two roles (1): perform analytics for the data acquired from the platform
and then sell the data to firm; (2): deliver ads to targeted consumers on behalf of the firm. After performing
analytics for the data, the data broker can decide to offer the data with different quality levels. Specifically,
the data broker can offer either high quality data (denoted by ) or low quality data (denoted by ). High
quality data implies that the data has more precise information about consumer characteristics and thus
can be more useful in helping the firm find the ‘matched’ consumers.
The data broker and the firm are asymmetric in their capability of ads delivery. Since the data broker is
more specialized in online ads delivery operations, its ads delivery capability is higher than that of the firm.
We assume the firm’s ads delivery capability is , where , and the data broker’s ads delivery
capability is normalized to one. Typically, a higher capability of ads delivery can help find the matched
consumers more efficiently and deliver the relevant ads to them, thus increase consumers’ utility by
enhancing the matching benefits. The firm’s decision on whether to employ the data broker’s ads delivery
depends on the tradeoff between the gain from the matching benefits and the cost from paying the data
broker’s service. The role of the costs of data analytics and ads delivery is intuitive and fully expected; To
focus on the key interaction of data broker’s data quality choice and firm’s ads delivery choice, we normalize
What is your data strategy?
Fortieth International Conference on Information Systems, Munich 2019 4
all costs to be zero.
Firm. The firm sells products and delivers ads to consumers. In addition to setting the product price, the
firm decides whether to employ the broker’s ads delivery. If the firm decides to employ the broker’s ads
delivery, it needs to pay a fee for the delivery service in addition to the price for data. Consistent with the
prior literature (Banciu et al. 2010; Liu and Tyagi 2011), the marginal production cost is normalized to zero.
Consumer. Consumers are heterogeneous in their reservation value of the products, denoted as , where
. We assume the market size is. The consumers decide whether to purchase the products based
on their utility, which is related to their reservation value, matching benefits, and the product price.
The sequence of the model is as follows: (i) the broker performs data analytics and then determines its data
quality and the ads delivery fee to offer to the firm, (ii) based on the data broker’s data quality and ads
delivery fee, the firm decides its product price, and whether to use the data broker’s ads delivery service;
(iii) consumers choose whether to purchase the products based on their utility. For ease of exposition, we
use the subscripts , to denote the data broker data quality decision, the subscripts , to denote firm’s
ads delivery decision. The sequence of the events is depicted in the Figure 1.
Figure 1. The Sequence of the Events
Analysis and Results
Pricing Decision and Ads Delivery Decision
The data broker decides the data quality and the ads delivery fee. Based on the data broker’s decision, the
firm decides its product price and whether to use the data broker’s ads delivery service. We use backward
induction to derive the optimal scenarios for the firm and the data broker. Based on the data broker’s data
quality decision and the firm’s delivery decision, we have four cases, i.e. , , , and case. We
analyze each case and then compare them to derive the optimal scenario.
Case. We begin with analyzing firm’s pricing decision given that the data broker offers high quality
data (i.e., ) and the firm employs the ads delivery service by the data broker (denoted by ). We first
discuss how advertising service affects consumer utility as below.
The data broker performs data analytics to learn consumer characteristics, which allows them to deliver the
ads to the “right” consumer. The match between ads and the consumer will reduce consumers’ searching
costs before purchase. In other words, advertising service provides the matching benefit for consumers,
which improves the consumer’s utility. Moreover, the matching benefit is associated with the data quality
and data broker’s ads delivery capability. On one hand, the higher quality data that contains more precise
information about consumer characteristic can provide more matching benefit for consumers. On the other
hand, a good delivery capability enables the data broker to find the “right” consumer within a short time
period. As a result, the consumer valuation about the product is amplified with high data quality and ads
delivery capability. Specifically, we model the amplification of consumer value as *1, where is the data
quality, 1 is the normalized ads delivery capability of the data broker. Consequently, the consumer gross
utility reaches *1*v=. Note that the multiplicative form of consumer utility function has also been
widely used in the previous literature (Xue et al. 2018). Therefore, the consumer net utility under case
is given by , where represents the product price. We next analyze the consumer
The data broker
decides the data
quality (
) and
the ads delivery fee
Firm decides the product
price, and whether to
employ the data broker’s
ads delivery service (
or
)
Consumers
decide whether
to purchase the
product
Data broker
Firm
Consumers
time
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Fortieth International Conference on Information Systems, Munich 2019 5
demand and the firm’s profit.
