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Impact of content characteristics and emotion on behavioral engagement in social media: literature review and research agenda


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We present a review of N = 45 studies, which deals with the effect of characteristics of social media content (e.g., topic or length) on behavioral engagement. In addition, we reviewed the possibility of a mediating effect of emotional responses in this context (e.g., arousing content has been shown to increase engagement behavior). We find a diverse body of research, particularly for the varying content characteristics that affect engagement, yet without any conclusive results. We therefore also highlight potential confounding effects causing such diverging results for the relationship between content characteristics and content engagement. We find no study that evaluates the mediating effect of emotional responses in the content—engagement relationship and therefore call for further investigations. In addition, future research should apply an extended communication model adapted for the social media context to guarantee rigorous research.
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Electronic Commerce Research (2021) 21:329–345
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Impact ofcontent characteristics andemotion
onbehavioral engagement insocial media: literature
review andresearch agenda
MelanieSchreiner1 · ThomasFischer1· ReneRiedl1,2
Published online: 10 May 2019
© The Author(s) 2019
We present a review of N = 45 studies, which deals with the effect of characteristics
of social media content (e.g., topic or length) on behavioral engagement. In addition,
we reviewed the possibility of a mediating effect of emotional responses in this con-
text (e.g., arousing content has been shown to increase engagement behavior). We
find a diverse body of research, particularly for the varying content characteristics
that affect engagement, yet without any conclusive results. We therefore also high-
light potential confounding effects causing such diverging results for the relation-
ship between content characteristics and content engagement. We find no study that
evaluates the mediating effect of emotional responses in the content—engagement
relationship and therefore call for further investigations. In addition, future research
should apply an extended communication model adapted for the social media con-
text to guarantee rigorous research.
Keywords Affective content· Content engagement· Content marketing· Emotional
effect· Engagement behavior· Social media communication
1 Introduction
Being present on social media platforms such as Facebook, Twitter, or Instagram
has become important for companies for a variety of reasons, be it to promote
their brand, their products, or for general publicity [2]. It is estimated that by
2019, the content marketing industry will have a volume of about $ 300 billion
Electronic supplementary material The online version of this article (https ://
0-019-09353 -8) contains supplementary material, which is available to authorized users.
* Melanie Schreiner
1 University ofApplied Sciences Upper Austria, Steyr, Austria
2 Johannes Kepler University Linz, Linz, Austria
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M.Schreiner et al.
1 3
and thus will have more than doubled its volume within only 5years (2014: about
$ 145 billion) [63]. User interaction with this content (i.e., content engagement)
is a key indicator of its popularity and is used to assess the success of social
media activities (e.g., [8]). Moreover, content engagement is a precondition for
the positive impact of social media content on company success (e.g., “likes” of
a company’s Facebook post may have a positive effect on sales performance [21],
or branding [22]). Hence, we initially want to summarize research on content
characteristics by answering the following question:
RQ 1. Which content characteristics have been shown to be positively
related to engagement?
In addition to the direct link between content characteristics and engagement, it
has also been shown that social media activities can elicit emotional responses.
For example, based on emotion self-reports, Lin and Utz [34] evaluated the posi-
tive (i.e., happiness) and negative (i.e., envy) emotional response when brows-
ing through Facebook messages. Their results show that a close relationship to
the sender of the post evokes more happiness. Further, using a range of neuro-
physiological measures (i.e., skin conductance, pupil dilation, blood volume
pulse, respiratory activity, electromyogram, electroencephalogram) Mauri et al.
[37] compared the experience of viewing one’s own Facebook page with relaxing
or stressful activities (i.e., viewing a slide show of panoramas or mathematical
tasks). They found clear neurophysiological evidence for a flow state elicited by
social media interaction, which is significantly different from relaxation or stress
and characterized by high arousal and high valence (i.e., a very positive and excit-
ing experience).
This study also highlights the conceptualization of the relationship between
content (input) and engagement (behavior) as we want to apply it in this review,
which is in accordance with the stimulus–organism–response (S–O–R) paradigm
[62]. Based on this conceptualization, we are interested in the general effect of
content as an input on engagement as an output, mediated by individual affective
processes (organism), which are represented by emotional responses. This leads
us to the second research question:
RQ 2. Which effects do emotions have in the context of content characteris-
tics and content engagement?
In line with traditional procedures for the analysis of mediator effects (e.g., [5]),
we further break down this research question into three additional sub-questions;
that is: RQ 2a on the content/emotion relationship, RQ 2b on the emotion/engage-
ment relationship, and we further include RQ 2c to report on studies which have
already investigated a potential mediating effect:
RQ 2a. Which effects do content characteristics have on emotions?
RQ 2b. Which effects do emotions have on content engagement?
RQ 2c. Has there been research into the mediating effect of emotions
between content characteristics and content engagement?
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Impact ofcontent characteristics andemotion onbehavioral…
The resulting research model, which will guide our literature review, is summa-
rized in Fig.1. Based on the involved relationships, the remainder of this article is
structured as follows: In the next section, we discuss our understanding of content
engagement, content characteristics, and emotions, and present working definitions
for each of the involved constructs. Then, we describe the methodology of our litera-
ture review including search, selection, and analysis of relevant publications, before
moving on to the presentation of our results. We then discuss our findings and high-
light potential limitations of our review. Finally, we close with a concluding state-
ment on our findings and outline avenues for future research.
2 Theoretical background
Social media. Social media enables the creation of content, which allows companies
to contact social media users and to communicate with them (e.g., [17]). Thus com-
panies can strengthen relations with their customers and build new relations with
potential customers (e.g., [61]). In the context of our study, we focus on social media
platforms providing companies with the possibility to publish so- called marketer-
generated content such as Facebook or Twitter.
Content engagement. In addition to the conceptualization as a psychological state
(e.g., [9]), engagement can be a synonym for the interaction with content, which
is the consequence of psychological processes (e.g., [16]). Such a conceptualiza-
tion has, for example, been proposed by van Doorn etal. [56], who identified five
essential dimensions of customer engagement behavior, including the valence of the
response (i.e., positive or negative feedback) and contextual factors (e.g., where and
when the feedback is placed).
Though the psychological processes (i.e., attitudes and behavioral intentions
involved in the formation of engagement behaviors) are important to get a full
understanding of the causal process behind the content/engagement relationship,
we follow the conceptualization by van Doorn etal. [56] and focus on behavioral
results (i.e., actual interaction, such as Likes). The main argument for this concep-
tualization is practical significance as actual behaviors are a more straightforward
measurement point for businesses if compared to antecedents of actual behavior. For
the purpose of our review, we therefore conceptualize content engagement as the
Fig. 1 Research model involving content characteristics, emotions, and content engagement
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M.Schreiner et al.
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measurable results of individual interaction with content on social media channels.
Importantly, such a focus does not imply that behavior antecedents such as attitudes,
behavioral intentions, or even neurophysiological processes (see, for example, [43])
are generally less important objects of study.
Content characteristics. In a communication process, content usually refers to a
message that the sender directs towards potential receivers. A way to further break
down the structure of a communication process, which was also used in the context
of research on social media before (e.g., [29]), was proposed by Lasswell [31] who
stated that the answers to the following questions represent the main characteristics
of an act of communication: (1) who, (2) says what, (3) in which channel, (4) to
whom, (5) with what effect?
To contextualize our review, we already answered the main part of these ques-
tions, as we are interested in corporate communication (1), in the context of social
media (3), leading to engagement (5). As we are mostly interested in general effects,
we do not narrow down the target group (4) of this communication process and
maintain the view that social media communication can be broadly targeted at every
individual with Internet access.
When we refer to content characteristics, we are now particularly interested in the
remaining element (2) “says what”, and extend it by adding “in which context”. We
are therefore particularly interested in those parts of a social media post that can be
manipulated by the sender during its creation. Hence, not only is the actual topic and
the way it is presented included here (i.e., the “what” of the content), but we also
consider design features such as whether pictures are included or not, or how many
lines of text are included. Moreover, we consider the context of the message (e.g.,
when it was posted or whether a particular position in a stream of messages was
chosen for this particular message).
