Monetização dos Dados=
de
Mídias Sociais: Uma Revisão Sistemática sobre os Estudos, Técnicas de Análi=
se e
Estratégias para Monetização
|
Cláudia
Rodrigues Maia https://orcid.org/0000-0=
002-7274-0118 |
=
Doutora em Administração. Universidade
Federal do Rio Grande do Sul – UFRGS – Brasil. claudiarmaia@hotmail.com |
|
Antônio Carlos Gas=
taud
Maçada https://orcid.org/0000-0002-8849-0117 |
Doutor
em Administração. Universidade Federal do Rio Grande do Sul – UFRGS – Bra=
sil.
acgmacada@ea.ufrgs.br |
|
Guilherme Lerch
Lunardi https://orcid=
.org/0000-0003-3250-2796 |
Doutor
em Administração. Universidade Federal do Rio Grande - FURG – Brasil.
gllunardi@furg.br |
ABSTRACT
Social media have become important B2B an=
d B2C
communication channels, generating huge amounts of data. However, most
companies struggle to measure the value of data =
and
their monetization is still an unknown opportunity. While organizations are
increasingly interested in extracting knowledge and monetizing their data by
analyzing the value chain, little research has been done to examine data-dr=
iven
business models and the challenges involved in monetizing the data generate=
d by
stakeholder interactions. This study addresses this concern, by conducting a
Systematic Literature Review on the monetization of social media data. The
search strategy resulted in 35 studies published in the main IS journals
identified in Scopus database. Thus, we aimed to map the characteristics of
scientific publications related to social media data monetization and the
monetizing techniques of analysis and strategies for value creation. We used
the bibliometrix package, in the R software. The
results present the state-of-the-art about social
media data monetization, highlighting different forms of monetization and
strategies for generating value through the social media data value chain. =
The
study contributes to the expansion of knowledge of scientific production ab=
out
social media data and their monetization by exploring value creation
opportunities for business.
Keywords: social media data; data monetization; data value=
; data
value chain.
RESUMO
As mídias sociais tornaram-se
importantes canais de comunicação B2B e B2C, gerando enormes quantidades de
dados. No entanto, a maioria das empresas enfrenta dificuldades para medir o
valor dos dados e sua monetização ainda é uma oportunidade desconhecida.
Enquanto as organizações estão cada vez mais interessadas em extrair
conhecimento e monetizar seus dados através da análise da cadeia de valor,
pouca pesquisa foi feita para examinar modelos de negócios orientados por d=
ados
e os desafios envolvidos na monetização dos dados gerados pelas interações =
das
partes interessadas. Este estudo aborda essa preocupação, realizando uma
Revisão Sistemática da Literatura sobre a monetização de dados de mídias
sociais. A estratégia de busca resultou em 35 estudos publicados nos princi=
pais
periódicos de SI identificados na base de dados Scopus. Assim, o objetivo do
estudo foi mapear as características das publicações científicas relacionad=
as à
monetização de dados de mídias sociais e as técnicas de análise e estratégi=
as
de criação de valor para monetização. Foi utilizado o pacote bibliometrix, no software R. Os resultados apresentam=
o
estado da arte sobre a monetização de dados de mídias sociais, destacando
diferentes formas de monetização e estratégias para gerar valor através da
cadeia de valor dos dados de mídias sociais. O estudo contribui para a expa=
nsão
do conhecimento da produção científica sobre dados de mídias sociais e sua
monetização, explorando oportunidades de criação de valor para negócios.
Palavras-chave: dados de mídias s=
ociais;
monetização de dados; valor dos dados; cadeia de valor dos dados.
Recebido em 16/01/2024. Aprovado em 28/01/2025. Avaliado pelo s=
istema
double blind
https://doi.org/10.22279/navus.v16.1851
1 I=
NTRODUCTION
The rapid growth in the volume of data
generated in the online context, and the development of digital services,
businesses, and devices (Van't Spijker, 2014) are enabling data to be
recognized as potential monetization assets. Refining and extracting valuab=
le
information and knowledge from massive amounts of data, which grows daily,
provides the formation of a new field of study called “data monetization”,
defined as the process of converting data and their analysis (analytics) on
financial return (Hanafizadeh & Harati Nik,=
2020).