The consumer who is indifferent in purchasing or not purchasing is characterized by the valuation ,
where
. The market demand is , where
. Thus, the firm’s revenue from selling the
products is .
The data broker has two revenue sources. One is to sell the data to firms, and charge a price for the data.
The other is to deliver the ads on behalf of the firm, and charge the firm the ads delivery fee. In terms of the
data price, it is related to the data quality and the price for per unit quality. We normalize the price per unit
quality to one to highlight the role of data quality in this study. As a result, the data price charged by data
broker is given by . As for the ads delivery fee, we adopt a widely used pricing model in the industry,
that is price per action model (CPA). The CPA model implies that the firm needs to pay the ads delivery fee
based on the realized consumer demand (Hu et al. 2015). Similarly, to focus on the role of data broker’s
delivery capability, we normalize the price per demand to one. Thus, the ads delivery fee charged by data
broker is . To summarize, the total fees data broker can charge are the sum of data price and ads
delivery fee. Mathematically, it equals to the sum of data quality and realized demand, i.e.,
. Note that this charging mode is consistent to the real-word observation. For example,
Mediamath
1
, a well-known advertising company, provide both DMP (focus on data related service) and DSP
(focus on ads delivery service) service for firms. Mediamath charges both data price and ads delivery fee
when firms use DMP and DSP service simultaneously.
We denote by as the total price for data and ads delivery fee. Therefore, the firm’s
profit is given by
(1)
By maximizing firm’s profit, we obtain firm’s optimal
. Substituting
into (1), we
obtain firm’ profit as
.
Case. We next analyze the case where the data broker offers high quality data but the firm does not
employ the data broker’s ads delivery service (denoted by ). The consumer utility function is given by
, where represents the matching benefit. Since the firm’s delivery capability is lower
than that of the data broker, there is a loss in the matching benefit compared to when employing the data
broker’s delivery service.
The indifferent consumer between purchasing and not is characterized by
. The market demand
is
. Different from the case, under the case the data broker charges only the price
for the data, where is only a function of data quality, i.e. . The firm’s profit
is given by
(2)
By maximizing firm’s profit, we can obtain the optimal price
. Substituting the
into (2), we
determine firm’s equilibrium profit
.
Now, we are ready to examine whether the firm would employ the data broker’s delivery service given that
the data broker offers the high quality data. By comparing the firm’s profit under and cases, we have
following results.
LEMMA 1. When
,we have ; Otherwise, when
, we have
.
1
For detail information, please refer to https://www.mediamath.com
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Fortieth International Conference on Information Systems, Munich 2019 6
Lemma 1 implies that when the firm’s delivery capability is small, i.e.,
, the firm would employ
the data broker’s ad delivery service; otherwise, it would choose to deliver the ads by its own. The intuition
is that as the firm’s delivery capability increases, the relative benefit of employing the data broker’s delivery
service diminishes. Besides, it is interesting to find that as the data quality () increases, the threshold
value increases, which implies that given a , the firm has a higher likelihood to employ the data broker’s
ad delivery service. This is because when the data quality is high, the firm’s gain from using the data broker’s
delivery increases even more compared to delivering by its own.
Case. Under the case where the data broker offers low quality data (i.e. ) and the firm employs the
data broker’s ads delivery service (denoted by), we analyze the firm’s pricing decision. Note that despite
the data broker offers lower quality data to firm, it can always exploit high quality data to deliver the ads if
the firm employs its ads delivery service. The data broker has the incentive to do so because high quality
data can improve the matching benefit, thus increase the realized market demand, which in turn increases
the data broker’s revenue. Therefore, when the firm uses the ad broker’s ads delivery service, consumers’
gain from the matching benefit is (i.e., ). The consumer utility function is given by
.
