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CustomerengagementonSocialMedia:Astudyofemo,onalappealsinbrandpostsonFacebookand

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Master’sThesisMasterofArtsinCorporateCommunicationAarhusUniversity,BusinessandSocialSciences

Author:MadsKistrupJørgensenStudentID:AU450874

Supervisor:BoLaursen

DepartmentofManagement

Source:www.researcher.watson.ibm.com/view_group.php?id=7424

Abstract

Purpose: The emergence of social media in today’s hyper-connected world has changed how

customers and brands interact. Communication symmetry has changed and customers now take an

active role in forming the image of brands online (Whitting & Deshpande, 2016). As a result, the

notion of customer engagement (CE) has emerged in the marketing literature, which seeks to

explore the interactions between brands and customers that goes beyond purchase transactions

(MSI, 2010). Emotional appeals in communication has been proposed as an antecedent of CE, but

research on the subject in a social media setting remains sparse. This paper will seek to address this

gap in the literature by answering the following research question: How do emotional appeals affect

customer engagement in a social media setting?

Method: This paper adopts a quantitative content analysis for analyzing emotional appeals in brand

posts on Facebook and Twitter. IBM’s artificial intelligence software “Watson” is used for

analyzing emotional sentiment and this study is, to the knowledge of the author, the first to adopt

such approach in this context. This approach makes it possible to conduct a large-scale analysis of

emotional appeals in brand posts. A total of 3536 tweets from Twitter and 1496 brand posts from

Facebook are analyzed in this study, representing 44 different brands across 4 major industries cars,

clothing, consumer electronics and financial services. The emotional tones of the brand posts are

related to the number and type of engagements each post receives. Engagement levels are

operationalized into three categories of engagement based on the works of Schivinski,

Christodoulides & Dabrowski (2016): consumption (“reactions” / “favorites”), contribution

(“comments” / “replies”) and creation (“shares” / “retweets”), which makes it possible to

compare engagement levels across Facebook and Twitter.

Findings: Based on the theoretical framework, four major hypotheses are proposed and sought

investigated in the analysis. An emotional appeal is found to be significantly related to the

consumption category of engagement on both Facebook and Twitter. It is further found that

different emotional appeals have a varying effect on CE: an appeal containing joy is found to be

positively related to consumption behavior, while an appeal containing sadness is found to be

positively related to creation behavior on Facebook. Furthermore, the analyses show that several

variables can influence the effectiveness of an emotional appeal on CE. Posts containing either a

link, a photo or a video are found to have a varying influence on the effectiveness of an emotional

appeal. An emotional appeal combined with a photo has the strongest influence on CE, while no

significant relationships are found for links and videos. The effectiveness of emotional appeals is

further found to vary between Facebook and Twitter as well as between different industries.

Discussion: The findings of this study underpin present research in the field of CEB on social

media (Swani, Milne & Brown, 2013) by validating the effectiveness of an emotional appeal in this

context. It further broadens our understanding of the dynamics by exploring the concept across

different social media channels (Facebook and Twitter) and industries, as well as investigating how

different post characteristics can influence the impact of emotions on CE. These findings underpin

the complexity of the concept and suggest that an array of potential variables should be taken into

consideration, when investigating this phenomenon in the academic literature or when practitioners

seek to foster engagement on social media.

Limitations: This study investigates the occurrence of engagement on social media as an effect of

emotional appeals but not the valence of this engagement. Customer engagement behaviors are

neither positive nor negative per se (Barger, Peltier & Schultz, 2016) and the findings should be

interpreted in this context. This entails, that the results should not be regarded as best practice

without reservations. As an example, a sadness appeal is found to correlate with creation behavior

on Facebook but it is not known whether these shares of brand post are in favor of the brands or

not. Lastly, adopting a quantitative content analysis for investigating communication has inherent

limitations. Adopting IBM Watson software makes it possible to conduct a large-scale analysis and

provide reliable and generalizable results which are argued to be needed in the field (Dessart,

Veloutsou & Morgan-Thomas., 2016). But on the other hand, this type of method can be argued to

reduce the validity of the study by not taking into consideration the complex nature of language.

These limitations should be held in mind, when interpreting the results of this research.

Keywords: Customer engagement behavior, social media communication, emotional appeal,

content analysis, IBM Watson, artificial intelligence.

Characters excluding spaces: 4222

Mads Kistrup Jørgensen AU ID: 450874

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CHAPTER1:INTRODUCTION 3

SCOPEOFRESEARCH 4RESEARCHQUESTIONANDCONTRIBUTION 5METHODS 6DELIMITATIONS 7STRUCTUREOFTHEPAPER 7

CHAPTER2:THEORETICALFRAMEWORK 9

CUSTOMERENGAGEMENT 10CONCEPTANDDEFINITION 10CUSTOMERENGAGEMENTBEHAVIOR 11EFFECTSOFCUSTOMERENGAGEMENT 14SOCIALMEDIA 15CUSTOMERENGAGEMENTONSOCIALMEDIA 16MESSAGESTRATEGIESFORCUSTOMERENGAGEMENTONSOCIALMEDIA 18EMOTIONSINCOMMUNICATION 21HYPOTHESISDEVELOPMENT 23HYPOTHESIS1 24HYPOTHESIS2 24HYPOTHESIS3 25HYPOTHESIS4 25

CHAPER3:METHODOLOGY 26

SCIENTIFICPOSITION 27METHODFORANALYSIS 28CONTENTANALYSIS 29IBMWATSONANDNATURALLANGUAGEPROCESSING 30TONEANALYZERANDCODINGOFTHEANALYSIS 32VALIDITYANDRELIABILITYOFTHECODES 33SAMPLING 34DATACOLLECTION 35TONEANALYZERANALYSIS 36STATISTICALANALYSIS 36DEFININGDEPENDENTVARIABLES 36INDEPENDENTVARIABLES 38OUTLIERS 38RECAPITALIZATIONOFTHEMETHODOLOGY 39

CHAPTER4:FINDINGS 41

PART1-DESCRIPTIVEANALYSIS 41INDUSTRIESANDEMOTIONALMESSAGEAPPEALS 41POSTCHARACTERISTICS 45CUSTOMERENGAGEMENTDESCRIPTIVE 47DESCRIPTIVEANALYSISCONCLUSION 53PART2–HYPOTHESESTESTING 53HYPOTHESIS1 54


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HYPOTHESIS2 55HYPOTHESIS3 57HYPOTHESIS4 59SUMMARYOFFINDINGS 61

CHAPTER5:DISCUSSION 63

H1:EMOTIONALAPPEALANDENGAGEMENTONSOCIALMEDIA 63HIERARCHYOFENGAGEMENTLEVELS 65H2:POSTCHARACTERISTICSANDEFFECTIVENESSOFEMOTIONALAPPEALS 66H3:IMPACTOFVARYINGEMOTIONS 67H4:EMOTIONSACROSSINDUSTRIES 69CUSTOMERENGAGEMENTONSOCIALMEDIA 71LIMITATIONSANDFURTHERRESEARCH 72METHODOLOGICALLIMITATIONS 72IBMWATSON 74POSSIBLETHIRDVARIABLES 76RECAPITALIZATIONOFTHEDISCUSSION 76

CHAPTER6:CONCLUSION 78

REFERENCES 80

LISTOFFIGURES

FIGURE1,CONCEPTUALMODELOFCUSTOMERENGAGEMENTBEHAVIOR..........................................................................................12FIGURE2.PERCENTAGEOFPOSTSCONTAININGANEMOTIONALAPPEALBYINDUSTRIES.......................................................................44FIGURE3.RELATIVEDISTRIBUTIONOFPOSTCHARACTERISTICSBYINDUSTRIES....................................................................................46

LISTOFTABLES TABLE1.CODINGOFTHETONEANALYZER.................................................................................................................................32TABLE2.USAGEOFVARYINGEMOTIONSBYINDUSTRY.................................................................................................................43TABLE3.USAGEOFVARYINGEMOTIONSBYINDUSTRY..................................................................................................................43TABLE4.FREQUENCYOFPOSTCHARACTERISTICSONFACEBOOK.....................................................................................................45TABLE5.RELATIVEDISTRIBUTIONOFPOSTCHARACTERISTICSBYINDUSTRY.......................................................................................47TABLE6.MEANENGAGEMENTRATIOSFORFACEBOOKANDTWITTER..............................................................................................48TABLE7.MEANENGAGEMENTRATIOSBYINDUSTRY(FACEBOOK)...................................................................................................50TABLE8.MEANENGAGEMENTRATIOSBYINDUSTRY(TWITTER).....................................................................................................51TABLE9.AVERAGEENGAGEMENTRATIOSBYPOSTCHARACTERISTIC................................................................................................52TABLE10.THEEFFECTOFEMOTIONALAPPEALCEONFACEBOOKANDTWITTER................................................................................54TABLE11.THEOFPOSTCHARACTERISTICSONTHEEFFECTIVENESSOFANEMOTIONALAPPEAL..............................................................56TABLE12.THEEFFECTOFVARYINGEMOTIONSONCE..................................................................................................................58TABLE13.EFFECTOFANEMOTIONALAPPEALACROSSINDUSTRIES...................................................................................................60TABLE14.SUMMARYOFRESULTS............................................................................................................................................62

AppendixA:Listofcompaniesincludedinthestudy

AppendixB:CompletedatasetforFacebook

AppendixC:CompletedatasetforTwitter


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AppendixD:Syntaxforrecodingvariables

CHAPTER 1: INTRODUCTION In today's hyper-connected world communication is rapidly changing and with the emergence of the

internet and online communication, Social Network Sites (SNS) have become an everyday part of

life. The largest SNS Facebook has more than 1.96 billion active users every month (Statista, 2017)

and the number of Facebook business pages have now surpassed 50 million. In fact, these users

make 2.5 billion comments on business pages each month (Facebook, 2015). This has not only

changed the way we communicate, but also the communication symmetry between brands and

customers (Whiting & Deshpande, 2016). This shift entails that marketing is no longer strictly one-

way communication, where brands tell customers what- and how to think about them, but

customers now take an active role in creating and distributing their perspective on brands online

(Whiting & Deshpande, 2016). As a result, brands can no longer control the flow of information

about them but can merely seek to shape the discussion (Mangold & Faulds, 2009). Customers

thereby take an active part in forming the discourses surrounding brands and products in the online

community.

The co-creative role and power customers have gained from the technological development, make it

essential for practitioners as well as academics to understand the dynamics within these

communities. Within recent years, the concept of customer engagement has emerged in the

marketing literature in an effort to explain the interactions between customers and brands that go

beyond purchase. At the most fundamental level, the Marketing Science Institute (MSI, 2010, p.4)

define customer engagement as a “customer’s behavioral manifestation toward a brand or firm

beyond purchase” and other authors follow the same line by proposing that customer engagement

involves experiences or interactions between a subject (a customer) and an object (brands, websites

and other customers) (Mollen and Wilson, 2010). From a practitioner’s perspective, a plethora of

reasons for adopting customer engagement as a part of the marketing strategy has been proposed.

Customer engagement has suggested to affect future business performance (Sedley, 2008), sales

growth (Neff, 2007), enhanced profitability (Voyles 2007) as well as a tool for gaining a

competitive advantage (Brodie, Juric & Hollenbeek, 2011). The academic literature has mainly

been focusing on conceptual- and qualitative studies, while quantitative research on the subject and

validation of the concept has remained sparse (Dessart, Veloutsou & Morgan-Thomas., 2016; de


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Vries, Gensler & Leeflang, 2012). De Vries et al. (2012, p.83) suggest that this is especially true for

research on CE within the context of social media. This field have been dominated by descriptive

studies, unable to explain the dynamics in depth and academics have merely proposed that brands

should experiment with different message strategies to achieve engagement (de Vries et al. 2012,

p83). The need for understanding the dynamics of CE on social media is underpinned by the fact

that more than 90% of companies use social media as a part of their marketing mix (Whiting &

Deshpande, 2016).

Scope of research In order to answer the call for more research in the field (de Vries et al., 2012; Dessart et al., 2016)

this study will seek to investigate customer engagement in the context of social networking sites,

more specifically CE related to online brand communities. Muniz & O’Guinn (2001, p.412.) were

the first to introduce the concept of brand communities to the marketing domain and described them

as “a specialized, non-geographically bound community, based on a structured set of social

relationships among admirers of a brand”. On Social Networking Sites (SNS) such communities

take the form of pages administered by brands, where brands can communicate directly with the

followers of the page. Followers can engage with these communities in different ways and with

varying valence, which can be expressed by commenting, sharing, liking, retweeting and reacting to

specific content dependent on the type of SNS.

Social networking sites have, as the name implies, a networked structure in which users are exposed

to messages that are being engaged with by their network (Swani, Milne & Brown, 2013). This can

both be a deliberate action, where a user shares content with their network (e.g. through a share or

retweet function), and less deliberate, when a user comments or reacts to a message. According to

the social monitoring website Socialbakers (Ross, 2014) a correlation between post engagement and

reach (a measure for how many users that are exposed to the post) can be established. The purpose

for brands to be present on SNS can therefore be regarded as twofold: 1) they can strengthen

relations with customers through customer engagement (Barger, Peltier & Schultz, 2016) and 2)

they can increase brand exposure to more users through the inherent network structure of a SNS

(Ross, 2014). Understanding the dynamics of customer engagement on social media is thereby an

area that is extremely valuable to marketers, who in 2017 are expected spend more than 20% of


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their digital advertising budget on social media in the US (eMarketer, 2015).

Research in the field has sought to reveal some of these dynamics by investigating how emotional

communication and different post characteristics affect customer engagement. De Vries et al. (2012,

p.83) found that the vividness of posts (the extent to which a post stimulates different senses) is

linked to the number of likes a post receives. In their comparison of B2B and B2C post popularity

on Facebook, Swani et al. (2013) investigated the impact of different message strategies, and found

that a “call to purchase strategy” generates relatively more likes in a B2C context compared to a

B2B, while the opposite was true for a “brand name strategy. Despite these differences, they found

that an “emotional content strategy” does not seem to be mediated by either a B2C or B2B context.

On the other hand, previous studies in other areas of research, have found differences between the

effect of emotional appeals in B2B and B2C context. Lothia, Donthu & Herschberger (2003) found

that an emotional appeal positively affects the effectiveness of online banner advertisements in a

B2C context, while doing the opposite in a B2B context.

In the field of interpersonal communication, the perception of emotional content in emails has been

found to differ significantly from how the sender’s intention of the email (Byron, 2008). Receivers

tend to perceive emotions more negatively or neutral than indented by the sender (Ibid). Byron

(2008) explains this gap by proposing that the information-richness of message affects how

emotions are perceived. Information-richness refers to medium’s ability to convey natural language,

provide rapid feedback and enable multiple cues (Byron, 2008, p.311). As a result, face-to-face

communication has the highest degree of information richness, while email communication is

leaner. The role and effectiveness of emotions in communications is rather unclear in the literature

and varying effects have proposed in the different areas of the literature. This study will seek to

address this gap in the literature.

Research question and contribution As the above have outlined, the technological development has empowered customers become co-

authors of brand stories on the internet (Whiting & Despande, 2016). As a result, brands have lost

their control of information and can merely seek to shape and facilitate online discussions about

themselves (Mangold & Faulds, 2009). In the marketing literature, this paradigmatic shift in

communication has led to the emergence of customer engagement. Despite the increasing

importance and influence of social media on both brands and customers, research on CE in this


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context remains sparse (de Vries and Carlson, 2014). Swani et al. (2013) have investigated how

different message strategies affect engagement on Facebook and found that emotional content

increase the number of likes a brand post receives. Other areas of research have found emotions to

increase advertisement effectiveness (Lothia et al., 2003) and suggested that communication

channels affect the perception of emotions (Byron, 2008).

This study will answer the call for more research in the field of CE and shed light on the role of

emotions in a social media setting by investigating how emotional appeals in brand posts on

Facebook and Twitter affect customer engagement. Will an angry emotional appeal foster more

engagement than a joyful appeal? Is emotional communication more suitable for certain industries

than others? Does a post’s information richness affect the effectiveness of an emotional appeal on

customer engagement? These are merely a few of the questions that will be sought answered

through the following research question: How do emotional appeals affect customer engagement in

a social media setting?

Methods As mentioned above, research on customer have been dominated by qualitative studies (Dessart et

al., 2016), and in order to contribute to this field of research, this study will seek to answer the call

for more quantitative research in the field (Dessart, et al., 2016). More specifically this research will

adopt a content analysis for investigating how emotional appeals affect customer engagement. A

content analysis is well suited for providing a quantitative validation of customer engagement

dynamics on social media due to its inherent reliability and generalizability (Krippendorff, 2013).

This will be done by collecting all brand posts in online brand communities on Twitter and

Facebook from 44 companies during a time period of 45 days between January 24th. and March 9th.

The total dataset contains 1496 Facebook posts 3536 Twitter tweets spread across the 44 companies

in four different industries. To conduct a content analysis on this large body of data, this study

adopts IBM’s Watson software as a tool for coding and analyzing the data. Watson is IBM’s

artificial intelligence tool and, to the knowledge of the author, this type of method has not been

conducted before within this field. This method makes it possible to analyze emotional tone in large

amounts of text, making it possible to achieve reliable and generable results, which is essential in

answering the call for more quantitative research in the customer engagement literature.

Furthermore, Watson’s ability to be conducted at scale is well suited for a fast-paced social media


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platform where 2.5 billion comments are made on brand pages each month (Facebook, 2015). A

thorough description of Watson as well as the implications of using this approach to analyzing data

will be provided in the methodology and discussion section of the paper. The emotional tones

derived from the text through Watson will be linked to customer engagement on social media. Each

brand post on Facebook and Twitter will be analyzed for the presence of an emotional appeal,

which will be compared to the number “reactions” / “favorites”, “comments” / “replies” and

“shares” / “retweets” each post receives on Facebook and Twitter respectively. Barger et al. (2016)

suggest that reactions, comments, retweets, shares etc. all can be seen as customer engagement

behaviors in a social media context and is therefore used as metrics for measuring customer

engagement. In order to measure these relationships four main hypotheses are developed in the

literature review, which will be sought supported or rejected through a statistical analysis that links

emotional appeals to customer engagement behaviors.

