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UNIVERSITEIT GENT
FACULTEIT POLITIEKE EN SOCIALE WETENSCHAPPEN
Wetenschappelijk artikel
Evelyne Blanckaert
MASTERPROEF COMMUNICATIEWETENSCHAPPEN afstudeerrichting COMMUNICATIEMANAGEMENT
PROMOTOR: DR. Peter Mechant
COMMISSARIS: DR. Evelien De Waele-De Guchtenaere
ACADEMIEJAAR 2014 – 2015
Social branding on Twitter: How global brands are using Tweets to interact with stakeholders
Aantal woorden: 9853
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Samenvatting
Deze studie onderzoekt aan de hand van een kwantitatieve inhoudsanalyse hoe 16 globale commerciële
merken Twitter inzetten als sociaal media kanaal. De resultaten tonen aan dat de primaire functie van
Twitter een “community-‐building” functie is, waarbij dialogische conversatie centraal staat. Echter blijkt
wel dat de mate van betrokkenheid van gebruikers minder hoog is bij “community-‐building” tweets in
vergelijking met actie en informatie boodschappen. Bovendien blijkt uit de resultaten dat de globale
commerciële merken verschillende levels van interactiviteit vertonen. Bij bijna 70% van alle tweets
verloopt de interpersoonlijke communicatie reactief door het gebruik van @Replies. Voor het gebruik
van hyperlinks, hashtags en media, moeten we besluiten dat bijna de helft van de tweets geen enkele
vorm van deze interactieve middelen bevat. Nochtans zullen gebruikers een hogere mate van
betrokkenheid vertonen wanneer tweets een combinatie bevatten van minstens twee interactieve
middelen.
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Abstract
This study aims to generate an overview of the different functions and interactivity levels, which global
commercial brands assign to their social media. In order to answer this question, the communication of
16 global brands was studied on Twitter. To this end, a digital scraping technique was used to extract
data from Twitter and a quantitative content analysis was set up. During 4 weeks 4629 tweets, drawn
from 16 official Twitter pages, were scraped from the web. Results showed that global commercial
brands are using Twitter mainly as a community-‐building platform and less as an action or informational
communication platform. Remarkably, the results also show that Twitter users show a higher level of
engagement for action and informational messages, than they do for community-‐building messages.
When looking into the different levels of interactivity, this study reveals that the global commercial
brands are using high degrees of interpersonal interactivity, though mainly reactive in nature, with
almost 70% of the tweets being @Replies. In terms of machine interactivity this study found that
approximately 40% of tweets did not contain any interactive features, such as hashtags, hyperlinks or
media. Even though, users showed a higher level of engagement when at least two interactive features
are combined. Overall the results show that global commercial brands are embracing the potential of
social media to a higher degree than is previously reported for non-‐profit and governmental
organisations.
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Introduction
Social media, such as Twitter, have reshaped the possibilities for organisations to communicate with
their stakeholders. Compared to the use of traditional media, this type of media enables organisations to
communicate in a more efficient and direct manner, especially with consumers (Kaplan & Haenlain,
2010). But the introduction of social media has also altered the expectations of consumers, in that they
have come to expect that organisations use the interactive and dialogical potential to deliver two-‐way
conversations (Kietzmann, Hermkens, McCarthy & Silvestre, 2011). The number of social media accounts
has increased by 12% globally, equalling 222 million new users worldwide in 2014 (Kemp, 2015). Twitter
was founded in 2006 and to date it is the third most popular social media channel (Duggan, Ellison,
Lampe, Lenhart & Madden, 2015). Although Facebook is still the number one platform, its overall growth
in the number of active users has decreased in 2014. However Twitter is still growing, with an increase of
7% in active users in 2014. For marketing and communications professionals Twitter is reported as the
number two social media platform for marketing (Stelzner, 2014). Although Twitter is only ranked
number two, following in the footsteps of Facebook, the platform is more suited for branding purposes
than Facebook. In a study by Smith, Fischer and Yongjian (2012) the use of Facebook, Twitter and
Youtube were compared. The study indicated that Twitter is used more often to publish content relating
to brands than Youtube and Facebook. Furthermore, it appears that more than one third of consumer
activities on Twitter are directly related to brands, products or companies, such as following, tweeting
comments and visiting their feeds. This brand centrality on Twitter makes it an important platform for
brands and companies to incorporate into their business activities (Stelzner, 2014).
Previous studies analysing the use of social media by organisations have been focused on
communication managers and professionals to get insights into the motivations for adopting social
media (Alikilic & Atabek, 2012; Eyrich, Padman & Sweetser, 2008; Kitchen & Panopoulos, 2010). Other
research sharing the same focus has looked into the behavioural effects social media elicits on those
communication professionals (Diga & Kelleher, 2009; Porter, Trammell, Chung, & Kim, 2007). Although
this kind of research is useful to identify the barriers and effects of social media adoption, it says little
about how social media are being implemented and used by organisations. Research on the actual usage
of social media has been focused on frameworks such as the four models of public relations (Grunig and
Hunt, 1984) and the dialogical communication theory (Kent and Taylor, 1998) to see whether
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organisations are implementing dialogical communication and to rate their overall use of the
relationship building potential on social media (Cho, Schweickart & Haase, 2014; Crijns, Hudders,
Cauberghe & Claeys, 2015; Edman, 2010; Rybalko and Seltzer, 2010; Saxton & Waters, 2014; Waters and
Williams, 2011). Most of these studies have highlighted the underuse of reciprocal communication and
the lack of interactivity shown by organisations.
However to date there are only a few studies that focus on the actual messages that are being sent on
social media. The first study to analyse the content of messages on twitter send by organisations was
conducted by Lovejoy and Saxton (2012). Through this analysis the researchers were able to identify
three main communication functions that Twitter is being used for. The results of this study indicate that
Twitter is not often being used as a community-‐building platform. It is merely used as an extension of the
traditional media channels made to push one-‐way information and advertising messages. Within these
kinds of studies there is also a clear gap in research focussing on organisations other than non-‐profit
ones and a focus on another type of organisations is needed. This research will therefore focus on global
commercial brands, to analyse if they are also underusing the community-‐building potential and if they
are also lacking in the implementation of interactive communication on social media.
The main goal of this research is to get insights into the use of Twitter by global commercial brands. First
we will conduct an analysis to see for what type of communication functions Twitter is being used.
Secondly, the degree of interactivity will be measured to see if global commercial brands are using the
potential for interactive communication on Twitter. To this end a digital scraping technique and a
quantitative content analysis are combined. First we will start off with an overview of important previous
insights, leading up to the research questions. After our method is explained in more detail, the results
will be presented and closed off by a discussion. This research will end with the description of some
limitations and recommendations for further research.
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Literature review
Social media and brand communication
Many authors have attempted to define social media, but a much cited definition is provided by Kaplan
and Haenlain (2010, p.62), who state that “Social Media is a group of Internet-‐based applications that
build on the ideological and technological foundations of Web 2.0, and that allow the creation and
exchange of User Generated Content”. This definition clearly distinguishes two concepts that are
interrelated to what we call social media today. The first concept, web 2.0, introduced a new version of
the World Wide Web in which end users are seen as valuable developers of content and where
interactivity and continuous adaptation to users needs are deep-‐rooted into its existence. The 2.0
version of the World Wide Web is mostly associated with the technological infrastructure making social
media possible, although it is also marked as a true fundamental shift in the thought process of software
developers and the way end-‐users started using the web (Arnhold, 2010). User generated content is
inherent to the advent of Web 2.0 and can be described as the collection of original information created
and uploaded by users on publicly available websites (Georgescu & Popescul, 2015; OECD, 2007). From a
less technical viewpoint Safko (2010) concludes that social media are the sum of all the activities,
practices and behaviour with the purpose of sharing information, knowledge and opinions taking place in
conversational communities.
The use of social media has become an important component in the communication strategies of
corporations today as it allows firms to engage with stakeholders in a timely and direct manner and at a
relatively low cost (Larson & Watson, 2011). Historically companies’ Internet activities were dominated
by a one to many paradigm where experts disseminated and created information with only limited
opportunities for reciprocity. The introduction of social media however has led to a many to many model
where consumers are also empowered to create, share and inform (Kaplan and Haenlein, 2010). This
caused a shift in the expectations of consumers, who are looking to create dialogical conversations with
organisations (Kietzmann et al., 2011). Companies are pressured by these expectations and professionals
are to consider the empowered consumer by implementing those social platforms in their
communication strategies and by participating in a world dominated by influential stakeholders. As more
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and more consumers will search and disseminate information through social media, it will become even
more crucial for companies to keep pace with different social media outlets (Larson & Watson, 2011).
It is evident that social media have reshaped the way traditional marketing and communications
operated on the web, with less control over what is being said and over the way brands are being
perceived. The loss in control that has been offset by social media can be counterbalanced by providing
the platforms, and by using those tools to engage with stakeholders and, as such, to create a community,
where participation, co-‐creation and conversation are encouraged (Mangold & Faulds, 2009). This
activity, whereby a company does not merely provide social platforms, but also embraces the need for
creating authentic conversations, shares a variety of content and gives its brands a human tone of voice,
is called social branding (Guldemond, 2011). Firms need to step away from treating their social media
platforms as an extension of their own work stream. Instead they need to incorporate the shifted
expectations of consumers and adapt their brands to social brands fostering dialogue and consumer
participation (Walsh, 2013). If managed well, this social media presence can act as an outbound channel,
complementing internal corporate services like marketing, advertising and customer service (Sollis &
Breakenridge, 2009). Firms that encourage social media conversation and treat consumers as their peers
will foster an environment that could have a significant impact on a firm’s reputation, sales and survival
(Kietzmann et al., 2011), through the enhancement of brand loyalty (Erdoğmuş & Cicek, 2012).
How organisations are using social media
Regarding the corporate use of social media there are different research streams that can be identified.
Few studies focus on the adoption of social media by PR-‐professionals (Alikilic & Atabek, 2012; Kitchen &
Panopoulos, 2010; Eyrich et al., 2008) and the impact it entails on those professionals (Diga & Kelleher,
2009; Porter et al., 2007). Through the use of surveys these researches gather data to explain social
media adoption and consequences. Kitchen and Panopoulos (2009) identified age, trialability and
working experience as various factors affecting the probability of adoption. Overall the studies conclude
that PR professionals appreciate social media, but the adoption varies according to the different types of
social media (Alikilic & Atabek, 2012). Once adopted Diga and Kelleher (2009) reported that social media
can have a significant impact on the structural, expert and prestige power of PR-‐practitioners. However a
previous study by Porter et al. (2007) reported that in a blogging context, this was only significant for
prestige and expert power.
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The focus of this study however is on the external use of social media for marketing and communication
purposes. Only a few researches have analysed how companies implement social media into their
business strategy and how they utilise the different functionalities social media has to offer. These
studies concentrate on different platforms and theories, but they share the common goal of describing
the way companies use social media. Most of this research centres either on Facebook (Crijns et al.,
2015; Cho et al., 2014; Saxton & Waters, 2014) or Twitter (Lovejoy & Saxton, 2012; Rybalko & Seltzer,
2010; Krüger et al., 2012; Waters and Jamal, 2011). This is not surprising considering their popularity
among consumers (Smith et al., 2012).
Most of the research investigating how organisations make use of social media focuses on the
prevalence of the four models of public relations by Grunig and Hunt (1984). These models describe the
possible ways in which companies can communicate with the public. The first two models indicate the
presence of one-‐way communication, but are different in the kind of information that is carried out by
the company. In the first model, the press agentry model, the one-‐way communication consists of
persuasive information, while the second model, the public information model consists of objective and
verified information. The other two models, the two-‐way asymmetrical and the two-‐way symmetrical
model, are classified as interactive communication models. Here the company interacts with its
community by responding to reactions made by the public. The distinction here however, lies in the
response of the company. An asymmetrical model implies that the response of the company is led by the
incentive of its own benefits, while a symmetrical model means that a company responds in order to
create mutual benefits.
Waters and Jamal (2011) analysed 773 tweets from 27 non-‐profit organisations and concluded that those
non-‐profit organisations are primarily using their Twitter account for one-‐way communications. Another
content analysis regarding 60 governmental agencies’ use of Twitter also aligned with these findings, as
they also found that one-‐way communication is the dominant model used by these government agencies
(Waters & Williams, 2011). However, recently the results of a study by Crijns et al. (2015) on the use of
Facebook by 12 Belgian commercial companies, contradicts these previous findings. The researchers
extended the four models of PR with a fifth semi two-‐way communications model where publics will
react to a message without the company responding to this reaction. As a result the researchers found
that most of the communication on the Facebook pages was semi two-‐way communication, followed by
two-‐way communication. This study did not support previous findings as only 20% of all Facebook post
were categorised as one-‐way communication. The findings of an earlier study by Edman (2010) align with
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Crijns et al. (2015). The research analysed 47 commercial corporations’ use of Twitter and found that
symmetrical two-‐way communication was the most used communication model. This indicates that not
just sharing information, but also building relationships through symmetrical two-‐way communications,
is an important function of the commercial organisations’ social media profiles (Crijns et al., 2015;
Edman, 2010). Some studies investigating these four traditional models of PR have also looked at the
level of engagement created by the use of these models. In a research among 36 non-‐profit
organisations, Cho et al. (2014) concluded that in terms of engagement displayed on Facebook, higher
levels of Facebook comments were found for symmetrical two-‐way communication and thus indicating
greater interactivity.