=
As
companies move from selling products to selling or renting services, the va=
lue
of data increases exponentially (Parvinen et al=
.,
2020), requiring new possibilities and methods to be traded as commercial g=
oods
(Lawrenz & Rausch, 2021). Driven by the Internet and technology, many
organizations stopped being providers of products and services and became
facilitators of innovation and collaboration for new ideas in the digital
economy (Suseno et al., 2018). Thus, there is a=
need
to seek new ways to assess the creation of value in the current digital
ecosystem due to the growth in the number of companies operating in this
environment (Suseno et al., 2018). The value
generated in the social ecosystem is derived from experience with internal =
and
external stakeholders and multidirectional influence
flows (Gosline & Krithivasan, 2021). In this
sense, it is relevant to analyze social information arising from communicat=
ion
and collaboration between customers, consumers, and companies.
=
In
this new scenario, consumers are no longer simply passive recipients of
services and increasingly become co-creators of value. Today, social media =
has
been used as a space for discussing ideas about products, services, or
processes, generating a wealth of content created by themselves (Suseno et al., 2018). This suggests that value is cre=
ated
through dynamic interactions carried out on social media between the
stakeholders (Tantalo & Priem, 2016), rather than being created exclusi=
vely
by one or another digital company. According to Jimenez-Marquez et al. (201=
9),
analyzing what customers are talking about the company on social media is a=
key
factor for companies to succeed in the era of big data. However, analyzing =
data
from social media is a complex task, due to the subjectivity that exists in
proofreading the text and the additional resources employed in the raw data.
Nevertheless, asdata are becoming more valuable=
than
ever, many organizations have sought to adapt processes that deal with the
specifics of big data as they are capable of extracting knowledge and
efficiently monetizing data assets (Faroukhi et=
al.,
2020).
The numerous advantages associated with t=
he use
of social media to stakeholders have attracted the attention of researchers
from different fields, including Information Systems (IS) (Jeyaraj & Za=
deh,
2020; Dwivedi et al., 2021). However, little research has examined data-dri=
ven
business models, as well as the value and challenges involved in data
monetization (Parvinen et al, 2020). In order to
provide a comprehensive overview of how social media data can generate value
for the digital business ecosystem and be monetized by firms, this study
presents a systematic literature review aiming at (i)
mapping characteristics of the studies about data monetization, (ii)
identifying the techniques used in monetizing social media data, and (iii)
identifying the main monetizing strategies for value creation. The study se=
eks
to broaden the scientific knowledge about social media data and monetizatio=
n by
exploring value creation opportunities for businesses, in addition to raisi=
ng
awareness in the academic community about the potential for research on
monetization and the value of social media data. Next, we present a review =
of
the literature on social media data monetization, followed by the methodological procedures, results, and final
considerations.
2 LITERATURE REVIEW
2.1 Social Media Data
Social media are
important communication channels between companies and consumers, providing=
a
lot of data generated by users online. However, as they are unstructured,
subjective, and found in massive databases, they are not fully used (Chan et
al., 2016). When referring to social media, applications like Facebook,
WhatsApp, Twitter, YouTube, LinkedIn, and Instagram come to our mind. These
apps are powered by user-generated content and are capa=
ble of
influencing a variety of settings, from buying/selling behaviors,
entrepreneurship, and even political issues (Greenwood & Gopal, 2015). =
In
this environment of online interactions, consumer ratings and reviews expre=
ssed
on social media enhance the quality, credibility, and authenticity of the
information, bringing together people with similar interests and goals.
For Momot et al. (2020), the value of
social-network information derives especially from network connections or
friends. Thus, companies can use their data to improve internal processes a=
nd
decision-making as well as to improve their products and services, making it
necessary to identify the value of social media data so that it can generate
value for organizations. An example is the case of Alibaba, which aggregates
and analyzes data from users who are part of its digital ecosystem to obtain
unique insights into the preferences and purchasing habits of its buyers. T=
he
company shares these insights with its partners to help them make better
decisions and create new business models (Williamson & De Meyer, 2019).=
The
phenomenon of social media is already well recognized as an important topic=
by
the main IS journals (Dwivedi et al., 2021; Jeyaraj & Zadeh, 2020) and =
the
combination of measurement of human behavior facilitated by information
technology (IT) brings social media analytics to the forefront of IS intere=
st
(Jeyaraj & Zadeh, 2020). Thus, the massive volume of data generated by
social media interactions has been intensively researched and big data
analytics has emerged as an important area of research using various
traditional data mining and machine learning techniques (Ghani et al., 2019=
).