Delimitations This paper will investigate and focus on the role of emotional appeals in brand post on social media.

As a result, only the emotional appeal of each brand post is considered and the content per se and

the communicated subject is not investigated in this paper.

The effect of these emotional appeal is measured in consumer engagement behavior. This is

measured in occurrences of engagement and do not account for the valence of these engagement

behaviors. While the valence of engagement in response to emotional appeals could be of great

interest, it is beyond the scope of this study and would require a different methodological approach.

Structure of the paper A visualization of the structure can be found on the next page (image 1).

Chapter 1 (this chapter) Introduces the research question and the contributions of this study to

present literature.

Chapter 2 will provide a review of present literature on consumer engagement, which will be put

into the context of social media. The role emotional message strategies will be explored and linked

to consumer engagement. Lastly, four main hypotheses will be developed


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Chapter 3 introduces the methods adopted for investigating the four hypotheses and discusses the

scientific position of this paper. Content analysis is introduced as a method for analysis followed by

a description of IBM Watson, which is adopted in this paper as a tool for analysis. The validity and

reliability is discussed and followed by an introduction to the statistical procedure of the study.

Chapter 4 presents the findings of this study. First a descriptive analysis of the data will be

provided, which will be followed by a testing of the hypotheses, where a relationship between

emotional appeals and consumer engagement is sought proved.

Chapter 5 puts the derived findings into perspective by relating them to the introduced research in

the theoretical framework and discusses the implications of the findings.

Chapter 6 provides a recapitalization of the paper and highlights the main findings of the study.


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Image 1. Structure of the paper.

CHAPTER 2: THEORETICAL FRAMEWORK This study will investigate how customer engagement is affected by emotional appeals on social

media and this chapter will seek to explore the different concepts that are encompassed in the

research question. Firstly, an introduction to the concept of CE will be provided along with a

behavioral perspective on the concept followed by the potential effects of CE. Secondly, this will be

put into the context of social media, and literature in this field will be explored in relation to CE.

CHAPTER 1: INTRODUCTION

CHAPTER 2: THEORETICAL FRAMEWORK

Customer engagement

Social media

Emotions in communication

Hypotheses development

CHAPTER 3: METHODOLOGY

Scientific position

Content analysis

IBM Watson Tone Analyzer

Validity and reliability

Statistical procedure

CHAPTER 4: FINDINGS

CHAPTER 5: DISCUSSION

Part 1: Descriptive analysis Part 2: Hypotheses testing

CHAPTER 6: CONCLUSION

Hypothesis 1Emotional

appeals and engagement

Hypothesis 2Post characteristics and effectiveness of emotional appeals

Hypothesis 3Impact of varying

emotions

Hypothesis 4Emotions across

industries

Limitations and further research


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Thirdly, message strategies for fostering engagement is investigated with a focus on emotional

message strategies and how these can affect CE. Finally, based on the introduced literature a set of

hypotheses are developed which will guide the research of this paper.

Customer engagement The concept of “engagement” is not new in the organizational management literature, where it has

been explored in different contexts such as “employee engagement” and “stakeholder engagement”,

as well as in other academic fields such as social psychology, but it has remained relatively

unexplored in the marketing literature and have only started to receive attention within the last

decade with the emergence of the term “customer engagement” (Brodie, Hollebeek, Juric & Ilic,

2011; Hollebeek, Glynn, & Brodie, 2014). While some authors link customer engagement to

business outcomes such as enhanced profitability (Voyles 2007) and competitive advantages

(Brodie et al., 2011)., others are still trying to conceptualize and define the concept (van Doorn,

Lemon, Mittal, Nass, Pick, Pirner & Verhoef 2010; Hollebeek et. al., 2014). To make sense of these

different research agendas and approaches to customer engagement, the following section will

review present literature the subject. This will provide the rationale and background for the research

question of this study.

Concept and definition While the term “customer engagement” has been used in the literature prior to Brodie et. al. (2011),

their seminal work is considered among the first to provide a thorough review and

conceptualization of the concept in the marketing literature (France, Merrilees & Miller, 2016).

Brodie et. al (2011, p.253) distinguish customer engagement (CE) from existing concepts such as

customer involvement- and participation by proposing that CE revolves around interactive

experiences and co-creation of value between customers and brands, which involvement and

participation concepts fail to recognize to the same extent.

Through a content analysis of the existing literature and its mentioning of customer engagement,

Brodie et al. (2011) suggest that CE is conceptually rooted in the service-dominant (S-D) logic

found in the marketing literature, which focuses on the cocreative and interactive relationship

between customers and firms (Brodie et al 2011, p.253). In this content analysis, they include


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“consumer” and “customer” engagement studies, which are considered to cover the area of

research. This paper adopts this approach and do not distinguish between the two names of the

concept. Based on their content analysis, Brodie et. al (2011) provide five fundamental propositions

for conceptualizing CE and synthesize these into the following definition:

“Customer engagement (CE) is a psychological state that

occurs by virtue of interactive, cocreative customer experiences

with a focal agent/object (e.g., a brand) in focal service

relationships. It occurs under a specific set of context dependent

conditions generating differing CE levels; and

exists as a dynamic, iterative process within service relationships

that cocreate value. CE plays a central role in a nomological

network governing service relationships in which other

relational concepts (e.g., involvement, loyalty) are antecedents

and/or consequences in iterative CE processes. It is a

multidimensional concept subject to a context- and/or

stakeholder-specific expression of relevant cognitive, emotional

and/or behavioral dimensions.”

This broad definition conceptualizes CE as a multidimensional concept by acknowledging a

cognitive, emotional and behavioral aspect of CE, which make the definition applicable across a

broad range of situations (Brodie et al. 2011). It further encapsulates the cocreative aspect of CE as

well as its contextual dependence on the situation and stakeholders. Although Brodie et al.’s (2011)

definition provide a conceptual broad understanding it has been criticized for being too broad and

generic by combining all relevant behaviors with emotions and cognitions (Malthouse and Calder,

2011).

Customer engagement behavior Namely the behavioral aspect has the center of attention in van Doorn et al.’s (2011) conceptual

work on customer engagement behavior (CEB). In contrast to the broad conceptualization provided

by Brodie et al. (2011), van Doorn et al. (2011, p 253) provide a deeper understanding of the


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behavioral aspect of customer engagement and investigate “...why customers behave in numerous

ways that are relevant to the firm and its multiple stakeholders”. These types of engagement

behaviors “...go beyond transactions, and may be specifically defined as a customer’s behavioral

manifestations that have a brand or firm focus, beyond purchasing, resulting from motivational

drivers” (van Doorn et al. 2011, p.254).

Through a review of existing literature on the subject they provide a set of antecedents and

consequences of customer engagement behaviors as well as a set of variables that can influence the

impact of CEB. These relationships are visualized in figure 1 below.

Figure 1. conceptual model of customer engagement behavior (van Doorn et al., 2011, p.256)

Van Doorn et al. (2011) note that this is not a linear model and argue that each of the categories can

affect each other, the consequences of CEB might for affect the antecedents and thereby affect

CEB.


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The antecedents are the drivers of CEB and can be divided into customer-based, firm-based and

context based antecedents. Van Doorn et al. (2011) admit that the dynamics of the antecedents are

rather complex and each antecedent could be placed on a continuum between being an antecedent

or a moderator of CEB. They suggest that the most important customer-based antecedents are

attitudinal, and that variables such as customer satisfaction with a brand, brand performance

perception and brand attachment are key influencers of CEB. Very high or very low levels of these

attitudinal antecedents can lead to CEB (van Doorn et al. 2011).

With regards to the firm-based antecedents, the most important variable affecting CEB is the brand;

brands with a high reputation and a high equity are more likely to receive higher levels of positive

CEB. Lastly, the context-based antecedents stem from societal externalities such as the political

environment, the economic situation, legislation etc. (van Doorn et al. 2011). The combination of

these three types of antecedents is the catalyst for fostering CEB, suggesting that an array of

variables can affect CEB, making it heavily context dependent.

The engagement behavior per se can vary across a range of factors namely valence (positive or

negative), form and modality (ways of expression), scope (temporal and geographic), nature of

impact (immediacy, intensity, breadth and longevity) and the customers’ goals with the engagement

behavior (van Doorn et al. 2011).

Lastly, CEB have consequences on different levels. On a customer level, CEB can change and

affect the emotional state of the customer as well as shape and reinforce their social identity (van

Doorn et al. 2011), which fits well with the affective and cognitive dimension of customer

engagement found in the conceptual definition provided by Brodie et. al (2011) above.

The dynamics of CEB found in figure 1 combined with Brodie et al’s. (2011) conceptualization of

CE highlights some of the difficulties in defining and investigating the subject due to the plurality

of dimensions affecting CE. Both of the presented studies have been of a conceptual nature which

to some extent is symptomatic for the academic treatment of the subject, which mainly has been

investigated conceptually in present literature (Dessart et al. 2016, p. 402). Despite disagreement on

how customer engagement should be understood and conceptualized there seems to be agreement

on the potential positive outcomes of engaging customers, which will be elaborated upon below.


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Effects of customer engagement The underlying assumption in measuring the effect of CE is that customers can create value to a

firm through more ways than merely exhibiting purchase behaviour (Kumar, Aksoy, Donkers,

Venkatesan, Wiesel and Tillmanns, 2010). In their study, Kumar et al. (2010) propose different

ways in which the value of CE can be captured, which includes “customer referral value”,

“customer influencer value” and “customer knowledge value”.

The former of the three relates to the value of having engaged customers refer new customers to the

company minus the cost of having them to so. Customer referrals can be done in myriad of ways

both online and offline, with or without money incentives, but from a CE perspective the value lies

in having existing customers engaged in referring new customers (Kumar et al. 2010).

While the referral value of customers often is based on extrinsic motivation provided by the focal

brand (e.g. competitions, rewards, etc.), the customer influencer value is based on intrinsic

motivators. This type of value encompasses the information sharing and WOM among customers,

which can have a significant, positive or negative, impact on the behavior of other customers

(Kumar et al., 2010). Lastly, CE can be measured on “customer knowledge value”, which

comprises the co-creative value of engaged customers (Kumar et al., 2010).

This conceptualization of the effects of CE takes a rather managerial perspective by providing a set

of measurable business-oriented outcomes that can be linked to CE. While the former studies of CE

presented in this paper (Brodie et. al 2010 and van Doorn et al 2011) represent theoretical

discussions on the concept, Kumar et al. (2010) provide a raison d’étre for CE from a practitioner's

perspective by conceptualizing the business value and effects of CE.

The above review has sought to shed light on the concept of customer engagement as well as

highlighting the different dynamics affecting CE. While it is argued that CE can provide value to

firms in myriad of ways, the actual strategies for creating customer engagement has been left

unexplored in the presented conceptual papers. The definitions of CE provided by Brodie et al.

(2011) and van Doorn et al. (2011) both highlight the interactivity between a customer and a focal

brand. The effects and value of CE is likewise based on social interactions in Kumar et al’s. (2010)

conceptualization of the effect. Thus it can be argued that it would be fruitful to study CE in a social

context by investigating platforms that enable brand-to-customer interactions as well as customer-

to-customer interactions. As a result, social media is an interesting area for investigating CE due to


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its social and interactive nature. The following section will investigate the properties of social

media and review how CE and social media have been investigated in the literature.

Social media As McLuhan (1964, cited in Siapera, 2012, p.7) famously proclaimed “the medium is the message”,

suggesting that a message cannot be separated from the medium in which it is conveyed. The idea

that the medium affects the perception of a message has sparked an interest into the dynamics of CE

on social media in the literature. As mentioned previously, the pivotal point of customer

engagement is the interactive and cocreative experiences between brands and customers (Brodie et

al. 2011), which fits well with social media as a medium, which is characterized by having two-

way- and peer-to-peer communication properties (de Vries and Carlson, 2014). Media channels-

and platforms have been investigated in other parts of the communication literature. For example,

the level of engagement with a media platform has been found to affect how customers respond to

advertisements (Calder, Malthouse & Schaedel, 2009). This make social media an interesting area

for further investigation due to its high level of customer involvement, which could potentially

affect how customers respond to- and engage with brands on the platform.

In order to investigate customer engagement in a social media context, it is first of all necessary to

understand the dynamics and usage of the medium. Social media can be defined as “[...] online

means of communication, conveyance, collaboration, and cultivation among interconnected and

interdependent networks of people, communities and organizations enhanced by technological

capabilities and mobility” (Tuten & Solomon, 2015, p. 12). This definition stresses the networked

structure of social media, where content is distributed among users across their network. It further

encompasses the different types of communication flows in these networks for example customer-

to-customer as well as between organizations and customers.

These properties of the medium have affected the marketing domain and the usage of social media

among marketers has increased dramatically within the last decade. Marketers are forecasted to

spend 20% of their digital advertising budget on social media in 2017, a number that is only

expected to increase (eMarketer, 2015), and more than 90 % of companies now use social media as

a marketing tool (Whiting & Deshpande, 2016). Social media have had a significant impact on the

marketing mix and changed the way customers interact with brands. Traditionally marketing


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messages have relied on mass-communication where relatively few publishers have distributed

marketing messages to a broad range of potential customers (Whiting & Deshpande, 2016). The

emergence of social media has made it possible for customers to create and share their own beliefs

about brands with other customers, which has caused a shift in communication symmetry moving

from one-way communication to two-way communication in marketing (Whiting & Deshpande,

2016). Social media have furthermore empowered customers by providing them with a powerful

platform for expressing their opinion. An old marketing saying states that one dissatisfied customer

will tell ten people about their experience, but with the emergence of social media, customers can

potentially reach millions of other customers with their complaint (Mangold & Faulds, 2009). In

order to respond to this shift in information control and power, Mangold & Faulds (2009) propose

that social media should be seen as a hybrid element in the traditional marketing mix. On one hand

it can, like traditional marketing elements, be used as a way to communicate and talk to customers

through company owned / controlled platforms (e.g. Facebook brand pages). On the other hand,

social media facilitates customer-to-customer communication and brands only have a small amount

of control over the dissemination of content and information. As a result, brands cannot control the

conversation but only seek to shape it (Mangold & Faulds, 2009).

The above have sought to show how social media have made a paradigmatic shift in marketing

communication by changing the power of information distribution between brands and customers.

Social media have strengthened customers’ role in shaping the image of brands through two-way

communication between customers and brands as well as between customers and customers.

Mangold and Faulds (2009) argue that brands should seek to facilitate and shape the discussion on

social media but to do so, it is necessary to understand how and why customers engage with brands

on social media. The following section will therefore look at the dynamics of customer engagement

in a social media context.

Customer engagement on social media The usage of social media by marketers have been steadily increasing within recent years but only

relatively few companies have experienced an increase in customer engagement (Barger et al.,

2016). This might be due to a focus on accomplishing short-term goals on social media (e.g. selling


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products), while building relations through CE has not been a priority (Ibid.). This calls for a better

understanding of the dynamics of CE in a social media perspective, but the academic literature has

not kept pace with the increased value and significance of social media in practice (de Vries &

Carlson, 2014). Barger et al. (2016) seek to answer this call by establishing a framework explaining

the antecedents and consequences of customer engagement on social media through a review of

existing literature concerning motivators and drivers of different engagement behaviors.

This framework is grounded in the previously introduced conceptualization of customer

engagement behaviors proposed by van Doorn et al. (2011) and revolves around behaviors on social

media that can be seen as expressions of CE. In a social media context these co-creative and

interactive behaviors can be measured through inherent metrics of the medium (Barger &

Labreque., 2013) namely reactions to content, comments on content, sharing of content and posting

of user-generated content (Barger et al. 2016). Together these four engagement forms can be seen

as expressions of customer engagement on social media. In their review of the literature Barger et

al. (2016) provide a set of five antecedents that drives CE on social media.

First of all, the degree of CE is influenced by customers’ attitude towards a particular brand.

Sharing of content has been found to not only be influenced by the content per se but also to the

customer’s attitude toward the brand distributing the content (Huang, Su, Zhou & Liu, 2012).

Secondly a set of product related factors can affect CE on social media, which encompasses

customer’s experience with a product or service. For example, Chen, Fay & Want (2011) found that

very high or very low perceptions of quality are more likely to lead to a product review than an

average experience. Bad experiences with products have also been found to be the primary driver of

“brand sabotage”, where customers actively seek to harm a particular brand, and social media

provide an easily accessible platform with a great potential for inflicting harm on a brand (Kahr,

Nyffenegger, Krohmer & Hoyer, 2016)

Thirdly, customer based factors can also influence the level of customer engagement on social

media. Customers have been found to engage with social media for different reasons including

entertainment, the need for information, social relations and impression management (Barger et al.

2016).

The fourth antecedent of CE is content factors, which include the message appeal and emotional

sentiment. Customers are more likely to engage with posts that carry an emotional sentiment and

less likely to engage with posts trying to sell a product (Swani et al.,2013). Furthermore, the


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vividness of content has also been linked to post engagement on social media (De Vries et al.,

2012).

Lastly Barger et al. (2016) suggest that the social medium itself can have an effect on the type and

degree of CE but only a scarce amount of research has looked into the differences between today’s

many different types of social media from a customer engagement perspective.

