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Utilitarian Use of Social Media Services: A Study on Twitter
Dicle Berfin Köse
University of Jyväskylä
dicle.b.kose@jyu.fi
Alexander Semenov
University of Jyväskylä
alexander.v.semenov@jyu.fi
Tuure Tuunanen
University of Jyväskylä
tuure@tuunanen.fi
Abstract
This paper applies structuration theory (ST) and
service dominant logic (SDL) as lenses to study different
uses of information systems (IS). We argue that
resources provided by IS may be combined and
reproduced by appropriating them for different
purposes than the design purposes of the IS. The study
provides empirical data and analysis to showcase the
use of resources for utilitarian purposes in the context
of social media services (SMS). Through an analysis of
sponsored tweets on Twitter, we show that users employ
implicit and explicit resources for utilitarian outcomes.
Our findings imply that users create their own service
through appropriation of resources available in the
social context of service use; hence, they induce
different adaptations of the information system.
1. Introduction
IS have branched out from organizational contexts
and they are now used in different aspects of daily life
for various purposes. Examples include but are not
limited to social networking services, video games, e-
commerce websites, online banking services and
crowdsourcing platforms. One important characteristic
of these services is that they reside in a sphere in which
the service provider has no direct control over how users
utilize the information system. In addition, the
flexibility that has come with Web 2.0 (i.e., enablement
of users as content creators) has provided users the
freedom to decide how to use IS according to their own
imagination, needs and purposes.
This ability to determine the benefit, and the lack of
control in IS use enable their adaption for various
purposes. Utilitarian IS may be used for hedonic
purposes, hedonic IS may be adapted to utilitarian use;
moreover, resources external to IS may contribute to
these different uses. One case of such adaption is
utilitarian use of SMS for monetary purposes. For
instance, some Twitch broadcasters make a living out of
streaming their games through tips, subscriptions and
even through merchandise or sponsorships [1].
Instagram and Facebook host a growing number of
people who earn money by posting advertisements of
brands [2, 3]. Facebook is launching its marketplace tool
to ease buying and selling as a result of the fact that one
quarter of the site’s visitors trade on it, and there are
more than 450 million buying and selling groups [4].
The mixing of hedonic and utilitarian values in IS is
growing, and previous research has looked into effects
of hedonic and utilitarian value on technology
acceptance (e.g., [5, 6]), the change of use motivation
over time (e.g., [7–9]) and use of the same information
system for both hedonic and utilitarian purposes (e.g.,
[10, 11]). However, no previous study was found
regarding resources conducive to these different uses of
IS. Accordingly, the research question of this study is,
What resources contribute to utilitarian use of IS,
particularly SMS?
With this purpose in mind, the study investigates
utilitarian usage of Twitter by screening sponsored
tweets posted by people. Here people refers to those
who are not celebrities and is distinguished by the
absence of the “verified” badge provided by Twitter.
Sponsored tweets provide monetary gains to their
owners; hence, they are a source of utilitarian value in
the form of extrinsic rewards. Sponsored content was
chosen for analysis instead of other utilitarian use types
because today users are bombarded with information
and there is increasing concern regarding the
transparency of this information and what is genuine
content and what is not. Above all, owners of this type
of content make use of various resources to receive
monetary gains from their social media accounts.
Therefore, to study this, we analyzed accounts posting
these tweets to extract their profile characteristics and
tweeting behavior, which in turn helped identify what
resources they used.
The study is explorative in nature; however, it draws
on ST [12] and SDL [13, 14] for analysis of the data and
presentation of the results. Based on our findings, we
argue that utilitarian use of SMS is enabled by resources
available within the social context of service use and by
the different meanings people attribute to these
resources. Different combinations of resources and
values result in separate uses of the same service. From
that perspective, ST is suitable for analyzing the
Proceedings of the 51st Hawaii International Conference on System Sciences |2018
URI: http://hdl.handle.net/10125/50019
ISBN: 978-0-9981331-1-9
(CC BY-NC-ND 4.0)
Page 1046
processual change of IS use by treating them as social
systems that interplay with their users. On the other
hand, SDL provides a motivational perspective (values
of the IS users) and concentrates on operating on
resources in contrast to controlling resources as in ST
[15].