Emotions. It is particularly difficult to define “emotion” as there are other closely
related affective concepts, such as preferences or moods [45]. Hence, instead of a
unified definition, a model that has garnered much interest, particularly by introduc-
ing a classification scheme rather than a general definition of emotions, is the Cir-
cumplex Model of Affect proposed by Russell [44]. This model, which has also been
applied to the social media context before (e.g., [64]), uses the two dimensions of
valence (positive vs. negative) and arousal (arousing vs. relaxing) to classify affec-
tive states. The basic premise of this model is that emotions are the outcome of an
appraisal process (i.e., a stimulus is processed, which then leads to an observable
affective response). In this paper, we focus on the observable part of emotional pro-
cessing, though we retain the general classification using valence and arousal.
Emotions have already been recognized to have an essential influence on human
behaviour in the marketing context. For example, Bagozzi etal. [3] stressed emo-
tions as markers, mediators and moderators of human responses when conducting
a literature review. As such, the emotional context of an ad (e.g., happy or sad TV
program) effects the evaluation of these ads and their recall [18]. Conducting an
experiment using FMRI to capture brain activity and self-reports, Bakalash and
Riemer [4] found that the memorability of an ad is positively associated with the
elicited emotional arousal. Similarly, Berger and Milkman [7] could confirm that
content (online news articles) that evokes high-arousal positive (awe) or negative
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Impact ofcontent characteristics andemotion onbehavioral…
(anger or anxiety) emotions is more viral and engaging (i.e., gets shared more
often by email) by following a multimethod approach (i.e., content analysis and
3 Methodology oftheliterature review
Literature search and selection. We conducted our literature review based on the
guidelines by Webster and Watson [60] and vom Brocke etal. [58]. Therefore, we
first created a list of keywords based on landmark publications (e.g., [14]) and more
recent research (e.g., [15]) (e.g., content engagement, emotional engagement, affec-
tive engagement, marketing communication, content emotion, social media, social
network, marketing; see online appendix for details).We used keyword combinations
to search for relevant publications in a total of 125 peer-reviewed journals and 6
peer-reviewed conference proceedings (see online appendix for details).
Our literature search and selection process consisted of four steps. The first step,
using the aforementioned keywords and outlets for an initial search, resulted in a total
of 4746 records in journals and conference publications. By evaluating the title and
abstract in a first screening phase (done by the first author), we excluded 4611 records,
as they were not thematically related to our research questions. In a second step, we
removed 85 duplicate records from the remaining 135 publications, which left us with
50 publications for a full-text analysis. The full-text analysis in the third step involved
a more detailed look at the relevance of a publication for our research questions (e.g.,
exclusion of studies as they do not consider the social media context; exclusion of
studies as they do not consider our focal constructs; exclusion of studies that do not
focus on marketer-generated content—initially done by the first author and discussed
with the second and third author). This step resulted in 31 publications being removed.
In a fourth step, the remaining 19 publications were used as the basis for a backward
and forward search as recommended by Webster and Watson [60], using Google
Scholar for the latter part. Through this procedure, we identified an additional 26 pub-
lications that are relevant to our research context. Hence, we were left with a sample of
N = 45 studies for our literature review (see online appendix for details).
Content characteristics. To synthesize previous literature, we used the catego-
ries listed below for classification (see online appendix for details). To develop this
classification scheme, we used previous research that has already introduced related
categories such as topic, length or interactivity (e.g., [47]). It has to be noted that
the topic category in particular comprises a broad set of characteristics, which led
us to split it up further. The main topic category, includes the type of content such
as announcements (e.g., [12]), promotions (e.g., [47]) or messages with emotional
(e.g., [35]), functional or informational message appeal (e.g., [53]). The component
category includes additional elements that are used to, for example, increase media
richness, not necessarily related to a certain topic (e.g., video, picture). In the over-
view of our classification results (see online appendix for details), for each content
characteristic we highlight the publications which included at least one indicator to
measure them.
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M.Schreiner et al.
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(A) Topic: We included indicators for the thematic orientation of a social
media post—Example: Posts about specific events or celebrities that appear
as testimonials for the company or brand (e.g., [19, 65]).
(B) Component: We included indicators of the media richness of a social
media post—Example: Other components in addition to text, such as pic-
tures, links, emojis, or hashtags. (e.g., [23, 35]).
(C) Length: We included indicators of the expansion extensiveness of a social
media post—Example: The number of lines of text or its visual height (e.g.,
[14, 47]).
(D) Interactivity: We included indicators of social media posts that actively
call for interaction—Example: Requests for customer feedback or raffles with
the chance of winning a prize (e.g., [14, 47]).
(E) Shared or original content: We included indicators for the originality of
a social media post—Example: Whether a post was created by the sender or
whether it was drawn from a source and reposted (e.g., [32]).
(F) Timing: We included indicators for the time-related context for a social
media post—Example: The time when it was created (e.g., a specific week-
day) or the frequency in which new content is added to a social media chan-
nel (e.g., [42, 53]).
(G) Position: We included indicators for the prioritization of a social media
post in a communication stream—Example: When a post is “pinned” to the
top of a news channel rather than being listed within the typical chronologi-
cal order of posts (e.g., [14, 47]).
Emotion. In line with our working definition of emotional responses, we
assigned studies to this category if they measured emotional responses on the
individual level. This includes psychological metrics (e.g., arousal and valence
measured via self-assessment, e.g. [40]), physiological measures (e.g., valence
measured via EEG, [30]; see also [39]) or behavioral measures (e.g., facial
expression via facial recognition, [33]). It is important to note here, that this
excludes analyses of affect, which do not involve individual reactions, such as
sentiment analyses of social media posts (e.g., [50]).
Content engagement. In line with our working definition of content engage-
ment, we classified studies which included at least one type of metric of interac-
tion with social media content. This included, for example, liking social media
posts (e.g., [51]), sharing a social media post (e.g., [40]), or commenting on a
social media post (e.g., [14]). Importantly though, we did not classify studies
that indicated engagement intention or comparable constructs that are anteced-
ents of behavior as the focus of our research is on actual behavior (liking, shar-
ing, commenting, etc.). Due to platform specifics (e.g., Twitter), some studies
used alternative measures such as favorites (e.g., [41]) or post clicks [16] to cap-
ture engagement; we classified these measures as “other” forms of engagement.
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Impact ofcontent characteristics andemotion onbehavioral…
4 Results
An initial result of our literature review is the distribution of reviewed studies related
to the relationships that we have investigated, which indicates the relative interest in
our two research questions and the respective sub-research questions (Fig.1). Out of
our 45 reviewed studies, 39 investigated the content/engagement relationship (RQ
1), with 13 of them including emotion as a content characteristic (e.g., sentiment
of a social media post), though not measuring it on the individual level. Six stud-
ies focused on the impact of emotional responses in this category and particularly
on the effects of content characteristics on emotional responses (RQ 2a). The link
between emotional responses and content engagement (RQ 2b) has only been inves-
tigated in two studies. Only one study investigated the mediating effect of emotion
(RQ 2c). Overall, the studies focused mainly on the social media platform Facebook
(31) and considered hardly any other medium (Twitter (7), YouTube (3), Instagram
(1), Weibo (1), Taobao (1), Groupon (1)). Four studies mentioned no specific focus
at all. Please note that the assignment of studies to research questions and medium is
overlapping and not mutually exclusive.
4.1 Which content characteristics have been shown tobe positively related
tocontent engagement? (RQ 1)
Of the seven categories of content characteristics that we focused on during our lit-
erature analysis (A…Topic to G…Position—see online appendix for details), most
studies included at least one indicator for the topic of a social media post (i.e., 34
studies). In addition, the use of components, which may enhance the media richness
of a social media post was investigated frequently (i.e., 27 studies), all other catego-
ries received far less attention.
The topic (A34 studies) of a social media post has received by far the most
attention in our reviewed studies, which can be expected as this category was pur-
posefully defined in a wider fashion, to encompass the large variety of topics that
can be dealt with in a social media post (e.g., from general announcements to social
media posts with the explicit goal of promoting a company’s brand or sales, [46]).
The main focus in this category has been on the potential message (i.e., the appeal)
that is transported through a social media post. In most cases, studies focused on one
of five appeal categories including (i) providing information (e.g., providing infor-
mation about a company’s services and products), (ii) providing entertainment, (iii)
transformational messages (e.g., content that addresses the recipient’s self-esteem
and potential desires), (iv) transactional (e.g., content related to sales, such as dis-
count opportunities), and (v) emotional (e.g., content evoking romantic feelings)
(e.g., [13, 27]).