Together these five factors affect how customers interact with brands on social media, which will

influence the impact of the engagement on the firm. In line with van Doorn et al.’s (2011)

conceptualization, CE on social media can have consequences for how customers perceive a brand

and its products (Barger et al., 2016). These consequences can be strengthened by the inherent

sharing possibilities of social media, where information is distributed in the user’s own social

network. This is especially interesting taking into consideration that customers have the most

positive attitude towards recommendations from other customers compared to recommendation

from sellers (Lepkowska-White, 2013). This calls for a deeper understanding of the antecedents to

fully understand the drivers of CE on social media, and Barger et al. (2016) calls for more

comprehensive frameworks for each of the antecedents in their conceptualization. These five factors

differ in complexity and extent to which a brand can directly influence them, customer related

factors (e.g. personality traits) are arguably more difficult for brands to change than content factors

(e.g. format and emotional sentiment in messages). As mentioned above, Mangold and Faulds

(2009) argue that marketing practitioners should seek to shape and facilitate communication on

social media. Thus it is critical for marketers to understand how different communication- and

content strategies influence CE on social media, which will be sought understood in the following

section.

Message strategies for customer engagement on social media As argued above CE can lead to an array of positive outcomes for brands, and message strategies

that can foster engagement are thus of great value to practitioners. According to the social media

consultancy Socialbaker (Ross, 2014) a correlation can be established between engagement

(reactions, comments, shares etc.) with a brand post and the reach of the brand post (audiences who

are exposed to the message). This entails that post-engagement has an influence on individual users

who engage with the post, as well as the potential to spread the message to the entire social network


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of the individual user. This inherent feature of the medium gives engaging message strategies a

potential to deepen the relationship between customers and brands through CE (Barger et al. 2016),

and at the same time broaden the reach of a brand post to new audiences (Ross, 2014).

Despite these valuable properties of the medium it is still unclear how it should be utilized in the

best way to unfold its full potential (Sabbar & Hyun 2016; Li & Li 2014) and a theoretical

foundation has yet to be established (de Vries et al., 2012). In an effort to shed light on what makes

a brand post popular on social media de Vries et al. (2012) draw on relationship-marketing

literature as a conceptual framework. They investigate how different aspects of Facebook posts

relate to the number of likes and comments a post receive for example content, characteristics, and

position on the site. They found that the vividness of a post influences the number of “likes” it

receives (de Vries et al. 2012). Vividness relates to the richness of the medium: posts with a high

degree of vividness stimulate several senses (e.g. video which can be seen and heard), while posts

containing only a picture have a lower vividness. Thus, posts containing video elements get more

“likes” than posts containing pictures. Interactive elements of a brand post e.g. asking questions

were partially found to correlate with the number of comments a post receives (de Vries et al.,

2012). Content characteristics such as “informative content” and “entertaining content” were found

not to correlate with neither likes or comments (de Vries et al. 2012). The findings on content

characteristics are to some extent counterintuitive to similar research in the field, which have found

that “entertainment” and “information sharing” are among the top motivators for using Facebook

and Twitter (Alhabash & McAlister, 2015). Lastly, de Vries et al. (2012) provide valuable insights

into the dynamics of post engagement by suggesting that the effect of the influencing factors is

dependent on the type of engagement (i.e. “likes” or “comments”). Thus, the same strategies might

not be effective for getting customers to like a brand post compared to getting them to comment on

a brand post. The above have made it clear that it is not a straightforward task to identify effective

message strategies for fostering engagement due to a myriad of contingencies.

Alhabash & McAlister (2015) take a slightly different approach to investigating message strategies.

They propose that the demand of cognitive processing that is needed for engaging with a post

affects the type of customer engagement. This conceptualization suggest that Facebook users are

more likely to press “like” on a brand post, than “sharing” it, while they are least likely to

“comment” on it. For Twitter the “retweeting” function was found to be the most likely engagement

form followed by “replying” and “favorites” (Alhabash & McAlister, 2015). This adds an


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interesting perspective to how engagement on social media can be understood by creating a

typology for how different types of engagement requires differing levels of cognitive processing.

Schivinski, Christodoulides & Dabrowski’s (2016) have developed a slightly different typology for

measuring customer engagement on social media. They propose and validate three different

dimensions that can be used for categorizing engagement namely consumption, contribution and

creation. Consumption requires a minimum effort from the customer, who passively consumes the

brand-content without participation. This is the most frequent activity among social media users.

Contribution refers to users contributing to existing content on social-media either from a brand or

another user but do not create themselves. Lastly, creation entails that customers are highly engaged

and creates their own content and become the authors of the messages themselves (Schivinski et al.,

2016). More interestingly, they identified a hierarchical relationship between the three dimensions

and found that consumption is an antecedent of contribution and contribution is an antecedent of

creation (Ibid.). Customers who are in the contribute category are more likely to be active and enter

into the creation category than customers who are in the consumption category. This adds a

hierarchy to the notion of engagement on social media and makes it possible to evaluate different

message strategies’ effectiveness for sparking customer engagement.

Swani et al. (2013) have sought to measure the effect of different message strategies through an

empirical study of Facebook post from Fortune 500 companies. In contrast to the above studies,

Swani et al. (2013) only focus on one type of customer engagement behavior on social media

namely “liking” of brand posts. With point of departure in advertising- and WOM literature they

investigate how the effect of different message strategies differ in a B2B and B2C context.

Transactional message strategies with direct calls to purchase were found to generate significant

more likes in a B2C context compared to B2B, while emotional message strategies were found to be

positively related to the number of likes in both a B2C and B2B context (Swani et al. 2013).

These results are interesting for several reasons. First and foremost, they underpin the notion that

message strategies should be evaluated in their context by showing a difference between the effects

of message strategies in a B2C and B2B context. Secondly, they found that emotional strategies are

positively related to the number of likes a post receives (Swani et al. 2013). In their study Swani et

al. (2013, p 292.) define emotional content as a message that seeks to stir up either negative or

positive emotions but do not differ between the effect of these emotions. Thus, it is not investigated

how specific emotional appeals differ in effect on engagement levels but merely emotions as a


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broad construct. The following section will review how emotions have been studied in other

communication disciplines such as advertising and relate it to the context of social media.

Emotions in communication While emotions have received some attention in customer engagement theory and is regarded an

antecedent of customer engagement behavior on social media (Barger et al. 2016), only few studies

have sought to elaborate on the impact of emotions in this context. As an exception Swani et al.

(2013) provide evidence for the effect of triggering an emotional response from the receiver and

Alhabash, McAlister, Hagerstrom, Quilliam, Rifon & Richards (2013) have in a case study of an

online anti-bullying campaign found that a positive emotional tone fosters more likes than a neutral

or negative tone. These findings stress the potentials of leveraging emotional content for creating

engagement on social media but both studies adopt relatively broad conceptualizations of emotional

content. These broad conceptualizations have made the findings somewhat confusing and made it

difficult to compare findings of different studies. An example of this can by comparing the findings

by de Vries et al’s. (2012) and the findings by Swani et al. (2012). Entertaining message strategies

have been found not to correlate with engagement levels by de Vries et al. (2012), while

“entertainment” would arguably be considered to “stir up either positive or negative emotions” in

the study by Swani et al. (2012), who found a correlation between the use of emotions and

engagement. A more nuanced approach to the effect of emotions be useful in this regard. The use of

emotions has been considered in other communication branches such as interpersonal

communication and advertising, which can be adopted as a framework for investigating emotions in

a social media context.

In the field of interpersonal communication, emails’ ability to convey emotions have been

investigated by Byron (2008), who introduced a theoretical framework for understanding

miscommunication in emails by adopting a channel centric perspective. Compared to face-to-face

communication emails have a lower degree of “information-richness” due to the lack of instant

feedback and non-verbal cues (e.g. facial expressions), which make it less effective for

communicating ambiguous messages (Byron, 2008, p.311). As a result, Byron (2008) argues that

receivers perceive emails to be more emotionally negative or neutral than intended by the sender. A

medium’s richness and ability to convey non-verbal cues can therefore affect how emotions are


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perceived. In a social media context this stresses the importance of aligning form and content and

adds an extra emotional dimension to the previously introduced findings by de Vries et al. (2012),

which showed a correlation between post vividness and engagement on social media. Thus, the

richness of a message has both been found to influence “likes” on Facebook and affect the how

emotions are perceived.

Investigating why people talk have also been an area of interest in the interpersonal communication

literature, where emotions in communication have been found to predict whether people talk

(Hendriks, Putte & de Bruijn, 2014). More specifically message appeals that arouse amusement,

disgust and happiness are more likely to spark a discussion than message appeals that induce guilt

or sadness (Hendriks et al., 2014, p.685). These findings suggest that emotions have a varying

effect in their ability to initiate talk and discussions between people.

Research in interpersonal communication thereby provide two valuable findings that can be adopted

in this study, in order to understand the role of emotion on engagement in social media context: 1)

the richness of the message channel affects how emotional content is perceived and 2) emotions’

ability to spark discussion is dependent on the type of emotion at hand.

The role of emotions in communication has also been investigated in the advertising literature

where it has been related to persuasion theory. The pivotal point in this field of research has been to

determine the effectiveness of emotional appeals in contrast to cognitive appeals (Septianto &

Pratiwi, 2016). These two appeals are the most dominant appeals in advertising: the emotional

appeal often focuses not on the product itself but on the person, you become by using the product,

while the cognitive appeal often focuses on the product itself and the utilitarian value hereof

(DeBono, 2006).

A rationale for adopting an emotional appeal in advertisement is to make the customer create

favorable brand associations by evoking certain emotions, but there is little agreement on the actual

effect of this appeal (Panda, Panda & Mishra, 2013). Academics within the field of emotions and

communication struggle to reach an agreement on what the term “emotions” encompasses and only

few authors agree on the definition (Thamm, 2006 cited in Botha & Reyneke, 2013, p.162). This

has led to a body of literature full of disagreeing findings, where some authors argue for the

effectiveness of rational message appeals (e.g. Aaker & Norris 1982), while other authors have

found emotional message appeals to affect the effectiveness of advertisements (Panda et. 2013,

p11). Online advertisements expressing a positive emotional appeal have been found to be more


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effective in influencing online “click-through-rate” than advertisements expressing either a negative

or neutral appeal (Xie, Donthu, Lothia & Osmonbekov, 2004). In their paper on emotions in

advertising Panda et al. (2013) argue that the effectiveness of emotional appeals is heavily

dependent on the context and propose that the type of industry and product type can mediate the

effectiveness of emotions (Panda et al., 2013), which might explain some of the contradictive

findings in the field.

As outlined above, other fields of research can provide a useful insights into the effectiveness of

emotional appeals. The effectiveness of an emotional appeal is dependent on the type of emotion

that is adopted in the appeal as well as the context of the message. Contextual factors such as

message channel and product / industry, within which the brand is positioned, have been argued to

affect the effectiveness of emotional appeals. These findings have been derived through studies

relying on different forms of communication e.g. speech, email and online advertisement, which

differ from social media in their richness and communication symmetry. Thus, it is interesting to

investigate whether these factors also affect the effectiveness of emotional message strategies on

social media. As a result, these findings from other fields of research will serve as a framework for

investigating the effectiveness of emotional appeals on social media. In combination with the

introduced literature on customer engagement, these findings will be used for developing a set of

hypotheses, which will be elaborated upon in the following section.

Hypothesis development The above review has made it clear that customer engagement can lead to an array of positive

business outcomes such as sales growth, profitability and providing a competitive advantage (Neff,

2997; Voyles, 2007; Brodie et al., 2011). This paper adopts van Doorn et al.’s (2011) perspective on

customer engagement by acknowledging the importance of behaviors as the pivotal point for

understanding CE. The growing importance of social media in marketing and its role in facilitating

brand to customer interaction in a hyper-connected world (Whiting & Deshpande, 2016) have made

social media an area of great interest to both practitioners and academics. Nonetheless, research

regarding customer engagement on social media remains scarce and is dominated by conceptual

studies (Dessart et al., 2016). This study will seek to address the call for a better understanding of

the engagement dynamics on social media with point of departure in different types of emotional


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appeals on social media. This will be achieved through a set of hypotheses derived from the

research introduced above and will serve to answer the research question of this paper: How do

emotional appeals affect customer engagement in a social media setting?

Hypothesis 1 As introduced in the literature review, studies have sought to investigate the effectiveness of

different message strategies on social media, but the results remain limited and even contradicting

at some points. To answer Barger et al.’s (2016) call for a better understanding of the different

antecedents of customer engagement on social media, this study will investigate emotional appeals

in brand posts on social media as an antecedent of customer engagement. Earlier research has

suggested that type of content and message appeal can influence the level of engagement (de Vries

et al., 2012; Algabash & McAlister, 2014) and emotions have been proposed to play an important

role in this regard (Barger et al., 2016; Swani et al., 2013). Swani et al. (2013) found that the use of

emotional content affects the number of likes a Facebook message receives. Likes can be seen as

one among many forms of CE on social media (Barger et al., 2016) and as found by other authors,

different message appeals have different varying effects on the type of engagement for example

likes and comments (de Vries et al.,2012). Based on these findings the first set of hypotheses to be

tested will be the following:

H1: An emotional message appeal on social media will foster a higher level of customer

engagement than a message appeal without an emotional appeal.

H1a: An emotional message appeal will receive more “likes & favorites” than a neutral

message appeal

H1b: An emotional message appeal will receive more “comments / replies” than a neutral

message appeal

H1c: An emotional message appeal will receive more “shares / retweets” than a neutral

message appeal

Hypothesis 2 In order to provide a more nuanced understanding of emotional message appeals on social media,

this study draws on other branches of research for the hypothesis development. Research in


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interpersonal communication have found that the richness of communication channel affects how

emotional content is perceived (Byron, 2008). These findings combined with the findings by de

Vries et al. (2012) who found a Facebook post’s vividness to influence the number of likes it

receives, provide the foundation for hypothesis 2. This hypothesis will seek to explore whether the

effect of an emotional appeal is mediated by the information richness of the message.

H2: The effectiveness of an emotional appeal on social media is mediated by the information

richness of the message. A post with a high degree of information richness will foster more

engagement than a post with a low degree of information richness.

H2a: An emotional message appeal combined with a video will be more effective than an

emotional message appeal combined with a photo. An emotional message appeal combined

with a photo will be more effective than a text only emotional appeal.

Hypothesis 3 The research in interpersonal communication furthermore implied that the use of emotional content

can predict whether people talk and thus has the ability spark discussion (Hendriks et al., 2014). It

has been found that discrete emotions differ in their ability to initiate talk. Message appeals that

induced “happiness” were more likely to initiate discussion than messages inducing “sadness”

(Ibid.). Building on the fact that discrete emotions differ in their ability to spark discussion, it could

be hypothesized that they will also differ in their ability to foster engagement. The third hypothesis

will seek to investigate this.

H3: The effectiveness of an emotional message appeal on social media is affected by the type of

emotion the message seeks to induce. Discrete emotions will have a varying effect on the

occurrence of engagement as well as the type of engagement.

H3a: Messages appeals that seek to induce happiness will foster more engagement than

message appeals seeking to induce sadness.

Hypothesis 4 As introduced in the literature review, an array of different antecedents and mediators of customer

engagement exists. In van Doorn et al.’s (2011) broad conceptualization of customer engagement


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behavior, firm characteristics such as size and reputation regareded regarded as antecedents of CEB.

Swani et al. (2013) found that the effectiveness of emotional message strategies differ between

goods and service companies, and the advertising literature has proposed that the type of industry

and product can affect the effectiveness of emotional appeals (Panda et al., 2013). Thus, it is

interesting to investigate how the effectiveness of emotional appeals on CE differ across different

industries. There are no clear indications of which industries will receive more engagement and H4

will therefore be of an exploratory nature.

H4: The effectiveness of emotional appeals on social media is affected by the type of industry within

which the brand is positioned

CHAPER 3: METHODOLOGY


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In order to investigate the developed hypotheses, a content analysis has been chosen as the most

suitable way of analysis. Content analysis has an unique ability to systemize data in an objective

manner (Prior, 2014), which arguably is much needed in the field, which have been dominated by

conceptual and qualitative studies (Dessart et al. 2016). This chapter will provide the rationale

behind choosing this specific method as the preferred type of analysis. Firstly, an introduction to the

scientific position of the paper will be provided followed by a discussion of the rationale behind the

chosen method for analysis. Secondly, a general introduction to content analysis is provided along

with a description for how it will be integrated in this paper. This research takes an unusual

approach to content analysis by adopting IBMs artificial intelligence “Watson” for analyzing

content and emotional tones. To the knowledge of the author, this is the first study to adopt this

particular approach to content analysis in this context. To justify this choice, a rather large part of

this chapter is devoted to explaining IBM Watson and the rationale behind this choice. Lastly, the

approach for relating the analyzed emotional tones to customer engagement through a statistical

analysis will be described.

Scientific position This study seeks to investigate how customers engage with brands on social media with point of

departure in messages generated by brands. More specifically it seeks to establish a link between

the use of emotional message strategies and engagement behaviors among customers. This research

question entails that communication is not an empty vessel for delivering information but adheres to

the belief that communication, through discourses, has the power to construct a representation of

reality (Leeuwen, 2005, p.94). In this study, emotional appeals are seen as one way of representing

reality. When customers respond to this represented picture of an organization, the discourse

becomes real in its consequences (Hines, 1988, p. 257).

A brand message on social media has the ability to construct a representation of reality and by

responding to that picture of reality, e.g. through engagement, customers make it real in its

consequences. Adopting an emotional message appeals in communication can therefore be seen as a

way of representing reality. As a result, words are assumed to not only carry distilled information

but also have the ability to convey emotions, which can trigger a reaction by the receiver.

According to van Leeuwen (2009, p148) the analysis of text can be an instrument for understanding


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the social world. An important aspect of van Leeuwen’s (2005, p. 94) notion of discourses is that

discourses are plural and that several discourses about the same aspect of reality can exist.

Acknowledging that discourses are plural, have the power to construct representations of reality and

that these can be analyzed for understanding the social world, positions this study within the social

constructionist domain to some extent. The rationale for investigating the effectiveness of emotional

appeal on social media lies in communication’s ability to represent reality in a certain way through

discourses. In this study, emotional appeals are seen as a way of constructing discourses. On the

other hand, this study includes aspects, that are associated with the positivist tradition by assuming

that communication to some extent can be studied on its own (i.e. not in a social context) through a

content analysis. While this might seem counterintuitive, the following section on the methodology

will seek to justify this position.