The results inform us about the kinds of resources
people employ in the utilitarian use of SMS, particularly
in the case of Twitter and sponsored content. Previous
literature has focused on usability and functionality in
terms of IS features (e.g., TAM studies) and has
emphasized the organizational or educational contexts
when studying utilitarian use. However, the findings of
this study suggest that utilitarian use of IS is not bound
to these contexts; moreover, it is also resources external
to IS that enable their utilitarian use. The analysis shows
that people employ both direct and indirect network
externalities together with online identities as resources
in the case of sponsored tweets.
We argue that acknowledging the utilitarian input of
resources other than IS features is important for several
reasons. First, it enhances knowledge on IS use. Second,
it provides a new standpoint for IS design and user
engagement. And finally, it offers foresight into how IS
may diverge from their design purpose.
The paper is organized as follows. In the next section
we briefly introduce online advertisements. Thereafter,
we provide the theoretical background of the study. This
is followed by the research methodology and results.
Last, we discuss the findings and our conclusions and
consider the study’s limitations and potential avenues
for future research.
2. Online advertisements
The Internet is more or less a level playing field for
advertisements, as it provides fair reach to resources;
besides, establishing an initial presence online is
relatively easy and low cost, and it provides reach to an
international audience [16]. Furthermore, online social
networks facilitate this usage by providing a platform
comprised of networked people. According to The
Economist [17], publishing advertisements on social
media accounts is a growing business among celebrities.
Yet, it is not only the celebrities who get sponsored for
advertisements. People who are not of public interest
have also started using social media for monetary gains.
Consequently, there is increasing control of online
sponsored content. The Federal Trade Commission
(FTC) [18] states that online advertisements including
those on social media need to incorporate clear and
conspicuous disclosures. Furthermore, they elaborate
that the disclosure should exist in each advertisement in
a space-constrained ad, like those in tweets. Likewise,
the Word of Mouth Marketing Association (WOMMA)
recommends that, alongside the existence of a material
connection between the speaker and the company and/or
brand, disclosures should be made not only for ethical
and responsible communication but also to avoid
monetary, regulatory or legal risks [19].
One way to make money via online accounts is to
publish sponsored content (e.g., sponsored tweets).
Sponsored tweets are messages posted on Twitter and
sponsored by an advertiser to create word of mouth with
the aim of reaching potential customers. There is a
growing business network around social media
advertising; for instance, services such as adly and
SponsoredTweets bring together advertisers and
advertisement publishers. Relatedly, Park, Lee, Kim and
Chung show that online advertisements are more
effective when the publisher has a large audience (i.e.,
has a high number of followers) and is actively engaged
with the online social network (i.e., publishes a high
number of posts) [20]. Other services (e.g., Hootsuite,
quintly) enable brands to measure and boost their social
media impact, identify key interacting users, conduct
tweet analysis and perform many more activities.
3. Theoretical background
Here we review ST [12] and SDL [13, 14] to explain
the adaptation of SMS. The term adaptation stands for
emergence of new use types in the context of IS. In this
explanation, ST helps us understand the processual
change of SMS use, and SDL elucidates the role of
values and resources in this change.
First, ST was developed by Giddens [12] to explain
the recursive change in social systems through reflexive
and knowledgeable actions of human agents. It has been
applied to IS in many studies that investigate the
processual change of IS and IS use by their users’
adaptions (see, e.g., adaptive structuration theory [21]).
In our study, we will take on four main concepts of
ST: agents, structures, systems and the duality of
structure. Agents are the knowledgeable human actors
who act purposefully, rationally and by monitoring their
actions reflexively. Structures are the rules and
resources of a social system. They both enable and
constrain people’s actions and, at the same time, are
recursively formed by these same actions as properties
of the system. As for systems, they comprise the
relations and regular practices of actors and
collectivities that are organized and reproduced in
interaction settings. The last concept, duality of
structure, considers that the properties of social systems
transform recursively as a result of the practices they
accommodate; they are both a medium and an outcome
of these practices.
