Social Media Marketing: Who is Watching the Watchers?

Abstract

The ready access to and availability of social media has opened up a wealth of data that marketers are leveraging for strategic insight and digital marketing. Yet there is a lack of professional norms regarding the use of social media in marketing and a gap in understanding consumers’ comfort with marketers’ use of their social media data. This study analyzes a census-balanced sample of online adults (n = 751) to identify consumers’ perceptions of using social media data for marketing purposes. The research finds that consumers’ perceived risks and benefits of using social media have a relationship with their comfort with marketers using their publicly available social media data. The research extends the applicability of communication privacy management theory to social media and introduces marketing comfort—a new construct of high importance for future marketing research. Marketing comfort refers to an individual's comfort with the use of information posted publicly on social media for targeted advertising, customer relations, and opinion mining. In the context of the construct development, we find that targeted advertising is the strongest contributing component to marketing comfort, relative to the other two dimensions: opinion mining and customer relations. By understanding what drives consumer comfort with this emerging marketing practice, the research proposes strategies for marketers that can support and mitigate consumers’ concerns so that consumers can maintain trust in marketers’ digital practices.

Full text

Contents lists available at ScienceDirect
Journal of Retailing and Consumer Services
journal homepage: www.elsevier.com/locate/jretconser
Social media marketing: Who is watching the watchers?
Jenna Jacobson
a,⁎
, Anatoliy Gruzd
b
, Ángel Hernández-García
c
a
Ryerson University, Ted Rogers School of Retail Management, 350 Victoria Street, Toronto, Ontario, Canada M5B 2K3
b
Ryerson University, Ted Rogers School of Management, 350 Victoria Street, Toronto, Ontario, Canada M5B 2K3
c
Universidad Politécnica de Madrid, Department of Organization Engineering, Business Administration and Statistics, Av. Complutense, 30, 28040 Madrid, Spain
ARTICLE INFO
Keywords:
Digital marketing
Ethics
Social media
Consumer trust
Privacy
ABSTRACT
The ready access to and availability of social media has opened up a wealth of data that marketers are leveraging
for strategic insight and digital marketing. Yet there is a lack of professional norms regarding the use of social
media in marketing and a gap in understanding consumers’ comfort with marketers’ use of their social media
data. This study analyzes a census-balanced sample of online adults (n = 751) to identify consumers’ perceptions
of using social media data for marketing purposes. The research finds that consumers’ perceived risks and
benefits of using social media have a relationship with their comfort with marketers using their publicly
available social media data. The research extends the applicability of communication privacy management
theory to social media and introduces marketing comfort—a new construct of high importance for future mar-
keting research. Marketing comfort refers to an individual's comfort with the use of information posted publicly
on social media for targeted advertising, customer relations, and opinion mining. In the context of the construct
development, we find that targeted advertising is the strongest contributing component to marketing comfort,
relative to the other two dimensions: opinion mining and customer relations. By understanding what drives
consumer comfort with this emerging marketing practice, the research proposes strategies for marketers that can
support and mitigate consumers’ concerns so that consumers can maintain trust in marketers’ digital practices.
1. Introduction
Just as the use of social media is changing how people live (Quan-
Haase and Young, 2010), learn (Gruzd et al., 2016), and connect with
one another (van Dijck, 2012), fundamental shifts are also taking place
within businesses with the introduction and use of social media. Con-
sumers are using social media to generate information and share their
experiences with their friends, companies, and broader online com-
munities via posts, tweets, shares, likes, and reviews (Bailey et al.,
2018; Dimitriu and Guesalaga, 2017; Martín-Consuegra et al., 2018).
Businesses are taking notice as they adopt strategies and tools to engage
in social media listening (Misirlis and Vlachopoulou, 2018; Schweidel
and Moe, 2014). From a design retailer combining social media and
predictive analytics to gather sentiment on potential new products
(Amato-McCoy, 2018) to travel companies mining unstructured social
media data to present users with personalized offers (Western Digital,
2018), marketers are particularly interested in understanding what
their customers and the public are saying about their business (Tuten
and Solomon, 2017).
While social media listening has been shown to be extremely va-
luable for businesses to better understand what their customers and the
public are saying about their products or services (Lee, 2018; Paniagua
and Sapena, 2014), not all consumers might be comfortable with such
practices (Akar and Topçu, 2011;Dubois et al., 2018). And if they are
not comfortable with what and how marketers use social media data,
consumers may develop negative attitudes, which may in turn impact
consumers’ purchasing intention and lead to a loss of trust and a da-
maged relationship between the consumer and the company (Adjei
et al., 2010; Arnold, 2018; Goldfarb and Tucker, 2013). For example,
when a UK-based insurance company decided to rely on Facebook posts
to price car insurance, it created a backlash in the form of negative
publicity about the company and their data practice (Ruddick, 2016). In
addition, recent data breaches at Facebook and the platform's secretive
data sharing arrangements with other tech giants (Dance et al., 2018;
Kanter, 2018) have heightened people's privacy concerns and increased
their awareness of who might be accessing their data and for what
purposes (Cochrane, 2018; DMA, 2018a, 2018b). These recent cases
highlight the need for developing a more granular understanding of
consumers’ attitudes towards marketers’ use of their social media data.
Prior research has primarily focused on the organizational environment
and personal characteristics of marketers or decision makers in mar-
keting professions (Singhapakdi et al., 1996). While the perspective of
https://doi.org/10.1016/j.jretconser.2019.03.001
Received 15 September 2018; Received in revised form 4 March 2019; Accepted 4 March 2019
⁎
Corresponding author.
E-mail address: jenna.jacobson@ryerson.ca (J. Jacobson).
Journal of Retailing and Consumer Services 53 (2020) 101774
Available online 20 March 2019
0969-6989/ © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
T

marketers is important to understand the professional practices, there is
little knowledge about the public's attitudes towards marketers using
their social media data, which we seek to address in this study.
A unique aspect of our work is that we study people's attitudes to-
wards the use of publicly accessible social media data. While data
breaches (like in the cases mentioned above) do happen, typically
marketers would not have direct access to users’ data that is privately
shared with a selected group of friends or shared in members-only
online groups—at least, not without users’ consent. But the situation is
different when it comes to user-generated content shared publicly on
social media, such as a public post on Twitter or a comment in a public
Facebook page. Because of their business models, most major social
media platforms encourage data use for marketing purposes through
well-developed APIs—data sharing protocols and an ecosystem of third-
party applications that rely on APIs to offer business intelligence ser-
vices. Furthermore, few jurisdictions around the world have regulations
in place to limit or make these data mining practices more transpar-
ent—with some exceptions like the General Data Protection Regulation
in the European Union. We argue that even if data access and use is
possible and legal, marketing professionals have ethical responsibilities
that extend beyond the legal requirements.