The indifferent consumer between purchasing and not is characterized by
. The market demand is
. Thus, the firm’s revenue is . The data broker charges a price for the data
and delivery service, where . The firm’s profit is given by
(3)
By maximizing firm’s profit function, the firm’s optimal price is
. Substituting the
into (3),
we obtain firm’s profit as
.
Case. Under the case where the data broker offers low quality data (i.e. ) and the firm does not use
the data broker’s ads delivery service, the firm can only leverage on the low quality data and its own delivery
capability to deliver ads, thus the matching benefit is . The consumer’s utility function is
.
The indifferent consumer is characterized by
. The market demand is
. Different
from case, the data broker only charges a price for the data, where . Thus, the firm’s
net profit is given by
(4)
By maximizing the profit, we obtain firm’s optimal price as
. Substituting the
into (4), we get
the firm’s profit
. By comparing firm’s profit under and case, we can determine
whether the firm would employ the data broker’s ads delivery service. The result is summarized as below.
LEMMA 2. When
, we have ; when
, we have .
There are several important observations from Lemma 2. First, similar with the case where the data broker
offers the high quality data, we find that if the firm’s delivery capability is relatively low, it would employ
the broker’s ads delivery service. Second, compared with the threshold in Lemma 1, it can be shown that
, which implies that when the data broker offers low quality data, given a , the firm has
higher likelihood to employ the broker’s ads delivery service. This is because the low quality data makes less
contribution to consumers’ matching benefit compared to the high quality data, the firm is more likely to
employ the broker’s ads delivery service to increase the matching benefit.
Combine Lemma 1 with Lemma 2, we are able to show how firm’s choice of delivery service changes with
its own delivery capability and the data broker’s data quality (Figure 2).
What is your data strategy?
Fortieth International Conference on Information Systems, Munich 2019 7
Specifically, if the firm’s delivery capability is high, i.e.,
, then regardless of the data broker’s
data quality, the firm always chooses to deliver by its own (). Conversely, if the firm’s delivery capability
is low, i.e.,
, the firm always chooses to employ the broker’s delivery service (). The firm’s
choice of delivery service is more complicated when its delivery capability is at the moderate level. If the
data broker offers high quality data, the firm chooses ; while if the data broker sells low quality data, the
firm chooses . The result indicates that firm’s delivery service choice depends not only on its own delivery
capability, but also on the data quality offered by the data broker.
Figure 2. Firm’s Ads Delivery Decisions
Notes. The first letter (, ) in the parentheses represents the data broker’s data quality decision, the second letter (,
) in the parentheses represents firm’s ads delivery decision.
Data Broker’s Decision
Building upon the firm’s optimal pricing and the choice of delivery service, the data broker strategically
determines the data quality to maximize its payoff , where represents its data quality decision.
Recall that the data broker’s payoff if the firm chooses ; if the firm chooses , its payoff
, its payoff can be written as follows.
If the data broker sells high quality data, its payoff is given by
If the data broker sells high quality data, its payoff is given by
By comparing the data broker’s payoff
and
, we can obtain the data broker’s data quality decision. The
result is summarized as follows.
PROPOSITION 1.
(i) If
, the data broker offers high quality data, and its realized payoff is
.
(ii) If
, the data broker offers low quality data when
, and its
realized payoff is
; otherwise, the data broker offers high quality data, and its realized
payoff is .
(iii) If
, the data broker offers high quality data, and its realized payoff is .
Proposition 1 has several important implications. First, when is either low or high, the data broker would
offer high quality data. When is low, the firm always employs the broker’s delivery service; when is high,
the firm always delivers by its own. Thus, the best response of the data broker is to offer high quality data.
0
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Fortieth International Conference on Information Systems, Munich 2019 8
However, when is in the intermediate range, the data brokers may strategically choose to offer low quality
data, and that would incentivize the firm to enhance the matching benefit by employing the broker’s ads
delivery service. The data broker gains not only from data offering but also from ads delivery service. This
result highlights that data quality can be a strategic tool used by the data broker to affect firm’s ads delivery
service choice. The condition that
in (ii) implies that the data quality the broker offers
cannot be too low. Otherwise, the broker would lose too much from the low price of data and hence, offset
the gain from charging ads delivery service.