2.2 Data monetization for business
Data monetization has gained importance, =
both
in research and in practice, as an emerging phenomenon driven by current
technological trends in the context of big data. Business leaders are looki=
ng
to generate value from their massive data assets, increasingly seeking to
monetize them. However, knowing how to manage the data life cycle and extra=
ct
insights to generate business value requires a complete understanding of the
life cycle of operational, customer, and third-party data (Parvinen,
2020; Alfaro et al., 2019; Faroukhi et al., 202=
0).
Researchers have proposed distinct business models and strategies to moneti=
ze
data in different contexts (Faroukhi et al., 20=
20).
Najjar and Kettinger (2013) define data
monetization as the conversion of the intangible value of data into real va=
lue,
usually through sales or other tangible benefits. Before data can be moneti=
zed,
data needs to be processed and discovered first as it is a value creation
process that needs different technologies and business know-how (Najjar &am=
p;
Kettinger, 2013; Liu & Chen, 2015). The use and monetization becoming
important and increasingly relevant because it encourages companies to coll=
ect
and use or sell data for business decision-making, making this a true sourc=
e of
competitive advantage for businesses in the digital economy (Wixom & Ro=
ss,
2017). However, many companies do not know exactly how useful these data wi=
ll
be to them (Ray et al., 2020). Most companies find it difficult to value th=
eir
data (Parvinen et al., 2020), however, the glob=
al
data monetization market is expected to reach around US$370 million by 2023
(Allied Market Research, 2018).
According to the research by McKinsey Ana=
lytics
(2017), many companies monetize their data to a limited extent, indicating =
that
some are struggling to extract economic value from their data. In fact, a s=
tep
towards data monetization can be very challenging for organizations in
practice, as it often requires organizational changes and technology upgrad=
es
(Wixom & Ross, 2017).
To remain competitive, companies need to
identify the most promising opportunities for data usage, as well as their
benefits, to make monetization efforts (Wixom & Ross, 2017). However, t=
here
is still a lack of guidance and know-how for companies to start exploring
opportunities to create value from their data assets (Liu & Chen, 2015).
According to Hanafizadeh & Harati Nik (2020=
), it
is necessary to have a better understanding of the data monetization process
based on analytical insights that can result in greater effectiveness and
usefulness for organizations, capable of generating competitive and strateg=
ic
advantage. The idea of monetizing, creating new value, and revenue from dat=
a is
not new. But, based on the literature, the conclusion is that there are sti=
ll
few journals in the IS area that are dealing with the topic. According to <=
span
class=3DSpellE>Suseno et al. (2018), empirical studies are limited a=
nd
necessary to investigate value-creation practices as a =
result
of interactions between stakeholders.
3 METHODOLOGICAL PROCEDURES
According to Kitchenham (2004), Systematic
Literature Reviews (SLR) aim to summarize evidence on existing research,
identify gaps in the literature, build theoretical frameworks to support new
research activities, and collect empirical evidence to support, contradict =
or
generate new hypotheses. Consistent with the rigor for the existence of
science, in terms of objectives, this research is characterized as a
descriptive scoping review (Paré et al., 2015), as it seeks to describe asp=
ects
related to the characteristics, the potential size, and nature of existing
literature on an emerging topic. Scoping reviews can identify the conceptual
boundaries of a field, the size of the set of research, the types of eviden=
ce
available, and research gaps in the extant literature (Paré et al., 2015; X=
iao
& Watson, 2019).
This study is in line with the recommenda=
tions
of Webster and Watson (2002), whose approach is based on searching titles,
abstracts, and keywords to identify relevant articles available in the Scop=
us
database (Dwivedi et al., 2021). As in the study by Dwivedi et al. (2021), =
the
Scopus database was chosen to ensure the selection of only high-quality
studies. The initial search did not return results for the terms “social me=
dia”
AND “data monetization” in titles, abstracts, and keywords. Therefore, for =
the
selection of the portfolio of publications, the following filters were defi=
ned:
(i) the document should contain the words “soci=
al
media data” OR “data value” OR “data monetization” in the title, abstract, =
or
keywords (TITLE-ABS-KEY ("social media data") OR TITLE-ABS-KEY
("data value") OR TITLE-ABS-KEY ("data monetization"); =
and
(ii) the document should be an article or review published in the main IS
journals indicated by the Association of Information Systems (AIS). The eig=
ht
main IS journals indicated by AIS were selected (AIS Basket of eight Top Jo=
urnals),
appointed by researchers in the area as the journals that best reflect the
discipline based on bibliometric data (Lowry et al., 2013). They are as
follows: European Journal of Information Systems, Information Systems Journ=
al,
Information Systems Research, Journal of Information Technology, Journal of
Management Information Systems, Journal of Strategic Information Systems,
Journal of the Association for Information Systems, and MIS Quarterly. In
addition to these, other journals indicated by the AIS were selected: Infor=
mation
and Management, Decision Support Systems, Decision Sciences, MIS Quarterly
Executive, International Journal of Information Management, and Communicati=
ons
of the Association for Information Systems. The Journal of Big Data and the
Global Journal of Flexible Systems Management were also added to the filter
because they are related to the research topic and meet the quality criteria
defined for the study.