In this context, this study seeks to help marketing professionals
develop strong professional principles and guidelines while still being
able to benefit from many opportunities that social media has to offer to
both sides: consumers and businesses. We achieve this goal by ex-
amining relationships between consumers’ information privacy con-
cerns, social media use gratifications, and self-disclosure practices with
their comfort with marketers using their social media data. By under-
standing what drives consumers’ comfort with these emerging data
practices, we propose strategies for marketers that can support and
mitigate consumers’ discomfort with social media data use. Beyond the
practical reasons, the research is also important because of the evolving
marketing ethics. While marketers have always had to grapple with
various ethical considerations in their practices, the widespread adop-
tion and use of the internet has introduced new challenges for im-
plementing marketing ethics (Laczniak and Murphy, 2006). The re-
search addresses the link between marketing ethics and consumer
comfort with emergent marketing practices by introducing a new
construct: marketing comfort. As a theoretical lens, the research is
guided by communication privacy management. While communication
privacy management (CPM) theory has been applied to marketing
ethics, we extend CPM and assess its applicability in the context of
publicly available social media data.
In the following, we outline the: (1) relevant literature on social
media marketing and ethics in marketing, (2) use of Petronio's com-
munication privacy management theory to guide the research and the
three hypotheses, (3) methods and data analysis, (4) results of the data
analysis, (5) discussion, and (6) conclusions including the limitations
and implications of the research.
2. Literature review
2.1. Social media marketing
Social media marketing is used across sectors and refers to “the
utilization of social media technologies, channels, and software to
create, communicate, deliver, and exchange offerings that have value
for an organization's stakeholders” (Tuten and Solomon, 2017, p. 18).
In a systematic review of the social media literature, Kapoor et al.
(2018) find that social media has been widely adopted as a marketing
medium. In the private sector, social media is often used as a commu-
nication tool to promote and sell products and services; in the public
sector, social media is often used to share information and encourage
user engagement (Royle and Laing, 2014;Gruzd et al., 2018a). Beyond
being another medium to communicate with one's audience, social
media affords the opportunity for social and professional relationships
to be built, sustained, and strengthened with friends, family, and even
businesses. Marketers employ relationship marketing strategies to build
long-term relations that are mutually satisfying with key parties, in-
cluding customers (Kang and Kim, 2017; Murphy et al., 2007; Kamboj
et al., 2018; for a systematic literature review of social media marketing
see: Alalwan et al., 2017;Misirlis and Vlachopoulou, 2018;Felix et al.,
2017).
Research has analyzed the effectiveness of social media marketing
(Dwivedi et al., 2015; Kapoor et al., 2018; Lee and Hong, 2016) and
behavioural attitudes towards viral marketing (Citton, 2017; Eppler and
Mengis, 2004) and advertising (Alalwan, 2018; Lee and Hong, 2016;
Shareef et al., 2018, 2019). Factors such as interactivity (Jiang et al.,
2010), perceived relevance (Jung, 2017), perceived usefulness (Chang
et al., 2015), and organizational reputation (Boateng and Okoe, 2015)
have been found to impact consumers’ attitudes towards social media
marketing. Put simply, Alalwan (2018) explains, “customers who find
social media advertising beneficial and more advantageous are more
likely to be willing to purchase the targeted products of these ads” (p.
73).
Marketers are using publicly available social media data for three
common functions: opinion mining, targeted advertising, and customer
relations. First, marketers engage in opinion mining, which involves
leveraging the plethora of social media data to uncover knowledge,
insights, and patterns derived from structured and unstructured data
(He et al., 2013). Opinion mining may also involve tracking mentions or
particular phrases (Tuten and Solomon, 2017). Marketers then extract
actionable patterns that can be used to reach their strategic business
goals and provide a competitive edge in the marketplace (Gundecha
and Liu, 2012).
Second, the use of social media in marketing has contributed to the
individualization of marketing whereby organizations can commu-
nicate, collect data, and provide personalized responses and solutions
for customers (Royle and Laing, 2014; Simmons, 2008). Marketers can
therefore leverage social media to craft personalized messages and of-
fers for target audiences (Sterne, 2010). Personalized offers may deliver
five to eight times the return on investment (ROI) on marketing ex-
penditure and can increase sales by more than 10% (Cochrane, 2018).
Third, developing strong relationships with customers is the main
objective of marketing programs (Soler-Labajos and Jimenez-Zarco,
2016) and customer relations are improved using social media (Ainin
et al., 2015). As a tool for customer relations, social media is used to
attract customers with user-generated content, engage customers using
online two-way social interactions, and retain customers through
building relationships with other members (Wang and Fesenmaier,
2004). A key part of effective customer relations is delivering pertinent
information at the correct time and forming a personalized connection
with the customer (Peppers and Rogers, 2017). Traditional customer
relationship management (CRM) databases include personal informa-
tion about the customers and are now being augmented with social
CRM derived from social media data to obtain more detailed personal
information (Soler-Labajos and Jimenez-Zarco, 2016). Businesses can
add value to the customer experience by better understanding the
wants and needs of the customer.
In this study we focus on three common functions of using social
media data for marketing: (1) extracting insights via opinion mining, (2)
delivering information via targeted advertising, and (3) communicating
via customer relations with new or existing customers (Boerman et al.,
2017; Liu et al., 2017; Malthouse and Li, 2017; Sheng et al., 2018).
These functions speak to the three different informational exchanges:
pulling (i.e. opinion mining), pushing (i.e. targeted advertising), and
exchanging (i.e. customer relations). Opinion mining involves natural
language processing to identify the audience's overall mood about a
particular topic; for example, marketers can use opinion mining to
determine the success of a marketing campaign as well as what is or is
not working well for customers (Vinodhini and Chandrasekaran, 2012).
Targeted advertising refers to the segmentation of the population into
J. Jacobson, et al. Journal of Retailing and Consumer Services 53 (2020) 101774
2

subgroups based on user preferences and then the delivery of adver-
tisements for products and services that the subgroup will find desirable
(Yang et al., 2006); marketers use social media as the data source to
algorithmically group users and deliver more personalized advertise-
ments. Finally, customer relations refer to the relationship an organiza-
tion has with its customers and is hailed “the new marketing” due to the
customer's ability to share their issues on social media (Kietzmann,
2011); marketers can then use social media to build and foster re-
lationships with consumers.
