Visual Humor in Webcare Conversations

An Investigation of the Use And Effect of Visuals And Humor on Corporate Reputation And

Perceived Conversational Human Voice

Evelien van der Wel

SNR: 1275481

ANR: 302994

Master thesis

Communication and Information Sciences

Specialization Business Communication and Digital Media

Faculty of Humanities

Tilburg University, Tilburg

Supervisor: Dr. C.C. Liebrecht and L.J. van Maastricht, MA.

Second Reader: Dr. C.H. van Wijk

August 2017 MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Author’s Note

August 2017

This research endeavor represents all my hard work and everything I have learned during my pre-master and master Communication and Information Sciences. I would like to thank some people for their help, feedback and support during my thesis writing process. First and foremost, I would like to thank my supervisors: Christine Liebrecht and Lieke van Maastricht.

Without their sharp feedback, comments, ideas, and support, I could not have done it. I have learned a lot and gained valuable knowledge from my supervisors for which I am thankful. I also would like to thank my second reader, Mr. van Wijk, for his time and for reading my thesis. Another person who was also a huge support during my thesis time is my co-master thesis student Veronique le Duc. We have spent a lot of time together in the library, thinking, writing, and researching on our theses. Without her, I could not have done it. Also, I want to thank my family, friends, and boyfriend for their help and continuingly support during my pre-master and master. Without them, I would not have my degree. Finally, I want to thank you, the reader, for taking an interest and the time to read my thesis. That makes all my hard work worth it.

Evelien van der Wel

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Abstract

Webcare is the concept of brands responding to messages of customers online about their product or services in either a proactive or reactive manner (van Noort & Willemsen, 2012).

Previous studies on webcare have investigated the effects of corporate reputation and

Corporate Human Voice in webcare conversations extensively (e.g., Huibers & Verhoeven,

2014; Einwiller & Steilen, 2015). However, humor and humorous visuals have been understudied in a webcare context. This study sets out to investigate the usage and effects of the humor styles as identified by Martin, Puhlik-Dorris, Larsen, Gray, and Weir (2003) and humorous visuals in webcare conversations on corporate reputation and perceived CHV. In total, two studies were conducted: a content analysis study and an experimental study. The content analysis identified the usage of humor styles and visual types in webcare conversations by analyzing 100 humorous webcare conversations. Self-enhancing humor and affiliative humor were the most used humor types in Dutch webcare conversations. GIFs were the most used visual followed by a meme. Consequently, in the experimental study, a survey was created employing a 2 (humor style: affiliative vs. self-enhancing) x 2 (visual type: GIF vs. meme) x 3 (sentiment: negative vs. neutral vs. positive) mixed design. Humor style and visual type were used as a between-subjects variable and sentiment as a within-subjects variable. In total, 166 participants participated in the survey. The results revealed that sentiment of a webcare conversation did have significantly different effects on corporate reputation and perceived CHV when different styles of humor are used. However, no significant main and interaction results between humor style (affiliative humor or self- enhancing humor) or visual type (GIF or meme) on corporate reputation and CHV were found. Explanations and implications are discussed and future suggestions for research are given.

Keywords: webcare, humor, visuals, meme, GIF, reputation, perceived CHV.

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Table of Content Author’s Note ...... 2

Abstract ...... 3

1. Introduction ...... 7

2. Theoretical framework ...... 9

2.1. Webcare ...... 9

2.2. Webcare and Conversational Human Voice ...... 11

2.3. Webcare and Corporate Reputation ...... 12

2.4. Humor ...... 13

2.5. Online visuals ...... 17

2.6. The current study ...... 20

3. Study 1: Content Analysis ...... 22

4. Method ...... 23

4.1. Sample ...... 23

4.2. Instrumentation of Codebook ...... 24

4.3. Intercoder reliability ...... 27

4.4. Procedure ...... 27

5. Results of study 1 ...... 28

5.1. Frequencies of additional categories ...... 28

5.2. Frequencies of the main variables ...... 30

5.3. Humor style and Visual type ...... 31

6. Conclusion of Study 1 ...... 33

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

7. Study 2: Experimental Study ...... 34

8. Method ...... 35

8.1. Design ...... 35

8.2. Participants ...... 36

8.3. Materials ...... 37

8.4. Measures ...... 38

8.5. Procedure ...... 39

9. Results Experiment ...... 40

9.1. Main analyses ...... 41

9.2. Covariates: MANCOVA and ANCOVA ...... 47

10. Conclusion and Discussion ...... 48

10.1. Corpus study: Implications and future research ...... 49

10.2. Experimental study: Implications and future research ...... 51

10.3. Future research ...... 55

11. References ...... 56

12. Appendix ...... 65

Appendix A: Table of Companies of Content Analysis ...... 65

Appendix B: Codebook Content Analysis Humor in Webcare Conversations ...... 66

Appendix C: Intercoder reliability ...... 81

Appendix D: Frequencies tables of content study ...... 82

Appendix E: Contingency tables of joke type ...... 85

Appendix F: Stimuli of sentiment, humor style, and visual type...... 86 5

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Appendix G: Measures statements ...... 93

Appendix H: Survey screenshots ...... 94

Appendix I: Normality tests of various variables ...... 111

Appendix J: Statistical checks of evenly distributed data ...... 115

Appendix K: Demographic Variables ...... 116

Appendix L: Assumptions of a mixed MANOVA ...... 118

Appendix M: MANCOVA analysis ...... 121

Appendix N: Correlation analysis of humor style and sentiment ...... 122

Appendix O: Means and Standard Deviation of humor type per sentiment and visual type

for reputation...... 124

Appendix N: Means and Standard Deviation of humor type per sentiment and visual type

for CHV...... 125

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Visual Humor in Webcare Conversations: An Investigation of the Use And Effect of Visual

Humor Styles on Corporate Reputation And Perceived Conversational Voice

1. Introduction

Social media has enabled organizations but also consumers to communicate and interact with each other more easily in an online context (Mangolds & Faulds, 2009). These online conversations compel companies to take on a more ‘consumer-centric’ role, as they cannot control what is being said about their brand online (Kaplan & Haenlein, 2010). When potential, former, or current customers talk about a product or company via internet-based platforms, they are engaging in electronic word-of-mouth (eWOM) (Henning-Thurau,

Gwinner, Walsh & Grembler, 2004). eWOM may be positive or negative but once a complaint or compliment is shared online, other (potential) consumers may see it (Hong &

Lee, 2005; Lee & Song, 2010).

When companies reply to eWOM using social media, they are participating in webcare. Webcare is the phenomenon of brands responding to messages of customers online about their product or services in either a proactive or reactive manner. Ever since webcare emerged in 2006, it has become an interesting field to study for academics (Willemsen & Van

Noort, 2015). The effects of webcare interactions on corporate reputation or Conversational

Human Voice (i.e., an indication of the natural communication style of an organization towards a customer, Kelleher, 2009) have been studied extensively (e.g., Huibers &

Verhoeven, 2014; Van Noort & Willemsen, 2012; Demmers, Dolen & Weltevreden, 2014;

Einwiller & Steilen, 2015).

An aspect that has been relatively understudied is the effect of humor in a webcare context. Humor in webcare has only been studied as a part of Conversational Human Voice

(CHV). According to a survey on Dutch Webcare in 2016, only 9% of the webcare practitioners deliberately chose not to employ humor in a webcare message. In comparison,

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

29% do employ humor and 62% sometimes use humor in webcare messages (van Os,

Hachmang, Derksen & Keuning, 2017).