After applying the publication selection
criteria, 35 articles were identified with the necessary specifications, wh=
ich
make up the final research portfolio (Figure 1). To meet the study objectiv=
es
and guide this review, they were broken down into the following research
questions:
RQ1: What are the main characteristics of
publications on data monetization, data value, and social media data?
RQ2: What are the social media data analy=
sis
techniques used in the studies?
RQ3: What are the main social media data
monetization strategies found in the literature?=
First,
a bibliometric analysis was carried out, focused on themes and the main
keywords. This was done through a keyword correlation analysis and thematic
map, supported by visual analytics, with research trends in IS. The R softw=
are
was used, more specifically the bibliometrix pa=
ckage,
developed by Massimo Aria & Corrado Cuccurullo. The package in question=
is
intended to assist in the realization of comprehensive and complex scientif=
ic
mappings involving big data, becoming a useful tool nowadays, when the volu=
me
of scientific production gradually increases and
science is constantly changing (Aria & Cuccurullo, 2017).
Figure 1. The selection process of the papers=
Subsequently, the articles w=
ere read
in full, and their content was classified according to the study objectives.
The results of the study are presented in the next section.
4 RESULTS
4.1 Bibliometric analysis of the
characteristics of the studies
The search resulted in 35 articles addres=
sing
the monetization of social media data (Appendix). When analyzing Figure 2, =
it
is clear that publications related to the topic only started to receive
attention in 2013 (2 articles). The number of publications was two articles=
or
less per year until 2017. From 2016 onwards, there is a significant growth =
in
the number of publications, reaching nine articles published in 2020 and th=
ree
articles published in 2021 (annual growth rate: 5.2%). Given this growth, we
can see the importance of the theme today and its relevance in the academic
environment.
In order to verify the quality of the articles, we analyzed the most cited stud=
ies
on the subject and the journals where papers were published. The publicatio=
ns
are mainly concentrated in three journals, the International Journal of
Information Management – IJIM (8 articles), the Decision Support Systems (7
articles), and the Journal of Big Data (4 articles), representing more than=
50%
of the final research portfolio. The most cited articles were those by He et
al. (2013), with 520 citations, and the one by Stieglitz et al. (2018), with
298 citations – both published in IJIM.
Figure 2. Te=
mporal
Evolution of studies
Next, a quantitative analysis of the 20 m=
ost
cited keywords was performed, which provided a broader view of the topic.
Figure 3 shows the word cloud with the highest frequencies pointed out by t=
he
authors. It was identified that the term “social media” stands out first in=
the
cloud, followed by the terms “sentiment analysis”, “big data”, “Twitter”, a=
nd
“data monetization”. Corroborating the analysis, Jeyaraj & Zadeh (2020)
pointed out in their study that the phenomenon of social media is already
recognized as an important topic by the main IS Journals, which was also fo=
und
in this study.
Figure 3. Authors' keyword cloud
To obtain a comparative view of the centr= ality and relevance of the keywords, we used the thematic map function. This func= tion creates a map based on co-words network analysis and clustering, inspired by Cobo et al. (2011). The thematic map (Figure 4) shows the network of the ma= in clusters of occurrences of co-words, considering the maximum number of 250 keywords and the maximum number of three keywords for each cluster. Accordi= ng to Tayebi et al. (2019), through the visual design of the thematic map, it = is possible to analyze the themes and in which quadrants they are located. Fig= ure 4 shows that the largest cluster comprises social media, Twitter, and data mining framed as driving themes. The second and third most significant clus= ters are related to some techniques widely used in social media analysis: sentim= ent analysis, natural language processing (NLP), and text analytics- framed as = main themes. The fourth-largest and central cluster represents big data, indicated as a relevant topic, while the fifth cluster comprises the data monetization framed as emerging and undeveloped, marginal themes. The latter has a lower level of importance (density) concerning the four clusters described above; however, it appears as more central and rele= vant when compared to the clusters related to social media, social media analyti= cs, and sentiment analysis. In this sense, the classification of the monetizati= on theme, in the left quadrant, is pointed out as a special, important theme, = but not yet developed. The smallest clusters identified were related to social networks and machine learning –framed as a basic and well-developed theme.<= o:p>
Figure 4. Thematic map of keywords=
Through the thematic map, it is possible =
to see
that the themes of big data, social media, and data monetization are still
loosely associated in the IS literature, indicating the relevance of this s=
tudy
for a better understanding of the theme in the academic environment. After =
the
bibliometric analysis, the main techniques for analyzing social media data =
are
presented, followed by the strategies found in the literature for data
monetization.