In the internet era, the possibility of collecting massive amounts of
personal consumer data has caused a shift in consumers’ privacy con-
cerns (Goldfarb and Tucker, 2013), which has critical implications for
evaluating the ethical practices in marketing, as discussed in the fol-
lowing section.
2.2. Ethics in marketing
Social media is celebrated as giving people the opportunity to ex-
press themselves and their ideas via user-generated content (van Dijck,
2009), yet many people express privacy concerns with the use of their
social media data by third parties (Acquisti and Gross, 2006;Gruzd and
Hernandez-Garcia, 2018; Gruzd et al., 2018b;Marwick and Hargittai,
2018). In the current marketing communities, there are scholarly de-
bates surrounding ethics, including normative ethics (what should be),
positive ethics (what is or could be), consumer ethics (what moral rules
guide consumers), and virtue ethics (what is ethical) approach (Hunt
and Vitell, 1986, 2006; Murphy et al., 2007; Vitell, 2003). Unlike tra-
ditional marketing, which involves a one-way dissemination of in-
formation, the use of the internet affords two-way communication
thatposes different ethical and privacy considerations for marketers
(Malhotra et al., 2004). Even with publicly available social media data,
individuals may still have expectations of privacy (Gruzd and
Hernandez-Garcia, 2018; Gruzd et al., 2018b). Serious privacy and
ethical considerations are raised when organizations seek to capitalize
on the wealth of data from social media and the internet more broadly
(Malhotra et al., 2004; Ward, 2018).
Previous research has sought to explore how marketers come to
make ethical decisions based on personal characteristics (Singhapakdi
et al., 1996). Hunt and Vittell (1986) contend that people respond
differently to ethical questions or situations because of their ethical
sensitivity. Sparks and Hunt (1998) find that when marketing profes-
sionals are placed in a decision-making situation, many marketers will
fail to recognize the ethical issues, which is even more complicated as
marketers explore opportunities to leverage social media data. Re-
garding social media marketing, Barger et al. (2016) argue that there is
fragmentation in the discipline and call for further research to under-
stand how consumer engagement can be embraced for the benefit of
consumers and companies.
While understanding the decision-making process of marketing
professionals provides insight into what “is” the current state of social
media marketing, it does not contribute to an understanding of what
marketing ethics of using social media data “ought” to be. As Malhotra
and Miller (1998) state, “Remembering that the consumer is an essen-
tial part of the marketing process cannot be ignored, it seems that more
energy should be devoted towards targeting efforts to the consumer
(client, respondents, and public) perspective of ethical dilemmas in
marketing research, rather than solely through the eyes of the business
(researcher)” (p. 271). Thus, understanding the consumer perspective
on marketers’ use of social media data needs to be considered.
3. Theoretical framework and hypotheses
With the focus on consumers, we turn to Petronio's (2002) com-
munication privacy management (CPM) theory that explores how
people regulate information they consider to be private. At its core, the
theory describes how individuals develop their own privacy rules to
calculate the risks and benefits of disclosing information. The theory
contends that privacy management is dialectic in that people need to
disclose private information to fulfill social functions and needs, while
also concealing information to maintain their protection (Baruh et al.,
2017). In recent years, CPM has been widely adopted by scholars ex-
amining information privacy concerns in the context of social media use
(e.g., Cavusoglu et al., 2016;Child et al., 2012;DeGroot and Vik, 2017;
Waters and Ackerman, 2011).
Businesses may be overestimating not just consumers’ comfort with
sharing their personal data, but also the extent to which they feel they
receive fair value in exchange (Conroy et al., 2014). By applying the
CPM theory, this study explores the tension between users’ information
privacy concerns (Alashoor et al., 2017; Bellman et al., 2004; Hazari
and Brown, 2013; Proudfoot et al., 2018) and the benefits associated
with social media use—such as supporting self-presentation, social re-
lationships, entertainment, and information sharing (Blatterer, 2010;
Debatin et al., 2009; Fox and Moreland, 2015; Quinn, 2016; Sundar and
Limperos, 2013). Importantly, we examine this tension in relation to
people's attitudes towards marketers using their publicly available so-
cial media data; thus, we hypothesize:
H1. Consumers’ perceived risks of using social media have a negative
relation with the comfort with marketers using their publicly available
social media data.
H2. Consumers’ perceived benefits of using social media have a positive
relation with the comfort with marketers using their publicly available
social media data.
While considering both risks and benefits of being social, consumers
may engage in various information privacy protective responses (IPPR),
such as posting less often or posting less accurate information (Das and
Kramer, 2013;Gruzd and Hernandez-Garcia, 2018;Hayes et al., 2005;
Son and Kim, 2008). From the CPM theory perspective, IPPR can be
viewed as a mechanism to manage one's privacy boundaries (Jeong and
Kim, 2017). Petronio (2002) theorizes that individuals set their privacy
boundaries from completely open to completely closed. One way to
engage in IPPR—and to assess one's privacy boundaries—is to measure
the amount, depth, intent, polarity, and accuracy of one's self-disclosure
on social media (Gruzd and Hernandez-Garcia, 2018). Our expectation
is that open boundary individuals may be more comfortable with
marketers using their publicly available social media data; thus, we
hypothesize:
H3. Consumers’ self-disclosure practices on social media have a positive
relation with the comfort with marketers using their publicly available
social media data.
4. Method
4.1. Data collection
The research hypotheses were tested with data from a cross-national
survey based on the internet panel hosted by Research Now. Research
Now has been used by academic researchers to access panels of in-
dividuals based on specific criteria or as a representative sample of the
general population (Finucane et al., 2000; Freelon et al., 2008; Giles
et al., 2016; Zmud et al., 2016). The survey design was piloted and
refined over a one-year period. The broad research goal of the survey
was to understand individuals’ social media use, privacy concerns, and
comfort with third parties mining their publicly available information
on social media. Aligned with Research Now's typical protocol, parti-
cipants were given eRewards, which are points that can be transferred
to various loyalty rewards programs, upon completion. The research
proposal was approved by the university's Research Ethics Board in
Canada.