Page 1047
IS are combinations of rules and resources designed
for specific purposes. Yet, different configurations of
these IS rules and resources suited to given conditions
result in different types of uses. In this process,
acknowledging the knowledgeable and reflexive nature
of human agents provides an understanding of their
interpretive uses of IS [22]. At this point, SDL’s
customer-centric approach to IS provides a better
understanding of IS adaption into different uses and
their corollary adaptations.
SDL emphasizes customer-determined value
through the application of resources for the benefit of a
party or the party itself [13, 14]. According to SDL,
value is a judgment of the increase or decrease in the
well-being of an entity in some respect and is an
experiential concept determined individually and
contextually. It is the apprehension of the resource
integration process and the result of service experience.
According to SDL, users derive two types of value
from IS: utilitarian and hedonic. Utilitarian value is
incentivized by IS users’ extrinsic motivations [6, 23].
It is driven by conscious pursuit of intended outcomes
[24]. Tasks and accomplishments are prominent for
users with utilitarian orientation; hence, they approach
IS use rationally [25]. Instead of being an end, IS usage
becomes a tool for achieving a goal; therefore, usability
and functionality gain importance. Within utilitarian IS,
user efficiency and performance are prominent, and
hence, utilitarian value is quantifiable in terms of
objective measures [26].
On the other hand, hedonic value is driven by
intrinsic motivations [6, 23]. It represents activities
pursued out of inner interests without external pressures
[27]. Hirschman and Holbrook view it as the essence of
consumers’ psychological experience [28]. They state
that it is about emotional arousals, multisensory images
and fantasies; in other words, the activity may cause
historic imagery through reminders of past events or
fantasy imagery by evoking users’ imaginations.
Moreover, they state that hedonic value is affected by
the social aspects of consumer experience; therefore,
instead of its objective attributes, what the information
system represents gains importance. For these reasons,
hedonic value is a subjective concept and difficult to
measure [26, 28].
Hedonic or utilitarian, value is determined in use and
stems from the application of operant resources that may
be transferred through operand resources. Operand
resources are physical, static and finite materials and
may be manipulated for beneficial use. They are mostly
natural resources that become a resource when humans
find a use for them. On the other hand, operant resources
are intangible competences (i.e., skills and knowledge)
that act on operand resources to produce effects. These
effects may enhance the value of physical properties or
reproduce operant resources [13].
SMS create their own version of social systems with
their underlying programming code, relevant end-user
license agreements and terms of service [29]. What is
more, their flexible nature in terms of the miscellaneous
resources they provide enables their adaption for
different purposes. Users of these services determine
and propose value by utilizing these various resources.
The resources they employ may be their own skills and
knowledge, or they may also be of an operand nature.
As Hilton and Hughes put it, IS embody codified
operant resources of the service provider, and these
embedded resources become operand resources for the
benefit of IS users [30].
Among the resources available on SMS are network
externalities, presentation of online identity and features
intrinsic to the social media services.
The value consumers derive from a service is
dependent upon other agents in the service network [31].
When the value of membership is positively correlated
with the number of other users or the network size, those
markets are said to exhibit “network effects” or
“network externalities” [32]. Network externalities are
often conceptualized with two constituents: direct
network externalities and indirect network externalities.
Direct and indirect network externalities are extrinsic
attributes of a service, compared to its intrinsic attributes
such as its functionalities, technical specifications or
accessibility [33].
Direct network externalities stem from other users of
the service [31, 34]. In the context of SMS, it may be
conceptualized as, for example, the number of contacts
on the service. Zheng, Salganik and Gelman found that
the median number of acquaintances one has is 610,
with 90% of the population having an expected number
of contacts between 250 and 1710, according to their
analysis of 1370 individuals in American society [35].
However, through SMS, the number of people one can
reach or have in one’s circle may be tens of thousands
or even millions.