4.2 Social Media Data Analysis Techniques=
Social media platforms, in addition to te=
xtual
data, offer many possibilities of data formats, including images, videos,
sounds, and geolocation (Stieglitz et al., 2018) which can be called
unstructured data and structured data. According to Jimenez-Marquez et al.
(2019), some data are usually associated with the review texts, they are the
number of stars, the number of votes that considered the review useful, the
photo or video of the reviewer, popularity of the reviewer, number of revie=
ws
provided by the reviewer, images to illustrate or support the argument, typ=
e of
services provided (as indicated by customers), overall rating of the
service/product provider, etc. The=
se
resources generate huge amounts of information that are commonly called big
data or social media big data (Jimenez-Marquez et al., 2019) that are being
generated due to the growth of social media usage.
According to OLeary and Storey
(2020), social media users disclose substantial information, and some of th=
at
information can provide deep insights that can be used to create value. Many
companies are gaining valuable insights from this data by applying big data
techniques (Jimenez-Marquez et al., 2019). Although there is a lot of
literature on the challenges and difficulties of data analysis methods and
techniques, there is still no clear understanding of the stages of data
discovery, collection, and preparation (Stieglitz et al., 2018).
In this sense, the authors point out bene=
fits
for professionals who wish to collect and analyze social media data. The
article by Jimenez-Marquez et al. (2019) proposed a two-stage framework, du=
e to
the complexity involved in analyzing social media data. The authors indicate
that the proposed framework serves as a bridge between data analysis and
technology. Thus, machine learning algorithms were applied for the analysis=
of
unstructured text data obtained in the preparation stage.
OLeary and Storey
(2020) explored social media content through text sentiment analysis to
generate value. The authors point out that value can be realized from data
exhaust and suggest an approach to generate innovations and facilitate the
evolution of systems to benefit managerial decision-making. Furthermore, Lo,
Chiong & Cornforth (2016) demonstrated the possibility of identifying t=
he
main followers to help companies in decision-making. Semi-supervised and
supervised machine learning techniques were used (Twitter Latent Dirichlet
Allocation - LDA, Fuzzy Match, and Support Vector Machine - SVM) to identify
key members of the Twitter social network to differentiate their customers =
from
the general public, enabling market segmentation=
to
improve business decision-making (Lo, Chiong & Cornforth, 2016).
Another possibility for analyzing social =
media
data is identifying the most shared topics. Ibrahim & Wang (2019) colle=
cted
tweets associated with online retailers and used a combination of text
analytical approaches including topic modeling, sentiment analysis, and net=
work
analysis to identify emerging topics and areas that generate customer
dissatisfaction. These insights can help companies to better understand the=
ir
customers and enable them to translate information into meaningful knowledg=
e to
improve their performance (Ibrahim & Wang, 2019). However, the true val=
ue
of data is rarely discovered due to the information overload in social medi=
a,
but it would be possible to interpret these data and extract value with pro=
per
techniques.
Table 1.
=
Su=
mmary of
social media data analysis techniques
|
An=
alysis tech=
niques |
So=
urce |
|
Regression Anal=
ysis |
Hu et al. (2019) |
|
Trend Analysis |
Stieglitz et al. (2018) |
|
Network Analysis |
Ibrahim & Wang (2= 019) |
|
Content Analysis |
Suseno
et al. (2018); Stieglitz et al. (2018); Ibrahim & Wang (2019) |
|
Sentiment Anal=
ysis |
Lau & Liao (2014=
);
Stieglitz et al. (2018); Dong et al. (2018); Jimenez-Marquez et al. (2019); OLeary & |
|
Natural Langua=
ge
Processing-NLP |
Abbasi et al. (2018); Chang, Ku & Chen (2019); Jimenez-Marquez et al. (2019) |
|
Fuzzy=
Match |
Lo, Chiong & Corn= forth (2016) |
|
Latent Dirichlet Allocation – =
LDA |
Lo, Chiong & Cornforth (2016); Ibrahim & Wang (2019) |
|
Support<=
b> Vector Machine - SVM |
Lo, Chiong & Corn= forth (2016) |
|
Cluster analys=
is |
Van Dam & Van De Velden
(2015) |
|
Text<=
span
style=3D'color:black;mso-color-alt:windowtext'> Mining |
He, Zha &= ; Li (2013); Dong et al. (2018) |
Note. The table was created by the author bas=
ed
on multiple sources. Sources are listed directly in
the table. For complete references, see the reference list.