Humor does not necessarily have to be verbal or written but can also be displayed visually (Tsakona, 2009). Internet content partly consists of visual communication as visualization helps to spread the informative or entertaining message across (Baran, 2012).

The effect of the combination of (humorous) visuals and webcare on corporate reputation or

CHV has not been investigated yet. Previous research on humorous visuals has been limited to classification of humorous television commercials (Buijzen & Valkenburg, 2004), the usage and effects of humor in print advertising (McQuarrie & Mick, 1999), or the cultural differences of humor in advertising (Koudelova & Whitelock, 2001).

Therefore, the current study aims to investigate the usage visual humor in webcare conversations and its effect on corporate reputation and CHV. The goals of this study are twofold: to identify the different humor styles and visual types that are used in Dutch webcare conversations and to investigate the effects of the humor styles and visual types on corporate reputation and CHV. In total, two studies were conducted. The first study is an exploratory corpus study that aims to identify the different humor styles and visual types and investigate their use in Dutch webcare conversations. The second study provided deeper insights of the first study by using the two most used humor styles and visual types in an experiment. By conducting a survey, the experimental study aimed to investigate the effects of humor style and visual type on perceived CHV and reputation. In the end, the current research aimed to answer the following two research questions:

RQ1: Which humor styles and visual types are used in Dutch webcare conversations?

(Study 1).

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

RQ2: Do different humor styles and visual types in webcare conversations affect customers’ perceptions of corporate reputation and perceived conversational human voice?

(Study 2).

2. Theoretical framework

This chapter will discuss theoretical insights into webcare, humor, CHV, and corporate reputation.

2.1. Webcare

The phenomenon of webcare refers to online conversations with consumers to discuss consumer feedback (Van Noort & Willemsen, 2012). Webcare is defined as “the act of engaging in online interactions with (complaining) consumers, by actively searching the web to address consumer feedback (e.g., questions, concerns, and complaints)” (Van Noort &

Willemsen, 2012, p. 133). When webcare is employed effectively, it may have several advantages such as higher customer satisfaction and retention, improved corporate reputation and stakeholder relations, and positive brand- and product perceptions (Willemsen & Van

Noort, 2015).

According to Van Noort, Willemse, Kerkhof, and Verhoeven (2014), webcare has three main goals: providing customer service, achieving marketing goals, and realizing public relations goals. This is also in line with a report on Dutch webcare in which 97 organizations were surveyed on their views on webcare. Almost 95% said that the main goal of webcare was customer service, followed by Public Relations goals (55%) and marketing and sales goals (37%) (Van Os, Hachmang, Derksen, & Keuning, 2017). As these goals are important for webcare, there are many studies conducted investigating the effects of webcare on these goals (Huibers & Verhoeven, 2014, Dijkmans, Kerkhof & Beukeboom, 2015; Crijns,

Cauberghe, Hudders, & Claey, 2017).

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

But how can an organization employ webcare effectively? Many studies focused on questions such as how to respond to eWOM (e.g., tone of voice) (Park & Lee, 2013; Huibers

& Verhoeven, 2014), on when to respond to eWOM (e.g., depending on whether it is positive or negative) (Van Noort & Willemsen, 2012; Demmers, Dolen & Weltevreden, 2014;

Einwiller & Steilen, 2015), and what to respond to messages in webcare conversations (e.g., webcare strategies) (Huibers & Verhoeven, 2014). In terms of when to respond, Willemsen and Van Noort (2015) found that online eWOM, especially complaints, must be perceived as an opportunity and companies should react to them. A study by Purnawirawan, De

Pelsmacker and Dens (2015) on whether restaurants should respond to negative online reviews or not found that restaurant owners should reply to negative reviews. By replying to negative reviews, (potential) customers will not lose trust or develop negative feelings toward the restaurant. Similar results have been found by Kniesel, Waiguny, and Diehl (2014) who found that no response to an online hotel review is the worst type of response. Thus, it is important to reply to webcare messages but the consumer also expects a quick reply. A recent study by Istanbulluoglu (2017) found that a quick first response to the complaint and a quick final response or solution lead to higher customer satisfaction for complaint solving issues.

There are also different expectations regarding response time for complaint handling on different platforms. On Twitter, consumers expect the first response within one to three hours compared to three to six hours on Facebook.

Research in terms of what to respond has mainly focused on webcare response strategies. Huibers and Verhoeven (2014) identified several webcare strategies on an

‘aggravation-mitigation’ continuum where strategies were considered defensive or forthcoming. According to Huibers and Verhoeven (2014), the webcare strategies are information, apology, sympathy, denial, justification, compensation and corrective action.

Willemsen and Van Noort (2015) have claimed that strategies that were placed on the farthest

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS end of both extremes of the scale (i.e., denial being the most defensive and compensation being the most forthcoming) are rarely used by companies. Moreover, forthcoming webcare strategies led to a higher corporate reputation compared to defensive webcare strategies.

2.2. Webcare and Conversational Human Voice

In terms of how to respond, this is mostly investigated in terms of Conversational Human

Voice (CHV). Conversational Human Voice is defined as “an engaging and natural style of organizational communication as perceived by an organization’s public, based on interactions between individuals in the organization and individuals in publics” (Kelleher, 2009, p177).

CHV is generally measured using eleven items and one of the items is employing a sense of humor in communication (Kelleher & Miller, 2006). Other items that are used to measure

CHV are focused on personal ‘human’ communication (e.g., ‘tries to communicate in a human voice’ or ‘is open to dialogue’) (Kelleher & Miller, 2006). Consumers appreciate it when companies communicate in a more ‘informal’ tone compared to a corporate tone

(Kerkhof, Beugels, Utz & Beukeboom, 2011). By employing CHV, a company shows that it is not a rigid company only concerned about producing and selling products, but that there are also ‘real’ individuals behind that company that genuinely care about customer welfare

(Dijkmans, Kerkhof, Buyuckan-Tetik & Beukeboom, 2015).

Van Noort and Willemsen (2012) reported results that perceived CHV was a mediator for negative webcare. Schamari and Schaefers (2015) tested this notion as well and found that personalized webcare by employing CHV had a positive effect on consumer engagement on consumer-generated platforms. Moreover, Dijkmans, Kerkhof, Buyukcan-Tetik, and

Beukeboom (2015) performed a longitudinal study and found that CHV mediated the positive relationship between consumer’s exposure to a brand’s activities on social media and the brand’s reputation. A recent study by Crijns, Cauberghe, Hudders, and Claeys (2017)

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS confirmed the findings of Dijkmans et al. and added that perceived CHV had a positive effect on corporate reputation on Facebook.

CHV in the context of social media such as blogs (Kelleher & Miller, 2006; Park &

Cameron, 2014), and review websites (Zhang & Vásquez, 2014) all show a positive effect of

CHV on a company’s reputation. The positive effect that CHV has on the perception of a company might be explained by the fact that CHV implies transparency and receptiveness in conversations between a company and (potential) consumers (Scoble & Israel, 2006).

However, there is also an indication that CHV in the context of webcare is not as important as initially thought. Huibers and Verhoeven (2014) conducted a 2 x 3 research design on the effect of webcare strategies and CHV and did not find a main effect. It appears that CHV as a mediator or a dependent variable leads to different results.