Indirect network externalities occur as a result of
complementary services related to the original service
[31, 34]. These complementary services enhance
perceived value for users, as they augment available
actions [36]. For instance, the Twitter developer
network offers various services by making use of
Twitter data and developer tools. One example is
quintly, which provides services for follower and tweet
analysis, interaction analytics, customer care metrics
and identification of key interacting users [37]. Another
example is Hootsuite, which offers services for
measuring and boosting Twitter impact [38].
Finally, online identity is another important resource
on SMS. This is because these services provide a
Page 1048
Figure 1. Adaptation of social media services
separate medium than the offline world for identity
production. Their features enable the portrayal of
different identities formed through concepts of lifestyle,
connections and media consumption [39]. In Sundén’s
words, they allow members to “type oneself into being”
[40]. As Miller indicates, one type of online identity
presentation is self-promotional, which is similar to the
display of an electronic curriculum vitae or of services
provided by the person [41]. However, this display is
dependent upon the sense of an actual and imagined
audience [39]. According to the imagined audience,
profile owners use various methods to target different
followers, balance authenticity and perform self-
censorship [42].
By recognizing these resources and applying their
own competencies, agents use SMS for hedonic or
utilitarian values. Proliferation of these uses, in turn,
results in their acceptance and integration into the
service. This is a recursive process in which users’
activities on the service change the service, which in
turn affects again how people employ the information
system. Figure 1 above depicts this process: Users of the
social media service become proponents and
determinants of value, and according to the type of their
usage, they employ different sorts of resources. The
social media service, in turn, provides hedonic or
utilitarian value according to how it is used. Users’
choice of resources is depicted with the arrow from
agents to structures, and the employment of varying
resources to deliver hedonic or utilitarian value is
indicated with the arrow from structures to systems. Yet,
it should be noted that these arrows do not represent
causal relationships; rather they indicate that users
“determine/provide” value according to the resources
they “identify.” The loop displays the interplay between
social media users and the service. As users’ different
employments proliferate, they become part of the
service’s value proposition.
4. Research methodology
The methodology of this research study analyzed
data directly extracted from Twitter. First, we collected
data in two different ways to encompass various uses.
Second, we applied filtering and randomization
techniques due to the amount of collected data so that it
is feasible to manually analyze the data set. The
following sections describe the methodology in detail.
4.1. Data collection
Data collection was done in two steps. The first step
involved collecting Twitter accounts created in the name
of G20 leaders and the top 21 Twitter accounts with the
highest number of followers according to Twitter
Counter [43]. These accounts were collected because
public identities were deemed to be resources for online
identity presentation on SMS. The second step involved
streaming tweets that contained advertisement hashtags.
For the first step, the collection of accounts and
tweets for public personas was conducted during the
third week of February 2017. In order to extract Twitter
users’ screen names, we used the Twitter API function
“users/search” [44]. This API function returns 1000
accounts with a matching full name or other criteria. We
used the first and last name of each public person in
quotes as the function parameter; therefore, we
performed 42 requests. However, as each query returned
a maximum of 20 results, we had to query the API
Page 1049
function repeatedly. As a result of the execution of the
aforementioned procedure, we obtained a list of Twitter
screennames corresponding to the names of the 42
public persons in question. Next, we used the API call
“statuses/user timeline”; this call retrieves data on the
3200 most recent public Tweets of a user specified in
the parameter. Data are represented as a JSON
(JavaScript Object Notation) object containing a
number of fields, such as status text, date, information
on retweet, and so forth. We refer the reader to [45] for
a detailed description of the object. The collected data
were stored in MongoDB NoSQL database [46]. The
result of this collection is presented in Table 1 below.
The collection of profiles created in the name of the
selected 42 personas resulted in a total of 28,529 Twitter
accounts; their timeline posts totaled close to 36.6M
tweets.
Table 1. Descriptive statistics for accounts and tweets
collected per public persona
Statistics
Accounts
Tweets
Mean
679.26
871,177.1
Standard deviation
400.62
846,194.3
Min.