The importance of two-‐way symmetrical communication is also highlighted by its ability to build
relationships with the public (Hon & Grunig, 1999). This relationship function is enabled by the
interactivity that is inherent to social media platforms such as Facebook and Twitter. To investigate
whether social media are utilising the potential for creating relationships with the public, researchers
(Rybalko & Seltzer, 2010; Lovejoy et al., 2012) have been depending on the dialogical communication
theory of Kent and Taylor (1998). This theory identifies five dialogical principles that can be used to build
relationships. By means of the first principle, the usefulness of information, the public is given useful
information about the organisation. Secondly, a company can also invest in the retention of stakeholders
on their social media pages. This principle is called the conservation of return visits. For companies it is
also important to invest efforts into getting people to revisit the social media account. Therefore the
third principle, the generation of returns, will aim at inducing frequent visits. This is a critical principle
when creating a relationship. As a good relationship can only be formed over a period of time, this
implies that reoccurring visits have to take place (Taylor, Kent & White, 2001). The fourth principle is the
ability to create a dialogical loop. This principle can easily be accomplished through the inherent
interactive functions social media carries and makes the creation of a relationship possible by offering
dialogical communications to the public. Social media offers the means for stakeholders to directly reply
to messages and show their interest by using actions such as liking, retweeting and sharing and thus
facilitating dialogue. The last principle, the ease of interface, determines whether a platform is easily
accessible and usable by the public.
Rybalko and Seltzer (2010) examined how 93 Fortune top 100 companies are implementing these
dialogical features on Twitter. Using a content analysis, 930 Tweets were examined and only 61% of the
companies in their sample were considered to be dialogical. Looking at these principles more closely, it
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became clear that dialogical companies showed a greater degree of the conservation of returns principle
than non-‐dialogical companies. However non-‐dialogical companies showed a greater degree of the
generation of return visits. For the usefulness of information principle no difference was found between
dialogical and non-‐dialogical companies. With regards to the most important relationship indicator, the
dialogical loop, results indicated that, although companies frequently react to comments, stimulate
discussing by asking questions and asking follow-‐up questions to stakeholders, they still do not fully
exploit this principle. According to these relationship indicators the non-‐profit Fortune companies on
Twitter do not fully use the relationship-‐building aspect available on social media.
Other studies measuring the utility of the relationship-‐building potential of social media have indicated
the same underdeveloped use of its potential. Waters, Burnett, Lamm, and Lucas (2009) reported that
while non-‐profit companies use social media to disclose information to the public, disseminating
information and creating involvement were rarely used strategies. Bortree and Seltzer (2009) also
highlighted that the environmental advocacy groups in their study did not facilitate true dialogue with
stakeholders. They are merely adopting social media without effectively utilising the dialogical strategies
the platforms have to offer.
What’s in a Tweet? Twitter Functions Revealed
Research about the used dialogical strategies and public relations models have created important
insights into the way social media platforms like Twitter are being implemented by companies. This
research is valuable for determining whether social media are used to their full theoretical potential., but
tell only little about the actual functions the platforms are being used for. To date there is little emphasis
on the most primary feature of social media, i.e. the messages (Saxton & Waters, 2014). By studying
these messages we can gain insights into the communication functions that Twitter is being used for.
To deal with this gap in research, Lovejoy and Saxton (2012) analysed the content of 2437 tweets
belonging to the Twitter account of 73 non-‐profit organisations, in order to create a typology of the
microblogging functions. Their study is the first one classifying Twitter messages on an organisational
level. In their work the authors revealed three major functions of the organisations’ Twitter accounts:
information, community and action. The information function is present when a tweet is sent simply to
inform stakeholders about the organisation and its activities or any other news that might be relevant to
stakeholders. Here the communication is characterised as one-‐way information. A second function that
Twitter is used for by organisations is the community creating function. Here all tweets that aim to build
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relationships through dialogical and interactive communication are considered. Lovejoy and Saxton
(2012) identify two different aspects of this function. The first group of tweets aim at creating interactive
conversations and thereby facilitate dialogue. The second group of tweets are formulated with the goal
of strengthening ties with an online community. This aspect differs from the previous one because of the
absence of interactive expectations underlying the message. Messages such as ‘giving recognition’ are
not formulated with the intent of creating a dialogue, but they are still community building. The third
and last function is action. Here messages are grouped as they all stimulate stakeholders to undertake
specific actions, such as donating money or buying a product. When the typology was clearly defined,
Lovejoy and Saxton (2012) also analysed the frequency in which the selected organisations are making
use of the information, community and action functions. The results indicate that the tweets are mostly
belonging to the informational function and are less community building or action driven tweets.
Nonetheless all three categories are being implement on Twitter, although the informational function
seems to be the primary function for non-‐profit organisations on Twitter.
Crijns et al. (2015) have also tackled this gap in literature, but focused on Facebook instead of Twitter.
Also they chose to analyse commercial companies instead of non-‐profit organisations. The researchers
wanted to know for what kind of corporate communication Facebook is being used. Therefore they
analysed the messages of tweets and classified them according to two distinct types of corporate
communication, being public relations and marketing communication. These two categories differ in
their ultimate goals, as public relations messages will try to enhance the reputation of a company (e.g.
customer service and stakeholder engagement), while marketing communication messages only aim to
boost sales (e.g. discounts and advertisements). They discovered that the analysed Facebook pages were
mostly used as a platform for public relations and less for content related to marketing communications.
Even though these recent studies have began investigating the use of social media by organisations on a
message level, more investigation is still needed to enrich this research domain. In their study, Lovejoy
and Saxton (2012) are recommending future research that investigates a different type of organisation
on Twitter, such as for-‐profit organisations. The study of Crijns et al. (2015) did already study messages
of for-‐profit organisations, but they did so on Facebook instead of Twitter. To date there are no studies
analysing the communication functions of the Twitter accounts of global commercial brands and more
research is needed to fill this gap.
In addition to analysing the organisational use of Facebook by non-‐profit organisations in terms of
information, action or community building communication, Saxton and Waters (2014) also studied the
8
level of engagement that is being achieved through measuring the comments and sharing of Facebook
messages. Studying engagement is important, as it reveals how the social media publics are reacting to
the actions of organisations and brands (Saxton and Waters, 2014). Taking the function of engagement
into account can shed light on the type of communication that is preferred by the social media publics.
Social media engagement can be measured by looking at the number of shares, likes, retweets and
favourites that messages are inducing. Focussing on Facebook, Saxton and Waters (2014) found that the
publics share and comment more on messages that are community building in nature and solicit a desire
for creating dialogue. This is consistent with the findings of Cho et al. (2014) that two-‐way
communication model of public relations is better at creating engagement than the one-‐way information
model. Although to a lesser degree, Saxton and Waters (2014) did find that action messages are also
creating a lot of engagement and they stress the need for future research to focus more on this
communication function. Crijns et al. (2015) also found different levels of engagement for marketing
communications and public relations on Facebook. They concluded that public relations posts were
shared more often than the marketing communications messages. On the other hand, the companies
responded more often to customer reactions about marketing communication posts than otherwise.
In order to fulfil our research goal we need to elaborate on previous research studying the
communication functions on social media and also take into account the level of engagement these
functions are creating. Therefore the following research question is formulated:
Research question 1: For what type of communication functions are global commercial brands using
their Twitter accounts?
• Are the different communication functions leading to a difference in the level of
engagement?
• Is there a connection between the brand industry and the type of communication functions
used?
•
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Tweets and their Interactive Features
In comparison to the more statistic functions of traditional websites, social media are constructed in a
way that they can facilitate interactive organisational behaviour (Saxton & Waters, 2014). Previous
studies all agree on the potential of using the interactive features that social media have to offer (Burton
and Sobleva, 2011; Saffer, Sommerfeldt & Taylor, 2013). But despite the growing importance of
interactivity in corporate communication and the potential of interactive features on social media, to
date there is a lack of agreement on a clear definition of interactivity. As a consequence interactivity on
social media is studied differently according to the definition that is chosen.
Using the interactive features on Twitter can have positive effects on the relationship between
organisations and their Twitter followers (Saffer et al., 2013). By using a quasi-‐experiment Saffer et al.
(2013) measured the effect of interactivity on the quality of the relationship with the public. Here
interactivity was defined as ‘contingency interactivity’ and is described as the interactive role between
the sender and the receiver of a message. On Twitter this kind of interactivity was measured by looking
at the number of replies an organisation had sent to it followers. Results showed that higher levels of
interactivity led to a better quality of relationships with their users on Twitter.
The importance of interactive communication was recognised long before the arrival of social media.
What Saffer et al. (2013) are referring to as ‘contingency interactivity’ has long been incorporated into
the four models of public relations by Grunig and Hunt (1984), where it is embodied as two-‐way
communication models. In these communication models a company interacts with its stakeholders
either in a symmetrical or in an asymmetrical way. Interactive communication is also incorporated into
the dialogical communication theory of Kent and Taylor (1998) as one of the five dialogical principles to
build relationships on. By using the fourth principle, the dialogical loop, organisations create
conversations and stimulate dialogue. A lot of different researchers already pointed out that non-‐profit
organisations aren’t using the two-‐way interactive communication on social media to its optimal
potential (Bortree & Seltzer, 2009; Lovejoy & Saxton, 2012; Waters & Jamal, 2011). Lovejoy, Waters and
Saxton (2012) analysed 4,655 tweets, but could only identify less than 20% of those tweets as interactive
conversations. Yet a recent study by Crijns et al. (2015) did find contrasting results indicating that
commercial organisations are using two-‐way symmetrical communication to a much higher degree than
is stated for non-‐profit organisations.
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However, by only taking into account the use of two-‐way communication as a measurement for
interactivity, these studies do not capture other dimensions related to interactivity on social media.
Besides the dialogical communication aspect of interactivity, Twitter offers a lot more interactive
features that can be employed, such as using hashtags, multimedia and links to webpages. Burton and
Sobeleva (2011) recognised this complexity in studying interactivity on social media by splitting
interactivity into two layers. In their study, organisational interactivity on Twitter is analysed by
classifying tweets according to two contrasting views on interactivity. The first one, the ‘interpersonal
view’ refers to interactivity as being ‘involved communication’ between individuals and/or organisations,
ranging from non-‐interactive communication to fully interactive communication. This vision is equal to
the definition used by Saffer et al. (2013). The second view on interactivity is based on the structure of
the medium. This kind of interactivity is called ‘machine interactivity’ and describes the use of links and
multimedia included into tweets. They studied 12 for-‐profit corporate Twitter accounts and concluded
that the organisations showed a range of different interactive strategies. These strategies were ranging
from highly interactive to merely reactive Twittering by only replying to users instead of using hashtags,
retweets and incorporating media into their tweets to create high interactivity with their followers
(Burton and Sobeleva, 2011). Lovejoy et al. (2012) described the use of hyperlinks, media and retweets in
their study on non-‐profit organisations’ use of Twitter. They found that and 68% of the non-‐profits’
tweets contained hyperlinks, 16.2% were retweets and almost 30% of the tweets contained hashtags.
Most studies that touch upon organisational use of interactivity on social media have been focused on
non-‐profit organisations. Moreover, they have been focusing to a greater extend on the interpersonal
view of interactivity, whilst failing to describe the use of other interactive features (Bortree & Seltzer,
2009; Crijns et al., 2015; Saffer et al., 2013; Waters & Jamal, 2011). Following in the footsteps of Burton
and Sobleva (2011) it would thus be interesting to see whether global commercial brands are indeed
embracing the interactive possibilities on Twitter.
Research question 2: How interactive are global commercial brands on Twitter?
• Is there a connection between the type of industry and the level of interactivity?
• Is there a connection between the communication functions and it the level of interactivity?
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Methodology
The aim of this research is to analyse how global commercial brands are using Twitter to communicate
with the publics. To accomplish this goal a quantitative content analysis is used. This research technique
makes it possible to study the large volumes of data on Twitter and allows for an objective description of
the available data (Krippendorrf, 2013). The data was gathered by using a data scraping technique. The
availability of the Twitter Application Program Interface (API) allows researchers to automatically
retrieve data from Twitter by using custom written scripts. A couple of previous studies have used
custom written Phyton scripts to extract social media data (Lovejoy and Saxton, 2012; Saxton and
Waters, 2014). For this study we used TAGS (Version 6; Hawksey, 2014) to scrape the Twitter accounts.