Method for analysis In contrast to much of the prior research in the field of customer engagement, which have been

dominated by qualitative research (Dessart et al., 2016, p.403), this study adopts a quantitative

approach to studying the phenomenon. According to Dessart et al. (2016) the body of literature is

dominated by qualitative and exploratory studies seeking to conceptualize CE. This white space in

the field can also be found in the context of social media, where research has not kept pace with the

increasing interest in customer engagement and thus lack empirical validation of the dynamics

affecting CE on social media (de Vries & Carlsson, 2014). To answer this call, this study adopts a

quantitative content analysis to provide empirical validation of the influence of emotions on

customer engagement in a social media context. A quantitative research design provides more

generalizable- and less subjective results than a qualitative approach (Bryman, 2012, p.405) and

such results seem scarce in the present literature. Obviously, this methodological choice comes at a

cost and it is acknowledged that adopting a quantitative approach for investigating communication

will reduce the level of contextual richness. On the other hand, quantifying emotional discourses

will make it possible to provide valuable insights into the dynamics of CE by conducting a large-

scale analysis. In order to contribute with more generalizable results, it is necessary to accept a

decrease in the contextual richness. The following will elaborate on the reasoning for adopting a

content analysis in this research.


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Content analysis Content analyses can be defined as ”…a research technique for making replicable and valid

inferences from texts (or other meaningful matter) to the contexts of their use” (Krippendorf, 2013,

p 24.). This definition stresses content analyses’ ability to provide reliable findings about a

phenomenon while emphasizing that these findings will always be dependent on the context of their

use. Krippendorf (2013, p.28) further states that texts have no objective on its own but are

dependent on the reader of it and as he puts it “a text does not exist without a reader”. Thus, there

is no single meaning of a text to be identified it will always depend on the interpreter. The

reliability of the findings in content analysis should therefore be understood in this context, the

interpreter and the chosen codes will guide what is analyzed.

The modern usage of content analysis can be traced back to the beginning of the 20th century with

the emergence of quantitative newspaper analysis as a response to the increase in mass production

of newsprint (Krippendorf, 2013, p11.). In the last couple of decades, the way we consume content

has changed dramatically and so has the methods for content analysis. While content analysis in the

beginning the 20th century relied on manual coding and reading of texts, the technological

development has made computer aided content analysis possible (Krippendorf, 2013). Computer

aided content analysis has dramatically improved the efficiency of content analysis by enabling

researchers to analyze large bodies of data e.g. organizational documents (Pollach, 2012). While

this might seem like the holy grail of research, computer aided content analysis is still limited by

the coding of researchers. Computers are sequential machines and must be programmed to look for

the right codes in the content. Their operations are deterministic and therefore perfectly reliable

within their programming (Krippendorf, 2013, p208) as a result, a computer aided analysis will

never be better than its coding. This study will adopt a computer-aided approach to content analysis

due to the reliability, generalizability and ability to comprehend large bodies of textual data this

method present, which is argued to be needed in the field. While computer aided content analysis is

no new phenomena within communication, this paper will use IBM Watson and its Tone Analyzer

tool for conducting the analysis, which to the knowledge of the author has not been done in the

literature before. In order to explain the rationale behind this choice the following section is devoted

to explaining IBM’s artificial intelligence, Watson, and discussing the methodological implications

of this choice.


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IBM Watson and Natural Language Processing The name “Watson” is as an umbrella term for a wide range of different artificial intelligence

applications including image recognition, voice recognition, natural language processing and

sentiment analysis (IBM, 2017a). The term artificial intelligence (AI) has been around for decades

and Merriam Webster (2017) defines AI as “the capability of a machine to imitate intelligent human

behavior” and traces the first usage of the word back to 1955. The technological development has

advanced significantly since 1955 and so has the development of AI. The Watson technology is

probably best known for its appearance in the American television show Jeopardy! in 2011, where

it competed against two of Jeopardy’s great champions and won (Ferrucci, 2012). While this could

be regarded as a mere marketing stunt it served to prove the development in artificial intelligence

and its ability to understand natural language by processing the open-ended questions proposed by

the host of the show. Watson had to “understand” this question and based on its previous learnings

provide an answer depending on its evaluation of the likeliness that the answer was correct

(Ferrucci, 2012). From a content analysis perspective, this is interesting since one of the greatest

challenges in computer aided content analysis is to make computers recognize language in the same

way as humans do (Krippendorf, 2013, p.208-210). The difficulties in getting computers to

recognize language in a meaningful way is to a large extent due to the unstructured nature of

language. While humans for example understand, and interpret language based on context and

previous knowledge, computers need to be taught these patterns of recognition. The process of

learning computers to “understand” language is often referred to as Natural Language Processing

(NLP) (Ferrucci, 2012). This is not a straight forward task, the implicit and contextual nature of

language can even make humans interpret the same message in different ways (Ferrucci, 2012).

NLP is a computerized technique that seeks to infer meaning from text by analyzing different

variables such as usage patterns and syntax in order to derive meaning. These techniques are

individually not capable of understanding the full complexity of language but in combination, these

algorithms provide a more nuanced approach to reflecting natural language. In the Watson AI these

are combined in a deep-learning architecture, where algorithms not only make isolated evaluations

of their specific NLP technique, but are combined and interact in order to achieve a better and

deeper understanding of the complexity of language (Ferruci, 2012). Watson draws on an array of

known features of language and use machine-learning algorithms for determining how these


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features in combination explain language in the most precise way. While this architecture is unique

to Watson, the usage of NLP in content analysis has been used in the literature before.

Crowston, Allen & Heckman (2012) adopt NLP for coding in content analysis and provide a useful

explanation for justifying the usage of NLP in a content analysis. NLP assumes that language can

be understood and analyzed through rules and patterns, which generally can be divided into two

different approaches: statistical and symbolic (Crowston, Allen & Heckman, 2012). Statistical NLP

applies mathematical models to predict how words are used in a context based on a large corpus of

data while symbolic NLP uses human-developed rules such as syntaxes, discourses and semantics

to extract meaning from text (Crowston et al., 2012, p 527). Crowston et al. (2012) adopt the latter

approach to NLP in their study. They argue, that NLP can be used to gain valuable insights of the

social world and further claim that NLP can be adopted as a tool for content analysis. They possess

that social researchers often employ a qualitative approach for understanding the social world but in

content analysis, this requires extensive manual coding, which limits the possible scale of such

studies (Crowson et al., 2012, p.525). Thus, they propose that NLP can be used as a tool for coding

in content analysis and propose that it can enhance the reliability of such studies by making a

deeper understanding of language possible at scale.

NLP plays an important role when assessing the validity of Watson and is an essential part of the

technology. The previous introduced Jeopardy! appearance of Watson, can now serve to exemplify

the fundamental principles of Watson. When Watson is prompted a question, hundreds of different

algorithms, based on NLP (e.g. semantic relatedness, source reliability, classification, etc.) starts

analyzing the input but none of these algorithms are assumed to understand the questions on its own

(Ferrucci, 2012). Based on prior machine-learning of how these algorithms are best weighted

against each other, each result is scored and the scores are weighted into one overall score. The final

result will provide a list of candidate answers all weighted with a score that measures Watson’s

confidence of the answer being correct (Ferrucci, 2012).

The above provides a brief overview of the technology and it is acknowledged that the technology

is more complex than this review propose, but this description delivers a rationale for adopting IBM

Watson technology in this resaerch. The following will briefly introduce the Tone Analyzer tool,

which is adopted in this study and is one among many tools within the Watson technology.


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Tone Analyzer and coding of the analysis The Tone Analyzer tool uses linguistic analysis to infer different tones from text, which include

emotional tones, social tones and language tones (IBM, 2017b). This study focuses on emotions in

communication and the emotional tones will therefore be the point of departure for this analysis.

The Tone Analyzer measures the likeliness that a piece of text will be perceived as containing a

discrete emotion. The Tone Analyzer is coded to look for 5 different emotions, which are presented

in Table 1.

Emotion Description

Joy Joy or happiness has shades of enjoyment,

satisfaction and pleasure. There is a sense of

well-being, inner peace, love, safety and

contentment.

Fear A response to impending danger. It is a

survival mechanism that is a reaction to some

negative stimulus. It may be a mild caution or

an extreme phobia.

Sadness Indicates a feeling of loss and disadvantage.

When a person can be observed to be quiet,

less energetic and withdrawn, it may be

inferred that sadness exists.

Disgust An emotional response of revulsion to

something considered offensive or unpleasant.

It is a sensation that refers to something

revolting.

Anger Evoked due to injustice, conflict, humiliation,

negligence or betrayal. If anger is active, the

individual attacks the target, verbally or

physically. If anger is passive, the person

silently sulks and feels tension and hostility.

Table 1. Coding of the Tone Analyzer.


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The Tone Analyzer looks for the occurrence of each of these discrete emotions and provides a

likeliness score for each of the emotions being present (i.e. that the text will perceived as having

that discrete emotion) on a scale from 0 to 1. Scores below 0.5 are not likely to have a given

emotion present, while scores above 0.5 are like to have an emotion present. These five discrete

emotions are thus the codes adopted this content analysis. Coding is an essential part of content

analysis since it guides what is observed and how it is interpreted and as Berelson (1952, p.147)

puts it a “content analysis stands and falls by its categories”. This analysis relies on codes provided

in the Tone Analyzer, which bears some inherent limitations and a discussion of the validity reality

is therefore provided

Validity and reliability of the codes Validity refers to whether Tone Analyzer in fact measures emotional tones, while reliability refers

to consistency of the measurement (Bryman, 2012, pp.169.) As mentioned computer-based analysis

is perfectly reliable within their coding due to the deterministic operations of computers

(Krippendorf, 2013, pp. 208-210) if another researcher follows the same procedure as this study,

they will derive at the same result. This reliability comes at a cost, which is the validity of the

method. Despite several inquiries, IBM has declined providing a comprehensive list of the features

that the Tone Analyzer relies on. Thus, it has not been possible to evaluate the validity of the

individual algorithms that are used the Tone Analyzer due to the secrecy regarding the technology.

This is a critical limitation of the study, since it reduces the transparency of the analysis. On the

other hand, IBM provides examples of features the Tone Analyzer infers emotions based upon

which includes: emoticons, curse words, greeting words, sentiment polarity and n-grams (IBM,

2017.) All the different algorithms are combined in an ensemble framework in which they derive at

a prediction about the likeliness of a particular emotion being present (IBM, 2017b).

The Tone Analyzer has been benchmarked against two standard emotional datasets the International

Survey on Emotional Antecedents and Reactions (ISEAR) and Semantic Evaluation (SEMEVAL).

Both of these datasets are based on large bodies of textual that have been evaluated and classified

by humans to represent different emotional states e.g. guilt, anger and joy (Muresan, Stan, Giurgio

& Potolea, 2013). The Tone Analyzer ensemble model scores a macro-average F1 score of 41% and

61% respectively for the two emotional datasets. The macro-average F1 score is an averaged score

of the model’s precision and recall and can be compared to the macro-average F1 scores of other

models seeking to infer emotions from text (IBM, 2017a). According to IBM (2017a) the Tone


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Analyzer outperforms the top-reported state-of-the-art models in the field whose macro-average F1

scores are 37% and 63% for the two datasets. It is acknowledged, that the Tone Analyzer is not

capable on inferring emotions with the same precisions as humans can, but it presents state-of-the-

art technology within computer-aided emotional analysis. While it is acknowledged, that Watson

cannot outperform human coders on validity, the strength of the Tone Analyzer lies in is capability

of analyzing large amounts of textual data in a reliable manner, which is difficult to achieve with

qualitative human coding of data (Crawston et al., 2012). This trade-off was deemed necessary for

the study in order to answer the research question as well as the call for quantitative studies in the

field (Dessart et al., 2016). The following section will describe the sampling procedure adopted to

collect textual data for the content analysis.

Sampling

In order to investigate the role of emotions in customer engagement on social media, this study has

chosen to focus on brand owned pages. Brand pages on social media enables brands to post a

message which customers can respond to in different ways. The brand message is therefore

controlled by the brand and messages can thus be seen as one fraction of a company’s total

corporate communication. Social plug-ins on social media enables users to respond to a message by

various means e.g. reacting, commenting or sharing content, which can be used to assess the level

of engagement (Barger et al., 2016). As argued by McLuhan (1964, cited in Siapera, 2012 , p7.),

“the medium is the message”, and since social media platforms differ in characteristics two

different platforms have been chosen for this study, Facebook and Twitter. Facebook is by far the

largest social media platform with 1.96 billion monthly users, compared to Twitter’s 319 millions

monthly users (Statista, 2007). Following the example of Ashley & Tuten (2015), the sample was

conducted using Interbrand’s Best Global Brand list, which ranks the top 100 best global brands

according to brand equity. Based on the notion, that large companies are more likely to receive

engagement (van Doorn et al., 2013), it was assessed that recognized brands would be best suited

for this analysis.

Lastly, it has been suggested that brand industry can influence social media engagement and that

companies who are closely related to the end-customers are more sensible to social media

marketing since they communicate directly with the customer (Tuten & Solomon, 2015). In order

respect these perspectives the first step of the sampling followed the below procedure:


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1. The company should be present on Interbrand’s Best Global Brand List

2. Have presence on both Facebook and Twitter with a global site

3. Use English for communication

4. Be a B2C oriented company

In order to account for the differences between industries the list of companies was grouped into

categories e.g. “Clothing” and “Cars”. In order to make these industry labels as representative of the

industry as possible it was determined that a minimum of 5 companies should have the same label

for a category to be included.

Through this procedure, a total of 44 companies were found and included in the sample. These 44

brands were divided into Cars, (14) Clothing (12), Financial Services (9), and Consumer electronics

(9). These were assessed to be closely linked to their respective end customer, 3 of them mainly

through physical products and 1 of them through services. Appendix A provides a complete list of

the companies included in the study.

Data collection In order to analyze the effects of emotion on customer engagement, two types of data was needed in

this study. The brand posts communicated by the companies as well as the reaction to this content.

The former was collected through their textual messages and the latter through the social plug-in

mechanisms on Facebook and Twitter (e.g. like, share, retweet functions).

Since this study seeks to analyze a large body of text, manual data collection was deemed

unsuitable for this study. Both Facebook and Twitter allow users to generate an access token that

makes it possible to scrape historical information. These tokens were obtained and put into the

social media monitoring tool, Quintly, which was used for scraping the 44 brands’ Facebook and

Twitter account. The data was scraped on March 23th and collected between January 24th and March

9th, 45 days in total, where all posts by each brand were obtained from both Facebook and Twitter.

The 14 days between the last post and the collection of data was included to make sure that the

latest messages had reached their potential engagement levels. Afterwards posts containing a blank

text field (i.e. not containing any written words) were removed from the dataset, since these were

not suitable for a textual analysis. Lastly, all retweets were removed since these were not written by


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brands themselves but by third parties. The data set ended up containing 3606 tweets and 1536

Facebook posts after removing blank posts and retweets.

It should be noted, that this analysis only analyzes written words of the brand posts and do not

account for content within links, photos or video. It is acknowledged that this is limitation of the

study but it is reasonable to assume that the caption of a link, photo or video will seek to describe

the content and convey a similar emotional tone.

Tone Analyzer analysis The retrieved social media posts were analyzed through IBM Watson’s Tone Analyzer feature,

which has been described above. Each message was put into the Tone Analyzer individually and a

JSON file containing a score for each of the 5 emotions was returned for each message. These

scores were merged into an Excel sheet along with the engagement metrics (likes, shares, comments

etc.) for each message making it possible to compare engagement and emotions for each message.

The complete dataset containing messages, emotional scores and engagement levels for each brand

post can be found in Appendix B (Facebook) and Appendix C (Twitter).

Statistical analysis The above sheet will be used for describing tendencies in emotional message appeals across

industries and platforms. The sheet has further been used as the dataset for statistical analysis in

order to link emotional content to customer engagement. IBM SPSS Statistics version 24 is used for

conducting this analysis.

Defining dependent variables It can be assumed, that the numerical number of engagement is dependent on the number of

followers a Facebook or Twitter account has. A Facebook brand page with 1 million followers will

most probably receive more likes for a post than a Facebook brand page with 1000 followers. In the

dataset the lowest number of followers for a brand page was 33.354 (Facebook) and 36.043

(Twitter), while the highest number of followers were 27.696.896 (Facebook) and 8.395.359

(Twitter). To account for this significant difference in followers, the number of engagement

occurrences is divided by the number of followers for the respective brand, which provides a ratio

that can be compared between brand pages with varying number of likes. It furthermore makes it


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possible to compare engagement levels between Facebook and Twitter. The engagement ratios are

generally extremely small since these pages can have millions of users and in an effort to make the

ratios more comprehensible the ratios are multiplied by a factor of 100 000.

!"#$#%&%"')$'*+ = NumberofengagementsNumberofbrandpagefollowers ∗ 100.00

These engagement forms differ slightly between Facebook and Twitter. A user can react (e.g. like,

love etc.), share or comment on a post on Facebook, while they can retweet, reply or favorite a post

on Twitter. An engagement ratio was calculated for each type of engagement. Engagement ratios

for each brand post on Facebook can be found in appendix b and appendix c provides the

engagement ratios for Twitter.

As found in the literature review, different cognitive processing goes into each of these engagement

levels. In order to create a common typology for the engagement levels Schivinski et al.’s (2016)

framework of consumption, contribution and creation of content on social media is adopted and

modified slightly for the present study.

Consumption is the most widely adopted activity among social media users. According to

Schivinski et al.’s (2016) users do not engage actively on this level. Since this study cannot

investigate what users do not do, this category will contain the lowest levels of engagement which

will be argued to be Reactions for Facebook and Favorites for Twitter.