0
0
Max.
1,000
2,438,334
Median
988
664,058.5
Sum
28,529
36,589,439
The second step collected tweets according to their
hashtags using streaming API. For hashtags, the words
“advertisement” and “sponsored” and their
abbreviations were used. These two words were chosen
because they were the two referred examples in both
FTC regulations and WOMMA guidelines. The
hashtags were chosen according to their usage
frequency. A cross-check of the hashtags via top-
hashtags website [47] showed their popularity as
demonstrated in Table 2 below.
Table 2. Popularity of advertisement hashtags
Hashtag
Usage
Amount
Hashtag
Usage
Amount
#ad
3.38M
#sp
10.15M
#advert
152.0K
#spon
101.2K
#advertisement
580.8K
#sponsored
577.5K
Hence, #ad, #sp, #advertisement and #sponsored
hashtags were used for the collection: tweets that
contained these hashtags were collected for 24 hours
together with the profile information of their owners. As
a result, nearly 72K tweets were collected.
4.2. Data analysis
Analysis of the two data sets was done separately.
Analysis of the first data set was conducted in two steps:
The initial step distinguished unverified profiles that
have advertisements in their tweets; the second step
analyzed characteristics of these advertising profiles
from qualitative aspects.
4.2.1. Analysis of the first data set. The initial phase
of analysis commenced by querying tweets containing
strings and hashtags that indicate an advertisement.
Indication of the advertisement was established by the
existence of “advertisement” and “sponsored” words,
their abbreviations and hashtags. To this end, the queries
extracted tweets that contained strings of “ad,” “advert,”
“advertisement,” “sp,” “spon” and “sponsored”; and
their hashtags “#ad,” “#advert,” “#advertisement,”
“#sp,” “#spon” and “#sponsored.” The numbers of
tweets resulting from these queries are presented below
in Table 3. Due to the high number of tweets in “ad” and
“sp” files, they were downsized to their 10% by
randomization. In the end, there were 3661 tweets in the
ad and 854 tweets in the sp file. In conclusion, a total of
12,796 tweets were analyzed.
Table 3. Results of tweet queries
Query String
Number of
Tweets
Query String
Number of
Tweets
ad
36,793
sp
8,348
#ad
2,262
#sp
302
advert
1,427
spon
0
#advert
12
#spon
426
advertisement
960
sponsored
2,363
#advertisement
6
#sponsored
523
The resulting tweets from the queries were analyzed
manually in order to distinguish whether they were
advertisements. During this content analysis, tweets
showing certain characteristics were eliminated. For
instance, tweets in the “advert” and “advertisement”
files mostly stated opinions about running
advertisements, so they were not sponsored tweets. “Sp”
mostly stood for São Paulo, or a political party. The
word “sponsored” was also used to share externally
sponsored events: announcing an event that was
sponsored—these weren’t classified as advert. Besides
this, there were topics related to politics and government
that included phrases such as “government sponsored,”
“state sponsored,” “sponsored terror” and “sponsored
bill.” Advertisements for jobs were discarded, as it was
assumed that they weren’t sponsored. Tweets from
Page 1050
accounts that were sharing their own sponsorship were
not counted as sponsored. Posts related to giveaways or
sweepstakes were also eliminated.
After screening the tweets, corresponding profiles
were marked. Only unverified profiles were transferred
to the second step of the analysis due to the assumption
that verified profiles belonged to people who are
assumed to act in the interest of the public.
In the second phase, compiled advertisement tweets
and their associated profiles were analyzed once more
to ensure that they were posting sponsored tweets.
Profiles were coded and classified according to the
purpose stated in each profile bio; tweets were coded
according to the type of advertisements and Web links
they posted. Profiles were also analyzed according to
other profile information, which included profile
picture, profile name, number of contacts (followers and
followings), profile creation and last activity date,
number of tweets, number of retweets, number of
received retweets and posted URLs, photos and videos.