While perceived CHV is mostly studied as a mediator in webcare studies, it is interesting to find out what its effect is when studying humor in webcare conversations. As humor is one of the items used to measure perceived CHV, it makes intuitive sense that when the level of humor is high, perceived CHV is also more noticeable.

2.3. Webcare and Corporate Reputation

The concept of corporate reputation has different definitions and perspectives based on divers previous literature (Walsh, Mitchel, Jackson & Beatty, 2009). In the current study, the definition of corporation reputation as given by Walsh and Beatty (2007) is used as they define corporate reputation from a consumer-based point of view. This definition is very fitting for the context of webcare as webcare is also concerned with managing relationships with consumers and is thus also very consumer-oriented. Walsh and Beatty (2007, p. 129) define corporate reputation as “the customer’s overall evaluation of a firm based on his or her reactions to the firm’s goods, services, communication activities and interactions with the

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS firm and/or its representatives or constituencies (such as employees, management or other customers) and/or known corporate activities”.

Previous research has studied the effects of webcare on corporate reputation. For example, previous studies have shown positive correlations between exposure to a brand’s social media activities and perceptions of a brand (Dijkmans, Kerkhof & Beukeboom, 2015).

This notion has been further investigated in other studies. For example, Huibers and

Verhoeven (2014) found that when a company gave no webcare response to an online complaint, this led to a worse corporate reputation compared to when a company did give a webcare response. Furthermore, positive results were found in a longitudinal study by

Dijkmans, Kerkhof, Buyukcan-Tetik, and Beukebom (2015) on the effects of a brand’s social media exposure on corporate reputation mediated by CHV. This indicates that being exposed to a brand’s social media activities leads to a better perspective of a brand’s reputation even over time.

2.4. Humor

Many previous studies have studied the concept of humor in different contexts (e.g., Martin,

Puhlik-Dorris, Larsen, Gray, and Weir, 2003; Booth-Butterfield & Booth-Butterfield, 1991;

Catenescu & Tom, 2001; Gervais & Wilson, 2005). However, as humor is studied from various perspectives, there is no clear definition of humor which makes it an ambiguous concept. Some scholars examine humor from the perspective of the sender (Imalwi & Gregg,

2014), while others investigate humor from a receiver’s perspective (Lefcourt & Martin,

1986). Other academics define humor as a presence or outcome of an interaction (Reyes,

Rosso & Buscaldi, 2012; McGraw, Warren & Kan, 2014). The definition that is used most and that will also be used in this study is the one by Booth-Butterfield and Booth-Butterfield

(1991, p. 206) which states that humor is “intentional verbal or nonverbal messages which

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS elicit laughter, chuckling, and other forms of spontaneous behavior taken to mean pleasure, delight and/or surprise in the targeted receiver.”.

McGraw, Warren, and Kan (2014) use the benign violation theory to explain their view on humor. This theory suggests that humor happens when something that seems to threaten (i.e., a violation) an individual’s comfort, identity, or belief structure while instantaneously appear acceptable (i.e., benign). For example, a ‘pun’ joke violates language norms but are still deemed benign which results into humor. The benign violation theory proposes that the influence of humor on consumer-to-consumer discourse is contingent on the valence of communication (i.e., complaining or approval) (McGraw, Warren & Kan, 2014).

This means that according to McGraw, Warren and Kan (2014), that whether the sentiment of the conversation is either negative (complaining) or positive (approval) has an influence on the humor use among consumers. It is interesting to test this notion in the context of webcare conversations when the sentiment can be positive (e.g., a compliment), neutral (e.g., an observation) or negative (i.e., a complaint).

Scholars have identified different types and styles of humor. Martin et al., (2003) individual humor styles and created four dimensions of humor: self-enhancing, affiliative, aggressive, and self-defeating humor. These dimensions were conceptualized in a 2 x 2 model that made two distinctions, which are valence-based and relationship-based. The relationship- based distinction focuses on whether humor is employed to enhance oneself or to enhance one’s relationship with others. When humor is used to enhance oneself, it can be used as a

‘protection’ of a person by, for example, using humor as a means of dealing with stress or as a defense mechanism (Martin et al., 2003). When humor is used to enhance relationships with others, it is used to improve someone else’s feelings or to strengthen relationships by raising group morale. Another distinction that can be made is the valence of the humor. Humor can either be positive and caring by considerate and receptive of oneself and others. Or, humor

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS can be negative and harmful at the expense of oneself or others. Humor might also be negative and harmful at the expense of oneself or others. The dimensions and humor styles are displayed in Table 1.

Table 1.

2 x 2 conceptualization of humor styles as identified by Martin et al (2003).

Valence Relationship

Enhance oneself Enhance others

Positive Self-enhancing humor Affiliative humor

Negative Aggressive humor Self-defeating humor

Self-enhancing humor is used by people who have a general humorous perspective on life, even when confronted with stress or difficulty (Kuiper, Martin & Olinger, 1993). In a webcare context, this means that a company could make a joke that is about the company itself that presents them in a favorable manner compared to the customer. The humor is directed at the company itself rather than at the person that the company is engaging with online.

Affiliative humor focusses on enabling relationships, e.g. when people say funny and witty things to entertain others (Lefcourt, 2001). Individuals who engage in affiliative humor do so by using humor that is accepting of oneself but also of others (Martin et al., 2003). In a webcare context, that means that the humor is directed at the consumer the company is engaging with rather than the company itself. The joke is intended to improve the relationship between the company and the consumer.

Aggressive humor is a form of sarcasm or teasing that disregards its possible effect on others (Martin et al., 2003). Aggressive humor includes humor that people use because they

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS find it difficult to resist the urge to say funny things at the expense of others. In a webcare context, aggressive humor may be employed when a company makes a sarcastic remark or negatively makes a joke about somebody else than the company or the webcare employee.

The Self-defeating humor style is used when an individual makes negative jokes at their own expense in order to gain approval of others. Individuals who engage in self- defeating humor make themselves the punchline of the joke and laugh with others when they are being made fun of or ridiculed (Martin et al., 2003). In a webcare context, this would mean that the webcare employee is making fun of him or herself in a negative way by being the punchline of the conversation.

While underrepresented in studies on webcare, humor and its effects have been studied in several other contexts, e.g., in advertising (Einsend, 2009), or in an online recruitment context (Oikarinen & Söderlund, 2016), or in an offline customer service setting (Mathies,

Chiew & Kleinaltenkamp, 2016) Especially humor in advertising and customer service provide interesting results that might also have implications for humor in a webcare context, because both fields have similar objectives (i.e., customer service, marketing/advertising goals and relationship management goals). Previous studies on humor in advertisements have provided insights and results such as a positive effect of humor on the attention that consumers give to the advertisement (Weinberger & Gulas, 1992; Eisend, 2009) and consumers’ attitude towards the advertising company (Alden, Mukherjee & Hoyer, 2000;

Eisend, 2009). The role of humor in customer services experiences has shown mixed results:

Mathies, Chiew, and Kleinaltenkamp (2016) argue that the use of humor by customer service employees may improve customer experience, whereas Söderlund, Oikarinen, and Heikka

(2017) claim that jokes in customer service encounters reduce customer satisfaction.