TAGS is a free to use Twitter Archiving Google Sheet allowing us to scrape Twitter using Google Drive.
Once the specific search requirements are defined, TAGS collects recent tweets and data and
automatically pulls them into a Google sheet, while also updating them regularly. Although there is a
limit in the amount of tweets TAGS can extract, this was not an issue in this research. To ensure the TAGS
sheet was running accurately, a test download was conducted and 50 tweets from the Google Sheet
template were examined to ensure the data file was an exact copy of the activity on the Twitter account.
The extraction was completely accurate for all of the captured tweets.
Sample
Once the research method and data gathering instrument were selected, the next phase in this study
was determining which brands could be selected for our sample. Here we opted to use Forbes “The
World’s Most Valuable Brands” list, consisting of a total of hundred global brands across different
industries. Those brands are most likely to have a strong social media presence compared to non-‐global
and less valuable brands, as they spend a lot of money on company advertising. In this list each brand
was then assigned an industry type and we used this categorisation to select 16 brands belonging to four
different industries: apparel, automotive, technology and beverages. Once the industry types were
selected, the brands were chosen in an order of decreasing value, only if they met our set criteria. To this
end, a one-‐day search strategy was conducted on the 23 of February. The search criteria were adapted
from previous studies (Crijns et al., 2015; Edman, 2010). To meet the first criterion the brand had to own
a global twitter account. We began by searching the brand’s website and added a Google search if the
12
link was not available on the brand website. If a global account was found, there had to be at least one
tweet posted by the brand in the week preceding our search, to ensure activity on the twitter account.
This search strategy was repeated until each of the industry categories consisted of four brands. Table 1
illustrates a list that contains the selected brands.
Table 1 Selected brands
Brand Name Industry Rank
Coca-‐Cola Beverages 4
Samsung Technology 8
Toyota Automotive 9
BMW Automotive 11
Intel Technology 13
Mercedes-‐Benz Automotive 17
Honda Automotive 20
Nike Apparel 21
Budweiser Beverages 23
Pepsi Beverages 28
Nescafé Beverages 29
H&M Apparel 31
HP Technology 36
Audi Automotive 38
Zara Apparel 51
Adidas Apparel 63
We scraped the data using the TAGS script for 1 month, from the 1st of March until the 28th of March.
After a month a total of 4629 tweets were captured. In order to guarantee that our study results are a
realistic representation of the activities of the chosen global commercial brands, all tweets that were
posted during the selected timeframe were coded using the quantitative content analysis. Unlike
previous studies we did not only code a mere subsample of the collected Twitter data (Lovejoy & Saxton,
2012; Saxton & Waters, 2014).
13
Code development
To answer the research questions, a coding sheet was developed. This instrument allows us to make
sense of the large amount of tweets by allocating them to predefined categories. The categorisations
were adapted from previous studies (Lovejoy and Saxton, 2012; Lovejoy et al. 2012) and were redefined
to meet the needs of this specific research. Coding started on the organisational level first, capturing
some of the static information about the brands’ Twitter accounts. To start with, the name of the brand
was coded, as well as the number of its followers. We also coded the number of people the brand is
following. Since this study revolves around the actual content of the tweets, we did not gather any more
information on this organisational level.
For the tweet-‐level analysis coding, we started by specifying the type of tweet that had been used. On
twitter there are different kind of tweets to communicate with the publics. Therefore it is important to
identify these types in order to get a better understanding of how they are being implemented by
brands. The total amount of tweets can be broken down into five different categories (Bruns & Stieglitz,
2013). When a tweet originates from the brand and does not mention any other users, it’s coded as an
Original Tweet. On Twitter however, it is also possible to refer to other users in a tweet. We call this type
of tweets @Mentions. Twitter users can also reply to other tweets, in which case tweets will start with
mentioning the user they are replying to. To distinguish reply reactions from @Mentions, this type of
tweet is coded separately as @replies. Besides these authentic messages, tweets can also be duplicated
from other users. This type of tweet resembles the sharing option available on Facebook. We call them
Retweets and they are identifiable through the presence of the letters ‘RT’ before mentioning the
original sender. It is also possible to add a message into a retweet, to share an opinion on the matter.
This kind of retweet is coded separately as an Edited Retweet.
Once a tweet was categorised into different types, the content of the message had to be analysed to
determine its underlying communication function. Here the coding scheme was adapted from the
previous research of Lovejoy and Saxton (2012) on non-‐profit organisations. The researchers identified
three main communication functions on Twitter: information, community and action. The first function
can be operationalized as one-‐way information-‐sharing type of communication. This type of
communication is used by organisations to inform their Twitter users about company activities, news or
other information that might seem relevant to share with their followers. By using the second
community function, organisations or brands on Twitter try to engage their stakeholders by using
14
dialogical communication or by building relationships. The action function on the other hand, is
operationalized as all the communication efforts that aim at getting users to do something. Here the
organisation has an underlying motive that directly benefits the organisation’s mission and the purpose
is not solely to inform or to create a community.
Each communication function has various subcategories, but because Lovejoy and Saxton (2012)
analysed non-‐profit organisations on Twitter, not all of their identified subcategories can be used to
describe the communication for global commercial brands. Therefore an adapted classification scheme
was needed. To adapt the categorisation scheme of Lovejoy and Saxton (2012), 100 tweets were
reviewed prior to the actual start of the quantitative content analysis. Through this inductive process we
were able to identify the different subcategories that commercial brands use on Twitter. An overview of
the functions and subcategories is displayed in table 2.
To answer research question number two we also needed to operationalize the interactive features that
can be added to tweets. Therefore, each tweet was coded according to the presence or absence of
hashtags, hyperlinks and media. When a hyperlink was used, the type of hyperlink was also examined.
This way more specific information about hyperlinks’ usage could be gained. Hyperlinks were coded into
two groups, either leading to websites or to other social media sites. We also coded for the ownership of
the hyperlink, as hyperlinks can either lead to owned websites or owned social media channels, but also
to websites and social media that are not controlled by the brand. The presence of media in a tweet was
also analysed. Additional coding was provided to see which kind of media is most often used in tweets.
Here six categories can be identified. Twitter enables their users to implement a picture or video within
their tweets. These 2 categories are referred to as ‘Twitter picture’ and ‘Twitter video’. Brands are also
implementing Youtube videos into tweets. But it is also possible to use other video applications that can
implement videos directly into a tweet. Further, the static Twitter pictures can also be distinguished from
animated pictures. Finally, the last type of media is an image providing a webpage preview.
15
Table 2 Communication functions and subcategories
Category Example
Information
Company activity + news Coca-‐Cola: Coke becomes one of the largest acceptors of #ApplePay
with 100,000 enabled vending machines.
Event BMW: Preparing the stage for the start of the @GVAMotorshow.
http://t.co/himCrtnFbF #GIMS #BMWgeneva http://t.co/3PeF5pnTax
Consumer interests HP: Extending your battery life is easy with these simple tips.
http://t.co/9x9uhNwI95 http://t.co/2TlYWA4mmS
Product BMW: .@BMWi is excited to provide one of the apps for Apple Watch
when it becomes available in April. Stay tuned! http://t.co/CSfKmneOAT
Action
Brand advertising + product advertising Zara: New collection: Soft Wear. Check it at http://t.co/jUtlpbB9fF
#zaralookbook http://t.co/qHR3gmE9u2
Follow activity + participate in activity Mercedes-‐Benz: Now live! Download the new Mercedes-‐Benz Magazine
app for your iOS device: http://t.co/AXpIS16aCG
http://t.co/LPdAyaubo1
Promotions Honda: We’ve got a reward for doing your spring-‐cleaning – deals on
your favorite Honda vehicles. http://t.co/04Z9iCjBBW
http://t.co/GyMvJCbtIe
Community
Conversation Toyota: @2Wired2Tired You will always look good in the
#swaggerwagon
Customer service: questions H&M: @neocronica All graphics have been designed by our in-‐house
team and has not taken any inspiration from real or existing bands.
Customer service: complaints Dell: @nlupus -‐ We hate to hear you've had a bad experience. Looping
in our @DellCares team for assistance here on Twitter.
Social activity participation Pepsi: See your favorite bands perform. IRL. Tag a pic of your Pepsi with
#OutOfTheBlue #Entry and you could get lucky!
http://t.co/P5dcx0AmnD
16
After coding the interactive features, machine interactivity was measured by counting the used features
for each tweet. Four categories emerged, ranging from high machine interactivity to an absence of
machine interactivity. In the high category all possible features are present in a tweet: a hyperlink,
hashtags and media. The medium category only holds two interactive features and the low category only
holds one. As it is also possible to use none of the available features, a fourth category was coded for
containing tweets without interactive features.
Interpersonal interactivity can be measured by looking at the type of tweets, as this reflects the way
organisations are communicating with the publics. Here tweets coded as original tweets do not show any
interactivity, while using @Mentions and retweets shows a willingness to interact with other users.
Interpersonal interactivity can also be reactive when organisations use @Replies to answer to users’
tweets.
17
Results
The 16 brands in this study had an average number of 1,538,747 followers on their Twitter accounts (SD
= 1,670,150.1) ranging from a minimum of 40,755 followers for Nescafé and a maximum of 5,015,918
followers for H&M. On average the brands were following 8737 users (SD = 14,807.2) and this number
ranged from zero followings for Budweiser to 43,041 followings for Pepsi. The data from Twitter was
scraped during one month, from the 1st of March until the 31rd of March. During that time the brands
posted an average of 289 tweets (SD = 270.0). The brand with the lowest amount of tweets posted was
Nescafé, who only posted 22 tweets. Dell on the other hand posted the maximum of 895 tweets in
March. When taking a closer look at the different industries represented in this study we see that the
automotive sector has sent the highest average amount of tweets (M = 546.3, SD = 99.8) and that the
beverages industry has sent the lowest average amount of tweets (M = 69.8, SD = 54.9). It is also the
beverages industry, which, on average, is followed by the smallest number of users (M = 828,427, SD =
1,335,660.9), but, on the other hand, those brands are most active in following other users (M =
19,382.0, SD = 22,624.9). The industry with the highest average of followers is the apparel industry with
an average of 3,074,402 users (SD = 2,092,507.5). This industry is also the one following the least number
of Twitter users (M = 157,3, SD = 80.5).
Research question 1: Type of communication functions
The first purpose of the content analysis was to determine for which communication functions the global
commercial brands are using their Twitter accounts. The analysis revealed a percentage of 78.1 tweets
belonging to the community function (n = 3615). The informational function was represented in 9.5% of
all tweets (n = 441), with the remaining percentage of tweets belonging to the action function (n = 573,
12.4%). To get a more detailed view of these three categories, we also analysed the distribution of
tweets among the different subcategories, as shown in table 3.
18
Table 3 Distribution of communication and subcategories in sample
Category Freq. (%)
Information 441 9.5%
Company activity + news 116 2.5%
Event 102 2,5%
Consumer interests 173 3.7%
Product
50 1.1%
Action 573 12,40%
Brand advertising + product advertising 491 10.6%
Follow activity + participate in activity 38 0.8%
Promotions
7 0.2%
Community 3615 78.1%
Conversation 2493 53.9%
Customer service: questions 427 9.2%
Customer service: complaints 657 14.2%
Social activity participation 38 0,8
For this first research question we also wanted to know if there is a connection between the
communication functions and the level of engagement users show. Engagement was measured by the
number of times users retweeted a message. To answer this question a one-‐way ANOVA was conducted.
The results of the ANOVA revealed a statistically significant difference among the three communication
functions in the total amount of retweets they generated (F(2, 4626) = 72.041, p < .001). Because equal
variances were not assumed a Games-‐Howell post hoc test was chosen to get a more detailed view of
the differences between the three functions. The post hoc test revealed that all three functions showed
significant differences in the number of retweets (Table 4). This analysis found that twitter users engage
less with community building messages than they do with information tweets. But the most engagement
is generated by action messages.