The profiles that did not have multiple sponsored posts
and did not otherwise indicate an advertising purpose in
their bios were eliminated.
4.2.2. Analysis of the second data set. Prior to analysis
of the second data set, it was first cleaned of retweets;
then, the remaining tweets were downsized to a random
10% of the original, which resulted in 3360 tweets.
Afterward, the analysis followed a different procedure
compared to the first data set due to the amount of
resulting profiles. The tweets were content-analyzed
multiple times, first to distinguish advertisements from
non-advertisements. Then, advertisement tweets were
coded according to the links, the frequency of the same
type of links they posted and the description of the
profiles. For this, the links shortened by Twitter were
reverted back to their original forms. Coding of the Web
links enabled discovery of the services that were used
for advertisements.
5. Results
The results of the analysis show that advertising
accounts used miscellaneous resources. These resources
were of both operand and operant nature. An overall
classification of these resources resulted in two main
groups: network externalities and online identity.
Network externalities were further divided into direct
and indirect network externalities. Furthermore, it was
seen that the accounts used different combinations of
these resources to extract utilitarian value from Twitter.
In most cases, the accounts described the nature and
purpose of the Twitter profile in their bios.
5.1. Network externalities
5.1.1. Direct network externalities. In the context of
Twitter, direct network externalities stem from the
social network reached through Twitter, in other words,
the number of contacts. They enable users to accumulate
potential social capital.
The analysis of the accounts showed that many of
them had a very high number of followers or followings
outside the boundaries identified by Zheng et al.’s study
[35]: 250–1710. In the first data set, 73 advertising
accounts were identified with a mean of 2281.88
followings and 30,976.93 followers, and a median of
1575 and 3691, respectively. Table 4 displays
descriptive statistics for the number of followings and
followers of advertising profiles in the first data set;
Figure 2 shows the frequency of accounts (y-axis) per
number of followers in the specified intervals (x-axis).
As may be seen in the table, there is a substantial
difference in the numbers between the followings and
the followers. This might be due to the prominence of
follower numbers for the social reach of advertisement
tweets.
Table 4. Descriptive statistics for following and
follower numbers in the first data set
Statistics
Following
Follower
Mean
2,281.88
30,976.93
Standard deviation
3,007.24
163,052.10
Min.
0
91
Max.
21,263
1,375,260
Median
1,575
3,691
Figure 2. Frequency of accounts according to their
follower numbers
5.1.2. Indirect network externalities. Twitter provides
its users with extending indirect network externalities.
An increasing number of consumers use Twitter in
combination with other online services or
Page 1051
complementary services. In the case of advertisements,
these services may be grouped as other SMS, e-
commerce websites and services combining their value
offerings with the Twitter service.
Among the SMS that were used were YouTube,
Instagram and paper.li. Accounts shared their content in
multiple channels. E-commerce websites were used for
the sale of merchandise. For instance, in the second data
set, more than 10% of the tweets were linked to products
sold on Amazon. Amazon and eBay have affiliate
marketing programs called Amazon Associates and
eBay Partners, respectively. Members of these programs
may earn money when their website visitors or social
followers click their advertisement links and make
purchases [48, 49]. Others posted advertisements for
merchandise sold on websites such as mercado livre,
FUT Fanatics and others.
In the second data set, more than 55% of the tweets
linked to short domains that differed from Twitter’s link
service. Therefore, we may argue that short domains
seem to be a common method to increase webpage
click-through rates (i.e., the ratio of clicks per views an
ad receives).
Finally, there was also an account with a
subscription to SponsoredTweets. SponsoredTweets is a
service that brings together brands and consumers; in
this connection, brands tap into consumers’ networks
for advertisements, and consumers get compensated for
publishing commercials [50].
5.2. Online identity
The analysis showed that online identity was used as
a resource in two ways. In the first case, the presentation
of identities concentrated on professional skills such as
being a designer or photographer. Also, it emphasized
expertise in specific topics like beauty, fashion,
decoration, recipes, gardening and fitness. Accordingly,
the advertisements posted by these profiles related to the
specialization of the profile owner. Some examples of
bios belonging to these profiles are as follows:
Wife & Mom. Creator & Photographer. Sharing
fun and frugal decor, recipes and gardening. @eBay
Influencer. Shop my designs: @society6 and @etsy.