Söderlund, Oikarinen, and Heikka (2017) speculate that the reason for these mixed results is that employing humor is not generally better as not employing humor in a communicative

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS setting. As webcare is a combination of customer service, marketing, and public relations goals, it is interesting to investigate what the effect is of humor on corporate reputation and

CHV in a webcare context.

2.5. Online visuals

While a large part of the internet exists of visuals, little research has been done on visual communication on the internet (Baran, 2012). An example of topics of visual internet communication that have been studied is internet memes. The term ‘meme’ was first used in

1976 to refer to a “viral spread of an idea” (Taecharungroj & Nueangjamnon, 2015). In the current internet era, known as Web 2.0, a meme has transformed to define a “piece of culture, typically a joke, which gains influence through online transmission” (Davidson, 2012).

Internet memes consist of an image macro and a short text (e.g., “One does not simply X”).

Internet memes are considered funny, may reference to contemporary culture and are reused by other individuals over a period of time (Goncalo Oliveira, Costa & Pinto, 2016). For example, the “One does not simply X” meme is derived from the 2001 “Lord of the Rings” movie and consist of a picture of a character of the film. The text template is an analogy for the original line in the movie “One does not simply walk into Mordor” (Goncalo Oliveira,

Costa & Pinto, 2016). An example of how this meme was taken and altered over time can be seen in Figure 1.

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Figure 1. An example of how the “One does not simply X” meme has changed over time.

Numerous memes are created of customized pictures and the main purpose seems to be either humorous or sarcastic (Wiggins & Bowers, 2014). Memes transform over time as they undergo a process of variation, competition, selection, and retention (Taecharungroj &

Nueangjamnon, 2015).

Memes have been studied as headlines for news (Goncalo Oliveira, Costa & Pinto,

2016), in an online discourse about political or news topics (Tay, 2015) and as visual rhetoric

(Huntington, 2013). Taecharungroj and Nueangjamnon (2015) investigated memes in relation to humor styles and found that affiliative and aggressive humor styles in memes are most used on Facebook. The most used humor types used in Facebook memes are sarcasm and silliness

(Taecharungroj & Nueangjamnon, 2015). Sharing humorous jokes is an important aspect of online activities and jokes are particularly shared in the form of memes (Levy, 2001; Shifman,

2012). A study on image sharing on Twitter in the United States and the United Kingdom

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS found that the most common shared visuals were advertising related (7.8% of the total sample of 400 tweets) or jokes (4.3% of the total sample of 400 tweets). According to Shifman

(2012), internet memes denote three main features of humor, specifically playfulness (i.e., meme readers are asked to not take the meme content with earnest intent); incongruity (i.e., humor is a consequence from two contrasting scripts); and superiority (i.e., the user of the meme perceives himself/herself as superior).

Another form of visuals in online media is animated GIFs. GIFs (i.e., Graphical

Interchange Format) are animated graphics displayed as a sort of electronic flip book that gives the illusion of continuous movement (Grădinaru, 2016). Compared to memes, little research has been done on GIFs on contemporary online platforms. An example of research that has been done on this subject is the study of Bakshi, Shamma, Kennedy, Song, de Juan, and Kaye (2016). Bakshi et al. (2016) analyzed the engagement (in the form of a “like” or

“reblog”) of over 3.9 million Tumblr posts and found that posts containing GIFs were more engaging than posts that contained other media. Furthermore, Bakshi et al., (2016) found that multiple factors, like the absence of sound, low bandwidth, storytelling abilities, imminent consumption capabilities, the ability to express emotions and little time demands contribute to making GIFs engaging content on a social media platform like Tumblr.

The main difference between GIFs and memes is that GIFs are moving images that continuously play in a loop while memes are image macros containing text that do not move at all. However, this distinct difference might also mean that memes and GIFs have different effects on corporate reputation and CHV in humorous webcare messages. This assumption can be explained via Media Richness Theory. Media Richness theory assumes that the aim of communication is to resolve ambiguity and decrease uncertainty. Media Richness theory postulates that media can be ‘rich’ or ‘poor’ (i.e., the quantity of information that a medium is able to send in a given time period) and this has an influence on the level of decreased

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS ambiguity and uncertainty (Daft & Lengel, 1986; Kaplan & Haenlein. 2011). This means in a digital media context, a video message is considered more ‘rich’ compared to an e-mail, which is only text based. It is logical to assume that GIFs are more ‘rich’ compared to memes because GIFs are continuous looping images existing of multiple factors (e.g., the ability to express emotions) whereas memes are non-moving image macros containing a piece of text that is intended to be funny and may change over time. This distinction of abilities might result in different effects on corporate reputation and perceived CHV in a webcare conversation.

The usage of online visual communication like memes and GIFs has been studied in conversational comment sections (Herring & Dainas, 2017) but also in terms of virality

(Taecharungroj & Nueangjamnong, 2015). However, the combination of a (humorous) visual with a webcare message has not been studied before. It is interesting to find out to what extent the results of previous research on humorous visuals in TV advertising, print advertising and especially in Internet visuals are applicable in a webcare context.

2.6.The current study

The literature reviewed in this chapter makes clear that the usage of humor and humorous visuals such as GIFs and memes in webcare conversations and their effects on corporate reputation and perceived CHV remains understudied. Therefore, the current research endeavor aims to fill this gap in the existing literature on humor, humorous internet visuals and webcare. The current study has theoretical and practical relevance. Humor has been mainly studied in other contexts such as advertising (Einsend, 2009), online recruitment

(Oikarinen & Söderlund, 2016) or in an offline customer service setting (Mathies, Chiew &

Kleinaltenkamp, 2016). In a webcare context, humor has only been studied as an item of perceived CHV (Kelleher & Miller, 2006), not as a variable on its own. While previous

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS studies have shown the effect of webcare conversations on corporate reputation (Huibers &

Verhoeven, 2014) and perceived CHV (Crijns, Cauberghe, Hudders & Claeys, 2017), the usage and effect of humor on these variables have not been investigated. Another aspect that have not been studied yet in a webcare context is the usage and effect of humorous visuals such as pictures, memes, and GIFs. While visuals take up a large part of internet content

(Baran, 2012), little is known about the effects of these visuals on corporate reputation and perceived CHV. The current study also offers practical relevance for webcare practitioners. A

2016 survey conducted among Dutch webcare practitioners showed that 29% of the Dutch webcare employees used humor and 62% sometimes used humor in webcare conversations

(van Os, Hachmang, Derksen & Keurning, 2017). The current study can provide suggestions to webcare practitioners on what humor style and visual type will yield the highest score on corporate reputation and perceived CHV.

Considering the two research questions, this research will be divided into two studies.

For now, only hypotheses for the first study will be formulated because the results of corpus study will influence the experimental study. A content analysis of webcare responses (i.e., study 1) will help to determine the most used webcare response humor styles and types. Based on this information, an experimental study in form of a survey (i.e., study 2) will be conducted to explore the effect that visual humor has on corporate reputation and perceived

CHV in a webcare context. The first study aims to answer the first research questions by investigating the hypotheses below.

The humor styles as identified by Martin et al. (2003) are self-enhancing humor, affiliative humor, aggressive humor and self-defeating humor. Based on how each humor style is operationalized in this chapter, the following hypothesis on humor style are formulated.

H1: The self-enhancing humor style is the most used humor style in webcare responses.