19
Table 4. Comparison of the communication functions for engagement
Engagement Mean score
information
function
Mean score
action
function
Mean score
communication
function
F-‐value P-‐value
Retweets 43.14 (SD =
153.01) a 77.84 (SD =
212.27) b
10.79 (SD =
108.05) b
72.04 .00
a,b = Communication functions that significantly differ in engagement
Looking at the communication subcategories we see that for the action function, product and brand
advertisements received the highest average amount of retweets (M = 86.56, SD = 227.86), followed by
tweets persuading users to follow an activity or participate in a brand activity (M = 27.01, SD = 30.20)
and tweets mentioning promotions (M = 10.43, SD = 7.80). For informational messages the highest
average number of retweets was found in tweets that were giving information about a product or
service (M = 105.48, SD = 388.24). Information about events (M = 47. 46, SD = 73.95) and tweets merely
containing information relevant to consumers’ interests (M = 38.35, SD = 108.08) also received a high
average retweet count. Tweets containing information about company activities and company news
received the least retweets in this category (M = 19.61, SD = 27.90). The communication function with
the least amount of retweets is the community function. Here customer service tweets with questions
(M = 0.24, SD = 0.698) and complaints (M = 0.14, SD = 0.431) received a very low average number of
retweets. Tweets supporting conversation (M = 15.11, SD 129.74) and tweets promoting participation in
social activities (M = 29.97, SD = 40.92) did receive higher amounts of engagement.
To assess if there is a connection between the type of industry and the used communication functions a
one-‐sample chi-‐square test was conducted. The results of the test were significant, χ2 (6, N = 4629) =
300.84, p < .001. Next, a chi-‐square post hoc analysis (Garcia-‐pérez & Núñez-‐antón, 2003) was
performed using the adjusted residuals of each cell to calculate the corresponding chi-‐square values.
These chi-‐square values were used to calculate the adjusted z-‐scores and corresponding p-‐values. The p-‐
values were then compared to the Bonferonni corrected p-‐values to correct for type I errors. This post
hoc chi-‐square analysis was able to reveal the significant differences between each of the groups, as
illustrated in figure 1. The information function was used significantly more in the beverages sector (z =
11.03, p < .001). The action function was significantly more present in the apparel sector (z = 9.06, p <
.001) and automotive sector (z = 5.26, p < .001). The communication function was used significantly
more in the technology sector (z = 7.12, p < .001).
20
Figure 1 Comparison of communication functions with industries
Research question 2: How interactive are global commercial brands on Twitter?
According to Burton and Sobleva (2011) interactivity on Twitter can be divided into ‘interpersonal
interactivity’ and ‘machine interactivity’. In this study Interpersonal interactivity on Twitter refers to the
process of communication between the global commercial brands and Twitter users. Here the specific
manner in which brands communicate on Twitter is analysed, whereas machine interactivity looks at the
interactive features that are being added to tweets. These features supplement and enrich the
interpersonal communication in tweets.
Figure 2 shows that in terms of interpersonal interactivity the quantitative content analysis revealed that
13.6% of the 4629 tweets in our sample are original tweets (n = 630). Only 8.6% of all the tweets are
unedited retweets (n = 400), meaning that the brands have not added original text to another users’
tweet. Retweets that were edited by brands only amounted for 0.4% (n = 20). The second-‐to-‐last way of
communicating on Twitter is by using @Mentions, this category was only used in 8.4% (n = 387) of the
cases. The remaining percentage of tweets were all @Replies and accounted for 69.0% of all tweets (n =
3192).
21
Figure 2 Distribution of interpersonal interactivity levels
Looking more closely at machine interactivity displayed in figure 3, we can see that only one third of the
tweets were accompanied by a hyperlink (n = 1524, 32.9%) and even lower amount of tweets were
carrying a media attachment (n = 1222, 26.4%). Hashtags were the most used interactive features, with
more than half of all the tweets containing one or more hashtags (n = 2349, 50%). Overall only 10.7% of
all the tweets were categorised as high machine interactivity, containing all three of the interactive
features (n = 495). Tweets showing medium machine interactivity, only contained a combination of two
interactive features. This category was present 26.1% of the cases (n = 1209). Low interactive tweets only
carried one interactive features, this was the case for 25.8% of the tweets in our sample (n = 1192). The
remaining tweets did not show any interactive features, this group of tweets accounted for 37.4% of all
the tweets (n = 1733).
Figure 3 Distribution of machine interactivity levels
22
To see whether a connection exists between the communication functions and the use of interactive
features a chi-‐square analysis was conducted and results are shown in figure 4 and 5. The test showed a
statistically significant link between the three communication functions and the level of machine
interactivity, χ2 (6, N = 4629) = 1808.42, p < .001). A chi-‐square post hoc analysis (Garcia-‐pérez & Núñez-‐
antón, 2003) revealed that tweets without any interactive features (z = 27,4, p < .001) and tweets with
low machine interactivity (z = 10.8, p > .001) were used significantly more in the community-‐building
function than in the other communication functions. On the other hand tweets with high machine
interactivity were more used for the action function (z = 24.4, p < .001) and the information function (z =
20.7, p < .001) and were rarely used for the community function. The same was true for tweets showing
medium interactivity in the action function (z = 12.9, p < .001) and information function (z = 9.3, p <
.001). The same test was used to see if the same significant connection exists for interpersonal
interactivity. These results were also significant, χ2 (8, N = 4629) =3612.38, p < .001. The post hoc test
revealed that original tweets, containing the least interpersonal interactivity, were used significantly
more in the information (z = 17.66, p = < .001) and action function (z = 38.66, p = < .001) compared to the
community function. @Mentions were also more present in the information (z = 29.51, p < .001) and
action function (z = 7.92, p < .001) and the same trend was found for @Mentions. For edited retweets no
significant differences were found, but this category only contained 20 tweets. For @Replies a reverse
trend was found, as these tweets were more commonly used in the community function (z = 53.32, p <
001).
Figure 4 Comparison machine interactivity for communication functions
23
Figure 5 Comparison interpersonal interactivity for communication functions
We also conducted a chi-‐square test to see if there is a connection between the industry brands belong
to and the level of machine interactivity the tweets display. The results of the test were significant, χ2 (9,
N = 4629) = 992.90, p < .001. Once more we’ve conducted a post hoc test to see where this significant
connection originated from (Garcia-‐pérez & Núñez-‐antón, 2003). The test showed that tweets without
interactive features were more used by brands from the automotive sector (z = 9.46, p < .001) and the
technology sector (z = 7.86, p < .001) and that they were least present in the beverages and apparel
sector. Low machine interactivity was most present in the apparel sector (z = 22.19, p < .001) compared
to the other sectors. Medium interactivity was more present in the beverages (z = 5.92, p < .001) and
technology sector (z = 11.30, p < .001) than in the other two industries. The highest level of interactivity
appeared more in the apparel sector (z = 8.85, p < .001) and the beverages sector (z = 3.03, p = .002)
compared to the two remaining categories.
24
Figure 6 Comparison between machine interactivity and industries
The same chi-‐square test also revealed a statistically significant link between interpersonal interactivity
and the industry type, χ2 (12, N = 4629) = 324.03, p < .001. Although there is a significant connection, the
post hoc test only revealed significant differences for some of the cases. The results are illustrated in
figure 7. Original tweets, low in interpersonal interactivity, were mostly used in the beverages sector (z =
11.89, p < .001) compared to the technology sector. The opposite was true for @Replies, a reactive form
of interpersonal interactivity (z = 7.52, p < .001). @Mentions were also more frequently used in the
beverages sector (z = 7.07, p < .001). Unedited retweets were mostly used by brands in the apparel
sector, in comparison the other industries (z = 7.95, p < .0.001). Lastly, edited retweets were more
common in the automotive sector than in the others (z = 3.77, p < .001)
25
Figure 7 Comparison between interpersonal interactivity and industries
A one-‐way anova was also conducted to see if different levels of machine and interpersonal interactivity
generate a different level of engagement. The results of the two one-‐way ANOVA’s were significant,
indicating that different levels of machine interactivity lead to a different amount of retweets, F(3, 4625)
= 46.544, p < .001. The same result was found for interpersonal interactivity, F(4, 4624) = 96.517, p <
.001. Because equal variances were not assumed a post hoc Games-‐Howell was conducted. Table 5
shows that in terms of machine interactivity the test shows that tweets belonging to the low, medium
and high category were retweeted significantly more than tweets lacking in machine interactivity.
Tweets with low interactivity were less retweeted than tweets with medium and high interactivity. But
no significant difference was found between the medium and high category in terms of the number of
retweets. For interpersonal interactivity, table 6 reveals that @Replies were retweeted significantly less
than the tweets in the other categories. Retweets that were not edited by a brand received significantly
more retweets than the original tweets and @Mentions did.
26
Table 5 Comparison between machine interactivity levels and engagement
a,b = Machine interactivity levels that do not significantly differ
Table 6 Comparison between interpersonal interactivity levels and engagement
a,b and c,d = Interpersonal interactivity levels that do not significantly differ
Engagement Mean
score no
interactivity
Mean score
low
interactivity
Mean score
medium
interactivity
Mean score
high
interactivity
F-‐value P-‐value
Retweets .31 (SD =
1.49)
14.24 (SD =
110.47)
44.05 (SD =
153.58) a
64.40 (SD =
267.33) b
46.54 .00
Engagement Mean
Score
Original
tweets
Mean
score
Unedited
retweets
Mean
score
Edited
retweets
Mean
score
@Mentions
Mean
score
@Replies
F-‐value P-‐value
Retweets 51.31 (SD
= 145.62)a
119.73
(SD = 335.99)c
64.10 (SD
= 59.49)b,d
51.30 (SD =
203.58)b
.40 (SD =
3.43)
96.52 .00
27
Discussion and conclusion
The goal if this study was to analyse how global commercial brands are using Twitter to communicate
with the publics. Previous research had focused on the use of theories to analyse the different
communication models implemented by organisations and to define the extent to which they are using
the potential to build relationships on social media (Grunig & Hunt, Kent & Taylor). The focus of this
study however, turned to the actual content of the messages, in order to determine the different
communication functions for which Twitter is being used. Because previous research on this specific
topic had concentrated only on non-‐profit organisations, this study rather analysed the implementation
of Twitter into the communication strategy of global commercial brands. To this end a digital scraping
technique was used to collect all the tweets and a quantitative content analysis was performed.
The results of this study indicate that global commercial brands are using Twitter more to build a
community with the publics, rather than to push one-‐way information or for advertising purposes. These
findings do not align with the research on the use of Twitter and Facebook by non-‐profit organisations
(Lovejoy and Saxton, 2014; Saxton and Waters, 2014). For non-‐profit organisations the informational
function was reported as the primary communication function, followed by community building and in
last instance the action function. In our study the information function was least used, showing us that
global commercial brands are using Twitter differently than is reported for non-‐profit organisations.
Contradictory to the findings of different researchers (Bortree & Seltzer, 2009; Lovejoy, Waters &
Saxton, 2011; Rybalko & Seltzer, 2010), these global commercial brands are understanding the potential
Twitter has to offer for building relationships and are using the dialogical opportunities to their
advantage.
In terms of engagement this study found that, although community building is the primary function for
the commercial brands, this function also generated the least amount of retweets. The highest level of
engagement was produced by tweets belonging to the action function, more specifically by tweets
designed for product or brand advertising. These findings contradict those of Saxton and Waters (2014),
who found that, on Facebook, community building messages led to a higher number of shares than
informational messages. The low number of retweets for community building messages in our study can
possibly be explained by the one-‐to-‐one conversations belonging to the community function. Because a
lot of tweets in this category are conversations with one single or a few Twitter users, it is evident that
28
those conversations will not be picked up as much by other users. Also, users following a brand on
Twitter are probably more interested in seeing content that directly relates to that brand, instead of
following the conversations with other users. On the other hand, Saxton and Waters (2014) did also find
that action messages produced the highest level of engagement in terms of liking of the messages on
Facebook. Together with our findings, this clearly indicates that action messages, as opposed to
community messages, are also important to incorporate in social media, as they elicit favourable
responses from the public.
This study also looked at the degree of interpersonal and machine interactivity that is being used by the
brands in our sample. Interactivity in terms of the intended direction of communication was measured
based on the frequencies of the different types of tweets. Results show that the global commercial
brands are implementing high levels of interpersonal interactivity. Original tweets show the lowest level
of reciprocal communication, as they are merely one-‐way communication in nature, but those tweets
only accounted for less than 15%. The two types of retweets, as well as tweets containing @Mentions,
show a high level of interpersonal interactivity, as they directly include other users into the conversation.
Those types of tweets were used more often than the original tweets. @Replies were used in almost
70% of the tweets, also showing a high level of interactivity. Nevertheless, those interactive tweets could
also be seen as merely reactive in nature, as they are typically used to directly respond to users. Overall,
we see that global commercial brands are using the interpersonal interactivity possibilities on Twitter.
For machine interactivity the results were less promising, as more than one third of all the tweets did not
contain any interactive features, such as hastaghs, hyperlinks or media. Especially the implementation of
media into tweets was not frequently being used. High machine interactivity was least found in tweets.
Although our results did indicate that users show a higher level of engagement for medium and high
machine interactivity, brands are not yet using these features to their full potential. Thus, if brands
would want to elicit a higher number of favourable public responses, they should start implementing
various combinations of hastaghs, hyperlinks and media into their tweets.