Some studies in our sample provided evidence for a weak impact of informa-
tional appeal (e.g., data about the company, its history, etc.) on content engage-
ment (e.g., [13, 26]). However, most other studies (e.g., [14, 52]) did not find
such an effect. Content that was aimed at entertaining its recipients (e.g., funny
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M.Schreiner et al.
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videos) has been shown to positively impact engagement behaviors in most stud-
ies (e.g., [13, 26]), but not in all studies that investigated this relationship (e.g.,
[14]). Transformational content that directly addresses certain needs or desires
[27] has been shown to be effective in eliciting engagement (e.g., [11]). Further,
Gavilanes etal. [16] and Kim etal. [28] found a positive association between the
use of transactional content and content engagement, particularly for announce-
ments of sales or promotions (e.g., discounts, giveaways, etc.). Yet, Schultz [46,
47] found a contradictory effect for transactional content that included references
to competitors (e.g., price comparisons with competitors could lead to a nega-
tive effect on engagement). Emotional appeal was mainly investigated by using
secondary data and then coding it applying a set of discrete emotion categories
(e.g., fear, humor, romance, [51, 53]). Some studies found an impact of emo-
tional appeal of textual content (e.g., [50]) or video content [49] on behavioral
For the use of specific content components (B27 studies) (e.g., links, text,
videos), we found diverging results, regarding the effect of different compo-
nents and the specific target of this effect (e.g., whether likes or shares were
positively affected). Whereas some studies found photo content to receive more
engagement (e.g., [28]), others reported the same for video content (e.g., [14]).
In addition, similarly to the topic category, we also found diverging results for
specific types of engagement behavior (e.g., like or share). For example, Cvijikj
and Michahelles [13] found that including videos enhanced like and share behav-
ior, but it did not have any significant effect on the likelihood that users com-
mented on a post. For components more specific to the social media context, such
as hashtags and mentions [23], it was found that the use of hashtags can increase
engagement slightly [47].
In our sample, one study reported an effect of content length (C10 studies) on
engagement. Wagner etal. [59] found that a complete lack of text or an overabun-
dance of text are less effective in evoking content engagement compared to a moder-
ate amount of text.
For interactivity (D17 studies), the studies in our sample indicate that includ-
ing at least one interactive element (e.g., a question or a call for user feedback) can
enhance content engagement (e.g., [46]). Yet, the effects of these elements differ
strongly. For example, while a contest is more likely to affect “like” behavior, ques-
tions are more likely to lead to engagement via comments (e.g., [14, 16]).
Regarding whether social media content was original or shared (E3 studies),
previous research revealed that reposting content that was originally created by users
can positively impact content engagement [21], but, in general, original content is
more likely to evoke engagement behavior than shared content [27].
For timing (F18 studies), one study in our sample found a significant effect
of weekdays versus weekend on engagement, with content generating more engage-
ment when published on days other than the weekend [13]. Other studies that also
included timing as a variable found no significant effects [47].
All of the studies in our sample that investigated the effect of the position (G5
studies) of a social media post within a content stream (e.g., [14, 47], found a small,
positive effect on engagement behaviors.
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Impact ofcontent characteristics andemotion onbehavioral…
4.2 Which effects doemotions have inthecontext ofcontent characteristics
andcontent engagement? (RQ 2)
Seven studies investigated the effect of emotions in the content/engagement rela-
tionship (RQ 2). According our research model (Fig.1) we assigned them to (i) the
effect of content characteristics on emotional responses (6 studies) (RQ 2a), (ii) the
effect of emotional responses on engagement behavior (2 studies) (RQ 2b), and (iii)
the relationship among all three construct areas (1 study) (RQ 2c) (see online appen-
dix for details).
Effects of content characteristics on emotional responses (RQ 2a). A first group
of studies investigated the effect of video ads on emotional responses. The first study
in this group by Teixeira etal. [55] investigated whether emotional responses (i.e.,
joy and surprise, assessed using facial recognition software) elicited by videos could
affect a number of dependent variables (e.g., retention of the video, attention dur-
ing the video). Yet, they did not explore, which specific content characteristics are
involved in the formation of these emotional responses and instead selected videos
that they themselves classified as neutral or emotional. Shehu etal. [48] also focused
on the emotional response to video content, but they used a self-report scale (rang-
ing from “do not like at all” to “like very much”) so participants could evaluate a
video when they watched it. Yet, they also did not report on specific content charac-
teristics that could be related to the likability of a video ad.
Lewinski etal. [33] conducted an online experiment to examine the emotional
responses to content with a supposedly emotional appeal. The stimulus material
consisted of six videos varying in perceived amusement (low, medium or high).
Using facial expressions (assessed via Facereader software), they classified the reac-
tions into discrete emotion categories (i.e., happiness, sadness, anger, surprise, fear,
and disgust). They only found that facial expressions which were classified as an
indicator of happiness (e.g., smiling) could be used to distinguish between amusing
and non-amusing videos (in high and medium conditions, not in the low amusement
In addition to the positive emotional response evoked by humor (i.e., highly
positive valence), Brown et al. [10] found that the combination with high levels
of arousal and potential negative valence, evoked by violence, can have crossover
effects. They conducted an online experiment, which involved humorous video ads
that varied in their level of violence intensity/severity (e.g., a fictional soft drink
commercial with a person being hurt to varying degrees so another person can get
to his soft drink). Violence intensity, severity and perceived humor were meas-
ured using self-report scales and it was found that perceived humor ameliorates the
potential negativity that might be a side-effect of violence depicted in ads.
Aside from video content and emotional appeal, some studies also investigated
the emotional response to content with transactional appeal. Yu [64] performed a
content analysis to examine the effect of content high in interactivity (e.g., using
greetings/wishes from the personified brand, questions aiming to engage) and with
a transactional appeal (e.g., product advertising, sales promotion) on engagement.
Using a semantic differential scale with six adjective pairs (i.e., happy/unhappy,
pleased/annoyed, content/melancholic, hopeful/despairing, satisfied/unsatisfied,
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M.Schreiner et al.
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and relaxed/bored, based on the valence and arousal conceptualization of the affec-
tive space by [38]), 13 independent coders classified the emotions evoked by the
content. They found that highly interactive content, that is using a personalization
of the brand, was perceived as more arousing and received more positive valence
ratings than transactional content intent on promoting certain products or sales
Transactional appeal should be investigated more thoroughly, as it can have posi-
tive effects as well, as shown by Kuan etal. [30]. In their laboratory experiment,
they showed product ads consisting of text and pictures to participants, which were
accompanied either by additional information or by testimonials (i.e., number of
people who bought the product and number of people who liked the product), or by
both or by none (control). The emotional effect (i.e., valence) of these variations was
measured using the Emotiv EPOC device (an EEG headset with 14 channels). They
found that “buy” information alone reduced valence, whereas the addition of “like”
information positively affected valence and “like” information alone as an addition
to the product ad led to the most positive effect on valence.
Effects of emotional responses on engagement behavior (RQ 2b). Only two stud-
ies in our sample focused on the relationship between emotional responses and
engagement behavior (i.e., [40, 64]). Nelson-Field etal. [40] used a sample of 800
video ads from Facebook (commercial as well as non-commercial), which were
coded by 28 raters using a list of 16 discrete emotion categories (i.e., hilarity, amuse-
ment, disgust, discomfort, inspiration, calmness, sadness, boredom, astonishment,
surprise, shock, irritation, exhilaration, happiness, anger, frustration). These discrete
emotions were then recoded into specific arousal and valence ratings. In general,
high arousal videos were found to be shared twice as often as low arousal videos
and positively valenced/high arousal videos were shared about 30% more than nega-
tively valenced/high arousal videos. Importantly, they found that valence of a video
alone did not lead to any statistically significant effects, with high levels of arousal
being a precondition for different sharing behaviors. Yu [64] investigated the effect
of different levels of arousal and valence evoked by brand posts on the propensity of
an individual to like, share, or comment on a post. While high levels of arousal and
or positive valence were positively related to the liking or sharing of a brand post, no
such effects were found for the engagement via a comment on the post.