Conclusion and Future Research
The increasing demand on turning data into insights has spawned the data broker industry, which has
played a critical role in the data-driven advertising ecosystem. Motivated by this new phenomenon, we build
an analytical model to investigate the interactions among the data broker, firm, and consumers. Specifically,
we analyze the interplay between the data broker’s data quality decision and the firm’s decisions on ads
delivery choice and product price, after acquiring consumer data from the platform. To the best of our
knowledge, this study is one of the first theoretical studies on data strategy in online advertising ecosystem.
We find that the firm’s choice of ads delivery service depends not only on its relative ads delivery capability
vis-à-vis that of the data broker but also on the data quality offered by the data broker. Particularly, when
the firm’s ads delivery capability relative to that of the data broker is either low or high, the data broker is
better off by offering high quality data to the firm. An unexpected yet interesting result is that when the
firm’s ads delivery capability is in the intermediate range, offering low quality data may be more profitable
for the data broker. The intuition is that by offering low quality data to the firm, the matching benefits
accrued from the low quality data is limited, incentivizing the firm to employ the data broker’s ads delivery
service. Hence, the data broker is better off. This result highlights that data quality can be a strategic tool
used by the data broker to influence firm’s ads delivery service choice.
Apart from the amount of data, data quality is another important factor that affects the matching efficiency
of data-driven advertising, but the issue has not been well studied. This study uncovers the strategy of the
data broker on data quality offering, i.e., preparing the data with different quality levels. Our result suggests
that the conventional wisdom that the data broker is always better off by offering high quality data may not
always be true. Thus, firms are advised to pay more attention to the data quality received and respond wisely
on the ads delivery choice.
There are several directions for future research. First, in the current model, we assume the data broker
collects data from other sources such as the social media platform. A natural and more realistic extension
is to incorporate the data disclosure level of the platform in our current model. It would be interesting to
study the interaction between the platform and the data broker, i.e., the impact of platform’s data disclosure
level on the data broker’s decisions of data quality offering. We expect that our current results on the
optimal data quality and ads delivery choice remain hold, though the market parameter space for each
optimal choice may change. Second, while highly targeted ads fueled by high quality data can benefit
consumers by reducing the searching cost, collecting and disclosing consumers’ personal data have
threatened the consumer privacy (see Acquisti et al. (2016 for the extensive survey). The privacy loss leads
to the disutility of consumers (e.g., nuisance cost), which has also caused increasing attention from policy
makers (e.g., the release of general data protection regulation (GDPR)). Thus, it is imperative to investigate
the net impact of these interactions among all stakeholders on the consumer welfare by taking into account
of the consumers’ privacy concerns. We aim to provide some policy implications regarding the privacy
protection. Lastly, we consider a monopolistic platform and consumers resides in only one advertising
market. In general, consumers may have multi-homing behavior and there may be two platforms providing
similar services and competing for consumers (Haberer and Schnurr 2018; Krämer et al. 2018). It is
worthwhile to study the competition between platforms, and how the competition affects the disclosure
level of data, as well as the subsequent decisions of the data broker and the firm. We leave all these issues
into future research.
What is your data strategy?
Fortieth International Conference on Information Systems, Munich 2019 9
References
Acquisti, A., Taylor, C., and Wagman, L. 2016. "The Economics of Privacy," Journal of Economic Literature
(54:2), pp. 442-492.
Anand, B. N., and Shachar, R. 2009. "Targeted Advertising as a Signal," QME (7:3), pp. 237-266.
Asdemir, K., Kumar, N., and Jacob, V. S. 2012. "Pricing Models for Online Advertising: Cpm Vs. Cpc,"
Information Systems Research (23:3-part-1), pp. 804-822.
Banciu, M., Gal-Or, E., and Mirchandani, P. 2010. "Bundling Strategies When Products Are Vertically
Differentiated and Capacities Are Limited," Management Science (56:12), pp. 2207-2223.