He, Zha & Li (2013) also point out th=
at
companies need to monitor and analyze information from their competitors, a=
nd
to do this, text mining techniques were applied to analyze unstructured
Facebook and Twitter data from the largest companies in the sector. In
addition, Pääkkönen & Jokitulppo (2017) sug=
gest
that many techniques based on Machine Learning (ML) have been used to assess
aspects related to the quality of social media data. Jimenez-Marquez et al.
(2019) claim that to obtain better results in data analysis, organizations =
are
using several ML techniques in their analysis. In Table 1 we present a summ=
ary
of these techniques.
4.3 Social Media Data Monetization Strate=
gies
After identifying the main analysis techn=
iques
applied in social media data analysis, this section presents the paths and
strategies for data monetization identified in the literature. Some models,
approaches, and paths were proposed to monetize data in different contexts.
Najjar and Kettinger (2013) indicated three pathways that can help companie=
s to
monetize data: (i) high analytical capacity, (i=
i)
high technical data infrastructure, considering hardware, software, and net=
work
aspects, and (iii) data sharing with suppliers to improve their analytical
capabilities and also avoid high costs (Faroukhi et al., 2020).
Parvinen et al. (2020) present three approaches to data monetization. In this
sense, companies can sell data, data analyses, and data-based services. The
authors also identify that the growing resources available allow companies =
to
start integrating their internal data with external data, considering new
usages through data enrichment. Alfaro et al. (2019) also described three
monetization approaches that were applied at a financial institution: selli=
ng
information solutions, improving operations by using data to generate retur=
ns
through operational gains and wrapping offers with analytical capabilities =
or
experiences, aiming to increase a product's price, wallet share, market sha=
re,
or customer loyalty.
Hanafizadeh & Harati Nik (2020), in turn, developed a data monetization
configuration based on themes identified in a systematic review. The themes
identified were the monetization layer, refinement layer, base layer, and
accessing and processing restrictions layer, which represent the layers that
play an important role in the data monetization mechanism. In the monetizat=
ion
layer, the themes are related to direct sales, analyses and insights, and
end-consumer. Concerning the data refinement process, themes such as assets,
models, data-driven operations, and value were identified, and, within the
third aspect, which provides a basis for the success of data monetization, =
the
themes are related to people, perceptions, analytical and technical
capabilities, and platforms. In the last layer are the legal, ethical, and
privacy issues, which affect all other layers
In the context of monetization, and more
specifically of social media data, organizations need to consider privacy a=
nd
security issues, since data include user information (Gerlach et al., 2015).
Many companies do not monetize data due to reputation risks and issues of t=
rust
and property of data that can foster many conflicts between sellers and buy=
ers
in cases of data marketing (Thomas & Leieponen,
2016). In this way, the limited understanding of the regulation on data pri=
vacy
becomes a barrier for monetization (Mendonça, 2021).
Table 2.
=
St=
rategies
for monetizing social media data
|
Strategies |
Description |
Authors |
|
Strategy
for gaining revenue |
Directly sell social media =
data
or generate insights to make decisions, improve operational efficiency and
gain competitive advantage. |
Hanafizad=
eh & Harati |
|
Strategy
for reducing costs |
Identify
opportunities for improvement in processes and operations based on availa=
ble
data. |
Hanafizadeh & Harati Nik (2020); Parvinen
et al. (2020); Alfaro et al. (2019);
Chen et al. (2017)
|
|
Strategy
for developing and using analytical capabilities |
Develop capabilities to ext=
ract
value from data by generating insights. |
Hanafi=
zadeh & Harati Nik (2020);
Najjar & Kettinger (2013) |
|
Strategy
for data governance |
Develop
skills for proper use of data, considering security, privacy, ethics, and
data quality. |
Gerlach
et al. (2015); Hanafizadeh & Harati Nik (=
2020);
Parvinen et al. (2020) |
Note.=
The
table was created and adapted by the author based on multiple sources.