Contribution reflects customers’ contribution to brand-related content and will therefore include

the Comment function for Facebook and Reply function Twitter, which constitutes an intermediate

level of engagement

Creation refers to the strongest engagement level, where customers create or co-create content.

Sharing a brand-message distributes it to the user’s network (Ashley & Tuten, 2015) and is

regarded as the highest level of brand engagement. This will include Sharing on Facebook and

Retweeting on Twitter, where customers actively choose to share a brand message with their


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network and thereby facilitate customer-to-customer communication revolving around the particular

message.

Adopting this typology makes it possible to compare engagement levels between Facebook and

Twitter pages.

Lastly, H2 assumes that post vividness will influence the effect of emotions. The API used for

scraping data from Twitter did not classify the type of post, which makes it impossible to analyze

the effectiveness of this variable on Twitter. On Facebook each brand post is categorized as either

containing a status, an event, a link, a photo or video. As a result, H2 will only be investigated on

Facebook.

Independent variables Based on the previously introduced theory, it is assumed that the presence of emotions in brand

messages will affect the level of consumption, contribution and creation behaviors.

The emotional scores retrieved from the Tone Analyzer range between 0 and 1, scores above 0.5 are

considered likely to be perceived as having a particular emotion, while scores below 0.5 are

unlikely to present this emotion. Since this score represents a confidence level it and not a degree

(IBM, 2017), the scores variables were transformed into a binary value where scores < 0.499 were

coded as 0 (no presence of emotional content) and scores > 0.5 were coded as 1 (having emotional

content). This recoding of variables was conducted on each of the variables Joy, Angry, Sad,

Disgust and Fear. The syntax for the recoding procedure can found in appendix d.

Based on these variables a new variable all_emotions was coded to represent the presence of

emotional content in general and all_emotions was defined as contain Joy OR Angry OR Sad OR

Disgust OR Fear (appendix d).

Outliers It was found necessary to exclude a small portion of the original dataset due to a large variance in

the engagement ratios. On Twitter the lowest engagement ratio, when multiplied by a 100.000, was


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0,225 while the highest engagement ratio was 3003 and on Facebook the numbers were 0.39 and

30832 for the lowest and highest respectively. To decrease this enormous variance and create a

more normal distributed data set it was decided to remove the top 20 posts with the highest

engagement ratios and the bottom 20 post with the lowest ratios from Facebook and the top and

bottom 35 posts for Twitter. The difference in number of cases removed was due to the difference

in total amount of posts for each medium. The outliers account for 1.9% of the total Twitter posts

and 2.6 % of the Facebook posts. After removing the outliers, the engagement ratios rage between

1.18 and 344,03 for Twitter and between 2.36 and 1491 for Facebook. While it is always debatable

to remove outliers, it was assessed to be necessary for the analysis. First of all, it decreases the

skewness of the data and creates a more normal distribution. Secondly, there are myriad of reasons

why these outliers exist but one reason could be that the respective brands have used a significant

amount of money on buying reach and engagement for the posts. Another reason could be, that

these posts have gone viral. Nonetheless assessing the reach of paid posts or the antecedents of why

posts become “viral” is beyond the scope of this paper. While these are interesting subjects for

further research, these posts skew the data significantly and as a result it was chosen to exclude

these outliers from the analysis. The final dataset thus ended up containing 3536 tweets from

Twitter and 1496 posts from Facebook.

Recapitalization of the methodology This paper adopts a rather unusual method for conducting a content analysis and the above

elaboration on the method was deemed necessary for justifying this choice. To conclude this

section, this paper adheres to the belief that communication can construct reality through discourses

and that emotional framing is a way of affecting discourses. A quantitative content analysis can

provide reliable and generalizable results, which are needed in the field of study (de Vries et al.

2012; Dessart et al., 2016). Watson’s Tone Analyzer and 5 emotional coded tones are adopted in

this research, making it possible to investigate the usage of emotional strategies in a large body of

textual data. Lastly these results are sought related to customer engagement through a statistical

analysis of customer engagement behaviors on social media by investigating the relationship

between likes / favorites, comments / replies and share / retweets for Facebook and Twitter

respectively, and the analyzed emotional appeals.

Obviously, these methodological choices come at a cost. Adopting a quantitative approach to

studying a communicative phenomenon reduces the contextual richness, which decreases the


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validity. This specific method is to a large extent sender-centric by focusing on brand messages on

social media, while the receivers’ perception of these messages have been reduced to their

behavioral response to the message. This choice of focus has its inherent limitations, for example

the behavioral response by receivers is measured as occurrences, but do not take the valence of the

response into consideration. A comment or reply to a brand message on social media can both have

a positive or negative valence towards the brand, but this study does not analyze valences of the

behavior to a brand message but only the occurrence. A perception analysis could provide valuable

insights into the subject but is beyond the scope of this study.

On the other hand, the quantitative approach adopted in this study makes it possible to investigate

how different emotional discourses affect customer engagement behavior, which is well in line with

the research question of the study. Furthermore, this approach answers the call for more

quantitative- and validating research in the field of customer engagement. Thus, it is assessed that

this approach, despite its limitations, can provide valuable insights in the field and is the most

suitable method for investigating the research question.


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CHAPTER 4: FINDINGS The presentation of the findings will be twofold. Firstly, a descriptive analysis of the findings will

be provided. This will give an overview of the usage of emotional message strategies, post

characteristics and engagement levels and assess how these differ across industry and platform.

This will provide valuable insights on how emotional discourse are adopted by brands on social

media. These findings are based on the content analysis conducted with IBMs Watson Tone

Analyzer tool, which was described in the methodology section. Secondly a relationship between

the usage of emotional discourses and customer engagement will be sought established in order to

provide evidence of the potential effects of these message strategies. This will be established by

conducting a linear regression analysis to measure the significance and strength of the assumed

relationship between usage of emotional appeals and level of engagement.

Part 1 - Descriptive analysis As introduced in the theoretical framework industries (van Doorn et al., 2011; Panda et al., 2013) as

well as the medium for the message (Byron, 2008; de Vries et al., 2012) are expected to influence

the influence of emotional strategies. This also provides a rationale for investigating to what extent

the usage of emotional appeals differs across these variables.

Industries and emotional message appeals Table 2 (Twitter) and Table 3 (Facebook) show the inclusion of different message strategies across

industries divided into the five emotions. As mentioned in the methodology section each of these

discrete emotions are transformed into binary codes that categorizes the emotion as being absent or

present (emotional scores < 0.499 = absent, emotional scores > 0.500 = present). The frequency

count in the tables measures the occurrences of each emotion i.e. number of brand posts containing

that emotion. The percentage columns in the tables measure the amount of posts containing an

emotional appeal compared to the total number of posts in that category. As an example, the

industry cars adopts an emotional appeal in 661 posts on Twitter, which translates into 50,3% of the


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total posts (1313) in the car industry. Across all industries 1774 out of 3536 brand posts contain

emotional appeals on Twitter and 607 out of 1496 brand posts contain an emotional appeal on

Facebook

Table 2 how that 47,3 % of brand posts on Twitter contain emotional content, which is defined as

having at least one of the five discrete emotions present. Joy is by far the most present emotional

appeal and accounts for 43,6 % of the total brand posts on Twitter. Sadness is the second most used

emotion but it only accounts for 1,8% of the total brand post. Sadness is followed by disgust

(1,4%), fear (0,3%) and anger (0,2%). The same pattern can be observed in table 3, where 39,5 % of

brand posts on Facebook contain emotional content, where joy accounts for 34,6 %, sadness 3,5%,

disgust 1,3%, anger 0,7% and fear 0,2%.

As the tables clearly show, joy is the primary adopted emotional appeal for brand posts. The counts

for fear and anger are especially low, with less than 5 occurrences of these appeals for each of the

industries on both platforms. These few occurrences make it impossible to measure the possible

effects of adopting an anger or fear appeal on social media since the data pool for these categories

is too small. Nonetheless, they show how emotional appeals are adopted relative to each other and

it is evident that joy the most often applied emotional discourse among B2C brands on social media.

Table 2 and 3 furthermore reveal that the inclusion of emotional appeals differs across industries.

On Twitter the usage of joy as a message appeal is more frequently adopted by the industry cars

with 47,1 % of their posts containing joyful content. In contrast only 36,1 % of the posts obtained

from the industry finical services had the emotion joy present. Financial services is likewise the

industry containing least joyful message appeals on Facebook, while consumer electronics contains

the most. There seems to be a clear pattern of the relative importance of each discrete emotion on

both Twitter and Facebook where Joy is by far the most frequently used emotional appeal on both

channels compared to the other emotional appeals.

As mentioned, the low adoption of anger, disgust, sadness and fear makes it difficult to observe any

clear patterns. Sadness is the emotional appeal that differ most in usage between industries and

communication channel. On Facebook, financial services and consumer electronics adopts these

appeals in 5,2 % and 5,7 % of their total number of posts respectively compared to 2,8% and 2,9%

of posts in the cars and clothing industry. Furthermore, sadness is used in 3,5% of all Facebook

posts compared to 1,8% of Twitter posts. This indicates, that messages containing content that


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induce sadness is more common on Facebook than Facebook although the relative usage of this

appeal is still very low compared to the usage of joy

Table 2. Usage of varying emotions by industry (Twitter). n: 3536

Table 3. Usage of varying emotions by industry (Facebook). n: 1496

Twitter Joy Anger Disgust Sadness Fear Total no. messages

containing emotions

Freq. Pct. Freq. Pct. Freq. Pct. Freq. Pct. Freq. Pct. Freq. Pct.

Cars 619 47,1% 3 0,2% 8 0,6% 28 2,1% 3 0,2% 661 50,3%

Clothing 413 45,7% 0 0% 15 1,7% 6 0,7% 1 0,1% 435 48,1%

Consumer

electronics 266 41,1% 3 0,5% 10 1,5% 15 2,3% 3 0,5% 296 45,7%

Financial

services 243 36,1% 1 0,1% 18 2,7% 16 2,4% 4 0,5% 282 41,8%

Total 1541 43,6% 7 0,2% 51 1,4% 65 1,8% 11 0,3% 1774 47,3%

Facebook Joy Anger Disgust Sadness Fear Total no. posts containing emotions

Freq. Pct. Freq. Pct. Freq. Pct. Freq. Pct. Freq. Pct. Freq. Pct.

Cars 243 35,7% 5 0,7% 5 0,7% 19 2,8% 0 0% 269 39,6%

Clothing 140 33,6% 1 0,2% 7 1,7% 12 2,9% 1 0,2% 159 38,1%

Consumer

electronics 86 37,9% 1 0,4% 4 1,8% 13 5,7% 1 0,4% 104 45,8%

Financial

services 48 27,9% 4 2,3% 3 1,7% 9 5,2% 1 0,6% 65 37,8%

Total 517 34,6% 11 0,7% 19 1,3% 53 3,5% 3 0,2% 607 39,9%


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The above have outlined how the adoption of emotional appeals differ across industries.

Furthermore, it is observed that Joy is by far the most widely adopted emotional appeal across all

industries.

Table 2 and 3 further indicate that the usage of emotional appeals differs between Facebook and

Twitter. Figure 2 shows the total percentage of messages containing emotional appeals for each of

the industries on both platforms. This suggests that a larger share of brand posts on Twitter contains

emotional appeals than on Facebook. This tendency is true for the industries cars, clothing and

financial services, where the usage of emotional content relative to total number of posts is greater

on Twitter than on Facebook. Consumer electronics is the exception, where the relative adoption of

emotional appeals is slightly higher on Facebook (45,9%) than on Twitter (45,7%). This indicates

that brands are more likely to include emotional appeals on Twitter than on Facebook. Al though

this tendency does not show any difference in effect it serves to show that brands adopt varying

emotional discourses dependent on the platform for the message. It should be noted that the posts

for Twitter and Facebook are collected during the exact same period of time and the content on the

channels can therefore be assumed to revolve around the same themes and subject, but as figure 2

suggests, with a varying emotional framing.

Figure 2. Percentage of posts containing an emotional appeal by industries. n=1496 (Facebook), n=3536 (Twitter)

0

10

20

30

40

50

60

Cars Clothing Consumerelectronics Financialservices

%oftotalpostscontainingemotionalcontent

Facebook Twitter


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Post characteristics As mentioned in the theoretical framework the richness of a message can have an effect on the

perception of emotions (Byron, 2008; de Vries et al.,2012). H2 seeks to investigate this relationship

by investigating if the effect of emotional appeals is affected by media richness. In order to

investigate this relationship in the second part of the analysis, a descriptive analysis of how

different post types are used among the industries is provided in table 4. As pointed out in the

methodology, this part of the analysis will only focus on Facebook since information about post

characteristics could not be retrieved from the Twitter API. Table 4 shows the distribution of brand

posts on Facebook categorized as either containing an event (link to a Facebook event), a link (link

to a webpage), a photo, a status update or a video. It should be noted that the content analysis

adopted in this study has only looked at the text accompanying the respective events, links, photo

and videos as described in the methodology. Posts only containing an event, a link, a photo or a

video without any accompanying text have been removed from the dataset. More than half of the

brand posts on Facebook contain a photo (54%), followed by video posts which accounts for 34,1%

and lastly, links account for 11,3% of the posts. Post containing only a status or an event are almost

non-existing and in combination those two categories only account for 0,4% of the total brand posts

on Facebook.

Facebook Frequency Percent

Event 2 0,1%

Link 162 10,8%

Photo 810 54,1%

Status 5 0,3%

Video 517 34,6%

Total 1496 100%

Table 4. Frequency of post characteristics on Facebook. n=1496


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While there seems to be a clear pattern in the usage of these categories, with photos being the most

adopted post type, the post characteristics can also be broken into the four different industries,

which provide a more nuanced picture of the pattern. Figure 3 shows the distribution of posts across

industries. Cars, clothing and consumer electronics have a rather similar distribution of post types

with photos being the most widely used followed by videos and links. On the other hand, table 5

shows that financial services differ significantly from the others and video content is the primary

format of brand posts (47,1%) followed by photos (26,2%) and links (25%). This usage of links and

videos are significantly higher for financial services than the other industries, while its usage of

photos is significantly lower. This indicates that similar to the usage of emotional discourses, the

choice of brand post type differs across industries. Characteristics

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

100%


Post characteristicsbyindustry

Photo Video Link Status Event

Facebook Photo Video Link Status Event

Cars 55,4 36,5 8,1 - -

Clothing 63,1 28,5 7,4 0,5 0,5

Consumer

electronics 55,1 30,4 14,5


Page 47 of 87

Figure 3. Relative distribution of post characteristics by industries. n=1496 (Facebook)

Table 5. Relative distribution of post characteristics by industry. n=1496 (Facebook)

The above have outlined how the usage of emotional discourses on social media differs across

industry and communication channel (Facebook or Twitter). Furthermore, the usage of different

post characteristics differs across industry type as well. The emotional discourses and post

characteristics are hypothesized to drive customer engagement in this study and thus regarded as

independent variables in the analysis. The following section will describe the dependent variable,

customer engagement, in more depth and review how this variable differs across industries and

media platform.

Customer engagement descriptive As described in the methodology section, the customer engagement variable had to be transformed

in order to account for the differences in followers for each brand. This has been achieved by

measuring CE on a ratio instead of their absolute values. These ratios have been operationalized

into the three categories consumption, contribution and creation to make them comparable across

Facebook and Twitter. Lastly a total engagement variable has been computed as an overall measure

for engagement, this variable is constituted by the sum of the consumption, contribution and

creation ratios. As mentioned, the engagement ratios have been multiplied by a factor of 100 000

to make them more comprehensible for an analysis.

Table 6 provides an overview of the average engagement ratio for consumption, contribution and

creation. The mean engagement ratio indicates how many engagements a post can be expected to

receive based on the number of followers of a given brand page. As an example, the mean total

engagement ratio for Facebook is 79.64 (table 6). This indicates that a Facebook page with 100.000

followers will on average receive 79 engagements (no. of followers x engagement ratio / 100.000).

The ratios in figure 3 visualizes how engagement ratios differ across Facebook and Twitter.

Overall, users are engaging significantly more with posts on Facebook than on Twitter. The average

engagement ratio for a Facebook post is more than three times the average of a brand post on

Twitter. This tendency is most outspoken when it comes to consumption where the mean

engagement ratio for Facebook is 67,31 compared to 20,08 for Twitter. The same tendency is true

Financial

services 26,2 47,1 25 1,7 -


Page 48 of 87

for the contribution engagement ratio, while the creation engagement ratio is more equal across

channels. Similar for both channels are the relative importance of each type of engagement with

consumption being the most common type of engagement, followed by creation and contribution.

This indicates that customers are more likely to react / favorite a brand post than sharing /

retweeting it, while commenting / replying is the least common type of engagement. It should be

noted that these differences in engagement ratios should also be seen in the light of post frequency.

The brand posts for Facebook and Twitter have been collected during the same period of time but

twice as many tweets (3836) were collected compared to Facebook posts (1496). This suggests that

tweets are higher in frequency but lower in engagement level, while Facebook posts have a lower

frequency but a higher engagement level.

Facebook Total engagement ratio

Consumption ratio

(reactions)

Contribution ratio

(comments)

Creation ratio (shares)

Mean 79,64 67,31 4,89 7,43

Twitter Total engagement ratio

Consumption ratio

(favorites)

Contribution ratio

(replies)

Creation ratio (retweets)

Mean 26,94 20,08 0,58 6,28

Table 6. Mean engagement ratios for Facebook and Twitter. n=3536 (Twitter); n=1496 (Facebook). Engagement ratios are

multiplied by a factor of 100.000


Page 49 of 87

As previously found in the analysis, different industries adopt different emotional appeals and post

characteristics. Thus, it could be expected that engagement ratios will differ across industries. Table

7 and 8 show the total engagement ratio for each industry on Facebook and Twitter. Consumer

electronics and financial services on Facebook have a significantly higher engagement ratio than all

of the other industries. This could indicate the existence of extreme outliers but as mentioned in the

methodology actions has been taken to avoid the outliers. A further investigation into this difference

revealed that among the top 50 most engaging post on Facebook, 35 of them stemmed from either

financial services or consumer electronics. This suggest that there is a general tendency for these

industries be more engaging on Facebook and that the distribution is therefore not caused only by a

few outliers. On Twitter, the industry cars (38,7) has the highest average engagement ratio followed

by consumer electronics (27,68), financial services (18,32,) and lastly clothing (15,76). These

findings provide a more nuanced picture of the differences in engagement ratios on Facebook and

Twitter and show that there is a significant variance in the engagement levels on Facebook across

industries, while this variance is more moderate on Twitter. Furthermore, it shows that the

difference between engagement ratios on Facebook and Twitter described above differ across

industries. This is illustrated in figure 5.