This study also found that the level of personal and machine interactivity varies across the different
communication functions. In terms of machine interactivity, the action and information function showed
higher levels of interactivity than the community building function. This could be explained by looking at
the interpersonal interactivity, which shows that @Replies are more commonly used in the community
building function, unlike @Mentions, retweets and original tweets. Given that @Replies are merely
answers to users’ questions, brands might probably invest less effort in implementing interactive
29
features, as they are focused on the formulation of a proper response. This could explain the low level of
machine interactivity and also the low number of favourable responses, measured trough the amount of
retweets.
Interestingly, this study also found a connection between the type of industry and the use of Twitter to
communicate with users. The four different industries in this sample showed significant differences in
the use of communication functions, but also in interpersonal and machine interactivity. In their study,
Lovejoy and Saxton (2012) did not find any significant differences in the field in which organisations
operated. In our sample however, different industries were using different communication strategies.
The technology sector and the automotive sector showed a higher amount of community building
messages. Those brands were implementing conversational tweets, but also customer service activities,
on Twitter at a higher rate than the others. The beverages sector was using more informational tweets,
while the retail and the automotive sector were sending more action tweets.
Overall this study has shown that global commercial brands are embracing the potential of social media,
unlike what has been previously reported for non-‐profit brands. All the brands in our study use mixed
communication strategies, but they also invest their time in building a twitter community. Also, this
study found that for brands it is not favourable to only send community-‐building tweets, because the
publics are responding to action and information messages more often than to community building
messages. The global commercial brands could improve user engagement by implementing more
machine interactivity into their tweets and by using at least two interactive features to complement their
messages. In summary, we could say that the best way to go is to offer a variety in communication
functions, while keeping in mind the opportunities Twitter has to offer for creating dialogical
conversations and interactivity.
30
Limitations and Recommendations for Future Research
Although this research was able to produce useful insights into the way global commercial brands are
using Twitter, there are still some limitations that need to be considered. In this study we did not solely
focus on analysing how Twitter is being implemented in the communication strategy by brands. We have
taken into account the recommendations from previous research to also focus on the users’ reaction in
terms of engagement (Crijns et al. 2015; Lovejoy and Saxton; 2012). But because we used a data scraping
technique to extract tweets from Twitter, we also had to deal with the limitations of this script. Using
this technique we were able to scrape data about the amount of retweets, but we could not however,
also scrape the same information for the number of favourites a message received. Coding this data
separately from our data file would have caused a misrepresentation of the relative quantity of
favourites as opposed to retweets, because a shift in time would have occurred. Therefore we were only
able to capture one dimension of engagement. Future research could take the limitations to scraping
software into account by making sure favourites can also be scraped from Twitter.
There are also some limitations associated with using a quantitative content analysis. Despite delivering
valuable insights into the content of tweets and the use of interactivity, a quantitative analysis does not
tell us anything about the motivations behind the Twitter usage of organisations. To meet this limitation
future research could complement the quantitative content analysis with qualitative research such as
surveys. Conducting surveys with communications managers could lead to a better understanding of
how social media are integrated into business and what the motivations are behind the difference in
usage between the communication functions and the interactivity. Ultimately this multi-‐method design
could increase the quality of research extensively.
In this study only brands belonging to the Forbes “top 100 most valuable brands list” were chosen.
Future research could expand our sample by also including smaller brands and less known brands into
their sample. This mixed sample could then lead to overall insights into the use of Twitter for different
forms and sizes of commercial organisations. Finally, it would also be interesting for researchers to
analyse the use of different social media channels. The focus in this study was limited to the usage of
Twitter. Future research could expand these Twitter insights by including different social media channels
owned by the same selection of brands. This way, comparisons can be made to expose to differences in
strategies behind social media usage.
31
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35
Appendix A: Codesheet and codebook
A1 CODESHEET
1.Name of Twitter account
1 Adidas 2 BMW 3 Budweiser 4 CocaColaCo 5 Dell 6 H&M 7 Honda 8 HP 9 Intel 10 Mercedes-‐Benz 11 Nescafé 12 Nike 13 Pepsi 14 Samsung 15 Toyota 16 Zara
2. Type of Tweet: type corresponding number
1 Original Tweet
2 Retweet (Unedited)
3 Retweet (Edited)
4 @Mention
5 @Reply
36
3. Tweet Function
1 Information
2 Action
3 Community
4. Specific Tweet Function
Information:
1 Company Activity + News
2 Event
3 Consumer interests
4 Product
Action:
5 Brand Advertising + product advertising
6 Follow activity + Participate activity
7 Promotions
Community Building
8 Conversation
9 Customer Service: Questions
10 Customer Service: Complaints
11 Social Activity Participation
37
5. Interactivity
Hastags: Yes (1) or No (0)
URL: Yes (1) or No (0)
URL to where:
1. Website
2. Not Owned Website
3. Owned social media (Facebook, Instagram, Linkdin, Youtube, other TV and video sharing
applications (Periscoop, Twitch, Vimeo, Yahoo screen, Meerkat TV, Livestream and Vine), Other
social media (Hootsuite, slideshare and Tumbler)
4. Not Owned social media (Blogs, Instagram, Linkdin, Youtube)
Media: Yes (1) or No (0)
Type of media:
1. Twitter Picture
2. Twitter Video
3. Youtube
4. Other Video
5. Animated Picture
6. Website Preview
6. Engagement: Number of retweets
38
A2 CODEBOOK
1. Attach this code to all tweets from particular brand
2. Type of Tweet: type corresponding number next to tweet
Original tweet: Post originating from brand (without RT or an @user in the message)
Retweet (Unedited): This can be identified when tweets begins with ‘RT’ followed by @user
Retweet (Edited) A tweet containing a short message that precedes ‘RT’ @user
@mention A tweet that does not contain ‘RT’ but does contain @user. Except for
tweets beginning with @user
@reply Tweets beginning with @user that don’t contain ‘RT’
3. Tweet Function: Attach one function to every tweet
Information
Action
Community
4. Specific Tweet Function
Information = tweets containing only informational messages. The main purpose of
these tweets is to inform, without a secondary agenda.
Company Activity + News = information about company, its activities and other newsworthy company
information.
Event = information about a company event, without the main goal being
mobilisation.
39
Consumer interests = tweets containing facts that would appeal to the public – spreading
interesting information without secondary agenda.
Product = information about the brands products that is not an advertising effort.
Action = tweets that aim at getting the public to do something, like buying
product, going to event. The main purpose of these tweets is to fulfil the
brand’s goal.
Brand Advertising/product advertising = tweets that are advertisements made to persuade Twitter users.
Follow activity/Participate activity = tweets that aim at mobilising users and achieve company goals.
Promotions = tweets announcing reductions or promotions.
Community = all the tweets attempting to create social communities, and aim at
creating dialogue and interaction.
Conversation = all types of conversation such as thanking users, acknowledgements,
random interactions and response solicitations.
Customer Service: Questions = all tweets answering specific questions about customer service issues,
without being actual complaints.
Customer Service: Complaints = all tweets responding to complaints made by Twitter users.
Social Activity Participation = all tweets that try to engage with consumers through the creation of
activities designed for social media.
40
5. Interactivity
Hashtags: Indicate Yes (1) or No (0) when a hashtags is used in the tweet.
URL: Indicate Yes (1) or No (0) when a hyperlink is used in the tweet. Click on the link to identify it. And
specify by one of these options below:
1. Website
2. Not Owned Website
3. Owned social media (Facebook, Instagram, Linkdin, Youtube, other TV and video sharing
applications (Periscoop, Twitch, Vimeo, Yahoo screen, Meerkat TV, Livestream and Vine), Other
social media (Hootsuite, slideshare and Tumbler)
4. Not owned social media (Blogs, Instagram, Linkdin, Youtube)
Media: Indicate Yes (1) or No (0) when media is seen in the tweet. Specify the type of media by these
options below:
1. Twitter Picture
2. Twitter Video
3. Youtube
4. Other Video
5. Animated Picture
6. Website Preview
6. Engagement: Specify the number of retweets for every tweet
41
APPENDIX B: Ouput SPSS
B1. Descriptive analysis
Descriptives: number of followers, following and tweets
Descriptive Statistics
N Minimum Maximum Mean Std. Deviation
Followers 16 40755 5015918 1538747,31 1670150,062
Following 16 0 43041 8736,94 14807,181
Tweets 16 22 895 289,31 269,968
Valid N (listwise) 16
Descriptives tweets functions
Statistics
Tweet Function
N Valid 4629
Missing 0
Mean 2,69
Std. Deviation ,637
Minimum 1
Maximum 3
42
Frequency Percent Valid Percent
Cumulative
Percent
Valid Information 441 9,5 9,5 9,5
Action 573 12,4 12,4 21,9
Community 3615 78,1 78,1 100,0
Total 4629 100,0 100,0
Frequencies type of tweets: interpersonal interactivity
Type of Tweet
Frequency Percent Valid Percent
Cumulative
Percent
Valid Original tweet 630 13,6 13,6 13,6
Unedited retweet 400 8,6 8,6 22,3
Edited Retweet 20 ,4 ,4 22,7
@Mention 387 8,4 8,4 31,0
@Reply 3192 69,0 69,0 100,0
Total 4629 100,0 100,0
43
Frequencies machine interactivity: URL, Hashtags and Media
Statistics
URL Hashtags Media
N Valid 4629 4629 4629
Missing 0 0 0
Mean ,33 ,51 ,26
Std. Deviation ,470 ,500 ,441
Minimum 0 0 0
Maximum 1 1 1
URL
Frequency Percent Valid Percent
Cumulative
Percent
Valid No 3105 67,1 67,1 67,1
Yes 1524 32,9 32,9 100,0
Total 4629 100,0 100,0
Hashtags
Frequency Percent Valid Percent
Cumulative
Percent
Valid No 2280 49,3 49,3 49,3
Yes 2349 50,7 50,7 100,0
Total 4629 100,0 100,0
44
Media
Frequency Percent Valid Percent
Cumulative
Percent
Valid No 3407 73,6 73,6 73,6
Yes 1222 26,4 26,4 100,0
Total 4629 100,0 100,0
Frequencies Machine interactivity
Machine_interactivity
N Valid 4629
Missing 0
Mean 1,10
Std. Deviation 1,026
Minimum 0
Maximum 3
Frequency Percent Valid Percent
Cumulative
Percent
Valid No interactivity 1733 37,4 37,4 37,4
Low interactivity 1192 25,8 25,8 63,2
Medium interactivity 1209 26,1 26,1 89,3
High interactivity 495 10,7 10,7 100,0
45
Total 4629 100,0 100,0
Frequencies type of media and specific URL
Type of media
Frequency Percent Valid Percent
Cumulative
Percent
Valid 0 3405 73,6 73,6 73,6
Twitter picture 1050 22,7 22,7 96,2
Twitter video 40 ,9 ,9 97,1
Youtube 93 2,0 2,0 99,1
Other video 23 ,5 ,5 99,6
Animated
picture 12 ,3 ,3 99,9
Website preview 6 ,1 ,1 100,0
Total 4629 100,0 100,0
46
URL To
Frequency Percent Valid Percent
Cumulative
Percent
Valid 0 3105 67,1 67,1 67,1
Owned website 1174 25,4 25,4 92,4
Not owned website 192 4,1 4,1 96,6
Owned social media 127 2,7 2,7 99,3
Not owned social media 31 ,7 ,7 100,0
Total 4629 100,0 100,0
Descriptives number of followers, following and tweets X industry types
Descriptives
N Mean Std. Deviation Std. Error
95% Confidence
Interval for Mean
Lower Bound
Followers Beverages 4 828427,00 1335660,939 667830,469 -‐1296907,61
Automotive 3 873752,33 326413,791 188455,090 62895,53
Apparel 4 3074402,25 2092507,529 1046253,765 -‐255244,18
Technology 4 1479294,75 1751803,878 875901,939 -‐1308216,14
Total 15 1609983,53 1703423,332 439822,013 666659,13
Following Beverages 4 19382,00 22624,853 11312,426 -‐16619,19
Automotive 3 762,67 752,782 434,619 -‐1107,35
Apparel 4 157,25 80,467 40,233 29,21
Technology 4 9995,50 15212,207 7606,103 -‐14210,52
47
Total 15 8028,47 15043,570 3884,233 -‐302,38
Tweets Beverages 4 69,75 54,999 27,500 -‐17,77
Automotive 3 546,33 99,801 57,620 298,41
Apparel 4 152,25 160,099 80,049 -‐102,50
Technology 4 483,75 344,003 172,002 -‐63,64
Total 15 297,47 277,397 71,624 143,85
Descriptives
95% Confidence Interval for
Mean
Minimum Maximum Upper Bound
Followers Beverages 2953761,61 40755 2823675
Automotive 1684609,14 577115 1223443
Apparel 6404048,68 727269 5015918
Technology 4266805,64 463928 4092293
Total 2553307,93 40755 5015918
Following Beverages 55383,19 0 43041
Automotive 2632,68 80 1570
Apparel 285,29 65 261
Technology 34201,52 1266 32777
Total 16359,32 0 43041
Tweets Beverages 157,27 22 149
Automotive 794,25 432 616
48
Apparel 407,00 37 389
Technology 1031,14 106 895
Total 451,08 22 895
Descriptives subcategories tweet functions
SpecificTweetFunction
N Valid 4629
Missing 0
Mean 7,51
Std. Deviation 2,155
Minimum 1
Maximum 11
49
B2. Oneway ANOVA
Connection communication function and engagement
Descriptives
Retweet_count
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Lower Bound Upper Bound
Information 441 43,14 153,007 7,286 28,82 57,46
Action 573 77,84 212,265 8,867 60,42 95,25
Community 3615 10,79 108,045 1,797 7,27 14,31
Total 4629 22,17 132,050 1,941 18,37 25,98
Minimum Maximum
Information 0 2714
Action 0 3365
Community 0 4590
Total 0 4590
Test of Homogeneity of Variances
Levene Statistic df1 df2 Sig.