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

H2: The self-defeating humor style will be the least used humor style in webcare responses.

Webcare is employed to focus on customer service, marketing, and PR goals (van

Noort, Willemse, Kerkhof & Verhoeven, 2014). Therefore, it is likely to assume that webcare practitioners will probably make jokes mostly about themselves to enhance their own cause or business. This description fits best with the description of self-enhancing humor. Using similar reasoning, it is not likely for webcare practitioners to use humor that might make the company look bad as the webcare practitioners represent the company online. Therefore, it is predicted that self-defeating humor will be the least used humor style.

In terms of visuals, Bakshi et al., (2016) found that GIFs are the most used visual type in their study on the platform Tumblr. Bakshi et al., (2016) explained that GIFs are used the most because of the multiple abilities that a GIF has such as the ability to portray certain emotions. This ability can also help webcare practitioners to further express themselves with humor online. Therefore, the following hypothesis is formulated.

H3: GIFs will be the most used visual type in webcare responses.

3. Study 1: Content Analysis

The content analysis study aims to get insights into the usage of humor styles and visual types of Dutch webcare employees. It was investigated which humor style (i.e., affiliative, self- enhancing, aggressive or self-defeating humor) and which visual type (i.e., Meme, GIF,

Picture or no visual) were most employed by Dutch webcare teams. Therefore, a content analysis was conducted. A quantitative content analysis is a research method described as a systematic technique of assigning different communication content to groups according to a set of rules and the statistical analysis of the possible relations between those groups (Riff,

Lacy & Fico, 2014). As such, it seemed the best method for the goal of identifying which humor types are employed mostly in Dutch webcare conversations by webcare teams. The

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS content analysis study was conducted as part of a larger research of humor in webcare conversations. The content analysis was carried out together with a fellow master thesis student conducting research on the effects of gender on humor in webcare conversations.

4. Method

4.1. Sample

The sample consisted of ten webcare conversations on Twitter by ten different Dutch companies (N = 100 tweets). The sample was created by selecting ten companies who were nominated for ‘The Best Social Media Awards’ between 2014 and 2016. ‘The Best Social

Media Awards’ is an event of the Social Media Company ‘Studio Broekhuizen’ who give out awards for companies who are the best on social media (“About”, n.d.) The first selection criterion was to select the companies that were nominated the most times. The next step in the selection process included a review of the Twitter account of each company to make sure that the companies were frequently participating in (humorous) webcare conversations. This was a necessary step because companies could be nominated multiple times for a Social Media

Award but their presence on social media was not necessarily webcare related. Based on the first two steps, six companies of the top ten most nominated companies of the Social Media

Awards were selected. The other four companies were selected from the list of the remaining

Social Media Awards nominees between 2014 and 2016 based on their frequent webcare activities.

There were also criteria set for the tweets that were sampled using purposive sampling methods. Purposive sampling method is a sampling method where the researchers set specific criteria for each item in the sample (Treadwell, 2016). The criteria existed of three rules: (1) the tweet had to be humorous, (2) the tweet had to be a reaction of the company to a message by the customer and (3) the tweet had to be written in Dutch. While searching for these

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS humorous tweets, the researchers found that three companies (two airlines and a furniture store) barely used humor in their webcare responses. The researchers suspected that corporate social media guidelines prevented these companies from using humor in webcare responses.

Research has shown that 9% of the Dutch webcare teams explicitly choose not to employ humor (Van Os, Hachmang, Derksen & Keurning, 2017). After reaching out to one of the airlines on Twitter, this suspicion was confirmed. Thus, it was decided to exclude these companies from the final sample and they were replaced by three other randomly selected companies: one telephone and internet provider, a grocery store and one internet provider.

The final selection consisted of various companies from different branches ranging from telecommunications companies to the financial industry and entertainment industry. A full list of the names, industries, and Twitter handles can be found in Appendix A.

The social media monitor tool ‘Coosto’ (2017) was employed to harvest tweets within a two-week time span of Wednesday 1st of March till Wednesday15th of March company. The researchers selected ten humorous tweets per company using a purposive sampling method.

The used search queries and the total amount of Tweets found by Coosto (2017) and the

Twitter handles are represented in Appendix A.

4.2. Instrumentation of Codebook

In order to code the tweets in a structured way, a codebook was developed detailing exactly how each tweet should be coded. The finalized codebook can be found in Appendix B. The codebook consisted of 23 different categories with different classifications. Only the categories relevant to this study will be briefly discussed. To the author’s knowledge, no previous study has investigated humor and visual usage in webcare. Therefore, some categories were added to gain more insights into humor usage and visuals in webcare

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS conversations. All categories were described and operationalized by using examples in the codebook.

Two researchers did the coding so the first category specified the coder of the message

(i.e., 0 = Researcher 1, 1 = Researcher 2). The next categories existed of the link of the coded tweet as an URL, date of the tweet (i.e., dd/mm/yyyy) and company that participated in webcare (e.g., 1 = Coolblue, 10 = Ziggo). The next categories classified the sentiment expressed at the start of the webcare conversation by the consumer (i.e., positive, neutral, negative or no sentiment if the conversation was not displayed correctly), the presence of humor in the tweet by the consumer (i.e., yes or no) and the moment when the humor was used (i.e., in first two messages, in the middle of the conversation, or in last two messages).

Humor style was coded based on the 2 by 2 model of Martin et al. (2003) existing of affiliative humor, self-enhancing humor, aggressive humor, and self-defeating humor. Each humor style is either positive (i.e., affiliative and self-enhancing) or negative (i.e., aggressive and self-defeating) and is either directed towards the consumer (i.e., affiliative and aggressive) or directed towards the company (i.e., self-enhancing and self-defeating). This was operationalized in the coding scheme by the valence of the humor (i.e., negative or positive) and the direction of the humor (i.e., towards the company or towards the consumer).

To gain more insights into humor usage in webcare conversations, type of joke was coded as well. The type of joke was coded into five categories namely pun, sarcasm, personification, exaggeration, and other. These categories were defined based on the operationalization of Taecharungroj and Nueangjamnong (2015) which are described and illustrated in the codebook in Appendix B. It was possible for a tweet to contain multiple jokes. Therefore, it was coded whether a certain joke was present or not present for each type of joke. The other category was added in case a type of joke was used in the tweet that did not fit the operationalization of pun, sarcasm, personification or exaggeration.

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Other categories that were encoded was the gender of the webcare employee and the gender of the consumer. Gender was determined by looking at an employees or consumers name and profile picture. A list of popular baby names for boys and girls was used as a reference point if a unisex name was used (“Sociale Verzekeringsbank”, 2017). For gender of the webcare employee, the categories were male (0), female (1), use of initials (2) or no signature (3). The webcare accounts sometimes use their initials to protect the identity of the webcare employees or no signature or name is used at all. These facts were taken into account for the codebook. For gender of the consumer, besides male (0) and female (1), the items of doubtable (2) and not traceable (3) were added. In some cases, it was not clear whether the consumer was male or female due to contradictions (e.g., female name, male picture). There were also instances where consumers used pseudonyms and pictures of objects so the gender of the consumer could not be determined.