The use of an online panel does not bias the survey results because
J. Jacobson, et al. Journal of Retailing and Consumer Services 53 (2020) 101774
3

the survey solely focuses on internet users. Quota sampling was used to
align with the demographics of the Canadian population to increase the
representativeness of the data; participants were screened to match the
distributions in the 2016 Statistics Canada Census
1
report including age
(at least 18 years old), gender,
2
and location. Participants that met the
quota sampling requirements were shown a consent form that described
the purpose, outlined what participants were being asked to do and
estimated time of completion, defined potential benefits and risks, as-
sured anonymity, outlined data protection and storage processes, de-
scribed incentives, identified rights of research participants, and pro-
vided contact information for the research team. All data was
anonymized and is presented in aggregate.
The online survey was hosted by Qualtrics and was open from June
1, 2017 to July 15, 2017. We engaged in active data cleaning
throughout the collection process and we ceased data collection once
we reached our target of 1500 completed respondents. We system-
atically excluded responses that did not answer the “trap question”
correctly to ensure high quality responses. The survey had a median
completion time of 17 min and 16 s. This research analyzes a subset of
people (n = 751) who have at least one public social media account
considering this is the data available to third parties (i.e. marketers).
Table 1 shows the demographic and social media characteristics of the
sample used in this study. The sample is balanced across different age
groups, but has slightly more women than men (52% vs 48%).
The research asks participants about “publicly available social
media data,” which refers to information posted by the user or about
the user by other people across platforms, for three reasons. First, even
if an individual does not have a social media account on a particular
platform, they can still be targeted; social media platforms create
“shadow profiles” of individuals who do not have an account on the
platform, yet have data about the individual from their social contacts.
Second, marketers can scrape publicly available data from various so-
cial networks and are able to aggregate the data for marketing pur-
poses. Finally, marketing messages are shown across platforms using
cookies. While we recognize that people use specific social media
platforms for different reasons and get different gratifications, for the
purposes of this study, publicly available data needs to be understood in
aggregate.
The survey asked respondents to indicate whether each of their
social media accounts were primarily public or private. The reason the
word “primarily” was used in these questions is because users can
maintain a public account while also restricting access to few items in
their profile to a selected group of users; or in the opposite case, a user
can have a restricted account with few items shared with a wider au-
dience.
4.2. Instrument design
Derived from the theoretical framework, the research defines the
three predicting variables—Information Privacy Concerns,
Gratification, and Self-Disclosure—as multidimensional, second-order
reflective-formative latent variables. The instruments and scales for
each construct have been validated by prior research. The target en-
dogenous construct, marketing comfort, is defined as a formative
composite and captures the three elements detailed in the literature
review and theoretical framework: comfort with the use of information
posted publicly on social media for targeted advertising, customer re-
lations, and opinion mining.
To measure privacy concerns, following Stewart and Segars (2002),
the Concerns for Information Privacy (CFIP) instrument assesses one's
concerns for information privacy in response to an organization's use or
potential use of their personal information across four dimensions:
collection (COL), errors (ERR), secondary use (SUS), and unauthorized
access (UAC). The research follows the Concern for Social Media In-
formation Privacy (CFSMIP) instrument developed by Osatuyi (2015) to
support sharing information on social media.
Following Cheung et al. (2015), we assess the Gratification (GRAT)
of social media based on the following four dimensions: Information
Sharing
3
(G-INF), New Relationship Building (G-SOC), Self-Presenta-
tion (G-SP), and Enjoyment (G-Ent).
Finally, self-disclosure captures four different dimensions (Lai and
Yang, 2015; Leung, 2002): (1) Amount and Depth (SDAD): how much
information people disclose on social media and to what extent people
reveal their personal and intimate information about themselves; (2)
Positive/Negative Valence or Polarity (SDPN): to what extent their
online disclosures show their most positive and desirable self-image; (3)
Accuracy (SDAc): the level of honesty and accuracy in one's disclosures;
and (4) Intention (SDI): whether people are fully aware of their dis-
closures on social media. The items, originally proposed by Wheeless
(1976, 1978), were modified to fit the social media use context (Lai and
Yang, 2015). The final items included in the research were previously
used in the refined instrument for self-disclosure by Gruzd and
Hernandez-Garcia (2018).
Some scales were reversed to better interpret the results; in parti-
cular, CFSMIP: from strongly agree (higher concerns) to strongly dis-
agree, GRAT: from strongly agree (higher gratification) to disagree,
Self-Disclosure: from strongly agree (higher levels of disclosure) to
strongly disagree (lower levels of disclosure), and marketing comfort:
from extremely comfortable to extremely uncomfortable (see Appendix
A).
4.3. Data analysis
To test the research model, the study uses Partial Least Squares
Structural Equation Modeling (PLS-SEM), a non-parametric method.
PLS-SEM is an appropriate technique when the research goal is to
predict key target products or identify key driver products in complex
models that include formatively measured constructs (Hair et al.,
2017). The analysis follows the recommendations of Hair et al. (2017)
for the application of PLS-SEM, and Hair, Sarstedt, Ringle, and Gun-
degan (2018) for assessment of hierarchical component models in PLS-
SEM. According to these recommendations, the analysis includes a
measurement model assessment—of both reflective and formative
variables—and structural model assessment using factor weighing
scheme.
4.3.1. Measurement model assessment
Internal reliability was tested by observing composite reliability
(ρ
c
), with all values higher than 0.85, well above 0.6. All factorial
loadings of the reflective indicators were above the cut-off level of
0.708. Convergent validity was confirmed upon observation of AVE
values, which were over the threshold of 0.5. As mentioned before,
measurement of the second-order variables proposed a reflective-for-
mative approach using Mode A for the higher order construct (Hair
et al., 2018). Regarding marketing comfort, defined formative, after
discarding potential multicollinearity issues upon observation of the
VIF values, a bootstrapping procedure with 5000 subsamples shows
that both comfort with the use of social media data for targeted
1
The market research company used for data collection, unfortunately, does
not include access to panel survey participants in Yukon, Northwest Territories,
and Nunavut.
2
The authors would like to acknowledge that we recognize gender is not
binary. The screening question is aligned with Statistics Canada's demographic
questions to recruit a representative sample for statistical analysis. Later in the
survey, participants were given the opportunity to respond to a more inclusive
question regarding gender.
3
Cheung et al. (2015) refer to this dimension as “Convenience of Maintaining
Existing Relationships”.