0

10

20

30

40

50

60

70

80

90

Consumption Contribution Creation Totalengagement

Engagemen

tratiox100.000

EngagementratiosonFacebookandTwitter

Facebook

Twitter

Figure 4 .Engagement ratios divided into engagement categories. n=1496 (Facebook), n=3536 (Twitter)


Page 50 of 87

Facebook

Total

engagement

ratio

Consumption

ratio

(reactions)

Contribution

ratio

(comments)

Creation ratio

(shares)

Cars (n=680)

72.707 62.01 3.077 7.61

Clothing (n=417)

42,06 38.47 0.97 2.65

Consumer electronics (n=227)

140,38 123.32 7.83 9.223

Financial services (n=172)

118,00 84.32 17.67 16.03

Table 7. Mean engagement ratios by industry (Facebook). n=1496, Facebook. Engagement ratios are multiplied by a factor of

100.000

Twitter Total engagement

ratio Consumption ratio

(favorites)

Contribution

ratio

(replies)

Creation ratio

(retweets)

Cars (n=1313)

38,70 29,73 8,18 0,75

Clothing (n=904)

15,76 11,599 4,01 0,15

Consumer electronics

(n=647) 27,68 20,33 6,93 0,41


Page 51 of 87

Financial services (n=674)

18,32 12,36 4,98 0,97

Table 8. Mean engagement ratios by industry (Twitter). n=3536.. Engagement ratios are multiplied by a factor of 100.000

Figure 5. Difference in engagement ratios by industries and media platform. n=1496 (Facebook), n=3536 (Twitter). Engagement

ratios are multiplied by a factor of 100.000.

The last aspect that needs to shed light on in relation to the engagement ratio is the distribution of

engagement ratios compared to post the previously introduced post characteristics. Table 9 shows

the average engagement ratios for Facebook posts containing links, photos and videos and figure 6

visualizes the differences. Videos have the highest average reaction rate followed by photos and

links. On the other hand, links seems to have a higher comment ratio than photos and videos. Photos

in fact have the lowest engagement rate for both comments and shares. While these are merely

descriptive statistics and the causality is not sought proved, these are interesting findings. Recall the

theoretical framework section in which it was introduced that vivid brand posts receive more

engagement (de Vries et al., 2012), which in this sample seems to be true for reactions but only

partially for comments and shares. Photos arguably have a higher degree of vividness than links but

have a lower average engagement ratio for comments and shares. These findings will be discussed

in more depth in the discussion section. How different post characteristics influence the

0

20

40

60

80

100

120

140

160


Engagementratiosacrossindustries

Facebook Twitter


Page 52 of 87

effectiveness of emotional appeals in fostering engagement will be investigated in the second part

of the analysis.

Facebook Total engagement

ratio Reaction ratio Comment ratio Share ratio

Link (n=162)

56,34 40,02 7,51 8,80

Photo (n=810)

79,40 70,54 3,822 5,03

Video (n=517)

87,94 71,37 5,74 10,82

Table 9. Average engagement ratios by post characteristic. n=1496 (Facebook). Engagement ratios are multiplied by a factor of

100.000.

Figure 6. Average engagement ratios by post characteristic. n=1496 (Facebook). Engagement ratios are multiplied by a factor of

100.000.

0

10

20

30

40

50

60

70

80

Reactionrate Commentrate Sharerate

Averageengagementratio/postcharacteristic

Link Photo Video


Page 53 of 87

Descriptive analysis conclusion This first part of the analysis has sought to outline the main findings and tendencies found in the

dataset. As it should have become clear many different aspects of the dataset have to be taken into

consideration when seeking to link emotional message appeals to customer engagement. Firstly, it

has been found that the adoption of emotional message strategies differs across industries as well as

across communication channel. In general, brands seem to be more likely to adopt an emotional

message strategy on Twitter than on Facebook. The two channels also differ in their ability to

engage customers. Brand posts on Facebook receives a higher engagement ratio than brand posts on

Twitter but Twitter is used by brands more frequently. Lastly, the usage of different post

characteristics has been introduced. Facebook brand posts are dominated by photos, followed by

video and links posts. This analysis points out some interesting results and their implications will be

elaborated upon in the discussion section. While this part of the analysis has showed the central

tendencies in the data, the second part will seek to establish a statistical relationship between these

variables in order to measure the effect of adopting an emotional message strategy on customer

engagement.

Part 2 – Hypotheses testing In order to test the relationship between the usage of emotional message strategies and customer

engagement several hypotheses were developed in the theoretical framework. This section will

investigate to what extent these relationships are significant for the dataset and the strength of the

relationships’ coefficient. This will be achieved by conducting a linear regression analysis for each

of the potential analysis, which will provide a measure of significance that indicates the degree to

which the relationship found in the dataset will be generalizable to the population from which the

sample was drawn (Bryman, 2012, p.346). The coefficient will reveal the strength and direction of

the relationship by showing the expected change of the dependent variable (engagement ratios) as

an effect of changing the independent variable (emotions) (Field, 2007, p.207-208). To make these

results more comprehensible the testing of each hypothesis will be presented with the significance

and coefficient levels individually and finally each hypothesis will be sought accepted or rejected.


Page 54 of 87

Hypothesis 1 The first hypothesis seeks to investigates the relationship between the adoption of an emotional

message strategy and customer engagement.

H1: An emotional message appeal on social media will foster a higher level of customer

engagement than a message appeal without emotional cues.

H1a: An emotional message appeal will receive more “likes & favorites” than a neutral

message appeal

H1b: An emotional message appeal will receive more “comments / replies” than a neutral

message appeal

H1c: An emotional message appeal will receive more “shares / retweets” than a neutral

message appeal

As mentioned in the methodology, in an effort to make each engagement type comparable across

Facebook and Twitter the dependent variables (engagement) were transformed into three categories

consumption, contribution and creation following the works of Schivinski et al. (2016). A

regression analysis has been conducted for each of these categories with emotional content as the

independent variable. This part of the analysis does not consider which type of emotion but only

whether a post contains an emotional appeal or not. Table 10 shows the result of the regression

analysis.

Table 10. The effect of emotional appeal CE on Facebook and Twitter. Significant at p < 0.05, n=1496 (Facebook), n=3536

(Twitter). Engagement ratios are multiplied by a factor of 100.000.

The coefficients B and b show the un-standardized and standardized measure of the coefficient,

respectively. It should be noted that the un-standardized figures, B, measure the increase in

Facebook Twitter

Consumption

(Reactions)

Contribution

(Comments)

Creation

(Shares)

Total

(All)

Consumption

(Favorites)

Contribution

(Replies)

Creation

(Retweets)

Total

(All)

B

(b) Sig.

B

(b) Sig.

B

(b) Sig.

B

(b) Sig.

B

(b) Sig.

B

(b) Sig.

B

(b) Sig.

B

(b) Sig.

19.175

(0.073) 0.004

.058

(.001) .970

2.447

(.040) .118

21.680

(.069) .007

2.447

(.040) .018

.185

(.027) .105

.275

(0.14) .396

2.908

(.036) .031


Page 55 of 87

engagement ratio when going from a non-emotional appeal to an emotional appeal, while the

standardized measure, b, indicates the coefficient on a standardized scale (Field, 2009) and can as

result not be directly translated into engagement levels. As previously mentioned the engagement

ratios are multiplied by a factor of 100.000, which should be remembered. A Twitter brand page

with 100.000 followers would therefore receive 2.44 more favorites if it contains emotional content

than if it does not (100.000x2.44/100.000). On both Twitter and Facebook a significant relationship

was found between the use of emotional content and consumption behaviors. Neither on Facebook

nor on Twitter could a significant relationship be found between the adoption of an emotional

appeal and contribution and creation. As outlined in the descriptive analysis the average

engagement was significantly higher for the consumption category and as a result of this relative

importance of reactions and favorites drives the significant relationship between total engagement

and emotional appeals on both platforms. All of the significant relationship are positively correlated

to the use of emotional appeals but the coefficients are rather weak (between .036 and .073).

Nonetheless, these relationships are considered significant, which indicates that there is a

relationship between the variables but this relationship only have a slight effect on the level of

engagement.

As a result. H1 and H1a can be supported for both Facebook and Twitter, while H1b and H1c have

to be rejected since these relationships were not found to be significant.

Hypothesis 2 The second hypothesis seeks to uncover how the information richness of the medium influence the

effect of emotional message appeals.

H2: The effectiveness of an emotional appeal on social media is mediated by the information

richness of the message. A post with a high degree of information richness will foster more

engagement than a post with a low degree of information richness.

H2a: An emotional message appeal combined with a video will be more effective than an

emotional message appeal combined with a photo.

H2b: An emotional message appeal combined with a photo will be more effective than a text

only emotional appeal.

This hypothesis was based on the notion that information richness affects how emotions are

perceived. A higher degree of information richness would therefore lead to a more precise


Page 56 of 87

perception of the given emotion, which is hypothesized to increase the engagement ratio. As

mentioned, it was only possible to conduct this analysis on Facebook posts and table 11 therefore

only considers this relationship on Facebook.

Consumption

(Reactions)

Contribution

(Comments)

Creation

(Shares)

Total

(All)

B

(b) Sig.

B

(b) Sig.

B

(b) Sig.

B

(b) Sig.

Link 5.391

(.027) .730

4.303

(.045) .573

4.719

(.061) .441

14.413

(.050) .527

Photo 22.105

(.089) .011

.351

(.012) .733

1.285

(.046) .187

23.741

(.082) .019

Video 18.959

(.064) .146

-1.766

(-.022)

.614

3.490

(.041) .357

20,683

(.058) .186

Table 11. The of post characteristics on the effectiveness of an emotional appeal. Significant at p < 0.05, n=1496 (Facebook),

engagement ratios are multiplied by a factor of 100.000

Table 11 shows the effect of emotional content combined with the respective post type on

engagement ratios. It was expected, that richness would enhance the effect but as table 11 shows

this relationship does not seem to exist. There are no significant relationships between posting a

video with an emotional appeal and any of the engagement ratios. On the other hand, a significant

relationship was found between posting a photo with an emotional appeal and consumption. No

significantly relationships were found between posting a link combined with an emotional appeal

and any forms of engagement. The post type photos is the only post characteristic that influence the

effectiveness of an emotional appeal significantly.

As a result, H2 can only be partially supported. Photos are assumed to have a higher degree of

vividness than links, which supports H2 but on the other hand, videos have higher degree of

vividness than photos but no significant relationships were found between the use of videos with

emotional content and engagement. This entails that H2a can be supported while H2b is rejected.


Page 57 of 87

Hypothesis 3 The first hypothesis, H1, confirmed that the adoption of an emotional message strategy is related to

customer engagement but it did not differentiate between appeals. As introduced in the theoretical

framework, different emotional appeals can be expected to have a varying effect and to investigate

this, H3 was proposed:

H3: The effectiveness of an emotional message appeal on social media is affected by the type of

emotion the message seeks to induce. Discrete emotions will have a varying effect on the

occurrence of engagement as well as the type of engagement.

H3a: Messages appeals that seek to induce amusement will foster more engagement than

message appeals seeking to induce sadness.

Table 12 shows the coefficient and significance levels for the five discrete emotions in relation to

consumption, contribution, creation and total for both Facebook and Twitter. As noted in the

descriptive analysis the number posts containing fear and anger for both Facebook and Twitter

were deemed too low to conduct any meaningful analysis and as a result these have been left out of

the analysis.


Page 58 of 87

Results that were found to be significant (p<0.05) have been highlighted with green, while

insignificant results have been highlighted with red. For Facebook joy was found to have the

strongest impact on consumption where the unstandardized coefficient is 20.923, suggesting that a

Facebook brand page with 100.000 followers would on average receive 20,9 more reactions for a

brand post with an emotional message appeal than one without. As noted earlier, reactions accounts

for a large percentage of the total engagement and as a result a significant relationship between total

engagement and the usage of joy can be observed. On Facebook, a significant relationship between

the adoption of a sadness appeal and creation can be observed, suggesting that a sadness appeal is

more effective in getting customers to share content than an appeal containing joy. It can be

concluded that emotional appeals on Facebook differ in their ability to foster engagement and

thereby confirming H3. On the other hand, the results are less clear for H3a, since joy overall is

more effective in fostering engagement but sadness is in fact more effective in making users share

Facebook Twitter

Consumption

ratio (Reactions)

Contribution ratio

(Comments)

Creation ratio

(Shares)

Total engagement

ratio (All)

Consumption ratio

(Favorites)

Contribution ratio

(Replies)

Creation ratio

(Retweets)

Total engagement

ratio (All)

B (b)

Sig. B

(b) Sig.

B (b)

Sig. B

(b) Sig.

B (b)

Sig. B

(b) Sig.

B (b)

Sig. B

(b) Sig.

Joy 20.923 (.078)

.003 -.419 (-007)

.797 .688

(.011) .670

21.195 (.066)

.011 2.597 (.042)

.013 -.031

(-.005) .785

.206 (.011)

.529 2.771 (.034)

.042

Sadness -15.380 (-.022)

.390 2.167 (.014)

.601 12.608 (.079)

.002 -.605

(-.001) .977

2.092 (.009)

.586 .203

(.008) .632

1.105 (.015)

.380 3.354 (.011)

.540

Disgust 2.558 (.002)

.930 -.540

(-.002) .937

-2.764 (-.010)

.686 -.716

(-.001) .984

-2.781 (-.011)

.519 -.036

(-.001) .940

-.271 (-

.003) .842

-3.098 (-.009)

.584

Anger N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A

Fear N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A

Table 12. The effect of varying emotions on CE. Significant at p < 0.05, n=1496 (Facebook), n=3536 (Twitter). Engagement ratios are multiplied

by a factor of 100.000


Page 59 of 87

content. H3a can therefore not be accepted without reservations and is therefore only partially

supported for Facebook.

For Twitter joy has a significant relationship with consumption, which makes the overall

relationship between joy and engagement significant as well. In contrast to the findings for

Facebook, sadness is not found be significantly related to engagement on Twitter for any of the

engagement categories.

It can be concluded that emotional appeals differ in their ability to engage customers on Twitter and

H3 can be supported. Furthermore, no significant relationships were found between the use of a

sadness appeal and engagement while joy were positively related consumption, supporting H3a for

Twitter.

Overall it is found that emotional appeals differ in their ability to foster engagement on both

Facebook and Twitter supporting H3. On the other hand, H3a can only be partially supported for

Facebook since a sadness appeal is more effective in fostering a contribution behavior than a joy

appeal. H3a is supported for Twitter, where no significant relationships were found between

sadness and engagement.

Hypothesis 4 As introduced in the theoretical framework, research on customer engagement indicates that

industry type can have a influence on the effect on CE. To test this in the context of emotional

appeals on social media hypothesis four was put forward

H4: The effectiveness of emotional appeals on social media is affected by the type of industry within

which the brand is positioned

Table 13 shows how significance levels and coefficients between the usage of an emotional appeal

(the usage of at least one of the appeals) and engagement differ across the four industries on

Facebook and Twitter. It is interesting to note that neither of the industries is found to have a

significant relationship on both Facebook and Twitter.


Page 60 of 87

The industries cars and financial services are found not to have any significant relationships

between adoption of an emotional appeal and engagement on neither Facebook nor Twitter. The

industry clothing is the only industry on Facebook where a significant positive relationship between

emotional appeals and engagement can be established. This relationship is true for both

consumption and contribution where the usage of an emotional appeal increases the average

engagement among customers. On Twitter no significant relationships can be established for the

clothing industry. In the consumer electronics industry a significant positive relationship can be

found for consumption on Twitter, while no significant relationships can be found for this industry

on Facebook.

Overall, significant relationships can be established in two of the four industries where the usage of

emotional appeals are positively correlated with customer engagement on either Facebook or

Facebook Twitter

Consumption

ratio

(Reactions)

Contributi-

on ratio

(Comments)

Creation

ratio

(Shares)

Total

engagement

ratio

(All)

Consumption

ratio

(Favorites)

Contribution

ratio

(Replies)

Creation

ratio

(Retweets)

Total

engagement

ratio

(All)

B

(b) Sig.

B

(b)

Si

g.

B

(b) Sig.

B

(b) Sig.

B

(b) Sig.

B

(b) Sig.

B

(b)

Si

g.

B

(b) Sig.

Cars 8.531

(.047) .223

.187

(.013)

.7

37

2.682

(.038) .318

11.399

(.050) .197

-.481

(-.007) .795

.446

(0.46) .094

.030

(.002)

.95

2

-.006

(.000) .992

Clothing 16.336

(.120) .014

.505

(.100)

.0

42

.894

(.053) .279

17.735

(.117) .017

.550

(.015) .661

-.050

(-.049) .145

-.934

(-.019)

.57

3

.107

(.002) .955

Consumer

electronics 19.458

(0.45) .499

-.217

(-.004)

.9

46

2.516

(.046) .494

20.964

(.044) .509

6.503

(.095) .016

-.024

(-.013) .747

.916

(.049)

.20

9

7.395

(.087) .027

Financial

services 46.321

(.125) .103

-1.815

(-

0.011)

.8

86

5.665

(.058) .451

50.171

(.102) .182

3.240

(0.56) .144

.244

(.033) .395

.426

(.022)

.56

6

3.910

(.050) .197

Table 13.Effect of an emotional appeal across industries. Significant at p < 0.05, n=1496 (Facebook), n=3536 (Twitter). Engagement

ratios are multiplied by a factor of 100.000.