Evidence for the mediating effect of emotional responses (RQ 2c). Although there
has been individual evidence for the relationship between content characteristics and
emotional responses (RQ 2a) and emotional responses and engagement behaviors
(RQ 2b), we only identified one study in our sample that included indicators for all
three areas (i.e., [64]). In this study, Yu evaluated the emotional response based on
a content analysis where the emotional perception of content was rated in terms of
emotional arousal and valence using two six-item semantic differential scales. They
found that social content (e.g., greetings, wishes or questions) is more arousing and
positively valenced than promotional content (e.g., advertising, sales promotion
or business information). Consequently, users who are more pleased and aroused
would rather engage with the content. Yet, in this study a mediating effect was not
statistically evaluated, which points to an existing research gap.
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Impact ofcontent characteristics andemotion onbehavioral…
5 Discussion andresearch agenda
Our review of the literature on the relationship between content, emotions and
engagement resulted in a number of insights and opportunities for research that
we want to point out. For this purpose, we first summarize the main findings for
each of our research questions below.
RQ 1. Which content characteristics have been shown to be positively related to
content engagement? We found that particularly the appeal of a social media post
and high media richness (e.g., the inclusion of components such as pictures or vid-
eos) can have a positive effect on engagement behaviors. Yet, these results are often
not conclusive and highly context dependent (e.g., the content strategy used by the
focal company). In addition, we found evidence for mediating effects. For example,
content including the chance for some sort of remuneration (e.g., posts that include
a raffle) has been shown to have a negative effect on likes but at the same time may
have a positive effect on comments [13]. As another example, Luarn etal. [36] found
that incentivized content generated more “like” behavior than entertaining content,
while entertaining content evoked more sharing behavior than incentivized content.
RQ 2a. Which effects do content characteristics have on emotions? The main
findings related to this question result from the analysis of the emotional responses
to video content (4 studies). All of them used pre-selected stimulus material in an
experimental setting. Yet, several ways to measure emotional response were applied,
and it is therefore challenging to compare the results. Only two studies considered
further content types and their effect on emotional responses. The contradictory
findings of content with transactional appeal could be attributed to the different
social media platforms that were used as medium. While Yu [64] analyzed content
effects on Facebook, Kuan etal. [30] focused on content from Groupon, a group
buying platform, which has a specific focus on transactional content.
RQ 2b. Which effects do emotions have on content engagement? Although
engagement was operationalized differently and content types did vary, emotional
arousal seems to enhance engagement behavior whereas emotional valence inter-
acts as a facilitator.
RQ 2c. Has there been research into the mediating effect of emotions between
content characteristics and content engagement? No, so far this relationship has
not been investigated systematically.
These descriptive results of our literature review highlight two main areas for
future research: Research Area 1—Investigations to resolve contradictory findings
related to the relationship between content characteristics and content engagement
and Research Area 2—Further investigations into the effects of emotional responses
in the relationship between content characteristics and content engagement.
Research Area 1 (Research Consolidation). Based on the communication
process as outlined in the communication model by Lasswell [31], we should
look for the reasons for contradictory findings in three main areas: (i) sender,
(ii) receiver, or (iii) medium. We want to point out that in each of these areas,
researchers should carefully argue for the choices related to their research design
and consider its benefits and remedies.
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M.Schreiner et al.
1 3
Sender. With many studies in our review being specific to a certain industry (e.g.,
[10, 52]), we argue that a lack in generalizability has led to very limited comparabil-
ity of studies on social media content engagement. This can hamper further inves-
tigations, particularly from a theoretical perspective and we therefore call for more
research focusing on the generalizability of results to varying industries.
Receiver. We argue that the analysis of the behavior of an anonymous mass of
receivers (as common in content analysis approaches) can lead to dubious insights.
As common in the social sciences, control variables that might influence focal con-
structs have to be specified beforehand, such as demographic (e.g., country of ori-
gin, [26]) or psychosocial factors (e.g., attitudes and motivations, [36]) and related
data has to be collected. Social media research is not excluded from this require-
ment and we argue that, even if content analysis is a comfortable way to collect and
analyze data whether it can actually form the basis for rigorous research should be
Medium. As social media platforms have different intended purposes (e.g., [25]),
the content/engagement relationship will also manifest itself differently within each
platform. Several researchers have called for the analysis of data from various plat-
forms in order to get insights on engagement behavior that are more generalizable
(e.g., [26, 36]). We follow this call for generalization and add that if an analysis of
several platforms is not possible (e.g., due to resource constraints), those selected
should be rigorously argued for and the consequences of this selection should be
explained, ideally including research propositions regarding a transfer of the results
to another platform.
Research Area 2 (Emotional Effects). Next to highlighting the scarcity of research
on the emotional effects of social media content, we want to highlight that insights
from marketing and information system research could help us to further substanti-
ate the link between emotional responses and content engagement and could serve
as a guide for future research.
In the information systems field, emotions have been prominent for a consider-
able amount of time. For example, Venkatesh [57] stressed the impact of emotion
on technology usage by extending the TAM (technology acceptance model). They
conducted three longitudinal field studies and used self-reports to acquire data on
categorical emotional response. The results show that perceived enjoyment has an
impact on the perceived ease of use and in consequence affects the behavioral inten-
tion to use. Similarly, based on a survey to capture categorical emotional response
(e.g., excitement, happiness, anger, and anxiety) Beaudry etal. [6] discovered that
excitement (i.e., high arousal) has a positive impact on task adaption and IT use.
In addition, previous research on human decision behavior in online auctions
could provide us with valuable insights on the potential mediating effect of emo-
tional responses between content and engagement. For example, Adam etal. [1]
conducted two experiments and captured arousal via skin conductance and self-
report and valence via heart rate. Following a mediation analysis approach [24]
they confirmed a mediating effect of arousal on the relationship between time
pressure and bidding behavior as well as the relationship between social com-
petition and bidding behavior. Using such a research process as an example, we
argue that a study design framework could be valuable for social media research,
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1 3
Impact ofcontent characteristics andemotion onbehavioral…
particularly when it comes to investigating the mediating effects of constructs
such as valence and arousal in the relationship between content and engagement.
We summarize our research agenda in Table1 and, finally, we also want to
highlight the limitations of our review, which can be opportunities for future
research. First, although we included a large number of publication outlets in our
review, we cannot completely eliminate the potential of publication bias. In addi-
tion, for the sake of greater clarity in our research goals we mostly used nar-
row definitions for our focal constructs. We resorted to the definition of engage-
ment as an observable outcome of interaction with content, though there are other
conceptualizations of this construct (e.g., [54]). We also resorted to an investi-
gation of emotional responses only although there are other affective constructs
(e.g., moods) that could be interesting in the context of social media content and
engagement (e.g., [3]). In general, our specific focus on content characteristics
that can be manipulated by the sender also leaves further room for extension.
6 Conclusion
Based on a review of N = 45 studies focusing on the relationship between content
characteristics and engagement behaviors in the context of social media, we found
initial indications for research investigating the mediating effects of emotional
responses. Yet, results are still only partly conclusive and it would thus be par-
ticularly worthwhile to use a classification of content, such as the one proposed
in this review, to compare social media content regarding its effect on emotional
responses and engagement behaviors, which will make future investigations more
comparable. In line with Gregor etal. [20], we also call for further investigations
in this context, using multi-method designs and a dimensional conceptualization
of emotional responses, to create a more elaborate understanding of the relation-
ship we initially depicted in Fig.1. Furthermore, we recommend future research
to use social media communication models as a framework in order to control for
confounders and to motivate their research designs.
Table 1 Research agenda
Research focus Future research areas
Content characteristics
on content engage-
1. Effects of content characteristics on engagement in various industry contexts
2. Effects of content characteristics on engagement on various social media
3. Consideration of receiver characteristics as control variables
4. Development of a communication model for the social media context
Emotional 1. Emotional effects of content characteristics on content engagement
2. Marketing and information systems research could provide results and
research methods that further elaborate on the effect of emotions
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M.Schreiner et al.
1 3
Acknowledgements Open access funding provided by University of Applied Sciences Upper Austria.
Compliance with ethical standards
Conflict of interest On behalf of all authors, the corresponding author states that there are no conflicts of
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 Interna-
tional License (http://creat iveco mmons .org/licen ses/by/4.0/), which permits unrestricted use, distribution,
and reproduction in any medium, provided you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons license, and indicate if changes were made.