J. Jacobson, et al. Journal of Retailing and Consumer Services 53 (2020) 101774
4

advertising and for customer relations are significant, with outer
weights of comfort with the use of information posted on social media
for opinion mining being not significant (p = 0.06); however, the outer
loading of comfort with the use of personal social media data for opi-
nion mining has a significant outer loading of 0.82 (p < 0.001). This
means that the indicator should be interpreted as absolutely important
for the measurement of marketing comfort, but relatively important
when compared to the other two indicators, and thus is retained for the
analysis. Appendix B and Appendix C summarize the results of internal
consistency and convergent validity analyses.
Discriminant validity was assessed using the HTMT criterion
(Henseler et al., 2015) (see Tables 2 and 3). The results confirm dis-
criminant validity between first-order constructs and between second-
order constructs as all values are lower than 0.85. There is one excep-
tion: the heterotrait-monotrait ratio of correlations between secondary
use and unauthorized access yields a value of 0.88. This result is aligned
with the findings of Osatuyi (2015) and Gruzd and Hernandez-Garcia
(2018). Considering that the value was lower than the less restrictive
limit of 0.90, and to preserve content validity, both variables were kept
independent.
5. Results
Analysis of the variance inflation factor (VIF) returned values below
3, which suggests that there are no multicollinearity problems in the
model. The path coefficients (see Fig. 1) from Information Privacy
Concerns, Gratification, and Self-Disclosure, to marketing comfort are
- 0.19, 0.30, and 0.14 respectively—all significant at p < 0.01, after a
bootstrapping procedure with 5000 subsamples. The value of R
2
for the
new construct, marketing comfort, is 0.18; in other words, the three
predicting variables explain 18% of the variance in marketing comfort.
Considering the exploratory nature of the research and that the model
introduces a new concept, this value may be considered acceptable. R
2
values of 0.20 may be considered high in consumer behaviour dis-
ciplines, even though they may be considered moderate to weak for
other marketing issues in success driver studies (Hair et al., 2017a; Hair
et al., 2017b). The R
2
value suggests low predictive accuracy of the
model, which might be partially explained by the high number of re-
sponses expressing extreme discomfort with the use of social media data
for marketing purposes, and also the strong positions about privacy
concerns regarding unauthorized access and secondary use of the in-
formation. The inclusion of additional variables, which will be dis-
cussed in the following section, could help to increase the predictive
power of the model. However, and regardless of proportion of variance
explained, the results of the analysis of the structural model confirm the
significance of the hypothesized relations. The observation of the f
2
effect sizes shows that self-disclosure has a negligible effect (f
2
= 0.02)
with a higher, yet small, effect of privacy concerns (f
2
= 0.04) and
gratification (f
2
= 0.08). The blindfolding procedure with a distance
omission of 7 returns positive values of Q
2
, which confirms the pre-
dictive relevance of the model; however, observation of the q
2
values
unveil the negligible predictive relevance of self-disclosure (q
2
= 0.01)
and confirm the predictive relevance, even though small, of privacy
concerns (q
2
= 0.03) and gratification (q
2
= 0.05).
Aligned with the CPM theory, all three hypotheses are supported,
which suggests that consumers are actively engaged in the assessment
of risks and benefits when forming their attitudes towards the practice
of marketers using the public's social media data—with gratification
being the strongest predictor of comfort. In accordance with H3, the
results also confirm that individuals with more open privacy boundaries
are more comfortable with this practice, but self-disclosure only ac-
counted for an additional 1.3% of the variance explained in marketing
Table 1
Sample demographics.
Demographic Category N Percentage Cumulative percentage Facebook YouTube Twitter Instagram LinkedIn Pinterest Snapchat Tumblr Reddit Blog
Gender Female 391 52.1% 52.1% 97 121 160 129 155 181 32 66 26 56
Male 360 47.9% 100.0% 135 160 139 75 164 48 29 16 33 50
Age Under 25 130 17.3% 17.3% 23 57 60 51 37 37 19 46 24 18
25–34 154 20.5% 37.8% 28 60 74 63 71 64 22 19 16 25
35–44 134 17.8% 55.7% 34 54 57 37 65 39 10 6 10 20
45–54 125 16.6% 72.3% 50 43 51 27 59 30 5 3 6 13
55 + 208 27.7% 100.0% 97 67 57 26 87 59 5 8 3 30
Total 751 100.0% 100.0% 232 281 299 204 319 229 61 82 59 106
Table 2
Discriminant Validity Assessment: HTMT (first-order constructs).
Heterotrait-monotrait ratio of correlations (HTMT)
COL ERR SUS UAC SDAc SDAD SDPN SDI G-INF G-SOC G-SP
COL
ERR 0.50
SUS 0.60 0.56
UAC 0.61 0.63 0.88
SDAc 0.08 0.18 0.16 0.15
SDAD 0.08 0.09 0.21 0.17 0.35
SDPN 0.06 0.18 0.14 0.18 0.64 0.47
SDI 0.08 0.21 0.34 0.35 0.74 0.15 0.67
G-INF 0.10 0.08 0.11 0.13 0.44 0.28 0.56 0.38
G-SOC 0.08 0.13 0.03 0.08 0.35 0.45 0.45 0.24 0.68
G-SP 0.06 0.11 0.11 0.14 0.43 0.30 0.66 0.42 0.72 0.71
G-ENT 0.11 0.05 0.08 0.13 0.38 0.27 0.50 0.35 0.83 0.68 0.68
Table 3
Discriminant Validity Assessment: HTMT (second-order constructs).
Heterotrait-monotrait ratio of correlations (HTMT)
CFSMIP SD
CFSMIP
SD 0.24
GRAT 0.13 0.58
J. Jacobson, et al. Journal of Retailing and Consumer Services 53 (2020) 101774
5

comfort, suggesting that user behaviour (self-disclosure practices) by
itself should not be used to determine one's comfort with the studied
practice.
Overall, while the majority of respondents were not comfortable
with marketers’ use of publicly available social media data (see
Table 4), the findings also suggest that social media users are not pas-
sive consumers of advertisements. Individuals are actively assessing
risks and benefits, which supports the use of CPM theory in the context
of social media data and marketing, with targeted advertising being the
strongest contributing dimension of marketing comfort.
6. Discussion
6.1. Theoretical contributions
In the marketing literature and professional practice, there is cur-
rently a lack of understanding of what the ethical norms are due to a
lack of research on people's expectations of privacy and comfort with
marketers using social media data. Our research fills this gap by de-
veloping a nuanced understanding of consumers’ comfort with social
media marketing practices. Even when marketers are using public data,
consumers still have concerns about the use of their social media data:
53.1%, 42.3%, and 41.9% are uncomfortable with marketers using their
social media data for targeted advertisement, opinion mining, and
customer relations respectively (see Table 4).