Page 61 of 87

Twitter. Although the relationships are found to be significant, it should be noted, that the strength

of these relationships are relatively weak (standardized coefficient range between .087-.120)

making the effectiveness of adopting an emotional appeal relatively small.

It has been found that the effectiveness of an emotional appeal is mediated by both industry and

channel. An industry might successfully foster engagement with emotional appeals on Facebook,

but be unsuccessful in fostering engagement with the same appeal on Twitter and vice versa. Thus,

it can be concluded that industry type mediates the effectiveness of an emotional appeal and H4 can

be accepted.

Summary of findings The descriptive part of the analysis highlighted important tendencies on how brands communicate

on different social media channels, which post types and message appeal they adopt and how

customers engage on social media. It was worth noting that the adoption of emotional message

appeals differs across industries and media channels. With regards to message channels, it was

found that brand posts on Facebook have a higher engagement ratio but a lower post frequency

compared to Twitter, which have a high post frequency but a relatively low engagement ratio.

These findings provide valuable insights into how brands engage with customers on social media

but do not per se prove a relationship between emotional appeals and customer engagement.

The second part of the analysis has sought to establish this relationship to provide evidence of the

potential effect of an emotional appeal on customer engagement behavior. First of all, the adoption

of an emotional appeal has been found to be positively related to customer engagement. This

relationship was found to be significant for both Facebook and Twitter but the strength of this

coefficient is relatively weak and posts with emotional content only fosters slightly more

engagement compared to a post without an emotional appeal. Secondly, no significant relationships

were found between a high degree of information richness in a post and the effectiveness of

emotional appeals. Thirdly, joy is the most often adopted emotional appeal by brands in the study

and joy is the only emotional appeal where a significant relationship can be established with the

overall engagement ratio. Although, Sadness was found to be related to contribution behaviors on

Facebook. Lastly, the effectiveness of an emotional appeal is found to differ across industries and

media channels. Table 14 summarizes the findings for the investigated hypotheses.


Page 62 of 87

Facebook Twitter

H1 Supported Supported

H1a Supported Supported

H1b Rejected Rejected

H1c Rejected Rejected

H2 Partially supported N/A

H2a Rejected N/A

H2b Supported N/A


H3a Partially supported Supported


Table 14. Summary of results.


Page 63 of 87

CHAPTER 5: DISCUSSION

The findings section has provided an overview of the results derived from the analysis and

investigated how these either support or rejects the proposed hypotheses. This section will elaborate

on the implications of the findings in relation to the literature introduced in the theoretical

framework. This will be followed by a discussion of the limitations of the study along with areas for

further research.

H1: Emotional appeal and engagement on social media The research question of this study seeks to explain how emotional appeals affect customer

engagement in a social media setting, which has been sought answered through a set of hypotheses.

Hypothesis 1 set out to explain the overall relation between the adoption of emotional appeals and

customer engagement on social media, and it was found that the adoption of an emotional appeal is

positively correlated with the occurrence of customer engagement behavior. As introduced in the

theoretical framework, the effect of adopting a message strategy containing an emotional appeal has

been studied in relation to receiving “likes” on Facebook by Swani et al. (2013). Swani et al. (2013)

investigated this relationship among 193 Fortune 500 companies through 1143 Facebook posts. Al

though the sampling procedure adopted in the study by Swani et al. (2013), and the sampling

procedure adopted for this study are not the exact same, they bear a lot of resemblance by both

concentrating on large multinational companies. This makes the findings of the two studies

relatively comparable despite the different methods adopted for analysis. Swani et al. (2013, p.284)

found that emotional content in Facebook brand posts is positively correlated with the number of

likes a post receives. A similar result was found in this study, where the usage of an emotional

appeal was found to be positively related to the total engagement ratio of both Facebook and

Twitter (Table 10). In fact, this total engagement ratio was mainly driven by a significant

relationship in the consumption category of this study, which includes likes on Facebook making it

similar to the findings provided by Swani et al. (2013). Thus, the findings of this research underpin

previous findings by Swani et al. (2013) regarding the overall effect of an emotional appeal on

customer engagement.

This research broadens the understanding of these findings by testing the effect of an emotional

appeal on both Facebook and Twitter. A significant relationship between adopting an emotional


Page 64 of 87

appeal and the consumption rate was found for both Facebook and Twitter, and the findings thereby

elaborate on Swani et al.’s (2013) findings by providing evidence of the effect across different

communication channels. Furthermore, this study has furthermore tested if the findings by Swani et

al. (2013), where applicable to other engagement forms than “likes”. It was not possible to

establish any relationship between an overall adoption of an emotional appeal and contribution or

creation behavior on neither Facebook or Twitter.

While the relationship found between an emotional appeal and consumption was relatively weak, it

can still provide value to brands. A Facebook post from a brand page with 100.000 followers can

expect 19,1 more likes for a post containing an emotional appeal than one without. For Twitter a

similar post could expect 2.4 more favorites. For contribution and creation no significant

relationships were found for neither Facebook nor Twitter.

While the actual impact of adopting an emotional appeal on reactions and favorites in numbers

might seem insignificant, it is not without value in practice. Recall Kumar et al.’s (2010)

conceptualization of the value that CE can provide: customer referral value, customer influencer

value and customer knowledge value. When customers engage with brand posts, the message is

spread across their own network (Ross, 2014), which could be categorized as personnel referral

value. The personnel referral value includes information sharing among customers driven by

intrinsic motivators (Kumar et al., 2010), which arguably is the case, when customers engage with a

brand post. According to Swani et al. (2013, p.285) “…a single like [on a brand post] has the ability

to send over 130 personnel referrals (WOM)”, which is the average number of friends a Facebook

users has. Using Swani et al. (2013) proposed impact of a like, a Facebook page with 1 million

followers can potentially increase the number of potential referrals by 24.830 (191 reactions x 130)

by using an emotional appeal compared to an appeal without emotional content. Taking this

perspective, even a small increase in customer engagement, which can be achieved by adopting an

emotional appeal, can be valuable to brands.


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Hierarchy of engagement levels The above mentioned study by Swani et al. (2013) studied the effect of message strategies in

relations to “likes” on Facebook, while this study has sought to broaden the understanding by

including different social media channels as well as varying engagement behaviors. The descriptive

analysis along with hypotheses 1a-c investigated how different customer engagement behaviors are

affected by an emotional appeal and how this effect differs across media channels.

This aspect of the study was motivated by Alhabash & McAlister (2015) who investigated

customers’ intention to engage with posts on Facebook and Twitter. Through a case-study they

found that users were more likely to “like” a post than “sharing” it, and least likely to “comment”

on it on Facebook, and more likely to “retweet” than “reply” and “favorite” on Twitter. The

relationship between the different types of engagement for this study was explored in the

descriptive analysis (table 6 and figure 4). The findings for Facebook are similar to the ones

provided by Alhabash & McAlister (2015) with reactions (which include “likes”) being the most

common behavior, followed by sharing and commenting. On the other hand, the findings for

Twitter were different, with favorites being the most common, followed by retweeting and replying.

This could indicate that there is a difference between customers’ intention to engage and their actual

engagement behavior. Adopting Schivinski et al.’s (2016) categorization of engagement behavior

(consumption, contribution and creation) showed that the hierarchy of behaviors was in fact similar

for both Facebook and Twitter with consumption being the most common type of engagement

across platforms followed by creation and contribution. But, this contrasts with the proposed the

findings by Schivinski et al.’s (2016) who found contribution to be an antecedent of creation. This

study has not investigated the relationships between the three engagement levels and it is therefore

not possible to determine how these are interrelated in this study, but it is arguably counterintuitive

that creation behavior is more common than the contribution behavior if contribution is an

antecedent. This could be due to the categorization of the engagement and especially the creation

part. In this study, creation includes shares and retweets for Facebook and Twitter respectively,

since these are arguably the most similar to Schivinski et al.’s (2016) definition of creation which

includes user-generated content. Sharing and re-tweeting enables users to create a caption the brand

message with their own perspective on the subject and share it within their network, which is

beyond control of the brands. As a result, these were regarded as the strongest level of engagement.


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On the hand, Alhabash & McAlister (2015, p.1321) argue from their cognitive processing

perspective that commenting on a brand post demands the most cognitive processing and is

therefore the least likely form of engagement. While this fit well with the findings of the descriptive

findings of this study (table 6), it questions the hierarchy between the categories proposed by

Schivinski et al.’s (2016). While this debate is beyond the scope of this paper and has not been the

main subject of investigation, the descriptive findings provide an interesting perspective on the

discussion for further research. The categorization of customer engagement behaviors has been an

essential part of the analysis and these contradictions in categorization are important to note.

H2: Post characteristics and effectiveness of emotional appeals Hypothesis 2 sought to investigate how post characteristics affect the influence of an emotional

appeal on customer engagement. The link between post characteristics and influence of emotional

content was based on the works of Byron (2008), who suggested that the information-richness of a

message affects how emotions are perceived. A high level of information-richness makes the

interpretation of the intended emotional appeal in the message more precise. Thus, it could be

assumed that a high-level of information richness would increase the effectiveness of an emotional

appeal. This assumption was strengthened by de Vries et al. (2012) who found that post vividness

affects the number of likes a Facebook post receives: a post containing a video receives more likes s

than a post containing a photo. As mentioned in the methodology, the effectiveness of post

characteristics was only explored on Facebook.

The descriptive analysis of this study showed that the reactions ratio on Facebook followed the

findings by de Vries et al. (2012) and post containing a video had a higher mean engagement ratio

than photos and links. The mean comment ratio was highest for post containing links followed by

videos and photos. These findings are in line with the study by de Vries et al. (2012) who found a

relationship between post vividness and number of likes, while a relationship between vividness

and comments was unsupported. A similarity tendency can be found in this study.

In addition to the findings by de Vries et al. (2012) this study has further explored the number of

shares a post receives compared to post characteristics. Here it was found that videos had the

highest engagement ratio, followed by links and photos respectively. These are merely descriptive

findings, but they underpin the findings by de Vries et al. (2012) by showing the same tendencies

and elaborate on their findings by including the number of shares compared to post characteristics.


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Inspired by the findings of Byron (2008), the second part of the analysis investigated how post

characteristics influence the effectiveness of emotional appeals. The findings were rather

inconclusive for this part of the analysis. The only significant relationship was found between a post

containing a photo combined with an emotional appeal and consumption behavior in Facebook

posts. This suggest, that a post containing an emotional appeal combined with a photo is more

effective in fostering engagement, than an emotional appeal without a photo. On the other hand, no

significant relationships were found between emotional appeals combined with a video and

engagement. Videos combined with an emotional appeal were expected to be more effective in

fostering engagement than photos due to the higher information-richness and vividness of a video

compared to a photo. Thus, this study finds that post characteristics differ in their ability to

influence the effectiveness of an emotional appeal but the hierarchy between the characteristics do

not clearly follow the one proposed by Byron (2008). More research is needed in this area to deepen

our understanding of how different post characteristics differ in their ability to convey emotions and

influence customer engagement behavior.

A limitation of the present study is that the adopted analysis has been limited to analyzing textual

content. While it is assumed, that the caption of a photo or video will reflect the content, an analysis

of the actual photo and video content could provide a more nuanced perspective on the subject. This

was deemed beyond the quantitative scope of this paper, since an analysis of these media would

require a more thorough qualitative analysis, which would be an interesting subject for further

research in the field.

H3: Impact of varying emotions The above discussion has focused on the impact of emotional content in general i.e. whether a

brand post contains an emotional appeal or not. Hypothesis 3 of this study sought to provide a more

nuanced picture of the role of emotional content by investigating how different emotional appeals

affect customer engagement behavior. This hypothesis was proposed in order to elaborate on prior

research in the field, which have been focusing on whether emotional content was present, but not

on the type of emotion being present (e.g. Swani et al. 2013; Xie et al., 2004). Within the field of

interpersonal communication, Hendricks et al. (2014) have found that discrete emotions vary in

their ability to spark a discussion. Thus, it could be assumed that varying emotional appeals on

social media would vary in their ability to foster engagement. This study set out to investigate 5

different types of emotions (joy, sadness, disgust, anger and fear) but during the descriptive study it


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was found that the usage of anger and fear among corporate brands were simply to sparse to

conduct any meaningful analysis for these emotions.

No significant relationships were found between the use of disgust and customer engagement on

neither Facebook nor Twitter. On the other hand, sadness was the only emotional appeal that was

significantly related to the creation behavior (only on Facebook), which implies that that the usage

of a sadness appeal in fact generates more shares than a post without this appeal. This finding

contrasts with Hendricks et al. (2014) who proposed that a happiness appeal will be more likely to

spark a discussion than a sadness appeal.

On the other hand, a joy appeal was found to be significantly related to consumption on both

Facebook and Twitter. Sadness and consumption was not found to be related on neither of the

platforms. This part of the findings is in support of the findings Hendricks et al. (2014) who

proposed a similar relationship between happiness (joy) and sadness.

It might not be surprising, that the usage of anger or fear appeals were nearly non-existing among

corporate brands on Facebook. Nor does it seem odd that a disgust appeal was found not to be

related to customer engagement. On the other hand, the fact that a sadness appeal was found to be

the only appeal that was positively related to the sharing of content on Facebook is rather

surprising. Again, it should be noted, that strength of the relationship is relatively weak but taking

into consideration, that joy is not positively related to sharing of content, makes it quite noteworthy.

Another noteworthy aspect of the findings is that joy was the only emotion found to be significantly

related to overall engagement on both Facebook and Twitter.

The sharing of content is of great value to brands and following the hierarchy of engagement on

social media provided by Schivinski et al.’s (2016) the creation category, which include sharing, is

the strongest form of engagement. With these findings in mind, the overarching question becomes:

Should brands actively seek adopt a sadness appeal on Facebook to make customer share their

content? While this might be the logical deduction from the findings, it is important to note that this

study has only looked at the occurrence of engagement and not the valence. This is an important

distinction, when interpreting the impact of the findings. Recall the theoretical framework of

customer engagement behavior by van Doorn et al. (2011), within which it is argued that customer

engagement behaviors can have a varying valence. A CEB is neither positive or negative per se, the

effect and impact on a firm is dependent on the valence of the CEB. Take for example a product

review, which can have either positive or negative valence towards a particular brand or product.

While the value and potential impact for each of these behaviors are very different, both can be


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regarded as a form of CEB. The fact that a sadness appeal increase the number of shares of a post is

not inherently in favor of a respective brand. This study has not investigated the valence of shares,

but merely the occurrence of the behavior. As a result, it is not known whether these shares are

supportive of the brand or customers are sharing a disagreement with the message. Studying the

valence of these behaviors could provide interesting insights into the value and impact of CEB.

Thus, the findings of this papers have to be interpreted and understood in their context and not be

seen as best-practice for how brands should adopt an emotional appeal without reservations.

H4: Emotions across industries The forth hypothesis of this study sought to investigate how the effectiveness of emotional appeals

vary across industries. This was based on the works of Panda et al. (2013) and Swani et al. (2013)

who both suggest that the effectiveness of a message is influenced by its industry. Swani et al.

(2013) investigated the effect of an emotional appeal in both a B2C and B2B context and between

product and service industries, and found that the effectiveness of a message appeal varies across

these factors. Their findings suggested that emotional appeals were positively correlated with the

number of likes a post receives within both a B2C and B2B context, but that the appeal was most

effective in a B2C context. Among the three different message strategies they investigated, they

found that an emotional appeal was in fact the appeal that was the most effective in generating likes

for service products (Swani et al., 2013, p.283).

This present study has sought to elaborate on the findings by Swani et al. (2013) by taking a deeper

look into the B2C industry, which has been divided into specific segments (cars, clothing,

consumer electronics and financial services). As a result, a discussion of the effectiveness of an

emotional appeal between B2B and B2C companies is beyond the scope of this paper. On the other

hand, a service industry was included in the analysis namely financial services, while the three

other industries cars, clothing and consumer electronics all represent product industries. No

significant relationships were found between the usage of an emotional appeal and any forms of

engagement on neither Facebook nor Twitter for the industry financial services. These findings are

rather contradictive to the study by Swani et al. (2013), where an emotional appeal in a B2C service

company were found to generate twice as many likes as an appeal without emotional content.

Furthermore, Swani et al. (2013) found that an appeal without an emotional appeal fosters more

likes in a B2C product oriented companies than message containing an emotional appeal. These


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findings are also contradictive to the results of this study, which have found a significant positive

relationship between the usage of an emotional appeal and engagement for clothing (on Facebook)

and consumer electronics (on Twitter), which both consist of B2C product companies. These

findings suggest that adopting an emotional appeal within these industries will foster more

engagement than an appeal without emotional content.

The contradictive findings between this study and the findings by Swani et al. (2013) could imply

that the role and effectiveness of emotional appeals on customer engagement in even more complex

than previously assumed. Distinguishing between product and service companies might be a too

generic approach for investigating the effectiveness of emotional appeals. There do not seem to be

any clear patterns supporting a difference in effect between these broad categories in the findings of

this study. In fact, the findings for each industry are all different. Cars, clothing and consumer

electronics all consist of B2C product companies, but no significant relationship was observed in

the cars industry, while significant relationships were found for clothing on Facebook, but not on

Twitter, and for consumer electronics on Twitter, but not on Facebook. These varying results of

effectiveness suggest that generalizing findings into product and service categories might be too

simplistic, since industries within each category can differ significantly. Taken this perspective a

step further, it would be interesting to investigate how brands within each of the industry

segmentation adopted in this paper differ, and investigate if other characteristics might be equally

good or better at predicting the effect of emotional appeals.