It should be noted that only one article =
was
found in this systematic review proposing a model of data monetization.
4.4 Value Generation from Social Media Da=
ta
Moore (2015)
introduced two types of monetization for customer-related data: direct
monetization and indirect monetization. The sale of data is known as direct=
and
the use of data for improvements in performance, processes, and products is
known as indirect (Hanafizadeh & Harati Nik,
2020). Alfaro et al. (2019) addressed the direct generation of benefits by
selling information solutions to external customers, improving operations,
applying analytical processing to guide products, and benefiting from the e=
conomic
impact of projects based on data monetization.
Despite the
importance of monetization, a few research has considered monetization (
The various ty=
pes of
transaction platforms such as advertising platforms (e.g., Facebook and
Google), e-commerce platforms (which include online marketplaces like Amazon
and eBay), service platforms (like Uber, Airbnb, and Spotify), and cloud
platforms (such as AWS, Google Cloud Platform, and Microsoft Azure) are use=
d to
monetize data and generate revenue for companies. Typically, all these
platforms link users' accounts to their social media profiles. Thus, the va=
lue
of user data arises when this data is compiled in large volumes and process=
ed
to provide insights and help companies, governments, and other organization=
s to
make their decisions based on the data of individuals.
Figure 5. Flow of value generation fro=
m social
media data, adapted from Kotorov (2017).
In this sense,=
based
on academic literature, we adapted the Data Value Chain approach proposed b=
y Kotorov (2017) and developed a framework (Figure 5) t=
hat
represents the value generation flow of social media data divided into six
steps: (i) generation and discovery of data, (i=
i)
collection and preparation for data enrichment, (iii) application of data
analysis techniques, (iv) visualization and exposition, (v) interpretation =
and
insights, and (vi) implementation of the strategy by organizations. Through=
this
flow, it can be seen that the indirect monetizat=
ion of
social media data depends on the application of techniques to generate insi=
ghts
and implement strategies by organizations. Direct monetization can occur at=
any
stage of the data value chain, whether through raw, pre-processed, or proce=
ssed
data.
5 FINAL CONSIDERATIONS
In the last de=
cade,
social media has become a critical channel for B2B and B2C communication,
generating vast amounts of data. However, many companies face challenges in
measuring the value derived from this data, leaving monetization as an
underexplored opportunity. This study aimed to address this gap by answering
three key research questions (RQs):
RQ1: What are =
the
main characteristics of publications on data monetization, data value, and
social media data?
Our review rev=
ealed
that publications on this topic began gaining attention in 2013 and grew
significantly from 2016 onwards, reflecting its increasing importance. Most
articles are published in high-impact journals such as IJIM, Decision Suppo=
rt
Systems, and Journal of Big Data. Keyword analysis highlights themes such as
social media, big data, sentiment analysis, and data monetization, though t=
he
latter remains an emerging and underexplored area. These findings emphasize=
the
need for further research to establish a cohesive framework linking these
concepts.
RQ2: What are =
the
social media data analysis techniques used in the studies?
Sentiment anal=
ysis
is the most frequently applied technique in the reviewed studies, offering
insights into customer opinions, preferences, and perceptions. Other widely
used techniques include text mining, Latent Dirichlet Allocation (LDA), Fuz=
zy
Match, Support Vector Machines (SVM), and Natural Language Processing (NLP).
These methods are essential for extracting actionable insights from
unstructured social media data, forming the foundation of monetization
strategies.
RQ3: What are =
the
main social media data monetization strategies found in the
literature?
We identified =
four
core strategies organizations can adopt to monetize social media data: (
Furthermore, we
propose a framework illustrating the value-generation process of social med=
ia
data, helping companies identify new monetization opportunities and navigate
the complexities of this process.
This study
contributes to the literature by providing a
comprehensive definition of social media data monetization as the process of
creating value from large volumes of social media data for monetary and
non-monetary benefits. Monetization can occur through direct data sales,
intra-organizational insights generation, or the commercialization of insig=
hts.
Our findings b=
enefit
both academics and practitioners by shedding light on the potential of soci=
al
media data monetization as a revenue source and cost-reduction mechanism. F=
or
future research, we recommend exploring tailored monetization models to add=
ress
big data challenges and examining sector-specific applications in industries
such as healthcare, retail, telecommunications, finance, and insurance.