1. Adam, M. T. P., Krämer, J., & Müller, M. B. (2015). Auction fever!: How time pressure and social
competition affect bidders’ arousal and bids in retail auctions. Journal of Retailing, 91(3), 468–485.
https :// i.2015.01.003.
2. Ashley, C., & Tuten, T. (2015). Creative strategies in social media marketing: An exploratory study
of branded social content and consumer engagement. Psychology and Marketing, 32(1), 15–27.
3. Bagozzi, R. P., Gopinath, M., & Nyer, P. U. (1999). The role of emotions in marketing. Journal of
the Academy of Marketing Science, 27(2), 184–206. https :// 70399 27200 5.
4. Bakalash, T., & Riemer, H. (2013). Exploring ad-elicited emotional arousal and memory for the ad
using fMRI. Journal of Advertising, 42(4), 275–291. https :// 367.2013.76806
5. Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psy-
chological research: Conceptual, strategic, and statistical considerations. Journal of Personality and
Social Psychology, 51(6), 1173–1182. https ://
6. Beaudry, A., & Pinsonneault, A. (2010). The other side of acceptance: Studying the direct and indi-
rect effects of emotions on information technology use. MIS Quarterly, 34(4), 689–710. https ://doi.
org/10.2307/25750 701.
7. Berger, J., & Milkman, K. L. (2012). What makes online content viral? Journal of Marketing
Research, 49(2), 192–205. https ://
8. Brettel, M., Reich, J.-C., Gavilanes, J. M., & Flatten, T. C. (2015). What drives advertising suc-
cess on Facebook? An advertising-effectiveness model: Measuring the effects in sales of “likes”
and other social-network stimuli. Journal of Advertising Research, 55(2), 162–175. https ://doi.
9. Brodie, R. J., Hollebeek, L. D., Juric, B., & Ilic, A. (2011). Customer engagement: Conceptual
domain, fundamental propositions, and implications for research. Journal of Service Research,
14(3), 252–271. https :// 70511 41170 3.
10. Brown, M. R., Bhadury, R. K., & Pope, N. K. L. (2010). The impact of comedic violence on viral
advertising effectiveness. Journal of Advertising, 39(1), 49–66. https ://
91-33673 90104 .
11. Coursaris, C., van Osch, W., Balogh McKay, B. A., & Quilliam, E. T. (2014). Social media market-
ing: Investigating empirical links between purchase involvement, strategy, content, and media type.
In Proceedings of the conference of the american academy of advertising, Atlanta (GA), USA (pp.
12. Cvijikj, I. P., & Michahelles, F. (2011). A case study of the effects of moderator posts within a
Facebook brand page. In Proceedings of the 3rd international conference on social informatics
(Socinfo), Singapore (pp. 161–170). https :// -0_21.
13. Cvijikj, I. P., & Michahelles, F. (2013). Online engagement factors on Facebook brand pages. Social
Network Analysis and Mining, 3(4), 843–861.
14. de Vries, L., Gensler, S., & Leeflang, P. S. H. (2012). Popularity of brand posts on brand fan pages:
An investigation of the effects of social media marketing. Journal of Interactive Marketing, 26(2),
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1 3
Impact ofcontent characteristics andemotion onbehavioral…
15. Dessart, L. (2017). Social media engagement: A model of antecedents and relational outcomes.
Journal of Marketing Management, 54(2), 1–25.
16. Gavilanes, J. M., Flatten, T. C., & Brettel, M. (2018). Content strategies for digital consumer
engagement in social networks: Why advertising is an antecedent of engagement. Journal of Adver-
tising, 47(1), 4–23.
17. Goh, K.-Y., Heng, C.-S., & Lin, Z. (2013). Social media brand community and consumer behav-
ior: Quantifying the relative impact of user- and marketer-generated content. Information Systems
Research, 24(1), 88–107. https ://
18. Goldberg, M. E., & Gorn, G. J. (1987). Happy and sad TV programs: How they affect reactions to
commercials. Journal of Consumer Research, 14(3), 387–403.
19. Gonzalez-Lafaysse, L., & Lapassouse-Madrid, C. (2016). Facebook and sustainable development: A
case study of a French supermarket chain. International Journal of Retail and Distribution Manage-
ment, 44(5), 560–582. https :// -01-2015-0012.
20. Gregor, S., Lin, A. C. H., Gedeon, T., Riaz, A., & Zhu, D. (2014). Neuroscience and a nomological
network for the understanding and assessment of emotions in information systems research. Journal
of Management Information Systems, 30(4), 13–48. https :// 42-12223 00402 .
21. Ha, S., Kankanhalli, A., Kishan, J. S., & Huang, K.-W. (2016). Does social media marketing really
work for online SMEs?: An empirical study. In Proceedings of the 37th international conference on
information systems (ICIS), Dublin, Ireland.
22. Hudson, S., Huang, L., Roth, M. S., & Madden, T. J. (2016). The influence of social media interac-
tions on consumer–brand relationships: A three-country study of brand perceptions and marketing
behaviors. International Journal of Research in Marketing, 33(1), 27–41. https ://
ijres mar.2015.06.004.
23. Hwong, Y.-L., Oliver, C., van Kranendonk, M., Sammut, C., & Seroussi, Y. (2017). What makes
you tick?: The psychology of social media engagement in space science communication. Computers
in Human Behavior, 68, 480–492. https ://
24. Imai, K., Keele, L., & Tingley, D. (2010). A general approach to causal mediation analysis. Psycho-
logical Methods, 15(4), 309–334. https :// 761.
25. Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities
of social media. Business Horizons, 53(1), 59–68.
26. Khan, I., Dongping, H., Wahab, A., & Lewandowski, D. (2016). Does culture matter in effectiveness
of social media marketing strategy? An investigation of brand fan pages. Aslib Journal of Informa-
tion Management, 68(6), 694–715.
27. Kim, C., & Yang, S.-U. (2017). Like, comment, and share on Facebook: How each behavior dif-
fers from the other. Public Relations Review, 43(2), 441–449. https ://
28. Kim, D.-H., Spiller, L., & Hettche, M. (2015). Analyzing media types and content orientations in
Facebook for global brands. Journal of Research in Interactive Marketing, 9(1), 4–30.
29. Knoll, J. (2016). Advertising in social media: A review of empirical evidence. International Journal
of Advertising, 35(2), 266–300. https :// 487.2015.10218 98.
30. Kuan, K. K. Y., Zhong, Y., & Chau, P. Y. K. (2014). Informational and normative social influence
in group-buying: Evidence from self-reported and EEG data. Journal of Management Information
Systems, 30(4), 151–178. https :// 42-12223 00406 .
31. Lasswell, H. D. (1948). The structure and function of communication in society. The Communica-
tion of Ideas, 37, 215–228.
32. Lee, J., & Hong, I. B. (2016). Predicting positive user responses to social media advertising: The
roles of emotional appeal, informativeness, and creativity. International Journal of Information
Management, 36(3), 360–373. https :// omgt.2016.01.001.
33. Lewinski, P., Fransen, M. L., & Tan, E. S. H. (2014). Predicting advertising effectiveness by facial
expressions in response to amusing persuasive stimuli. Journal of Neuroscience, Psychology, and
Economics, 7(1), 1–14. https :// 00012 .
34. Lin, R., & Utz, S. (2015). The emotional responses of browsing Facebook: Happiness, envy,
and the role of tie strength. Computers in Human Behavior, 52, 29–38. https ://
35. Liu, J., Li, C., Ji, Y. G., North, M., & Yang, F. (2017). Like it or not: The fortune 500’s Facebook
strategies to generate users’ electronic word-of-mouth. Computers in Human Behavior, 73, 605–
613. https ://
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
M.Schreiner et al.
1 3
36. Luarn, P., Lin, Y.-F., & Chiu, Y.-P. (2015). Influence of Facebook brand-page posts on online
engagement. Online Information Review, 39(4), 505–519.
37. Mauri, M., Cipresso, P., Balgera, A., Villamira, M., & Riva, G. (2011). Why is Facebook so success-
ful? Psychophysiological measures describe a core flow state while using Facebook. Cyberpsychol-
ogy, Behavior and Social Networking, 14(12), 723–731. https :// .2010.0377.