Another interesting finding in regard to the industry segmentation, is the varying effect of an

emotional appeal found between Facebook and Twitter. As introduced in the theoretical framework,

McLuhan (1964, cited in Siapera, 2012, p7.) famously proclaimed “the medium is the message”

and with point of departure in this notion it was proposed that social media could provide

interesting context for investigating customer engagement. This was further by underpinned by de

Vries & Carlsson (2014, p.498) who propose that CEB has yet to explored in a social media

perspective. In an effort to answer this call, this study has investigated the effect on both Facebook

and Twitter. The analysis of the effectiveness of an emotional appeal across industries further

showed, that an emotional appeal within a particular industry can be effective in fostering

engagement on one of the channels, without being effective in fostering engagement on the other

channel. Significant relationships were found between an emotional appeal and engagement in the

clothing industry on Facebook but not on Twitter, while the opposite was true for consumer


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electronics. Thus, while an emotional appeal might be effective on one media channel it does

necessarily entail that the same appeal will be effective on a different social media channel. This

finding has implications for both theory and practice. With regards to the literature, this elaborate

on the present research agenda in the field by proposing that different social media settings have a

varying influence on the effectiveness of an emotional appeal. This could also explain some of the

contradictive findings found between Swani et al.’s. (2013) study, which focused on Facebook, and

this study, which have investigated both Facebook and Twitter. Thus, when comparing research on

“social media” it is essential to bear the specific social media platform in mind. In practice, these

findings suggest that marketers should have in mind that the effectiveness of an emotional strategy

is heavily context dependent when it comes to fostering engagement on social media. These

findings serve to show that, similar to the findings in the advertising literature introduced in the

theoretical framework (Panda et al., 2013), the effectiveness of an emotional appeal on CEB in a

social media setting is heavily dependent on the context.

Customer engagement on social media The above sections have outlined how the individual findings of this study fit into the theoretical

framework of this paper. These all investigate varying aspects of customer engagement on social

media but in order to put the findings into perspective, this section will return to the overall

framework of customer engagement and see how this study contributes to the literature in a broader

perspective.

This paper has focused on customer engagement behaviors and have adopted van Doorn et al.’s

(2011) framework of CEB as the backbone of the research. In order to contribute to this broad

conceptualization of customer engagement, the works of Barger et al. (2016) was adopted. Barger

et. al (2016) base their framework on van Doorn et al.’s (2011) conceptualization but puts it into a

social media perspective, which fits well with the research question of this study. Recall the

framework of Barger et al. (2016), which provide 5 antecedent factors of CEB on social media

namely brand factors, product factors, customer factors, social media factors and content factors.

The findings of this study mainly relate to social media factors and content factors and to some

extent brand factors, while the investigation of the two other antecedents has been beyond the

scope of this paper. Within each of these factors an array of different antecedents exist including

emotional sentiment of a message. The overall effect of an emotional appeal on customer

engagement has been fairly weak but these results should be seen in the light of emotional


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sentiment only being one among many antecedents of CEB on social media. This paper thereby

contributes to the broader understanding of CEB on social media by investigating and validating the

degree to which emotional content can be regarded an antecedent. Furthermore, it underpins the

social media factors as an important antecedent of CEB by showing how the effect of emotional

appeals differ across social media platforms. Lastly, this study provide empirical evidence of how

the effectiveness of an emotional appeal is influenced by industry type, which can be related to

Barger et al.’s. (2016) brand factors.

Limitations and further research As it should have become evident from the above, the findings of this paper must be interpreted in a

context. This section will highlight the main limitations of this study, which should be held in mind

when considering the findings. The above have linked the findings to existing theory in order to put

the findings into perspective and it should have become clear that CEB on social media is a

complex phenomenon. This study has sought to shed light on one aspect of this complex

phenomenon, which has been sought achieved through a content analysis adopting IBM’s Watson

software to analyze emotional sentiment. The emotional appeals derived through this analysis has

been related to varying forms of engagement on different social media platforms. While this is

argued to be the best suited methodology for investigating the research question of this paper, this

choice has its limitations, which will be discussed below.

Methodological limitations As Bryman (2012, p.5) puts it “…the topics that are investigated are profoundly influenced by the

available theoretical positions”, implying that the findings derived in this study are influenced by

the adopted method and theoretical framework. Theory guides what is observed and as mentioned

previously, this study has focused on observing occurrence of engagement but not the valence of

engagement. Engagement levels have been treated quantitatively by observing the number of

occurrences for “likes / favorites”, “comments / replies” and “shares / retweets”. While these

provide interesting findings in combination with the emotional appeals, they do not provide insights

into the valence of the engagement. Thus, this study can merely measure the occurrence of

engagement but not whether this type of engagement is in favor of a brand. This further entails that

a “best-practice” for how brands should adopt emotional communication cannot per se be deducted


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from this study. It can be deducted that certain emotional appeals foster engagement and it is fair to

assume that a click on the “like” or “favorite” bottom on Facebook and Twitter respectively, will

most often be supportive of the brand message. The valence of comments and shares of brand post

could be more ambiguous with regard to valence, and further research could clarify how emotional

content affect the valence of customer responses in comments and shares. Interesting questions for

further research could for example be: How does emotional appeals affect the valence of customer

response on Facebook and Twitter? Do customers mirror the emotional tone by brands when

contributing to a brand post?

To answer such research questions a quantitative approach will not suffice, while a qualitative

approach focusing on the perception of the content would be more suitable. This would obviously

limit the scale of the study but it could provide valuable insights into how these emotional appeals

are interpreted by the customers and why it leads to a behavioral response in the form of CEB. The

link between the emotional appeal customers are exposed to and their behavioral response could

expand our understanding of how and why customers engage with emotional content. The focus on

emotions in this study entails only one aspect of communication is investigated. The content per se

of each brand post is not considered although this could affect the level of engagement, which is a

further limitation of the study.

The above limitations have focused on the engagement variable of this research, but the limitations

related to the keystone of this research design also needs to be addressed, namely the analysis of

emotional content. As discussed in the methodology section, a computer aided content analysis

makes it possible to gain reliable and generalizable results through large scale analysis on textual

data (Krippendorff, 2013). Investigating communication from this perspective has its inherent

limitations due to the lack of contextual richness. A content analysis reduces the contextual richness

of communication by categorizing certain aspects of the text into codes, and “stands and falls by its

categories” as Berelson (1952) puts it. This aspect of the content analysis combined with the notion

that the theory we adopt guides what we see (Bryman, 2012), highlight the limitations of the

method. This type of content analysis will only count aspects that fits perfectly with the codes, but

the highly contextual and implicit nature of language (Ferrucci, 2012), makes it difficult, if not

impossible, to fit all nuances of language into predefined codes. Especially the contextual and

implicit nature of language is a major limitation, when studying communication from a quantitative

perspective. A basic assumption of this study is that generalizations about “emotional content”


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found in text can be made, which entails that all customers understand, perceive and interpret

“emotional content” in the same way. This assumption obviously contrasts with the highly

contextual nature of language (Ferrucci, 2012), and the notion that customers understand language

(and social phenomena), through their own context and experiences. Understanding how each

individual customer perceives and interprets an emotional appeal in a brand post on social media

would provide an interesting perspective on the subject, but it would require a completely different

research approach. This approach would require an extensive qualitative analysis to uncover how

each customer understands and interprets the meaning of emotions. As argued earlier in the

introduction, such research could be a valuable elaboration on the findings from this research by

explaining why customers engage with emotional content, but it would not be possible to conduct

this type of analysis at a similar scale. To put this into perspective, this study has analyzed

emotional content from 5032 brand posts and related this to 1.56 million occurrences of

engagement (total number of engagement for Facebook and Twitter).

As it should have become clear from this discussion, the findings of this paper are deeply

influenced by the adopted method for analysis, which has its inherent limitations. This study has

sought to contribute to the customer engagement literature by answering the call for more validation

as well as quantitative research in the field of CEB on social media (de Vries & Carlsson; Dessart et

al., 2016). While it is acknowledged, that this study cannot fully grasp the contextual richness of

communication, the adopted method has sought to decrease the gap between contextual richness

and generalizability outlined above by adopting IBM’ Watson Tone Analyzer software. The

implications of adopting this approach will be discussion below

IBM Watson The above discussion has focused on the theoretical limitations of adopting a quantitative content

analysis compared to a more qualitative and contextual approach. While a bipolar understanding of

the world, like the presented divide between methods, is a persuasive habit of the human mind

(Berlin, 1990, p.46), reality is often more nuanced. The same is true for positioning IBM Watson in

a scientific tradition as a method for content analysis. To the knowledge of the author, this study is

the first to adopt the Tone Analyzer tool for a content analysis and as result, its position in present

research traditions has not been discussed before. In the methodology section of this paper, IBM

Watson is argued to be suitable for a quantitative content analysis and this study adopt it as such.


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But, this conceptualization of its position is to a large extent based on how we regard language and

how we distinguish between quantitative and qualitative research. Recall the description of the

Watson technology, which described how Watson is based on different Natural Language

Processing algorithms (Ferrucci, 2012). The usage of NLP is not unique to Watson but is merely a

way of learning computers to understand language. Crowston et al. (2012) adopt NLP for coding in

a content analysis and argue that this type of analysis can be categorized as a qualitative content

analysis. In contrast, this study holds that IBM Watson, at the moment, can be categorized as

quantitative approach since the emotional “codes” programmed into Watson are based on known

NLP patterns and the technology has arguably not mature yet. But, Watson is based on machine

learning and artificial intelligence, which entail that it through time will enhance its ability to

recognize emotions. While this is a hypothetical discussion, it is essential to address for future

research in the field. The key question is if one believes that artificial intelligence can reach a point

in which it understands language in the same way as humans do. Assuming that IBM Watson in fact

can learn to understand emotional tone in the same way as humans do, would the method thus

become a qualitative approach?

While these to some extent are futuristic speculations, they serve to show that adopting IBM

Watson in the present research agenda is neither a black or white when seeking to position it in a

methodological perspective. It highlights how the technological development could have the

potential to change how we investigate communication and pave the way for new areas of research

in the field. Whether IBM Watson can be adopted for further research is to a large extent based on

the validity of the findings. As mentioned in the methodology, IBM Watson currently outperforms

state-of-the-art models, when it comes to analyzing emotions in texts (IBM, 2017b). But, this does

not entail validity per se, it only shows that Watson is the best computer-aided method for analyzing

emotions. The question of validity is therefore on a more profound level regarding whether

communication can be analyzed meaningfully through different textual patterns of recognition

without taking the contextual richness into account. This paper holds, that this is possible but

acknowledges that it has some methodological limitations. While some might question the validity

of the “emotional” tones analyzed by Watson, the relationships found between what Watson

perceives as emotions and engagement levels are still established. This dispute about validity is not

unique to the adoption of Watson but is a general limitation of a content analysis (Krippendorf,

2013). A manual coding of the themes would likely have led to a similar discussion of validity,

since these would be guided by the coder’s interpretation of “emotional” tones.


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Possible third variables An important aspect to bear in mind, when discussing the findings of this paper is that the findings

of this type of analysis implies relationships not causality (Bryman, 2012, p.341). While causality

can be sought inferred by relating the findings of this study to existing literature in the field, as it

has been done above, there could be unknown variables affecting the relationships. While this study

has sought to account for an array of variables deducted from the theoretical framework, e.g. firm

size, industry, social media platform, varying emotions, different engagement forms etc., it cannot

be out ruled that other variables might mediate or drive the found relationships. The list of potential

third variables could be long but one important potential influencer, which has not been accounted

for needs to be addressed. The findings of this study are based on the assumption that all of the

brand post have had the same possibility to be distributed. In an effort to uphold this assumption,

the study accounts for the difference in follower size by calculating ratios of engagement instead of

actual numbers of engagement. But, since the algorithms for Facebook and Twitter are not publicly

available, it is not possible to account for unknown variables within the algorithm, that might favor

some brand posts over others. Lastly, this study cannot differentiate between “paid” and “organic”

posts on social media and it is impossible to infer the amount of money a brand might have spent on

a brand post. Although, this could influence the findings, there are no reasons to assume that brands

would more often advertise an emotional appeal than a non-emotional appeal or the other way

around, at least according to the existing theory presented in the theoretical framework.

Recapitalization of the discussion The above discussion has sought to relate the findings of this study to present research and discuss

its implications. First of all, the findings of this study elaborate on the works of Swani et al. (2013)

by providing evidence for the influence of an emotional appeal on customer engagement on both

Facebook and Twitter. The effectiveness of an emotional appeal is mediated by post-characteristics,

industry type and social media channel, making it a rather complex phenomenon to investigate.

Emotional sentiment in a message is only one among many antecedents of customer engagement

behavior on social media (Barger et al., 2016), which can help to explain why all of the significant

relationships found in the study have a relatively weak effect on CEB. Despite this weak

correlation, it was exemplified how even a small increase in CEB can lead to a high number of


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potential referrals (Swani et al., 2013) by creating customer referral value (Kumar et al., 2010).

Lastly, the methodological limitations of this study have been discussed in order to put the findings

into perspective. An important aspect in this regard is, that this study has only investigated the

occurrence of engagement and not the valence, which should be held in mind when interpreting the

findings. With regards to the content analysis, this study has adopted a rather unusual approach by

adopting IBM’s Watson Tone Analyzer tool for analyzing emotional content. This approach has its

limitations, which have been addressed in this discussion, but it also provides a new interesting

approach to analyzing communication. This method makes it possible to conduct a content analysis

of emotional content in a large scale, which provide reliable and generable results, but it also

reduces the contextual richness of communication and the validity of method is thus up for

question. Lastly, the possibility of potential third variables that could have influenced the

established found in between emotional appeals and engagement cannot be out ruled, why the

findings should only be interpreted as relationships not causality.


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CHAPTER 6: CONCLUSION As introduced in the theoretical framework, social-media have changed the way brands and

customers interact (Whitting & Deshpande, 2016). As a result, brands have lost control of

information in a social media environment, where customers discuss brands with other customers as

well as with the brands themselves. Marketers cannot control the online discussion, but can merely

seek to shape it (Mangold & Faulds, 2009). The concept of customer engagement seeks to adapt to

this changing environment by conceptualizing the interactions between customers and brands that

goes beyond purchase (MSI, 2010, p.4). This study has sought to broaden our understanding of the

dynamics that influence CE by investigating specific customer engagement behaviors in a social

media setting. More specifically, it has been investigated how different emotional appeals in brand

posts on Facebook and Twitter affect CEB.

Adopting IBM’s artificial intelligence, Watson, has made it possible to conduct a large-scale

content analysis of emotional content in social media posts. A total of 5034 brand posts on social

media have been analyzed for the presence of an emotional appeal containing either joy, sadness,

disgust, anger or fear. These 5034 posts have been related to 1.56 million occurrences of

engagement in order to establish relationships between the adoption of an emotional appeal in a

message and the occurrence of engagement.

The findings of this analysis suggest that a relationship between the adoption of an emotional

appeal and CEB on social media can be established. More specifically the adoption of an emotional

appeal is significantly related to consumption which encompasses “reactions” on Facebook and

“favorites” on Twitter. It has further been found, emotional appeals differ in their ability to foster

engagement. An appeal containing joy has been found to be positively related to consumption on

both platforms, while a sadness appeal has been found to foster creation (sharing) behavior on

Facebook. These findings provide valuable insights into the effectiveness of emotional appeals on

social media. They elaborate on the findings by Swani et al. (2013) by testing the effectiveness of

emotional appeals across different social media platforms. In this regard, it was found that a

particular emotional appeal can be effective on fostering engagement on Facebook but not on

Twitter and vice versa. It has further been explored how post characteristics and industry type can

mediate the effectiveness of an emotional appeal. The findings on post characteristics, more

specifically the effect of the information richness of the post, are to a large extent contrary to the

expected effect proposed in the theoretical framework (Byron 2008; de Vries et al., 2012). The


Page 79 of 87

findings of this study do not show a clear relationship between post vividness and the effectiveness

of an emotional appeal. A photo combined with an emotional appeal were found to increase the

effectiveness of the appeal, but no relationships could be established between the usage of a video

combined with an emotional appeal and effectiveness. Thus, post characteristics are found to

influence effectiveness but information richness does not seem to be the driver. Lastly, the

effectiveness of an emotional appeal was found to vary across industries. The findings in this regard

were contrary to the findings by Swani et al. (2013), who found an emotional appeal to be effective

in the B2C service companies, while this study did not find any significant relationships within this

industry. In the discussion of the findings, it was proposed that measuring “service” and “goods”

companies might not be the best suited predictor of the effectiveness of an emotional appeal. This

categorization could be too broad, but further research is needed in this field to deepen our

understanding of which factors mediate the effectiveness of an emotional appeal on social media.

An important aspect to have in mind, when interpreting the findings of this study, is that this paper

has only looked at the occurrence of engagement not valence. This entails that the findings of this

study cannot per se be seen as best-practice for how to adopt an emotional appeal in practice. The

valence of the engagement responses to emotional appeal would be an interesting subject for further

research in the field and could expand our knowledge of the consequences of CEB followed by an

emotional appeal.

The findings of this study are derived through a quantitative content analysis adopting IBM Watson,

which answers the call for more quantitative and validating research in the field (Dessart et al.,

2016) but it also has limitations. This method does not fully account for the contextual richness of

communication and the validity of the research is reduced by adopting IBM Watson for coding the

emotional tones. Furthermore, this study only investigates one aspect of communication, the

emotional sentiment, but not the content per se of each message.

Lastly, this study set out to investigate how do emotional appeals affect customer engagement in a

social media setting, which have been sought answered through the testing of four major

hypotheses. The results of this study provide valuable insights into one branch of customer

engagement by investigating engagement behaviors on social media in response to emotional

appeals. While emotional sentiment of content is only one among many antecedents of CEB on

social media (Barger et al., 2016), these findings can take part in explaining the complex

phenomenon of customer engagement on social media.


Page 80 of 87

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