The codebook ended with a few essential categories for the current study: visuals. As not all webcare conversations necessarily contain visuals, the category ‘visual presence’ determined whether a visual was not present (0) or present (1). To determine whether a visual was a GIF, Meme or picture, several categories were used. In the ‘type of visual’ category, it was coded whether a visual was not moving (0), moving (1) or whether no visual was present

(2). This way, it could be determined whether a visual was a GIF or not as GIFs were the only moving visuals (i.e., 0 = text not present, 1 = present, 2 = no visual present). By doing so, it could be determined during the analysis of the codings whether a visual was a meme (i.e., not moving visual with text) or a picture (i.e., not moving visual with no text). Furthermore, the place (i.e., 0 = in the written tweet, 1 = in the visual or a 2 = combination of text and visual) where the humor was used was also coded

Finally, to see if the tweets were popular or not, the retweets and likes of the humorous tweet were also documented.

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

4.3. Intercoder reliability

An intercoder reliability check was performed to ensure the reliability of the coding by the two coders. A sub-sample of 20 conversations (20% of the total sample) was selected and double coded. For each item, a Cohen's κ was calculated to determine if there was an agreement between the judgment of both coders. The intercoder reliability of all items can be found in Appendix C. According to this analysis, most of the variables had a good to very good judgment (κmin = .61, κmax = 1.00). Only the items of valence (κ = .46, p = .02), sarcasm

(κ = .58, p = .004) and other joke type (κ = .57, p = .01) had a moderate intercoder reliability.

In order to improve this judgment, the sub-sample was reviewed again and the codebook was adjusted to incorporate better agreement for these items. A different sub-sample was re-coded again only based on these items and the improved codebook and the judgment for all items improved to good or very good (valence: κ = 1.00 , p <.001; sarcasm: κ = .64, p = .002; other:

κ = .79, p < .001). The entire sample was then divided into two and each coder coded 50 tweets. The sub-samples did not need to be coded again and were included into the final encodings.

4.4. Procedure

After the sample was collected and inter-coder reliability was calculated, the two coders started coding using the codebook. This codebook was created by the two coders which benefited them greatly as they learned from this process. Moreover, creating the codebook also meant that the instructions were clear and that the coders were sufficiently trained. When checking the intercoder reliability, any differences were discussed and the codebook was altered accordingly. Both coders had previous coding experience of webcare messages and were knowledgeable on the topic of humor as they had reviewed the literature on humor

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS extensively. In total, the coding was done in two days. The double coding was done the first day and the remainder of the sample was coded the day after that. There were no guidelines for pauses such as time limits or breaks between coding.

5. Results of study 1

The frequencies of each category were first calculated to gain more insights into the humor use of Dutch webcare teams. Subsequently, statistical analyses of the main categories (i.e., humor style and visual type) were conducted.

5.1. Frequencies of additional categories

The frequencies tables of the additional variables such as sentiment, the gender of consumer and moment of humorous webcare response can be found in Appendix D. The sentiment of the webcare conversations prior to the humor use were seemed roughly equally distributed

(i.e., negative: N = 30; positive: N = 37; neutral: N = 31; no sentiment: N = 2), which contributes to the validity of the sample. The frequencies of humor in eWOM prior to the humorous tweet (i.e., no: N = 73; yes: N = 27) indicated that most of the time the company started the humorous intent of the webcare conversation in the sample. The frequencies of joke type show that most jokes were classified as an exaggeration (N = 27), puns (N = 21) or belonged to the category other (N = 42). Sarcasm (N = 18) and personification (N = 7) were used least. Because the other category was the largest category, the two coders revisited this category again to see if distinct subcategories could be identified. The coders found that most jokes in the other category could be placed in the ‘surprise’ category. Taecharungroj and

Nueangjamnong (2015) define the surprise category as a category where humor arises due to unexpected situations. In Figure 2, an example of a tweet is displayed that was first categorized as other, but upon revisiting the data was labeled as ‘surprise’. Figure 2 displays a

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS good example of one of the tweets in the sample in the surprise category. The webcare employee Brian of T-Mobile reacted in an unexpected manner by adding a funny picture of a bunny with sunglasses to the conversation while neither bunnies nor sunglasses were the topics of the conversation. For the purposes of this research, the webcare conversation in

Figure 2 is translated from Dutch to English.

Figure 2. Webcare response of T-Mobile including a surprising joke and visual.

Moreover, humor was applied more frequently in a webcare conversation towards a man (N = 60) compared to towards a woman (N = 30) and the webcare employee mostly used initials (N = 36) or no signature (N = 29) to sign the tweets. The place where the joke was made was generally in the first two messages (N = 60) or in the final two messages (N = 26) of a webcare conversation. Most times, the joke would be in the text of the tweet (N = 42). In

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS total, 26 times the joke was present in the visual and it was 32 times in a combination of text and visual. The number of times a tweet was retweeted or liked was also counted in order to determine whether consumers appreciated the tweet. In our sample, only 4 tweets were retweeted and approximately half of the tweets were not liked at all (N = 55). In total, only 37 tweets were liked once. The highest amount of likes a tweet received was 7 times. This might indicate that retweets and likes are not a very good indicator of how funny tweets are according to consumers.

5.2. Frequencies of the main variables

The frequencies of the different styles of humor are displayed in Table 2, which shows that self-enhancing humor and affiliative humor were the most used humor styles. In line with our expectations, aggressive humor and self-defeating humor were barely used in our sample.

Table 2

Frequencies of humor style in the sample.

Type of humor N

Self-enhancing 19

Affiliative 72

Aggressive 8

Self-defeating 1

It was also calculated how many times visuals were used and what type of visuals. The number of times that visuals were used in the sample, irrespective of the type of visual that was used (N = 56), was almost equal to the number of times in which visuals were absent (N =

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

44). When a visual was present, a GIF was used most commonly (N = 36), followed by a meme (N = 11), and a picture (N = 9).

5.3. Humor style and Visual type

To gain more in-depth insights of the sample, multiple chi-square tests were conducted with the variables humor style and visual type. A chi-square test was conducted to review the sample distribution between humor type (self-enhancing, affiliative, aggressive and self- defeating) and visual type (Meme, GIF, Picture or no visual). In total, 100 cases were taken into account. However, there were ten cases with an expected count less than five meaning that this assumption was violated and that the Fishers exact test needed to be reported. Table 3 shows the frequencies and contingency percentages of humor style and visual type.

Table 3

Contingency table of humor style and visual type

Visual type

Humor style GIF (N = 36) Meme (N = 11) Picture (N = 9) No visual (N = 44)

Self-enhancing 8 (42%) 1 (5%) 1 (5%) 9 (47%)

Affiliative 24 (33%) 10 (14%) 8 (11%) 30 (42%)

Self-defeating 0 (0%) 0 (0%) 0 (0%) 1 (100%)

Aggressive 4 (50%) 0 (0%) 0 (0%) 4 (50%)

Note: The percentages between the brackets represents the percentage within humor style.

The Fisher’s exact test showed a non-significant result, χ2(9) = 5.53, p = .888, between humor style and visual type. This indicates that it is not more likely that a certain humor style (e.g., affiliative humor) contains a certain visual type (e.g., GIF).

Another a chi-square test was conducted between the most used humor types

(affiliative humor and self-enhancing humor) and the most used visual types (GIF and

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Meme). In total, 43 cases were taken into account. However, the assumption of the expected count was violated as the expected count was lower than five thus the Fisher’s exact test will be reported. Table 4 displays the contingency table of the most used humor styles (i.e., affiliative humor and self-enhancing humor) and most used visual types (i.e., GIF and meme).