38. Mehrabian, A., & Russell, J. A. (1974). An approach to environmental psychology. Cambridge: The
MIT Press.
39. Müller-Putz, G. R., Riedl, R., & Wriessnegger, S. C. (2015). Electroencephalography (EEG) as a
research tool in the information systems discipline: Foundations, measurement, and applications.
Communications of the Association for Information Systems, 37, 911–948.
40. Nelson-Field, K., Riebe, E., & Newstead, K. (2013). The emotions that drive viral video. Aus-
tralasian Marketing Journal, 21(4), 205–211. https :// .2013.07.003.
41. Parganas, P., Anagnostopoulos, C., & Chadwick, S. (2015). ‘You’ll never tweet alone’: Manag-
ing sports brands through social media. Journal of Brand Management, 22(7), 551–568.
42. Pinto, M. B., & Yagnik, A. (2016). Fit for life: A content analysis of fitness tracker brands use
of Facebook in social media marketing. Journal of Brand Management. https ://
s4126 2-016-0014-4.
43. Riedl, R., & Léger, P.-M. (2016). Fundamentals of neuroIS: Information systems and the brain
(Studies in neuroscience, psychology and behavioral economics). Berlin: Springer.
44. Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychol-
ogy, 39(6), 1161–1178. https :// 714.
45. Scherer, K. R. (2005). What are emotions? And how can they be measured? Social Science Infor-
mation, 44(4), 695–729. https :// 18405 05821 6.
46. Schultz, C. D. (2016). Driving likes, comments, and shares on social networking sites. In Pro-
ceedings of the 18th annual international conference on electronic commerce (ICEC), Suwon,
Republic of Korea. ACM Press.
47. Schultz, C. D. (2017). Proposing to your fans: Which brand post characteristics drive consumer
engagement activities on social media brand pages? Electronic Commerce Research and Appli-
cations, 26, 23–34.
48. Shehu, E., Bijmolt, T. H. A., & Clement, M. (2016). Effects of likeability dynamics on consum-
ers’ intention to share online video advertisements. Journal of Interactive Marketing, 35, 27–43.
49. Southgate, D., Westoby, N., & Page, G. (2010). Creative determinants of viral video viewing.
International Journal of Advertising, 29(3), 349–368. https :// 04871 02012
50. Stieglitz, S., & Dang-Xuan, L. (2013). Emotions and information diffusion in social media-sen-
timent of microblogs and sharing behavior. Journal of Management Information Systems, 29(4),
217–248. https :// 42-12222 90408 .
51. Swani, K., Milne, G., & Brown, B. P. (2013). Spreading the word through likes on Facebook:
Evaluating the message strategy effectiveness of fortune 500 companies. Journal of Research in
Interactive Marketing, 7(4), 269–294.
52. Swani, K., & Milne, G. R. (2017). Evaluating Facebook brand content popularity for service ver-
sus goods offerings. Journal of Business Research, 79, 123–133.
53. Swani, K., Milne, G. R., Brown, B. P., Assaf, A. G., & Donthu, N. (2017). What messages
to post? Evaluating the popularity of social media communications in business versus con-
sumer markets. Industrial Marketing Management, 62, 77–87. https ://
54. Syrdal, H. A., & Briggs, E. (2018). Engagement with social media content: A qualitative explora-
tion. The Journal of Marketing Theory and Practice, 26(1–2), 4–22.
55. Teixeira, T., Wedel, M., & Pieters, R. (2012). Emotion-induced engagement in internet video adver-
tisements. Journal of Marketing Research, 49(2), 144–159. https ://
56. van Doorn, J., Lemon, K. N., Mittal, V., Nass, S., Pick, D., Pirner, P., et al. (2010). Customer
engagement behavior: Theoretical foundations and research directions. Journal of Service Research,
13(3), 253–266. https :// 70510 37559 9.
57. Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic moti-
vation, and emotion into the technology acceptance model. Information Systems Research, 11(4),
342–365. https :// .
58. vom Brocke, J., Simons, A., Niehaves, B., Riemer, K., Plattfaut, R., Cleven, A., etal. (2009). Recon-
structing the giant: On the importance of rigour in documenting the literature search process. In AIS
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1 3
Impact ofcontent characteristics andemotion onbehavioral…
(Ed.), Proceedings of the 17th European conference on information systems (ECIS 2009), Verona,
Italy (Vol. 9, pp. 2206–2217).
59. Wagner, T. F., Baccarella, C., & Voigt, K.-I. (2016). Antecedents of brand post popularity in Face-
book: The influence of images, videos, and text. In Proceedings of the 15th international marketing
trends conference (IMTC), Venice, Italy.
60. Webster, J., & Watson, R. T. (2002). Analyzing the past to prepare for the future: Writing a literature
review. MIS Quarterly, 26(2), xiii–xxiii.
61. Weiger, W. H., Wetzel, H. A., & Hammerschmidt, M. (2017). Leveraging marketer-generated
appeals in online brand communities. Journal of Service Management, 28(1), 133–156. https ://doi.
62. Woodworth, R. S. (1958). Dynamics of behavior. Oxford: Holt.
63. Young, J. (2015). Content marketing will be a $300 billion industry by 2019. Marketing Magazine.
64. Yu, J. (2014). We look for social, not promotion: Brand post strategy, consumer emotions, and
engagement—A case study of the Facebook brand pages. GSTF Journal on Media & Communica-
tions, 1(2), 32–41. https ://
65. Zhang, Y., Moe, W. W., & Schweidel, D. A. (2016). Modeling the role of message content and
influencers in social media rebroadcasting. International Journal of Research in Marketing, 34(1),
100–119. https :// mar.2016.07.003.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
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This chapter intends to analyze and develop the concept of the entrepreneurial university and social innovation ecosystem from multiple viewpoints, including HEI regional knowledge spillover and social innovation ecosystem theoretical approaches, as well as policy and research views. The emerging perspectives of the entrepreneurial universities in the knowledge economy are considered as an instrument for “innovation and development” that acts as an elixir for the social innovation paradigm. Due to its vital position in the horizon EU strategy, respectively entrepreneurial universities and the social innovation ecosystem are gaining increasing importance in the EU’s regional knowledge-based economic growth policy discourse. Since then, they’ve been used by policymakers around the world as building blocks for executing various innovation policies, including research and innovation, smart inclusive regional knowledge growth, social innovation, industrial development, and regional development policies. The responsiveness of entrepreneurial universities and the social innovation ecosystem is envisioned in this chapter as a “facilitator” for increasing knowledge-based economic development and innovation-driven regional growth.
... Importantly, the framework was widely applied in studies of online consumer behaviour (Eroglu et al. 2003;Manganari et al. 2009). Meanwhile, the most recent studies have applied the full S-O-R paradigm (Carlson et al. 2018a, b;Triantafillidou and Siomkos, 2018;Schreiner et al. 2021) or a part of the S-O-R framework to CEB on social media platforms' context (see Mishra, 2021). ...
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In the European Union, SMEs represent as much as 99% of businesses, but only 3 out of 10 companies have some international involvement. EU policy makers perceive SMEs internationalization as a desired path for global growth; thus, they have put forward certain tools which aim to boost the pace and scope of internationalization, i.e., by creating and facilitating access to support activities, sharing information, promoting cluster and networking initiatives, making support schemes consistent throughout the EU, etc. (Della Corte, Handbook of research on startegic Management in Small and Medium Enterprises. IGI Global, 2014). However, a vital point in creating a successful internationalization framework requires understanding that SMEs internationalization models may and do differ from those of multinational enterprises (MNEs). SMEs have a different structure, and they act differently, since their aims vary from those of MNEs (Knight and Liesch, Journal of World Business 51(1): 93–102, 2016; Buckley, Journal of World Business, 51(1): 74–82, 2016). Former studies of European SMEs indicate that there are specific traits of company characteristics that determine their internationalization process. Amongst the distinguished factors, size, activities performed, age, and experience counted as the most significant determinants of the expansion. However, these findings refer to occurrences dating back at least 5 years. In the era of rapid digitalization and—still—ongoing globalization, the impact of these factors might have diminished, making place for others. Therefore, the rising importance of digitalization calls for the need to identify new barriers and opportunities for SMEs to become international. The aim of this chapter is to see whether and how digitalization has influenced the internationalization models of Polish SMEs. We do not provide quantitative analysis that would allow us to statistically verify hypotheses on that matter; however, given the recent developments of the business world and internationalization trends, we assume that digitalization has had an impact on how companies expand abroad nowadays. The study has a screening aim and should allow us to determine whether, in the case of the Polish context, the matter requires further pursuit. The remainder of this chapter is structured as follows: first, we discuss the internationalization models which commonly referred to the international expansion of SMEs. Secondly, we discuss how digitalization can influence the process and its determinants. Finally, we present our research results based on quasi-focus group discussion with Polish SMEs. The study concerned the impact the digitalization has on the internationalization experience of those companies.