60,685 2 4626 ,000
50
ANOVA
Sum of Squares df Mean Square F Sig.
Between Groups 2437558,185 2 1218779,093 72,041 ,000
Within Groups 78262273,622 4626 16917,915
Total 80699831,807 4628
Post Hoc Tests
Multiple Comparisons
Dependent Variable: Retweet_count
Games-‐Howell
(I) TweetFunction (J) TweetFunction
Mean Difference
(I-‐J) Std. Error Sig.
95% Confidence
Interval
Lower Bound
Information Action -‐34,698* 11,477 ,007 -‐61,64
Community 32,348* 7,504 ,000 14,71
Action Information 34,698* 11,477 ,007 7,76
Community 67,045* 9,048 ,000 45,79
Community Information -‐32,348* 7,504 ,000 -‐49,99
Action -‐67,045* 9,048 ,000 -‐88,30
51
Multiple Comparisons
Dependent Variable: Retweet_count
Games-‐Howell
(I) TweetFunction (J) TweetFunction
95% Confidence Interval
Upper Bound
Information Action -‐7,76
Community 49,99
Action Information 61,64
Community 88,30
Community Information -‐14,71
Action -‐45,79
*. The mean difference is significant at the 0.05 level.
52
Connection between subcategories communication functions and engagement
Descriptives
Retweet_count
N Mean Std. Deviation Std. Error
95% Confidence
Interval for Mean
Lower Bound
Company activity + News 116 19,61 27,896 2,590 14,48
Event 102 47,46 73,949 7,322 32,94
Consumer interests 173 38,35 108,076 8,217 22,13
Product 50 105,48 388,236 54,905 -‐4,86
Brand advertising / Product
advertising 491 86,56 227,863 10,283 66,36
Follow activity / Participate
activity 75 27,01 30,199 3,487 20,07
Promotions 7 10,43 7,807 2,951 3,21
Conversation 2493 15,11 129,737 2,598 10,02
Customer service: questions 427 ,24 ,698 ,034 ,17
Customer service: complaints 657 ,14 ,431 ,017 ,11
Social activity participation 38 29,97 40,920 6,638 16,52
Total 4629 22,17 132,050 1,941 18,37
53
95% Confidence Interval
for Mean
Minimum Maximum Upper Bound
Company activity + News 24,74 1 163
Event 61,99 1 532
Consumer interests 54,57 0 1005
Product 215,82 1 2714
Brand advertising / Product advertising 106,76 0 3365
Follow activity / Participate activity 33,96 0 129
Promotions 17,65 4 27
Conversation 20,21 0 4590
Customer service: questions ,31 0 9
Customer service: complaints ,17 0 3
Social activity participation 43,42 0 179
Total 25,98 0 4590
Test of Homogeneity of Variances
Levene Statistic df1 df2 Sig.
20,497 10 4618 ,000
54
ANOVA
Retweet_count
Sum of Squares df Mean Square F Sig.
Between Groups 3147445,352 10 314744,535 18,742 ,000
Within Groups 77552386,456 4618 16793,501
Total 80699831,807 4628
Post Hoc Test
Multiple Comparisons
Dependent Variable: Retweet_count
Games-‐Howell
(I)
SpecificTweetFunction
(J)
SpecificTweetFunction
Mean
Difference (I-‐
J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Company activity +
News
Event -‐27,849* 7,767 ,020 -‐53,31 -‐2,38
Consumer interests -‐18,735 8,615 ,526 -‐46,78 9,31
Product -‐85,868 54,966 ,890 -‐271,33 99,60
Brand advertising /
Product advertising -‐66,948* 10,604 ,000 -‐101,23 -‐32,67
Follow activity /
Participate activity -‐7,401 4,344 ,831 -‐21,60 6,80
Promotions 9,183 3,926 ,450 -‐5,16 23,52
Conversation 4,500 3,669 ,979 -‐7,37 16,37
Customer service:
questions 19,373* 2,590 ,000 10,87 27,88
55
Customer service:
complaints 19,471* 2,590 ,000 10,96 27,98
Social activity
participation -‐10,362 7,126 ,927 -‐34,41 13,69
Event Company activity +
News 27,849* 7,767 ,020 2,38 53,31
Consumer interests 9,114 11,006 ,999 -‐26,62 44,85
Product -‐58,019 55,391 ,993 -‐244,65 128,61
Brand advertising /
Product advertising -‐39,099 12,624 ,074 -‐79,92 1,72
Follow activity /
Participate activity 20,447 8,110 ,301 -‐6,09 46,98
Promotions 37,032* 7,894 ,000 10,99 63,07
Conversation 32,349* 7,769 ,003 6,88 57,82
Customer service:
questions 47,222* 7,322 ,000 23,11 71,34
Customer service:
complaints 47,319* 7,322 ,000 23,20 71,44
Social activity
participation 17,487 9,883 ,795 -‐14,96 49,93
Consumer interests Company activity +
News 18,735 8,615 ,526 -‐9,31 46,78
Event -‐9,114 11,006 ,999 -‐44,85 26,62
Product -‐67,133 55,516 ,979 -‐254,11 119,85
Brand advertising /
Product advertising -‐48,213* 13,163 ,012 -‐90,74 -‐5,68
Follow activity /
Participate activity 11,333 8,926 ,973 -‐17,70 40,37
56
Promotions 27,918 8,731 ,061 -‐,63 56,46
Conversation 23,235 8,618 ,209 -‐4,82 51,29
Customer service:
questions 38,108* 8,217 ,000 11,30 64,92
Customer service:
complaints 38,205* 8,217 ,000 11,40 65,01
Social activity
participation 8,373 10,563 ,999 -‐26,13 42,88
Product Company activity +
News 85,868 54,966 ,890 -‐99,60 271,33
Event 58,019 55,391 ,993 -‐128,61 244,65
Consumer interests 67,133 55,516 ,979 -‐119,85 254,11
Brand advertising /
Product advertising 18,920 55,860 1,000 -‐169,01 206,85
Follow activity /
Participate activity 78,467 55,015 ,936 -‐107,13 264,07
Promotions 95,051 54,984 ,814 -‐90,46 280,57
Conversation 90,368 54,966 ,855 -‐95,10 275,83
Customer service:
questions 105,241 54,905 ,704 -‐80,06 290,54
Customer service:
complaints 105,338 54,905 ,703 -‐79,96 290,64
Social activity
participation 75,506 55,305 ,951 -‐110,89 261,90
Brand advertising /
Product advertising
Company activity +
News 66,948* 10,604 ,000 32,67 101,23
Event 39,099 12,624 ,074 -‐1,72 79,92
Consumer interests 48,213* 13,163 ,012 5,68 90,74
57
Product -‐18,920 55,860 1,000 -‐206,85 169,01
Follow activity /
Participate activity 59,547* 10,858 ,000 24,45 94,64
Promotions 76,132* 10,698 ,000 41,48 110,78
Conversation 71,448* 10,607 ,000 37,17 105,73
Customer service:
questions 86,321* 10,283 ,000 53,06 119,58
Customer service:
complaints 86,419* 10,283 ,000 53,16 119,67
Social activity
participation 56,586* 12,240 ,000 16,88 96,29
Follow activity /
Participate activity
Company activity +
News 7,401 4,344 ,831 -‐6,80 21,60
Event -‐20,447 8,110 ,301 -‐46,98 6,09
Consumer interests -‐11,333 8,926 ,973 -‐40,37 17,70
Product -‐78,467 55,015 ,936 -‐264,07 107,13
Brand advertising /
Product advertising -‐59,547* 10,858 ,000 -‐94,64 -‐24,45
Promotions 16,585* 4,568 ,035 ,69 32,48
Conversation 11,901 4,349 ,192 -‐2,28 26,08
Customer service:
questions 26,774* 3,487 ,000 15,19 38,36
Customer service:
complaints 26,872* 3,487 ,000 15,29 38,45
Social activity
participation -‐2,960 7,498 1,000 -‐28,08 22,16
Promotions Company activity +
News -‐9,183 3,926 ,450 -‐23,52 5,16
58
Event -‐37,032* 7,894 ,000 -‐63,07 -‐10,99
Consumer interests -‐27,918 8,731 ,061 -‐56,46 ,63
Product -‐95,051 54,984 ,814 -‐280,57 90,46
Brand advertising /
Product advertising -‐76,132* 10,698 ,000 -‐110,78 -‐41,48
Follow activity /
Participate activity -‐16,585* 4,568 ,035 -‐32,48 -‐,69
Conversation -‐4,683 3,932 ,976 -‐18,98 9,61
Customer service:
questions 10,190 2,951 ,171 -‐3,68 24,06
Customer service:
complaints 10,287 2,951 ,165 -‐3,59 24,16
Social activity
participation -‐19,545 7,264 ,238 -‐44,23 5,14
Conversation Company activity +
News -‐4,500 3,669 ,979 -‐16,37 7,37
Event -‐32,349* 7,769 ,003 -‐57,82 -‐6,88
Consumer interests -‐23,235 8,618 ,209 -‐51,29 4,82
Product -‐90,368 54,966 ,855 -‐275,83 95,10
Brand advertising /
Product advertising -‐71,448* 10,607 ,000 -‐105,73 -‐37,17
Follow activity /
Participate activity -‐11,901 4,349 ,192 -‐26,08 2,28
Promotions 4,683 3,932 ,976 -‐9,61 18,98
Customer service:
questions 14,873* 2,599 ,000 6,50 23,24
Customer service:
complaints 14,970* 2,598 ,000 6,60 23,34
59
Social activity
participation -‐14,862 7,129 ,593 -‐38,92 9,19
Customer service:
questions
Company activity +
News -‐19,373* 2,590 ,000 -‐27,88 -‐10,87
Event -‐47,222* 7,322 ,000 -‐71,34 -‐23,11
Consumer interests -‐38,108* 8,217 ,000 -‐64,92 -‐11,30
Product -‐105,241 54,905 ,704 -‐290,54 80,06
Brand advertising /
Product advertising -‐86,321* 10,283 ,000 -‐119,58 -‐53,06
Follow activity /
Participate activity -‐26,774* 3,487 ,000 -‐38,36 -‐15,19
Promotions -‐10,190 2,951 ,171 -‐24,06 3,68
Conversation -‐14,873* 2,599 ,000 -‐23,24 -‐6,50
Customer service:
complaints ,097 ,038 ,262 -‐,02 ,22
Social activity
participation -‐29,735* 6,638 ,003 -‐52,48 -‐6,99
Customer service:
complaints
Company activity +
News -‐19,471* 2,590 ,000 -‐27,98 -‐10,96
Event -‐47,319* 7,322 ,000 -‐71,44 -‐23,20
Consumer interests -‐38,205* 8,217 ,000 -‐65,01 -‐11,40
Product -‐105,338 54,905 ,703 -‐290,64 79,96
Brand advertising /
Product advertising -‐86,419* 10,283 ,000 -‐119,67 -‐53,16
Follow activity /
Participate activity -‐26,872* 3,487 ,000 -‐38,45 -‐15,29
Promotions -‐10,287 2,951 ,165 -‐24,16 3,59
Conversation -‐14,970* 2,598 ,000 -‐23,34 -‐6,60
60
Customer service:
questions -‐,097 ,038 ,262 -‐,22 ,02
Social activity
participation -‐29,832* 6,638 ,003 -‐52,58 -‐7,08
Social activity
participation
Company activity +
News 10,362 7,126 ,927 -‐13,69 34,41
Event -‐17,487 9,883 ,795 -‐49,93 14,96
Consumer interests -‐8,373 10,563 ,999 -‐42,88 26,13
Product -‐75,506 55,305 ,951 -‐261,90 110,89
Brand advertising /
Product advertising -‐56,586* 12,240 ,000 -‐96,29 -‐16,88
Follow activity /
Participate activity 2,960 7,498 1,000 -‐22,16 28,08
Promotions 19,545 7,264 ,238 -‐5,14 44,23
Conversation 14,862 7,129 ,593 -‐9,19 38,92
Customer service:
questions 29,735* 6,638 ,003 6,99 52,48
Customer service:
complaints 29,832* 6,638 ,003 7,08 52,58
*. The mean difference is significant at the 0.05 level.