Bergemann, D., and Bonatti, A. 2011. "Targeting in Advertising Markets: Implications for Offline Versus
Online Media," The RAND Journal of Economics (42:3), pp. 417-443.
Bergemann, D., and Bonatti, A. 2019. "Markets for Information: An Introduction," Annu. Rev. Econ (11),
pp. 1-23.
Bimpikis, K., Crapis, D., and Tahbaz-Salehi, A. 2019. "Information Sale and Competition," Management
Science).
Bounie, D., Dubus, A., and Waelbroeck, P. 2018. "Selling Strategic Information in Digital Competitive
Markets,").
Bourreau, M., Caillaud, B., and De Nijs, R. 2017. "The Value of Consumer Data in Online Advertising,"
Review of Network Economics (16:3), pp. 269-289.
Casadesus-Masanell, R., and Hervas-Drane, A. 2015. "Competing with Privacy," Management Science (61:1),
pp. 229-246.
Chen, J., and Stallaert, J. 2014. "An Economic Analysis of Online Advertising Using Behavioral Targeting,"
MIS Quarterly (38:2), pp. 429-449.
Goldfarb, A., and Tucker, C. 2011. "Online Display Advertising: Targeting and Obtrusiveness," Marketing
Science (30:3), pp. 389-404.
Gopal, R. D., Hidaji, H., Patterson, R. A., Rolland, E., and Zhdanov, D. 2018. "How Much to Share with
Third Parties? User Privacy Concerns and Website Dilemmas," MIS Quarterly (42:1), pp. 143-164.
Haberer, B., and Schnurr, D. 2018. "An Economic Analysis of Data Portability and Personal Data Markets,").
Hu, Y., Shin, J., and Tang, Z. 2015. "Incentive Problems in Performance-Based Online Advertising Pricing:
Cost Per Click Vs. Cost Per Action," Management Science (62:7), pp. 2022-2038.
Kastl, J., Pagnozzi, M., and Piccolo, S. 2018. "Selling Information to Competitive Firms," The RAND Journal
of Economics (49:1), pp. 254-282.
Kim, S. J. 2017. "Privacy, Information Acquisition, and Market Competition,".
Koh, B., Raghunathan, S., and Nault, B. R. 2017. "Is Voluntary Profiling Welfare Enhancing?," MIS
Quarterly (41:1), pp. 23-41.
Kontaxis, G., Polychronakis, M., and Markatos, E. P. 2012. "Minimizing Information Disclosure to Third
Parties in Social Login Platforms," International Journal of Information Security (11:5), pp. 321-
332.
Krämer, J., Schnurr, D., and Wohlfarth, M. 2018. "Winners, Losers, and Facebook: The Role of Social
Logins in the Online Advertising Ecosystem," Management Science.
Levin, J., and Milgrom, P. 2010. "Online Advertising: Heterogeneity and Conflation in Market Design,"
American Economic Review (100:2), pp. 603-607.
Liu, Y., and Tyagi, R. K. 2011. "The Benefits of Competitive Upward Channel Decentralization,"
Management Science (57:4), pp. 741-751.
Montes, R., Sand-Zantman, W., and Valletti, T. 2018. "The Value of Personal Information in Online Markets
with Endogenous Privacy," Management Science.
Ozcelik, A. B., and Varnali, K. 2019. "Effectiveness of Online Behavioral Targeting: A Psychological
Perspective," Electronic Commerce Research and Applications (33), p. 100819.
Wohlfarth, M. 2019. "Data Portability on the Internet," Business & Information Systems Engineering, pp.
1-24.
Xue, Y., Caliskan-Demirag, O., Chen, Y. F., and Yu, Y. 2018. "Supporting Customers to Sell Used Goods:
Profitability and Environmental Implications," International Journal of Production Economics
(206), pp. 220-232.
Zhao, X., and Xue, L. 2012. "Competitive Target Advertising and Consumer Data Sharing," Journal of
Management Information Systems (29:3), pp. 189-222.