#TravelBlogger & #LifestyleBlogger | Aspiring
Expat. Thrill Seeker. Animal Collector. Wanna be
Chef. Follow along: https://t.co/SE1axfIijv
In the second case, public personas (e.g., G20
leaders), entities (e.g., sports clubs) or hobbies (e.g.,
gaming) were utilized in the construction of the Twitter
profile. They named the profile in the name of these
public personas or entities and described the purpose of
the profile in the bio as providing news or a fandom
base. Some of them sold merchandise (e.g., posters, t-
shirts) about these public identities or entities. Some
examples of bios belonging to these profiles are as
follows:
Los Angeles Lakers News
Latest news from r/gaming. Posts may include
Amazon Affiliate Links, use them for your next
purchases at no additional cost:
We post live news & updates about Taylor Swift,
her appearances, events and concerts. Stay tuned for
photos, videos, set lists, & more!
#Collectibles About #NBA #Cleveland
#Cavaliers #LeBronJames #Sports #Shopping
#Bargains #Deals #eBay #Hot #Sales #Discount
#Deal #Sporting #Basketball #LeBron
Daily updates on everything Demi Lovato! Demi
rt'ed 7/12/11 & 4/3/14 в™Ў Store:
https://t.co/QKeCT1xqyI
Latest Celebrity News, Celeb Gossip &
Celebrities Stories. Get it all at
http://t.co/DfSYnbolBy Watch videos with the latest
celeb stories!!
The reason these accounts exist could be that
identities of public personas and entities as symbols or
representatives of particular ideologies or lifestyles
could be used as a networked resource due to their
potential value for attracting attention.
6. Discussion
Shaped by the social and cultural habits of its users,
SMS are sensitive to changing customs surrounding
them [51]. What’s more, they provide their users with
various resources that may be utilized for different
purposes. These include but are not limited to their
interface features and social nature. In the case of
Twitter, hashtags, retweets and mentions—which
enable communication within itself and across other
platforms—or its programming interfaces that are open
for developers are examples of these resources.
SMS are prone to be adapted for different purposes
due to their user-generated content, network of users and
the resources available to these systems. Although they
might have been designed for hedonic use, their feature
set enables their adaption for utilitarian purposes. Or, as
people become more affiliated with the social media
service, hedonic motivations lose importance, and
utilitarian purposes, which are enabled through existing
or add-on features, gain prominence in their usage [10].
Previous studies mainly concentrated on their hedonic
use and have found that social networking sites are used
for utilitarian purposes of immediate access and
coordination [10]; they have also found that direct
network externalities, in terms of people already known,
and indirect network externalities fortify both their
utilitarian and hedonic value [36].
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However, neither SMS’ utilitarian use nor the
resources that contribute to this type of adaption have
been widely studied. The aim of this study was to
uncover what resources were employed in the utilitarian
use of SMS, more specifically for monetary gains
through sponsored advertisements. The study was
conducted by qualitatively analyzing tweets and the
Twitter accounts posting them.
The findings of this study show that people use a
variety of resources to earn money via SMS. Network
externalities is the first type of resource that contributes
to monetary gains on Twitter. Direct network
externalities in the form of a high number of contacts is
beneficial for increasing social media reach. The
number of followers and/or followings of advertising
accounts in the first data set was outside the average
number of contacts (250 to 1710) identified by [35].
This implies that, in contrast to the findings of Lin and
Lu’s [36] study, unfamiliar people contribute to the
utilitarian use of SMS.
In terms of indirect network externalities, three types
of resources were observed in the case of Twitter: other
SMS, e-commerce services and services combining
their offering with the Twitter service. Profiles used
Twitter together with other SMS such as Instagram,
paper.li and YouTube. In addition, there were many
accounts with a high number of posts with links to
products sold on Amazon or eBay. Furthermore,
SponsoredTweets was another service used for
monetizing SMS by people.