61
Connection machine interactivity and engagement
Descriptives
Retweet_count
N Mean Std. Deviation Std. Error
95% Confidence
Interval for Mean
Lower Bound
No interactivity 1733 ,31 1,488 ,036 ,24
Low interactivity 1192 14,24 110,474 3,200 7,96
Medium interactivity 1209 44,05 153,578 4,417 35,38
High interactivity 495 64,40 267,327 12,015 40,80
Total 4629 22,17 132,050 1,941 18,37
95% Confidence Interval for
Mean
Minimum Maximum Upper Bound
No interactivity ,38 0 42
Low interactivity 20,51 0 2665
Medium interactivity 52,71 0 2775
High interactivity 88,01 0 4590
Total 25,98 0 4590
62
Test of Homogeneity of Variances
Levene Statistic df1 df2 Sig.
70,387 3 4625 ,000
ANOVA
Retweet_count
Sum of Squares df Mean Square F Sig.
Between Groups 2364976,153 3 788325,384 46,544 ,000
Within Groups 78334855,654 4625 16937,266
Total 80699831,807 4628
Post Hoc Tests
Multiple Comparisons
Dependent Variable: Retweet_count
(I) Machine_interactivity (J) Machine_interactivity
Mean Difference
(I-‐J) Std. Error
Scheffe No interactivity Low interactivity -‐13,929* 4,897
Medium interactivity -‐43,740* 4,877
High interactivity -‐64,098* 6,633
Low interactivity No interactivity 13,929* 4,897
Medium interactivity -‐29,811* 5,312
High interactivity -‐50,168* 6,959
Medium interactivity No interactivity 43,740* 4,877
63
Low interactivity 29,811* 5,312
High interactivity -‐20,358* 6,944
High interactivity No interactivity 64,098* 6,633
Low interactivity 50,168* 6,959
Medium interactivity 20,358* 6,944
Games-‐Howell No interactivity Low interactivity -‐13,929* 3,200
Medium interactivity -‐43,740* 4,417
High interactivity -‐64,098* 12,015
Low interactivity No interactivity 13,929* 3,200
Medium interactivity -‐29,811* 5,454
High interactivity -‐50,168* 12,434
Medium interactivity No interactivity 43,740* 4,417
Low interactivity 29,811* 5,454
High interactivity -‐20,358 12,802
High interactivity No interactivity 64,098* 12,015
Low interactivity 50,168* 12,434
Medium interactivity 20,358 12,802
64
Multiple Comparisons
Dependent Variable: Retweet_count
(I) Machine_interactivity (J) Machine_interactivity Sig.
95% Confidence
Interval
Lower Bound
Scheffe No interactivity Low interactivity ,044 -‐27,62
Medium interactivity ,000 -‐57,38
High interactivity ,000 -‐82,65
Low interactivity No interactivity ,044 ,23
Medium interactivity ,000 -‐44,67
High interactivity ,000 -‐69,63
Medium interactivity No interactivity ,000 30,10
Low interactivity ,000 14,96
High interactivity ,035 -‐39,78
High interactivity No interactivity ,000 45,55
Low interactivity ,000 30,71
Medium interactivity ,035 ,94
Games-‐Howell No interactivity Low interactivity ,000 -‐22,16
65
Medium interactivity ,000 -‐55,10
High interactivity ,000 -‐95,07
Low interactivity No interactivity ,000 5,70
Medium interactivity ,000 -‐43,83
High interactivity ,000 -‐82,21
Medium interactivity No interactivity ,000 32,38
Low interactivity ,000 15,79
High interactivity ,385 -‐53,33
High interactivity No interactivity ,000 33,12
Low interactivity ,000 18,13
Medium interactivity ,385 -‐12,62
Multiple Comparisons
Dependent Variable: Retweet_count
(I) Machine_interactivity (J) Machine_interactivity
95% Confidence
Interval
Upper Bound
Scheffe No interactivity Low interactivity -‐,23
Medium interactivity -‐30,10
High interactivity -‐45,55
Low interactivity No interactivity 27,62
Medium interactivity -‐14,96
High interactivity -‐30,71
66
Medium interactivity No interactivity 57,38
Low interactivity 44,67
High interactivity -‐,94
High interactivity No interactivity 82,65
Low interactivity 69,63
Medium interactivity 39,78
Games-‐Howell No interactivity Low interactivity -‐5,70
Medium interactivity -‐32,38
High interactivity -‐33,12
Low interactivity No interactivity 22,16
Medium interactivity -‐15,79
High interactivity -‐18,13
Medium interactivity No interactivity 55,10
Low interactivity 43,83
High interactivity 12,62
High interactivity No interactivity 95,07
Low interactivity 82,21
Medium interactivity 53,33
*. The mean difference is significant at the 0.05 level.
67
Machine_interactivity N
Subset for alpha = 0.05
1 2 3
Scheffea,b No interactivity 1733 ,31
Low interactivity 1192 14,24
Medium interactivity 1209 44,05
High interactivity 495 64,40
Sig. ,147 1,000 1,000
Means for groups in homogeneous subsets are displayed.
a. Uses Harmonic Mean Sample Size = 938.242.
b. The group sizes are unequal. The harmonic mean of the group sizes is used. Type I error levels
are not guaranteed.
68
Connection interpersonal interactivity and engagement
Descriptives
Retweet_count
N Mean Std. Deviation Std. Error
95% Confidence
Interval for Mean
Lower Bound
Original tweet 630 51,31 145,619 5,802 39,92
Unedited retweet 400 119,73 335,986 16,799 86,70
Edited Retweet 20 64,10 59,487 13,302 36,26
@Mention 387 51,30 203,575 10,348 30,96
@Reply 3192 ,40 3,428 ,061 ,28
Total 4629 22,17 132,050 1,941 18,37
95% Confidence Interval for
Mean
Minimum Maximum Upper Bound
Original tweet 62,71 0 2665
Unedited retweet 152,76 1 4590
Edited Retweet 91,94 5 217
@Mention 71,65 0 2775
@Reply ,52 0 123
Total 25,98 0 4590
69
Test of Homogeneity of Variances
Retweet_count
Levene Statistic df1 df2 Sig.
149,240 4 4624 ,000
ANOVA
Sum of Squares df Mean Square F Sig.
Between Groups 6218630,021 4 1554657,505 96,517 ,000
Within Groups 74481201,786 4624 16107,526
Total 80699831,807 4628
Post Hoc Tests
Multiple Comparisons
(I) TypeofTweet (J) TypeofTweet
Mean
Difference (I-‐J) Std. Error Sig.
95%
Confidence
Interval
Lower Bound
Scheffe Original tweet Unedited retweet -‐68,417* 8,114 ,000 -‐93,42
Edited Retweet -‐12,787 28,826 ,995 -‐101,61
@Mention ,010 8,197 1,000 -‐25,25
@Reply 50,913* 5,533 ,000 33,86
Unedited retweet Original tweet 68,417* 8,114 ,000 43,41
70
Edited Retweet 55,630 29,080 ,454 -‐33,98
@Mention 68,428* 9,049 ,000 40,54
@Reply 119,330* 6,732 ,000 98,59
Edited Retweet Original tweet 12,787 28,826 ,995 -‐76,04
Unedited retweet -‐55,630 29,080 ,454 -‐145,24
@Mention 12,798 29,103 ,996 -‐76,88
@Reply 63,700 28,468 ,287 -‐24,02
@Mention Original tweet -‐,010 8,197 1,000 -‐25,27
Unedited retweet -‐68,428* 9,049 ,000 -‐96,31
Edited Retweet -‐12,798 29,103 ,996 -‐102,48
@Reply 50,902* 6,831 ,000 29,85
@Reply Original tweet -‐50,913* 5,533 ,000 -‐67,96
Unedited retweet -‐119,330* 6,732 ,000 -‐140,07
Edited Retweet -‐63,700 28,468 ,287 -‐151,42
@Mention -‐50,902* 6,831 ,000 -‐71,95
Games-‐Howell
Original tweet Unedited retweet -‐68,417* 17,773 ,001 -‐117,08
Edited Retweet -‐12,787 14,512 ,901 -‐55,18
@Mention ,010 11,864 1,000 -‐32,45
@Reply 50,913* 5,802 ,000 35,04
Unedited retweet Original tweet 68,417* 17,773 ,001 19,76
Edited Retweet 55,630 21,428 ,078 -‐3,77
@Mention 68,428* 19,731 ,005 14,46
@Reply 119,330* 16,799 ,000 73,29
71
Edited Retweet Original tweet 12,787 14,512 ,901 -‐29,61
Unedited retweet -‐55,630 21,428 ,078 -‐115,03
@Mention 12,798 16,853 ,941 -‐34,96
@Reply 63,700* 13,302 ,001 23,70
@Mention Original tweet -‐,010 11,864 1,000 -‐32,47
Unedited retweet -‐68,428* 19,731 ,005 -‐122,40
Edited Retweet -‐12,798 16,853 ,941 -‐60,56
@Reply 50,902* 10,348 ,000 22,54
@Reply Original tweet -‐50,913* 5,802 ,000 -‐66,78
Unedited retweet -‐119,330* 16,799 ,000 -‐165,37
Edited Retweet -‐63,700* 13,302 ,001 -‐103,70
@Mention -‐50,902* 10,348 ,000 -‐79,26
72
Multiple Comparisons
Dependent Variable: Retweet_count
(I) TypeofTweet (J) TypeofTweet
95% Confidence Interval
Upper Bound
Scheffe Original tweet Unedited retweet -‐43,41
Edited Retweet 76,04
@Mention 25,27
@Reply 67,96
Unedited retweet Original tweet 93,42
Edited Retweet 145,24
@Mention 96,31
@Reply 140,07
Edited Retweet Original tweet 101,61
Unedited retweet 33,98
@Mention 102,48
@Reply 151,42
@Mention Original tweet 25,25
Unedited retweet -‐40,54
Edited Retweet 76,88
@Reply 71,95
@Reply Original tweet -‐33,86
Unedited retweet -‐98,59
Edited Retweet 24,02
73
@Mention -‐29,85
Games-‐Howell Original tweet Unedited retweet -‐19,76
Edited Retweet 29,61
@Mention 32,47
@Reply 66,78
Unedited retweet Original tweet 117,08
Edited Retweet 115,03
@Mention 122,40
@Reply 165,37
Edited Retweet Original tweet 55,18
Unedited retweet 3,77
@Mention 60,56
@Reply 103,70
@Mention Original tweet 32,45
Unedited retweet -‐14,46
Edited Retweet 34,96
@Reply 79,26
@Reply Original tweet -‐35,04
Unedited retweet -‐73,29
Edited Retweet -‐23,70
@Mention -‐22,54
*. The mean difference is significant at the 0.05 level.
74
B3. Chi-‐square test
Industry type X tweet function
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent
IndustryType * TweetFunction 4629 100,0% 0 0,0% 4629 100,0%
IndustryType * TweetFunction Crosstabulation
TweetFunction
Information Action Community
IndustryType Retail Count 33 144 432
% within IndustryType 5,4% 23,6% 70,9%
% within TweetFunction 7,5% 25,1% 12,0%
% of Total 0,7% 3,1% 9,3%
Automotive Count 118 281 1407
% within IndustryType 6,5% 15,6% 77,9%
% within TweetFunction 26,8% 49,0% 38,9%
% of Total 2,5% 6,1% 30,4%
Beverages Count 79 34 166
% within IndustryType 28,3% 12,2% 59,5%
% within TweetFunction 17,9% 5,9% 4,6%
% of Total 1,7% 0,7% 3,6%
75
Technology Count 211 114 1610
% within IndustryType 10,9% 5,9% 83,2%
% within TweetFunction 47,8% 19,9% 44,5%
% of Total 4,6% 2,5% 34,8%
Total Count 441 573 3615
% within IndustryType 9,5% 12,4% 78,1%
% within TweetFunction 100,0% 100,0% 100,0%
% of Total 9,5% 12,4% 78,1%
IndustryType * TweetFunction Crosstabulation
Total
IndustryType Retail Count 609
% within IndustryType 100,0%
% within TweetFunction 13,2%
% of Total 13,2%
Automotive Count 1806
% within IndustryType 100,0%
% within TweetFunction 39,0%
% of Total 39,0%
Beverages Count 279
% within IndustryType 100,0%
% within TweetFunction 6,0%
76
% of Total 6,0%
Technology Count 1935
% within IndustryType 100,0%
% within TweetFunction 41,8%
% of Total 41,8%
Total Count 4629
% within IndustryType 100,0%
% within TweetFunction 100,0%
% of Total 100,0%
Chi-‐Square Tests
Value df
Asymp. Sig. (2-‐
sided)
Pearson Chi-‐Square 300,838a 6 ,000
Likelihood Ratio 271,368 6 ,000
Linear-‐by-‐Linear Association 1,433 1 ,231
N of Valid Cases 4629
a. 0 cells (.0%) have expected count less than 5. The minimum expected count is
26.58.