... Importantly, the framework was widely applied in studies of online consumer behaviour (Eroglu et al. 2003;Manganari et al. 2009). Meanwhile, the most recent studies have applied the full S-O-R paradigm (Carlson et al. 2018a, b;Triantafillidou and Siomkos, 2018;Schreiner et al. 2021) or a part of the S-O-R framework to CEB on social media platforms' context (see Mishra, 2021). ...
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Great Transformations like the Digital Transformation and Sustainability Transformation, their challenges and their consequences for society are increasingly discussed in theory and practice. In this context, especially the issue of dealing with fundamental and profound changes in economy, politics and society is coming to the fore. In countries with liberal democratic systems such as Germany, an entrepreneurial mindset is granted an important role in dealing with these transformations in a proactive and formative way. Thus, the issue as to how more entrepreneurial individuals and organizations—and therefore more “out of the box thinking”—can systematically be tapped and developed in Germany becomes essential. To this end, this chapter proposes a newly conceptualized interdisciplinary and integrative approach, which was distinctly designed for the addressing and winning of entrepreneurial individuals and organizations in a systematic and targeted manner. Because it is specifically tailored to the prevailing circumstances and structures in Germany, the approach emphasizes enlightened, voluntary and self-sustaining actions. Doing so, it offers an effective, but also legitimate way to address and win entrepreneurial personalities and organizations to contribute to actively shaping the Digital and Sustainability Transformation.
... Importantly, the framework was widely applied in studies of online consumer behaviour (Eroglu et al. 2003;Manganari et al. 2009). Meanwhile, the most recent studies have applied the full S-O-R paradigm (Carlson et al. 2018a, b;Triantafillidou and Siomkos, 2018;Schreiner et al. 2021) or a part of the S-O-R framework to CEB on social media platforms' context (see Mishra, 2021). ...
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The chapter sketches the past, present, and potential future of the dynamic capabilities framework. This essay is more by way of a personal reflection on the progress that has been made to date and the work remaining to be done. The dynamic capabilities framework has proved fertile ground for research and there is no evidence its momentum is slowing. In addition, I see the framework having numerous potential applications, several of which I have addressed in my own writing: (1) dynamic capabilities can serve as an overarching paradigm for teaching in business schools; (2) dynamic capabilities can potentially be built into a theory of the firm; and (3) dynamic capabilities is a policy tool for industrializing economies to help them understand the difference between accumulation and assimilation. Finally, innovation, including digital transformation, corporate entrepreneurship, and organizational behavior also contribute to the theoretical soundness of the dynamic capabilities framework.
... Importantly, the framework was widely applied in studies of online consumer behaviour (Eroglu et al. 2003;Manganari et al. 2009). Meanwhile, the most recent studies have applied the full S-O-R paradigm (Carlson et al. 2018a, b;Triantafillidou and Siomkos, 2018;Schreiner et al. 2021) or a part of the S-O-R framework to CEB on social media platforms' context (see Mishra, 2021). ...
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This paper aims to predict customer engagement behaviour (CEB), i.e. likes, shares, comments, and emoji reactions, on company posts on Facebook. A sample of 1109 brand posts from Facebook pages in Lithuania was used. The Random Forest method was used to train models to predict customer engagement behaviour based on features including time frame, content, and media types of brand posts. The data was used for training nine binary classification models using the Random Forest method, which can predict the popularity of a company’s posts. In terms of social score, accuracy of likes, comments, and shares varied from 68.4% (likes on a post) to 84.0% (comments on a post). For emotional responses, accuracy varied from 65.6% (‘wow’ on a post) to 82.5% (‘ha ha’ on a post). The data was collected from one single media platform and country, and encompassed emotional expressions at an early stage on Facebook. The findings of Random Forest prediction models can help organisations to make more efficient solutions for brand posts on Facebook to increase customer engagement. This paper outlines the first steps in creating a predictive engagement score towards diverse types of brand posts on Facebook. The same approach to features of brand posts might be applied to other social media platforms such as Instagram and LinkedIn.
... Importantly, the framework was widely applied in studies of online consumer behaviour (Eroglu et al. 2003;Manganari et al. 2009). Meanwhile, the most recent studies have applied the full S-O-R paradigm (Carlson et al. 2018a, b;Triantafillidou and Siomkos, 2018;Schreiner et al. 2021) or a part of the S-O-R framework to CEB on social media platforms' context (see Mishra, 2021). ...
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Eye-tracking application in social sciences including entrepreneurship education has increased significantly in the recent years. This technology has been used to investigate the learning process and how to foster it through instructions delivered, material used and the learning environment created. Traditional research with eye-tracking application mainly concentrates on visual aspects in the learning process including but not limited to text comprehension. A growing area of eye-tracking technologies is focused on entrepreneurship education including teacher education because schools are considered as an important stage for developing entrepreneurial competences. In general, the area of the application of eye tracking has become extremely wide in different sciences which also positively contributes to research in education. Transdisciplinary and multidisciplinary approaches are helpful to ensure multiple perspective as well as to ensure the validity of research data and results. This chapter is an attempt to critically reflect on how eye-tracking methodology is applied for research on entrepreneurship education and what are growing methodological challenges in it. At the end some implications for further studies in the field of entrepreneurship education are discussed as well as limitations of eye-tracking-based studies are highlighted.
Background During the COVID-19 pandemic, tribal and health organizations used social media to rapidly disseminate public health guidance highlighting protective behaviors such as masking and vaccination to mitigate the pandemic’s disproportionate burden on American Indian and Alaska Native (AI/AN) communities. Objective Seeking to provide guidance for future communication campaigns prioritizing AI/AN audiences, this study aimed to identify Twitter post characteristics associated with higher performance, measured by audience reach (impressions) and web behavior (engagement rate). Methods We analyzed Twitter posts published by a campaign by the Johns Hopkins Center for Indigenous Health from July 2020 to June 2021. Qualitative analysis was informed by in-depth interviews with members of a Tribal Advisory Board and thematically organized according to the Health Belief Model. A general linearized model was used to analyze associations between Twitter post themes, impressions, and engagement rates. Results The campaign published 162 Twitter messages, which organically generated 425,834 impressions and 6016 engagements. Iterative analysis of these Twitter posts identified 10 unique themes under theory- and culture-related categories of framing knowledge, cultural messaging, normalizing mitigation strategies, and interactive opportunities, which were corroborated by interviews with Tribal Advisory Board members. Statistical analysis of Twitter impressions and engagement rate by theme demonstrated that posts featuring culturally resonant community role models (P=.02), promoting web-based events (P=.002), and with messaging as part of Twitter Chats (P<.001) were likely to generate higher impressions. In the adjusted analysis controlling for the date of posting, only the promotion of web-based events (P=.003) and Twitter Chat messaging (P=.01) remained significant. Visual, explanatory posts promoting self-efficacy (P=.01; P=.01) and humorous posts (P=.02; P=.01) were the most likely to generate high–engagement rates in both the adjusted and unadjusted analysis. Conclusions Results from the 1-year Twitter campaign provide lessons to inform organizations designing social media messages to reach and engage AI/AN social media audiences. The use of interactive events, instructional graphics, and Indigenous humor are promising practices to engage community members, potentially opening audiences to receiving important and time-sensitive guidance.