The research also introduces a new construct, marketing comfort, to
address the link between marketing ethics and consumer comfort.
Marketing comfort comprises the three main functions of using social
media data for marketing purposes: pulling, pushing, and exchanging
information. The study, therefore, operationalizes marketing comfort as
an individual's comfort with the use of information posted publicly on
social media for targeted advertising, customer relations, and opinion
mining.
To identify the drivers of marketing comfort, the three research
hypotheses explore the relation between information privacy concerns,
uses and gratifications of social media, self-disclosure practices in social
Fig. 1. Results of the structural model assessment.
Table 4
Comfort with marketers using publicly available social media data.
Comfort level Targeted ads Opinion
mining
Customer
relations
Extremely comfortable 40 (5.3%) 62 (8.3%) 56 (7.5%)
Moderately comfortable 54 (7.2%) 89 (11.9%) 86 (11.5%)
Slightly comfortable 98 (13%) 106 (14.1%) 116 (15.4%)
Neither comfortable nor
uncomfortable
160 (21.3%) 176 (23.4%) 178 (23.7%)
Slightly uncomfortable 125 (16.6%) 104 (13.8%) 97 (12.9%)
Moderately uncomfortable 100 (13.3%) 88 (11.7%) 70 (9.3%)
Extremely uncomfortable 174 (23.2%) 126 (16.8%) 148 (19.7%)
J. Jacobson, et al. Journal of Retailing and Consumer Services 53 (2020) 101774
6

media, and consumers’ comfort with marketers using their publicly
available social media data. The results further support the application
of the CPM theory in the context of publicly available social media data:
individuals are assessing the benefits and risks (H1 and H2) and actively
managing privacy boundaries by considering what they disclose (H3).
Thus, both risks and benefits play into consumer comfort, which shows
the applicability of the CPM theory. Importantly, the study extends
Petronio's (2002) CPM theory in the context of publicly available social
media data, which is a major contribution to the literature. The results
confirm that, even though people are assessing their risks with the
practice, they are willing to compromise some privacy because they
also derive benefits from social media use. The findings also suggest the
applicability of the privacy calculus theory (Culnan and Armstrong,
1999) to the use of social media—users make a trade-off assessment of
the benefits of disclosure against their privacy concerns associated with
such disclosure.
6.2. Implications for practice
The research joins prior scholarship that has practical implications
to enhance marketing practices (Ismagilova et al., 2019; Kamboj et al.,
2018; Shareef et al., 2019). In particular, the study shows that while
Table A1
Measurement instrument.
Mean SD
Self-Disclosure (Wheeless, 1976;Lai and Yang, 2015)
When using your public account(s), to what extent do you agree with the following statements?(1 = Strongly agree, 7 = Strongly disagree)
Amount and Depth (SDAD) SDAD1 I usually talk about myself on social media for fairly long periods 5.84 1.47
SDAD2 I often discuss my feelings about myself on social media 5.35 1.64
SDAD3 I often express my personal beliefs and opinions on social media 4.52 1.86
SDAD4 I typically reveal information about myself on social media without intending to 5.34 1.61
SDAD5 I often disclose intimate, personal things about myself on social media without hesitation 5.81 1.48
SDAD6 When I post about myself on social media, the posts are fairly detailed 5.04 1.58
Polarity (SDPN) SDPN1 I usually disclose positive things about myself on social media 3.29 1.56
SDPN2 I normally express my good feelings about myself on social media 3.62 1.69
SDPN3 On the whole, my disclosures about myself on social media are more positive than negative 2.75 1.37
Accuracy (SDAc) SDAc1 My expressions of my own feelings, emotions, and experiences on social media are true reflections of myself 2.96 1.54
SDAc2 My self-disclosures on social media are completely accurate reflections of who I really am 3.12 1.51
SDAc3 My self-disclosures on social media can accurately reflect my own feelings, emotions, and experiences 3.31 1.58
SDAc4 My statements about my own feelings, emotions, and experiences on social media are always accurate self-
perceptions
3.08 1.48
Intent (SDI) SDI1 When I express my personal feelings on social media, I am always aware of what I am doing and saying 2.33 1.37
SD2 When I reveal my feelings about myself on social media, I consciously intend to do so 2.88 1.63
SDI3 When I self-disclose on social media, I am consciously aware of what I am revealing 2.32 1.34
Uses & Gratification (Cheung et al., 2015)
To what extent do you agree with the following statements:(1 = Strongly agree, 7 = Strongly disagree)
Information sharing (G-INF) G-INF1 Social media is convenient for informing all my friends about my ongoing activities 3.12 1.63
G-INF2 Social media allows me to save time when I want to share something new with my friends 3.03 1.62
G-INF3 I find social media efficient in sharing information with my friends 2.85 1.59
New Relationship Building (G-SOC) G-SOC1 Through social media I get connected to new people who share my interests 3.45 1.67
G-SOC2 Social media helps me to expand my network 3.36 1.62
G-SOC3 I get to know new people through social media 3.76 1.73
Self-presentation (G-SP) G-SP1 I try to make a good impression on others on social media 3.13 1.51
G-SP2 I try to present myself in a favorable way on social media 2.81 1.42
G-SP3 Social media helps me to present my best sides to others 3.54 1.56
Enjoyment (G-Ent) G-Ent1 When I am bored I often go to social media 2.97 1.80
G-Ent2 I find social media entertaining 2.81 1.48
G-Ent-3 I spend enjoyable and relaxing time on social media 3.16 1.55
Concerns for Information Privacy (Smith et al., 1996;Osatuyi, 2015)
To what extent do you agree with the following statements(1 = Strongly agree, 7 = Strongly disagree)
Errors (ERR) ERR1 Social media sites should take more steps to make sure that personal information in their database is accurate 2.31 1.39
ERR2 Social media sites should have better procedures to correct errors in personal information 2.43 1.36
ERR3 Social media sites should devote more time and effort to verifying the accuracy of the personal information in their
databases before using it for recommendations
2.47 1.42
Collection (COL) COL1 It usually bothers me when social media sites ask me for personal information 2.42 1.37
COL2 It usually bothers me when social media sites ask me for my current location information 2.42 1.43
COL3 It bothers me to give personal information to so many people on social media 2.52 1.45
COL4 I am concerned that social media sites are collecting too much personal information about me 2.53 1.39
Unauthorized access (UAC) UAC1 Databases that contain personal information should be protected from unauthorized access—no matter how much
it costs
1.75 1.10
UAC2 Social media sites should take more steps to make sure that unauthorized people cannot access personal
information on their site
1.71 1.07
UAC3 Databases that contain personal information should be highly secured 1.54 0.99
Secondary use (SUS) SUS1 Social media sites should not use personal information for any purpose unless it has been authorized by the
individuals who provide the information
1.65 1.05
SUS2 When people give personal information to social media sites for some reason, these sites should never use the
information for any other purpose
1.85 1.19
SUS3 Social media sites should never share personal information with third-party entities unless authorized by the
individual who provided the information
1.58 1.07
Marketing comfort (newly developed for this study)
How comfortable would you be if information about you or posted by you publicly on social media is used for …?(1 = Extremely comfortable, 7 = Extremely
uncomfortable)
Marketing comfort (MC) MCAD Targeted advertising 4.69 1.79
MCCR Customer relations 4.30 1.86
MCOM Opinion mining about products or services 4.25 1.85
J. Jacobson, et al. Journal of Retailing and Consumer Services 53 (2020) 101774
7

users still value using social media platforms as a source of entertain-
ment and information—where they can express and present themselves
in favorable ways—they are becoming more aware of the use of these
platforms as major data warehousing and advertising platforms, as well
as the platforms’ role in “surveillance capitalism” (Zuboff, 2015). To
understand this growing duality, it is useful to recognize different levels
of data access in terms of who is accessing social media data and for
what purposes. Common scenarios of social media data collection and
use include: (1) social media platforms collecting data for targeted
marketing; (2) social media platforms analyzing data for internal
Table B1
Internal reliability assessment: Outer loadings and weights (CFSMIP, SD and GRAT defined reflective-formative). In bold, indicator weights.