77
Tweet function X Machine interactivity
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent
TweetFunction *
Machine_interactivity 4629 100,0% 0 0,0% 4629 100,0%
TweetFunction * Machine_interactivity Crosstabulation
Machine_interactivity
No interactivity Low interactivity
TweetFunction Information Count 3 66
% within TweetFunction 0,7% 15,0%
% within Machine_interactivity 0,2% 5,5%
% of Total 0,1% 1,4%
Adjusted Residual -‐16,8 -‐5,4
Action Count 4 62
% within TweetFunction 0,7% 10,8%
% within Machine_interactivity 0,2% 5,2%
% of Total 0,1% 1,3%
Adjusted Residual -‐19,4 -‐8,7
78
Community Count 1726 1064
% within TweetFunction 47,7% 29,4%
% within Machine_interactivity 99,6% 89,3%
% of Total 37,3% 23,0%
Adjusted Residual 27,4 10,8
Total Count 1733 1192
% within TweetFunction 37,4% 25,8%
% within Machine_interactivity 100,0% 100,0%
% of Total 37,4% 25,8%
TweetFunction * Machine_interactivity Crosstabulation
Machine_interactivity
Medium
interactivity High interactivity
TweetFunction Information Count 197 175
% within TweetFunction 44,7% 39,7%
% within Machine_interactivity 16,3% 35,4%
% of Total 4,3% 3,8%
Adjusted Residual 9,3 20,7
Action Count 277 230
% within TweetFunction 48,3% 40,1%
% within Machine_interactivity 22,9% 46,5%
% of Total 6,0% 5,0%
Adjusted Residual 12,9 24,4
79
Community Count 735 90
% within TweetFunction 20,3% 2,5%
% within Machine_interactivity 60,8% 18,2%
% of Total 15,9% 1,9%
Adjusted Residual -‐16,9 -‐34,1
Total Count 1209 495
% within TweetFunction 26,1% 10,7%
% within Machine_interactivity 100,0% 100,0%
% of Total 26,1% 10,7%
TweetFunction * Machine_interactivity Crosstabulation
Total
TweetFunction Information Count 441
% within TweetFunction 100,0%
% within Machine_interactivity 9,5%
% of Total 9,5%
Adjusted Residual
Action Count 573
% within TweetFunction 100,0%
% within Machine_interactivity 12,4%
% of Total 12,4%
80
Adjusted Residual
Community Count 3615
% within TweetFunction 100,0%
% within Machine_interactivity 78,1%
% of Total 78,1%
Adjusted Residual
Total Count 4629
% within TweetFunction 100,0%
% within Machine_interactivity 100,0%
% of Total 100,0%
Chi-‐Square Tests
Value df
Asymp. Sig. (2-‐
sided)
Pearson Chi-‐Square 1808,422a 6 ,000
Likelihood Ratio 1878,363 6 ,000
Linear-‐by-‐Linear Association 1416,169 1 ,000
N of Valid Cases 4629
a. 0 cells (.0%) have expected count less than 5. The minimum expected count is
47.16.
81
Industry type X Machine interactivity
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent
IndustryType *
Machine_interactivity 4629 100,0% 0 0,0% 4629 100,0%
IndustryType * Machine_interactivity Crosstabulation
Machine_interactivity
No interactivity Low interactivity
IndustryType Retail Count 8,00 380,00
% within IndustryType 1,31 62,40
% within Machine_interactivity ,46 31,88
% of Total ,17 8,21
Adjusted Residual -‐19,77 22,19
Automotive Count 828,00 500,00
% within IndustryType 45,85 27,69
% within Machine_interactivity 47,78 41,95
% of Total 17,89 10,80
Adjusted Residual 9,46 2,41
Beverages Count 45,00 74,00
% within IndustryType 16,13 26,52
% within Machine_interactivity 2,60 6,21
82
% of Total ,97 1,60
Adjusted Residual -‐7,59 ,30
Technology Count 852,00 238,00
% within IndustryType 44,03 12,30
% within Machine_interactivity 49,16 19,97
% of Total 18,41 5,14
Adjusted Residual 7,86 -‐17,74
Total Count 1733,00 1192,00
% within IndustryType 37,44 25,75
% within Machine_interactivity 100,00 100,00
% of Total 37,44 25,75
IndustryType * Machine_interactivity Crosstabulation
Machine_interactivity
Medium
interactivity High interactivity
IndustryType Retail Count 93,00 128,00 609
% within IndustryType 15,27 21,02 100,0%
% within Machine_interactivity 7,69 25,86 13,2%
% of Total 2,01 2,77 13,2%
Adjusted Residual -‐6,54 8,85
Automotive Count 329,00 149,00 1806
% within IndustryType 18,22 8,25 100,0%
% within Machine_interactivity 27,21 30,10 39,0%
83
% of Total 7,11 3,22 39,0%
Adjusted Residual -‐9,79 -‐4,30
Beverages Count 115,00 45,00 279
% within IndustryType 41,22 16,13 100,0%
% within Machine_interactivity 9,51 9,09 6,0%
% of Total 2,48 ,97 6,0%
Adjusted Residual 5,92 3,03
Technology Count 672,00 173,00 1935
% within IndustryType 34,73 8,94 100,0%
% within Machine_interactivity 55,58 34,95 41,8%
% of Total 14,52 3,74 41,8%
Adjusted Residual 11,30 -‐3,27
Total Count 1209,00 495,00 4629
% within IndustryType 26,12 10,69 100,0%
% within Machine_interactivity 100,00 100,00 100,0%
% of Total 26,12 10,69 100,0%
Chi-‐Square Tests
Value df
Asymp. Sig. (2-‐
sided)
Pearson Chi-‐Square 992,903a 9 ,000
Likelihood Ratio 1102,108 9 ,000
Linear-‐by-‐Linear Association 6,762 1 ,009
N of Valid Cases 4629
84
a. 0 cells (.0%) have expected count less than 5. The minimum expected count is
29.83.
Interpersonal interactivity X Tweet function
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent
TweetFunction * TypeofTweet 4629 100,0% 0 0,0% 4629 100,0%
TweetFunction * TypeofTweet Crosstabulation
TypeofTweet
Original tweet Unedited retweet
TweetFunction Information Count 181,00 58,00
% within TweetFunction 41,04 13,15
% within TypeofTweet 28,73 14,50
% of Total 3,91 1,25
Adjusted Residual 17,66 3,54
Action Count 375,00 98,00
% within TweetFunction 65,45 17,10
% within TypeofTweet 59,52 24,50
% of Total 8,10 2,12
85
Adjusted Residual 38,66 7,70
Community Count 74,00 244,00
% within TweetFunction 2,05 6,75
% within TypeofTweet 11,75 61,00
% of Total 1,60 5,27
Adjusted Residual -‐43,32 -‐8,65
Total Count 630 400
% within TweetFunction 13,6% 8,6%
% within TypeofTweet 100,0% 100,0%
% of Total 13,6% 8,6%
TweetFunction * TypeofTweet Crosstabulation
TypeofTweet
Edited Retweet @Mention @Reply
TweetFunction Information Count ,00 200,00 2,00
% within TweetFunction ,00 45,35 ,45
% within TypeofTweet ,00 51,68 ,06
% of Total ,00 4,32 ,04
Adjusted Residual -‐1,45 29,51 -‐32,69
Action Count ,00 97,00 3,00
% within TweetFunction ,00 16,93 ,52
% within TypeofTweet ,00 25,06 ,09
% of Total ,00 2,10 ,06
86
Adjusted Residual -‐1,68 7,92 -‐37,82
Community Count 20,00 90,00 3187,00
% within TweetFunction ,55 2,49 88,16
% within TypeofTweet 100,00 23,26 99,84
% of Total ,43 1,94 68,85
Adjusted Residual 2,37 -‐27,25 53,32
Total Count 20 387 3192
% within TweetFunction 0,4% 8,4% 69,0%
% within TypeofTweet 100,0% 100,0% 100,0%
% of Total 0,4% 8,4% 69,0%
TweetFunction * TypeofTweet Crosstabulation
Total
TweetFunction Information Count 441
% within TweetFunction 100,0%
% within TypeofTweet 9,5%
% of Total 9,5%
Adjusted Residual
Action Count 573
% within TweetFunction 100,0%
% within TypeofTweet 12,4%
% of Total 12,4%
87
Adjusted Residual
Community Count 3615
% within TweetFunction 100,0%
% within TypeofTweet 78,1%
% of Total 78,1%
Adjusted Residual
Total Count 4629
% within TweetFunction 100,0%
% within TypeofTweet 100,0%
% of Total 100,0%
Chi-‐Square Tests
Value df
Asymp. Sig. (2-‐
sided)
Pearson Chi-‐Square 3612,377a 8 ,000
Likelihood Ratio 3480,683 8 ,000
Linear-‐by-‐Linear Association 1828,680 1 ,000
N of Valid Cases 4629
a. 2 cells (13.3%) have expected count less than 5. The minimum expected count
is 1.91.
88
Interpersonal interactivity X Industry type
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent
IndustryType * TypeofTweet 4629 100,0% 0 0,0% 4629 100,0%
Chi-‐Square Tests
Value df
Asymp. Sig. (2-‐
sided)
Pearson Chi-‐Square 324,033a 12 ,000
Likelihood Ratio 279,296 12 ,000
Linear-‐by-‐Linear Association 37,178 1 ,000
N of Valid Cases 4629
a. 2 cells (10.0%) have expected count less than 5. The minimum expected count
is 1.21.
89
IndustryType * TypeofTweet Crosstabulation
TypeofTweet
Original tweet Unedited retweet Edited Retweet
IndustryType Retail Count 81 104 0
% within IndustryType 13,3% 17,1% 0,0%
% within TypeofTweet 12,9% 26,0% 0,0%
% of Total 1,7% 2,2% 0,0%
Adjusted Residual -‐,2 8,0 -‐1,7
Automotive Count 272 129 16
% within IndustryType 15,1% 7,1% 0,9%
% within TypeofTweet 43,2% 32,3% 80,0%
% of Total 5,9% 2,8% 0,3%
Adjusted Residual 2,3 -‐2,9 3,8
Beverages Count 104 15 0
% within IndustryType 37,3% 5,4% 0,0%
% within TypeofTweet 16,5% 3,8% 0,0%
% of Total 2,2% 0,3% 0,0%
Adjusted Residual 11,9 -‐2,0 -‐1,1
Technology Count 173 152 4
% within IndustryType 8,9% 7,9% 0,2%
% within TypeofTweet 27,5% 38,0% 20,0%
% of Total 3,7% 3,3% 0,1%
Adjusted Residual -‐7,9 -‐1,6 -‐2,0
90
Total Count 630 400 20
% within IndustryType 13,6% 8,6% 0,4%
% within TypeofTweet 100,0% 100,0% 100,0%
% of Total 13,6% 8,6% 0,4%
TypeofTweet
@Mention @Reply
IndustryType Retail Count 35 389 609
% within IndustryType 5,7% 63,9% 100,0%
% within TypeofTweet 9,0% 12,2% 13,2%
% of Total 0,8% 8,4% 13,2%
Adjusted Residual -‐2,5 -‐2,9
Automotive Count 142 1247 1806
% within IndustryType 7,9% 69,0% 100,0%
% within TypeofTweet 36,7% 39,1% 39,0%
% of Total 3,1% 26,9% 39,0%
Adjusted Residual -‐1,0 ,1
Beverages Count 55 105 279
% within IndustryType 19,7% 37,6% 100,0%
% within TypeofTweet 14,2% 3,3% 6,0%
% of Total 1,2% 2,3% 6,0%
Adjusted Residual 7,1 -‐11,7
Technology Count 155 1451 1935
91
% within IndustryType 8,0% 75,0% 100,0%
% within TypeofTweet 40,1% 45,5% 41,8%
% of Total 3,3% 31,3% 41,8%
Adjusted Residual -‐,7 7,5
Total Count 387 3192 4629
% within IndustryType 8,4% 69,0% 100,0%
% within TypeofTweet 100,0% 100,0% 100,0%
% of Total 8,4% 69,0% 100,0%
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