The contingency table seems to indicate that for both humor styles, it is more likely that a GIF will be used compared to a meme. However, the Fisher’s exact test shows a non-significant result, χ2(1) = 1.25, p = .407. This indicates that it is not significantly more likely that GIFs are used in affiliative of self-enhancing humor style webcare conversations.

Table 4

Contingency table of the most used humor styles and visual type

Visual type

Humor style GIF (N = 32) Meme (N = 11)

Self-enhancing 8 (25%) 1 (9%)

Affiliative 24 (75%) 10 (91%)

Note: The percentages between the brackets represents the percentage within the visual type.

To gain more insights into which kinds of jokes are combined with which kinds of humor style, a statistical analysis was conducted with humor style and joke type as categories. It was possible for a joke to be in more than 1 category (e.g., a sarcastic pun). Therefore, multiple chi-square tests were conducted with the binary variables (i.e., present or not present) joke types pun, sarcasm, personification, exaggeration and other. For all chi-square analyses, there were 100 cases taken into account. Moreover, for each chi-square test, the violation of expected count higher than five was violated. Each item had four cells in which the expected count was less than five with the exception of exaggeration, which had three cells that were

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS less than five. This means that for all items the Fisher's exact test will be reported. The contingency tables of all types of jokes can be found in Appendix E. The results of the

Fisher’s exact test of the joke types can be seen in Table 5. None of the joke types show significant results with the exception of sarcasm, which is highly significant. This indicates that sarcastic jokes are more likely to be found in each humor style (e.g., self-enhancing) compared to the other joke types.

Table 5

Fisher’s exact test of humor style and joke type

Joke type df χ2 p

Pun 3 5.00 .140

Sarcasm 3 18.70 <.001

Personification 3 1.79 .815

Exaggeration 3 .92 .900

Other 3 4.64 .158

Note: p-values in bold are significant.

6. Conclusion of Study 1

The content analysis has provided useful insights into what kind of humor styles, types of jokes and types of visuals are being used in webcare conversations. In sum, the types of humor that are used most frequently are self-enhancing and affiliative humor. This means that

H1 is partly supported as affiliative humor is the most used humor type but self-enhancing comes in second place. The least used humor styles are aggressive and self-defeating humor.

Thus, H2 is supported. The jokes that were mostly used in our sample were exaggerations, puns, and other jokes. When taking a closer look at the other joke category, the coders found

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS that some jokes could also be categorized as ‘surprise’. More than half of the sample existed of visuals, of which GIFs and memes were used the most. Therefore, H3 is supported. This means that in the second study, the humor types of self-enhancing and affiliative humor will be used in the stimuli material combined with either a GIF or meme.

Since a non-probability sampling technique was used to create the sample, the results of this content analysis are only applicable to this particular sample and not necessarily to the population of humorous tweets. Nevertheless, the point of this study was to find out the characteristics of webcare responses containing humor and not necessarily how much or how many times humor was used in a webcare response. Therefore, the fact that the results are not applicable towards the population is relative.

The next chapters of this study will focus on the experimental study and answer the second research question on the effects of using humor and visuals in webcare conversations on corporate reputation and perceived CHV.

7. Study 2: Experimental Study

The second study was conducted to address questions on the effect of visual humor in webcare conversations on corporate reputation and perceived corporate human voice. An experimental quantitative survey was chosen as method to provide insights into this effect.

This particular method was chosen because of its several advantages: people can answer the questions quickly, many people may be asked to fill out the survey and the data can potentially be generalized to the population (Treadwell, 2016).

The content study showed that affiliative humor was used the most followed by self- enhancing humor. According to Martin et al., (2003), affiliative humor focuses on enhancing one’s relationship with others in a positive way. Self-enhancing humor is also considered positive but the emphasis is put on enhancing oneself (i.e., presenting yourself in the best

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS possible way) rather than others. Based on the definitions of Martin et al., (2003) and the results of the content analyses, it can be assumed that affiliative humor might be more favorable for consumer’s perception of corporate reputation and perceived CHV compared to self-enhancing humor. Therefore, it is assumed that affiliative humor in webcare contexts focusses on enhancing the relationship with the consumer whereas self-enhancing humor emphasizes on putting the company in a positive light. Consequently, the following hypothesis is formulated.

H1: Humor type (affiliate or self-enhancing) affects consumer’s perception of corporate reputation and perceived CHV. More specifically, consumer perception of corporate reputation and perceived CHV are more positive for stimuli containing affiliative humor than stimuli containing self-enhancing humor.

The study of Bakshi et al., (2016) showed that GIFs are considered ‘more engaging’ compared to other visuals. Bakshi at al., (2016) contributed this due to the many abilities of a

GIF such as expressing emotions. The outcome of the content study also showed that GIFs are the most used type of visual in our sample. Therefore, the following hypothesis is formulated.

H2: Kind of visual humor (Meme or GIF) affects consumer’s perception of corporate reputation and perceived CHV. More specifically, consumer perception of corporate reputation and perceived CHV are more positive for stimuli containing GIFs compared to stimuli containing memes.

8. Method

8.1. Design

In order to test these hypotheses, an experimental study with a 2 (humor type: affiliative vs. self-enhancing) x 2 (visual type: meme vs. GIF) x 3 (sentiment: negative vs. neutral vs. positive) mixed design was employed. The sentiment variables was used as a within-subjects

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS variable and humor style and visual type as a between-subject variable. The independent variables were humor type, visual type, and sentiment and the dependent variables were corporate reputation and perceived corporate human voice.

The experiment consisted of four conditions and the respondents were randomly assigned to one of those conditions by the survey tool Qualtrics (Version May 2017, 2017).

The conditions and number of participants can be found in Table 6.

Table 6

Research conditions and number of participants in each condition (N = 166)

Visual type

Humor type Meme GIF

Affiliative humor Condition 1 Condition 2

40 Participants (24%) 45 Participants (27%)

Self-enhancing humor Condition 3 Condition 4

39 Participants (24%) 41 Participants (25%)

8.2. Participants

All partial responses were excluded from further analyses. In total, 173 respondents completed the survey. In total, 8 respondents did not agree to give consent to participate in this study thus these respondents were thanked and excluded from any further analysis, which makes the sample (N = 166). The age of the respondents varied from 17 till 77 years old, the mean age was 33 (M = 32.61, SD = 12.9). The sample consisted of 44.2% male (N = 73) and

55.8% female (N = 92) respondents. The education level of the respondents was varied. In total, 10.9 cumulative percent possessed a high school degree (VMBO: N = 3; HAVO: N = 8;

VWO: N = 7). 16.4 % had a MBO degree (N = 27). The largest part of the sample 30.9% had an HBO Bachelor (N =51) and 6.1% had an HBO master degree (N =10). 8.5% of the

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS respondents had a WO bachelor degree (N = 14), 25.5% had a WO master degree (N =42) and

1 respondent had a Ph.D. degree. In total, 2 respondents filled out other and indicated that either they had an HBO degree (did not specify bachelor or master) or that they were from

Flanders and did not have the same levels of education as the Netherlands.