COL ERR SUS UAC SDAc SDAD SDI SDPN G-INF G-SOC G-SP G-Ent MKC
COL1 0.81
COL2 0.78
COL3 0.82
COL4 0.82
ERR1 0.88
ERR2 0.87
ERR3 0.87
SUS1 0.89
SUS2 0.82
SUS3 0.88
UAC1 0.85
UAC2 0.88
UAC3 0.86
SDAc1 0.86
SDAc2 0.83
SDAc3 0.76
SDAc4 0.85
SDAD1 0.83
SDAD2 0.86
SDAD3 0.73
SDAD4 0.78
SDAD5 0.82
SDAD6 0.78
SDI1 0.84
SDI2 0.81
SDI3 0.81
SDP1 0.84
SDP2 0.81
SDP3 0.79
G-INF1 0.88
G-INF2 0.91
G-INF3 0.92
G-SOC1 0.89
G-SOC2 0.83
G-SOC3 0.88
G-SP1 0.89
G-SP2 0.88
G-SP3 0.86
G-ENT1 0.85
G-ENT2 0.91
G-ENT3 0.91
MKC1 0.56
MKC2 0.35
MCK3 0.22
Table C1
Internal reliability and convergent validity assessment.
Construct reliability and convergent validity
α ρ
c
AVE
COL 0.82 0.88 0.66
ERR 0.85 0.91 0.76
SUS 0.83 0.90 0.75
UAC 0.83 0.90 0.74
SDAc 0.84 0.89 0.68
SDAD 0.89 0.92 0.64
SDPN 0.74 0.85 0.66
SDI 0.76 0.86 0.68
G-INF 0.89 0.93 0.81
G-SOC 0.84 0.90 0.75
G-SP 0.85 0.91 0.77
G-ENT 0.87 0.92 0.80
MKC - - -
J. Jacobson, et al. Journal of Retailing and Consumer Services 53 (2020) 101774
8

purposes (e.g., to improve the user experience on the platform); (3)
social media platforms sharing data with affiliates and partners; (4)
users sharing data with third-party developers; and (5) third parties
accessing data using the platforms’ API without users’ consent. While
there are many ways to collect and use social media data, our research
demonstrates that social media users still have concerns with un-
authorized access to, and secondary use of, their personal data.
From a marketing perspective, a major challenge is how to mitigate
privacy concerns while increasing the perceived benefits of using social
media data for marketing. The study supports the idea that creating
clear privacy policies is necessary (Arnold, 2018; Goldfarb and Tucker,
2013), but not sufficient. Prior research shows that most users skim or
do not read privacy policies and terms of use because the platforms
discourage engagement using confusing and time-consuming legalese
(Obar and Oeldorf-Hirsch, 2018). In order to achieve the potential
benefits of data sharing, social media platforms and brands should
adhere to transparent consumer-oriented privacy policy practices (e.g.,
OPC, 2018); they also need to empower users with a higher-level of
control over what information they want to share, with whom, and for
what purpose (Prince, 2018). Consumers trust in social media platforms
and comfort with digital marketing practices may increase if platforms
limit the access to individuals’ personal data, improve transparency
about the collection and use of personal data, implement opt-in pro-
cedures, and offer monetary or non-monetary benefits to consumers
(Jai and King, 2016). Furthermore, social media platforms may
leverage increased privacy controls and opt-in procedures to improve
the performance and effectiveness of targeted advertising (Tucker,
2014).
The study shows that targeted advertising is the strongest con-
tributing element to marketing comfort. It is likely that consumers are
more familiar with, and have more direct exposure to, targeted ads than
the other two dimensions: opinion mining and customer relations. The
use of social media data for opinion mining and customer relations are
more masked, which means the implications of these practices are less
apparent to average social media users. This finding points to the need
for consumer education on the emerging marketing practices that may
be less apparent in the day-to-day use of social media. Digital literacy
will continue to be crucial as technologies evolve and new ways to use
individuals’ data emerge. The onus does not only lie with individuals;
rather, third parties that use the data need to be held to higher ethical
standards.
Ethical issues have always arisen in marketing, but marketing pro-
fessionals are now tasked with more complex and insidious ethical si-
tuations that require a high level of technical and ethical literacy. The
impact of a particular decision or action made by a marketing profes-
sional may not be immediately obvious, or ever become apparent to the
public, but there may still be critical implications for consumers. Recent
high-profile news stories evidence the perils of social media marketing,
such as the ability to target advertising to racists and bigots on
Facebook (e.g., using Facebook's functionality to target “Jew-haters” as
a demographic variable or use the filtering mechanism to not display
ads to people with “ethnic affinities”) (Maheshwari and Isaac, 2017).