Finally, we
acknowledge that data monetization strategies may vary across sectors due to
differences in data characteristics, regulatory environments, and market ne=
eds.
Future studies should explore these variations to deepen understanding and
identify sector-specific monetization opportunities.
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Authors |
Title |
Year |
Source <=
span
class=3DSpellE>title |
Cited <=
span
class=3DSpellE>by |
|
Noshad, M., Choi, J., Sun, Y., Hero, A., Dinov, I.D. |
A data value metric for quantifying information content and utility=
|
2021 |
Journal<=
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International Journal of Information Management |
10 |
|
Sasikala, P., Mary Immaculate Sheela, L. |
Sentiment analysis of online product reviews using DLMNN and future
prediction of online product using IANFIS |
2020 |
Journal<=
/span> of Big Data |
5 |
|
Faroukhi=
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I., Gahi, Y., Amine, A. |
Big data monetization throughout Big Data Value Chain: a comprehens=
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2020 |
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22 |
|
Zheng, L., He, Z., He, S. |
A novel probabilistic graphic model to detect product defects from
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2020 |
Decision=
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3 |
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M., Rossi, M. |
Advancing data monetization and the creation of data-based business
models |
2020 |
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4 |
|
Wu, P., Li, X., Shen, S., He, D. |
Social media opinion summarization using emotion cognition and
convolutional neural networks |
2020 |
International Journal of Information Management |
22 |
|
Ray, J., Menon, S., Mookerjee, V. |
Bargaining over Data: When Does Making the Buyer More Informed Help=
? |
2020 |
Information Systems Research<= o:p> |
3 |
|
Hanafizadeh, P., Harati Nik, M.R.=
|
Configuration of Data Monetization: A Review of Literature with
Thematic Analysis |
2020 |
Global Journal of Flexible Systems Management |
8 |
|
Oleary, D., Storey, V.C. |
Discovering and transforming exhaust data to realize managerial val=
ue |
2020 |
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1 |
|
Ilk, N., Fan, S. |
Combining Textual Cues with Social Clues: Utilizing Social Features=
to
Improve Sentiment Analysis in Social Media |
2020 |
Decision=
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1 |
|
Chang, Y.-C., Ku, C.-H., Chen, C.-H. |
Social media analytics: Extracting and visualizing Hilton hotel rat=
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2019 |
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Suseno, Y., Laurell, C.,=
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2018 |
Journal of Strategic Information Systems |
40 |
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4 |
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Abbasi, A., Zhou, Y., Deng, S., Zhang, P. |
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2018 |
MIS Quarterly: Management Informat=
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|
2017 |
MIS Quarterly Executive |
33 |
|
Lo, S.L., Chiong, R., Cornforth, D. |
Ranking of high-value social audiences on Twitter |
2016 |
Decision=
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26 |
|
Gerlach, J., Widjaja, T., Buxmann, P. |
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cies
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2015 |
Journal of Strategic Information Systems |
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|
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2015 |
Decision=
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50 |
|
Lau, R.Y.K., Li, C., Liao, S.S.Y. |
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2014 |
Decision=
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|
Najjar, M.S., Kettinger, W.J. |
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2013 |
MIS Quarterly Executive |
38 |
|
He, W., Zha, S., Li, L. |
Social media competitive analysis and text mining: A case study in =
the
pizza industry |
2013 |
International Journal of Information Management |
520 |
<=
span
lang=3DEN-US style=3D'font-size:9.0pt;font-family:"Myriad Pro Cond",sans-se=
rif;
color:gray;mso-themecolor:background1;mso-themeshade:128;mso-ansi-language:
EN-US'>Monetization of Social Media Data: A Systematic Review of Studies,
Techniques of Analysis, and Strategies for
<=
span
class=3DSpellE>Value Creati=
on
<=
span
style=3D'font-size:9.0pt;font-family:"Myriad Pro Cond",sans-serif;color:gra=
y;
mso-themecolor:background1;mso-themeshade:128'>Cláudia Rodrigues Maia; Antô=
nio
Carlos Gastaud Maçada; Guilherme Lerch
Lunardi
|
IS=
SN
2237-4558 •<=
/span> Navus • <=
/span>Florianópolis
• SC • <=
/span>v.
16 • p. 01- |
|
|
ISSN 2237-4558 • Navus • &n=
bsp;Florianópolis •
SC • v.9
• n.2 • <=
/span>p.
XX-XX • abr./jun. 2019 |
|