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Penelitian ini bertujuan untuk menganalisis pengaruh waktu unggahan (hari unggah) dan tipe konten terhadap interaksi konten (jumlah suka, bagikan, dan komentar) yang terjadi pada TikTok. Total data yang terkumpul sebesar 815 unggahan yang bersumber dari 9 akun bisnis kuliner yang memasarkan produknya di TikTok untuk konsumen Indonesia. Pengumpulan data menggunakan metode analisis konten yang dianalisis menggunakan regresi binomial negatif pada SPSS. Hasil analisis menunjukkan bahwa hari unggah dan tipe konten memiliki pengaruh yang variatif terhadap interaksi konten (jumlah suka, bagikan, dan komentar) di TikTok. Penelitian ini dapat memperkaya literatur pemasaran media sosial dan content marketing dengan dibahasnya TikTok sebagai media sosial berbasis video pendek. Secara praktis, temuan dalam peneltian ini dapat membantu bisnis-bisnis di Indonesia untuk mengoptimalkan strategi pemasaran media sosial dan content marketing mereka.
Brand pages on social networking sites represent excellent vehicles for customer relationship management. These pages enable companies to interact with their customers and to foster their engagement. This study examines the relationship between, and moderating impact of, customer engagement behaviour and a COVID-19-related context as well as content types. The content of 1,946 company-generated Facebook brand posts of 16 media and technology companies was analysed. A hierarchical regression analysis was used to test the hypotheses. The results show that a COVID-19-related context and the content type are associated with customer engagement behaviour. These factors both partly interact and directly correlate with customer engagement behaviour. This study contributes to the literature by establishing (crisis-related) social media guidelines for media and technology companies.
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People are more and more using social networking sites (SNSs) like Facebook and MySpace to engage with others. The use of SNSs can have both positive and negative effect on the individual; however, the increasing use of SNSs might reveal that people look for SNSs because they have a positive experience when they use them. Few studies have tried to identify which particular aspects of the social networking experience make SNSs so successful. In this study we focus on the affective experience evoked by SNSs. In particular, we explore whether the use of SNSs elicits a specific psychophysiological pattern. Specifically, we recorded skin conductance, blood volume pulse, electroencephalogram, electromyography, respiratory activity, and pupil dilation in 30 healthy subjects during a 3-minute exposure to (a) a slide show of natural panoramas (relaxation condition), (b) the subject's personal Facebook account, and (c) a Stroop and mathematical task (stress condition). Statistical analysis of the psychophysiological data and pupil dilation indicates that the Facebook experience was significantly different from stress and relaxation on many linear and spectral indices of somatic activity. Moreover, the biological signals revealed that Facebook use can evoke a psychophysiological state characterized by high positive valence and high arousal (Core Flow State). These findings support the hypothesis that the successful spread of SNSs might be associated with a specific positive affective state experienced by users when they use their SNSs account.
A lack of a consensus regarding what constitutes engagement in the context of social media has made it difficult for scholars to generate theory in this area and has created challenges for managers attempting to demonstrate positive outcomes stemming from social media marketing. To address this issue, qualitative studies are undertaken with marketing practitioners and consumers to provide clarification and to formulate a formal definition of engagement in this context. The studies reveal it to be a psychological state of mind operating independently from interactive behaviors such as “liking” and sharing content. The findings offer new insight into consumer consumption of social media content and sow the seeds for future exploration.
Advertisers need to optimize their efforts on social networks to engage consumers effectively. Existing literature on this topic has not yet explained how social network advertising (SNA) can be categorized into different content types and how to conceptualize and operationalize digital consumer engagement (DCE) in social networks. Thus, we derive seven content categories for social network advertising and a four-level model for DCE based on consumers' intermediate mind-set responses. We propose the impact of different SNA categories as an antecedent of DCE. Our results confirm a significant but unequal impact of at least four content categories on various engagement metrics. We therefore distill the successful content strategies and content attributes for specific types of engagement and confirm intermediate responses to advertising in a real market situation.
This authored volume presents the fundamentals of NeuroIS, which is an emerging subfield within the Information Systems discipline that makes use of neuroscience and neurophysiological tools and knowledge to better understand the development, use, and impact of information and communication technologies. This book is an initial guide to this new research domain. The target audience primarily comprises PhD students and researchers, but the book may also be beneficial for graduate students and practitioners.
Directly engaging consumers with brand messages (posts) is one advantage of social networking sites. Using consumer engagement as a theoretical framework, the current study analyzes consumer engagement activities with brand posts, taking into account post characteristics, such as vividness, interactivity, content, and publication timing, while also controlling for post length, number of fans, and industry differences. The study identifies differences across different consumer engagement activities and industries. As such, vivid post characteristics yields mixed results, whereas post interactivity has a mainly positive effect on social interactions. If content categories address only some portion of the target audience, they negatively affect post interaction, compared to the baseline category. In terms of post publication, time at the top of the brand page increases the number of interactions, whereas weekday versus weekend has no effect on consumer engagement behavior. The findings challenge research and practice alike to account for these important differences.
Marketers using social media are struggling with its successful implementation, specifically in engaging their audiences through creation of popular brand content. Yet, creating popular brand content can lead to positive financial and brand outcomes. This research examines Fortune 500 companies' brand content strategies that contribute to Facebook content popularity metrics (i.e., number of likes and comments) for service versus goods offerings. Building on psychological motivation theory and the noted differences in culture and capabilities between goods and service firms, the article analyzes the key differences in service and goods brand content strategies in terms of branding, message appeals, and vividness. The findings from a multivariate multilevel Poisson model show that the use of corporate brand names is more popular for service messages whereas the use of product brand names, images, and videos is more popular for goods messages. Furthermore, service messages generate a higher number of comments than goods messages.
This study examines the effects of companies' social media communication strategies on users' electronic word-of-mouth behavior through a content analysis. The sample includes more than 15,000 posts from Fortune 500 companies' Facebook accounts in a five-year time frame. Three communication strategies are examined, including appeal, vividness, and interactivity. Results confirm the main effects of vividness and emotional appeals. However, interactivity does not help generate more eWOM behavior. Proposed interactions between vividness and appeal type, as well as between vividness and interactivity, are significant. Emotional appeals amplify the beneficial effects of vividness, whereas interactivity dampens the potency of vividness.
This article investigates individual-level antecedents and relational outcomes of social media engagement. Social media engagement approached in this study is a three-dimensional construct composed of affective, cognitive and behavioural dimensions. Surveying more than 48 Facebook pages, spanning nine product categories and 448 consumers, the results show that product involvement, attitude towards the community and online interaction propensity all impact social media engagement. The study also reveals that high social media engagement increases brand relationships significantly, particularly affecting brand trust, commitment and loyalty. Additionally, community engagement appears as a precursor of brand engagement. These findings provide insight into antecedents and outcomes of engagement for academic research and bring value to online brand and community management.
People engage in communication on Facebook via three behaviors—like, comment, and share. Facebook uses an algorithm that gives different weight to each behavior to determine what to show in user’s screen, suggesting that the strategic implication of each behavior may differ from the other. This study investigates when each behavior can be encouraged by organizational messages, thereby making clearer distinctions between three behaviors. A content analysis of organizational messages was conducted, where the researchers assessed message features and related them to each behavior separately. The findings indicated that different message features generated different behaviors: Sensory and visual features led to like, rational and interactive to comment, and sensory, visual, and rational to share. This suggests that like is an affectively driven, comment is a cognitively triggered behavior, and share is either affective or cognitive or a combination of both.
Purpose Along with traditional marketing channels, social media outlets are integrated as a part of the marketing mix. Social media has changed the dynamics of interaction between companies and consumers that foster this relationship. Managing brand fan pages on social networking sites is a specific way the companies are using. Customers can become brand fans on these pages and indicate that they like the brand posts, share on their wall or simply comment. The purpose of this paper is to analyse the impact of cultural differences on the effectiveness of social media metrics and scientifically tested brand engagement in terms of commitment, loyalty and brand recommendations. Design/methodology/approach The authors analysed 1,922 brand posts from five different brands of a single product category in three different countries. Ordinary least square and hierarchical moderation regression was used to test the hypotheses. Findings Results show that all determinants are not equally suitable for enhancement of number of likes, comments and shares. More specifically, vivid and interactive brand post determinants enhance the number of likes. Furthermore, interactive brand posts enhance the number of comments while vivid brand posts enhance number of shares. Moreover, impact and intensity vary across different cultures. Originality/value Brand fan page moderators can obtain guidance from the research in formulating their social media marketing strategies in order to decide which post determinants to place on the fan page.