Sparks and Hunt (1998) find that marketers’ ethical sensitivity is
achieved through socialization and an understanding of the ethical
norms. For social media marketing to be executed effectively and
ethically, the recipient of the marketing material—the con-
sumer—needs to be comfortable with the practices.
As the practices of using social media in marketing are still devel-
oping, we advocate for ethics and consideration of consumers’ concerns
to be integrated into marketers’ practices moving forward. Decision-
makers should consider the norms of all relevant communities and thus
ensure “a broad consideration of stakeholder interests” (Dunfee et al.,
1999, p. 28). The decision-makers—in this case, marketers—need to
recognize and consider all the stakeholders that may be impacted by the
decision or action, especially because customers’ trust and confidence is
a required factor for marketers to maintain a positive long-term
relationship with them. Therefore, it is not only an ethical practice, but
a practice that also makes sound business sense.
7. Conclusion
There is a lack of understanding related to consumers’ perceptions
about marketers’ use of their public social media data due to its recent
emergence, the complexity of the issues, and the dearth of research in
this area. Using communication privacy management theory as a the-
oretical lens, the research analyzes the public's perceptions of this
practice to better inform ethical marketing practices using a census-
balanced sample of the online adult Canadian population. There are
two major scholarly contributions of this research: (1) the extension
and applicability of communication privacy management theory to
social media, and (2) the introduction of marketing comfort, a new
construct of high importance for future marketing research. The 7-point
Likert scale measures individuals’ comfort with marketers’ use of social
media data by aggregating three common functions of using social
media data for marketing: extracting insights via opinion mining, de-
livering information via targeted advertising, and communicating via
customer relations with new or existing customers. We hope that the
construct will be further tested and we encourage other researchers to
apply this construct when using both public and private data (as this
research focuses on publicly available data), and in other countries to
confirm the results of this study.
From a practical perspective, while it is usually legally permissible
for third parties, such as marketers, to mine and use publicly available
social media data, our research evidences that many people are not
comfortable with this practice; as such, users’ attitudes may influence
their purchasing behaviour, which would critically impact marketers’
practices. Considering that the vast majority of online users are un-
comfortable with marketers using their publicly available social media
data, this research has implications for the wider marketing community
and marketing ethics. There is an opportunity for marketers to inform
and reassure the public about the ethical integrity of how they are using
the data (e.g., in aggregate rather than individually), but this requires
the development and communication of these ethical standards by all
marketing practitioners. The marketing community can, and must,
develop professional principles and guidelines on social media data use
that still affords them the ability to benefit from social media data, but
better speak to the concerns of consumers.
7.1. Limitations and future research directions
There may be cultural specificities that are important to consider in
understanding comfort with marketers use of social media (Tsai and
Men, 2012); thus, further research should seek to analyze this topic
cross-culturally. We also encourage future research to incorporate other
factors of the theory (e.g., culture, gender, motivation, trust, or context)
to improve the predictive power of this model.
As discussed in the methods section, a limitation of the research
model is its focus on social media in general, without consideration of
the platform being used. We acknowledge that different platforms may
provide different uses and gratifications, and they also may trigger
different privacy concerns—e.g., concerns about the use of personal and
activity data by Facebook after the Cambridge Analytica scandal— but
it is also worth noting that this research does not consider any specific
brand or company. This is a relevant issue because users might expect
that all the data they share with a social media platform is also col-
lected, analyzed, and used by the different brands they follow and in-
teract with, or at least every interaction with the company. Such a
consideration is outside the scope of the present study; however, if this
is the case, the level of perceived trustworthiness—e.g., the ability,
benevolence, and integrity (Mayer et al., 1995)—of the company might
help increase the predictive power of the model. This process may be
reinforced if trust transfer happens, be it between targets (i.e. the social
J. Jacobson, et al. Journal of Retailing and Consumer Services 53 (2020) 101774
9

media platform and the company) or from a context (i.e. from the
company's offline/online marketing practices to the social media con-
text) (Stewart, 2003). Future research can analyze whether the ex-
planatory power may be increased if social media platforms are ana-
lyzed in isolation. Aligned with Kamboj et al. (2018), all social media
platforms were considered in this research, but future research could
examine how these factors may manifest differently in different plat-
forms, as recommended by Alalwan (2018) and Alalwan et al. (2017).
The analysis also yields low predictive relevance of the model, with
a total variance explained of 18% of marketing comfort. This result does
not limit the validity of the relationships found in the analysis and is
partially explained by the high number of respondents that are ex-
tremely uncomfortable with the use of their personal and activity data
for marketing purposes—especially for targeted advertisement. Because
a group-based segmentation approach is beyond the scope of this re-
search, further research should investigate whether segmentation of
consumers based on their privacy concerns and their uses and gratifi-
cations of social media could better explain marketing comfort—e.g.,
pragmatists, fundamentalists, and unconcerned (DMA, 2018a). The
results point to the need for further refinement of the con-
cept—especially, for investigation of antecedents of marketing comfort
that may improve the accuracy of predictions, such as the above-
mentioned perceived trustworthiness or the consumers’ level of per-
ceived control over how their data are used (Goldfarb and Tucker,
2013).
Future work should also ask questions related to uses and
gratifications in the context of advertising (O′Donohoe, 1994). Fur-
thermore, future work should address consumer intention and beha-
viour as a response to concerns of marketers using consumers’ social
media data. Considering the recent changes to Facebook's ad platform
that explicitly identifies why users are seeing a particular ad, future
research should analyze how these disclaimers influence people's
comfort with the practice of microtargeting. As of 2018, Facebook only
applies this form of disclosure to political ads, but we contend this
practice should be expanded to other forms of advertising and other
social media platforms. Since the Cambridge Analytica scandal and the
new General Data Protection Regulation in the European Union, there
continues to be a growing need to understand consumers’ attitudes
towards the use of their social media data.
Acknowledgements
This research is supported in part through a five-year initiative on
“Social Media Data Stewardship” funded by the Canada Research Chairs
program (2015–2020; Principal Investigator: Gruzd, A.),
eCampusOntario research funding and the Ted Rogers School of
Management at Ryerson University. The authors would like to thank
Elizabeth Dubois for her collaboration in the development of the survey
design; Jordan Kilfoy, Christine Gagnon, and Jocelyn Stéphane Cadieux
for their help with the French translation of the survey; and members of
the Social Media Lab at Ryerson University for their feedback on the
survey design.
Appendix A
See Table A1
Appendix B
See Table B1
Appendix C
See Table C1
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