The ability to present one’s identity in desired ways
was another type of prominent resource employed in the
utilitarian use of Twitter. There were two kinds of
identity presentation in this case. In the first case, the
accounts presented themselves as specializing in certain
topics such as fashion, decoration or cooking. In the
second case, the profiles were constructed to provide
news about a human or nonhuman entity (e.g.,
celebrities, sports clubs). In a way, providing news
about these entities was the value offering of the account
owner for his or her followers.
In line with the framework presented in Figure 1, the
utilitarian use of Twitter is argued to be enabled by
various resources. In the case of sponsored content,
people utilize network externalities and online identity
as operand resources to get utilitarian value from the
Twitter service. In addition, their creativity, skills and
knowledge played the role of operant resources by
combining the offerings of the Twitter service with
other complementary services and contextual resources.
This way, they both determined and proposed value on
Twitter. Table 5 displays example combinations of these
resources in the case of utilitarian adaption of Twitter
through sponsored content.
Table 5. Resources and utilitarian use
Resources
Example
Online Identity + Direct NE
Newsfeed about celebrities
Online Identity + Direct NE
Presentation of skills and
knowledge
Indirect NE
Advertisements via
SponsoredTweets
Online Identity + Direct NE
+ Indirect NE
Advertisements of
merchandise about
celebrities via eBay Partners
7. Conclusion
This research applies ST and SDL to explain
utilitarian use of SMS. Previous literature investigated
effects of utilitarian and hedonic motivations on user
acceptance, and emphasized usability and functionality
when it comes to the utilitarian value of IS. In this study,
IS are seen as social systems that provide interaction
settings for people to engage in reproduced relations and
regular practices. These relations or practices that are
hedonic or utilitarian in nature shape IS use through
different applications of contextual resources. In
addition, this study shows that resources external to IS
may also contribute to their utilitarian value. In this
sense, the combination of ST and SDL provides a new
perspective for IS research and explains how IS may be
adapted for different uses other than their design
purposes. Another contribution of the study is that it
shows that the combination of resources may result in
different values for different people. Therefore, this
approach is also practical for forecasting alternative uses
of IS. Furthermore, a resource-based analysis of IS may
foresee its possible uses. It may be possible to minimize
the unprecedented consequences of IS use by
deconstructing its resources to anticipate how the
service may be utilized and how it may be combined
with other resources in its context of use.
Yet, no study is without limitations. First, we see
some limitations arise from the study’s Twitter data
analysis. In other words, we do not at this moment have,
for example, interview-based data to support the
findings. However, collected data are in essence user
generated; therefore, it does compare to self-reported
surveys or open-ended questionnaires. The applied
methodology here followed guidelines given for
qualitative analysis of social media data in IS research
[52]. This also provides uniqueness to the study and may
be presented as an example for the use of rich social
media data in qualitative research in the IS field. A
second limitation arises from the selection of the search
words (i.e., advertisement and sponsored). We
acknowledge that this narrows down the search results;
for instance, advertisement tweets labeled with
Page 1053
“promotion” or non-labeled advertisements were not
captured. However, the purpose of this study was not to
cover all instances of sponsored tweets, but rather to
illustrate the role of resources in utilitarian use of
Twitter. We believe that studying posts with, for
example, “promotion” would increase the variety and
amount of resources that contribute to this kind of usage
of the service. It should also be noted that we only
analyzed Twitter use, and only for sponsored content,
which limits the applicability of the findings to other
SMS use and other IS use in general.
Future research may investigate different types of
utilitarian uses of hedonic IS, such as the use of games
for educational or health-care services. Otherwise, the
reverse—hedonic adaption of utilitarian services—may
be examined. For instance, crowdsourcing services are
good candidates for this purpose. Other research may
look into the impact of IS features on the hedonic or
utilitarian use of IS. For instance, research may look into
differences in feature-level use between utilitarian and
hedonic adaptions.
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