Respondents were also asked about their knowledge of webcare and social media usage. 58.8% of the respondents indicated that they knew what webcare was prior to the survey (N =97) and 67.3% of the respondents (N =111) said that they had not participated in webcare conversations before. The respondents were also asked which social media sites they used on a daily basis. 92.1% of the respondents used Facebook on a daily basis, 54.5% used

Instagram, 38.8% used Snapchat and 21.1% used Twitter. The respondents were also asked, after completing the survey, if they understood the accompanying English text with the visuals and 98.8% said that they understood the text.

8.3. Materials

An informed consent form was used to inform potential respondents about the nature and length of the study and ensured anonymity. It also included instructions on what the respondents could expect during the study, highlighted the voluntary basis of participation, and included the researchers’ contact information in case of any questions. The respondents were given a choice if they gave their consent to participate. If a respondent did not agree with the explained conditions, they could click the survey away or choose the option ‘no’. In case a participant chose the latter option, the participant was thanked and excused from participating. This consent form and the entire survey can be found in Appendix H.

The survey consisted of six webcare conversations between the fictional airway company PennAirways and anonymous consumers. A fictional company was chosen so that the participants would not be biased with previous opinions or experiences and were forced to

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS create an opinion solely based on the webcare conversations they saw. Of those six conversations, three were experimental conditions and three of them were fillers to distract the respondent of what the true task was. Appendix F shows the manipulated stimuli that were created.

The tweets were modeled after real tweets using the social media monitoring tool

Coosto (2017) in order to increase external validity. For both the experimental conversations and the filler conversations, the conversation had a positive, neutral and negative sentiment prior to the webcare response of PennAirways. This was done to show a balanced variety of conversations to the respondents. The experimental conversations contained humor in the conversations and the filler conversations did not. The conversations in the GIF condition contained GIFs and the conversations in the meme condition contained memes. The only two conversations that did not contain a visual were the filler conversation with a negative and neutral sentiment. This was done in order to distract from the fact that visuals were also important in the study.

8.4. Measures

Corporate reputation was measured on a 7-point Likert scale (i.e., 1 indicates completely disagree and 7 means completely agree) designed by Walsh, Mitchell, Jackson, and Beatty

(2009). The original scale consisted of 15 items but this scale was adjusted and personalized to fit the scope of this study. Therefore, the scale used consisted of 6 items such as

‘Pennairways treats their customers in a fair manner’. The perceived corporate human voice was also measured on a 7-point Likert scale (i.e., 1 meaning completely disagree and 7 is completely agree) designed by Kelleher (2009). The original scale consisted of 12 items but was adjusted and personalized to fit the scope of this study. Therefore, the scale used in this study consisted of 6 items, e.g., ‘In this reaction PennAirways uses humor in the

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS communication’. The complete set of statements that was used to create the corporate reputation scale and the perceived CHV scale can be found in Appendix G.

At the end of the survey, the participants were also asked to fill out statements about their humor use using the humor style questionnaire. This questionnaire was created by

Martin, Puhlik-Doris, Larsen, Gray, and Weir (2003) and measures which type of humor by

Martin et al (2003) people use most frequently. The original questionnaire consisted of 8 items per humor style (e.g., 32 items in total). The survey only used affiliative humor type and self-enhancing humor type thus the other humor types were automatically excluded. Of the original 8 items of affiliative humor and self-enhancing humor, 3 items per humor type were selected (e.g., affiliative humor: “I like to make other people laugh”; self-enhancing humor:

“when I feel depressed, I can usually cheer myself up using humor.”). The items were measured on a 7-point Likert scale where 1 indicated ‘completely disagree’ and 7 indicated

‘completely agree’. The complete list of statements used to measure HSQ for affiliative humor and self-enhancing humor can be found in Appendix G.

8.5. Procedure

The survey was first spread on 17 May 2017 and closed on 29 May 2017. The survey was created in the survey tool Qualtrics and spread online. The respondents were found using network sampling via different online channels such as Facebook, Twitter, Instagram, and an internet forum. The survey could be accessed via a computer, laptop, iPad or mobile phone.

Webcare conversations generally take place on these devices, thus it increases the validity level of the survey.

The survey started with an introduction and a consent form that the participants had to sign which indicated that they allowed the collection of their data for academic purposes.

After consent was given, the participants were given instructions of what they needed to do in

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS the experiment. It was explained that they were going to see six twitter conversations between potential clients and Pennairways and answer questions about these conversations. A disclaimer was also given that some twitter reactions had visuals but due to the settings in the survey, they were displayed under the tweet instead of including the tweet. After this, the participants were randomly assigned by Qualtrics to a condition. After seeing the six webcare conversations, the respondents were asked to fill out the humor style questionnaire and some questions about demographics. Moreover, their experience in webcare and social media was also asked. After this, the respondents were given the opportunity to leave questions or feedback and were thanked for their participation. The participation in this study was completely voluntary as no compensation was offered.

9. Results Experiment

This section is dedicated to the statistical results of the experiment. Before any statistical tests were done, multiple tests were conducted to check if the data was distributed evenly and to see if any of the demographical variables have had an influence on the main. The results of these additional analyses can be found in Appendix I through K.

In the experiment, the participants were shown three conversations with different sentiments: negative, neutral and positive. It also might be possible that humor type and visual type have different effects when using different sentiments. Therefore, a correlation analysis for reputation and CHV was performed to see if the sentiments correlated with each other which are displayed in

Appendix N. Because the correlation between humor styles and the sentiment was significant, further analyses were conducted. The aim and hypothesis of this study are mainly focused on if humor style and visual type affect perceived CHV and reputation. As sentiment of the word-of- mouth was also considered in the design of the study, this factor is also taken into account in the statistical analyses. In order to present a complete picture on this matter, two factorial mixed

MANOVAs with a 2 (humor style: Affiliative and Self-enhancing) x 2 (visual type: GIF and

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Meme) x 3 (sentiment: negative, neutral and positive) design was conducted. The dependent within-subject variable was sentiment and the independent between-subject variables were the humor-type and visual type.

A mixed MANOVA was chosen over conducting multiple mixed ANOVA’s because a

MANOVA is designed to look at several dependent variables all at once. Moreover, when conducting multiple ANOVA’s, the risk for a Type I error increases (Field, 2013). The assumptions of the MANOVA were also checked and this can be found in Appendix L.

The first MANOVA was conducted for the scores on each sentiment for reputation and the second MANOVA was conducted with the perceived CHV scores. The result section is further structured as follows. First, the main result of the mixed MANOVA is reported, followed by the accompanying within-subjects ANOVA and between-subjects ANOVA. The result section is further structured as follows. First, the main analyses result of the mixed MANOVA is reported, followed by the accompanying within-subjects ANOVA and between-subjects

ANOVA. Following the results of the main analyses, are the results of the MANCOVA and

ANCOVA of various variables in order to exclude that those variables had influenced the main results in any way.

9.1.Main analyses

First, the assumptions of a mixed MANOVA were checked which can be found in

Appendix L. For reputation, two of the seven assumptions were violated and for CHV three of the seven assumptions were violated. Thus, it is important that the results of both analyses are interpreted with caution.

The results of the overall MANOVA of reputation scores can be found in Table 7.

Table 7

Main effects and interaction effect of MANOVA of sentiment, humor style and visual type - Reputation.

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Effect df1 df2 F p Pillai’s Trace η²

Sentiment 2 161 52.99 <.001 .40 .40

Sentiment * Humor style 2 161 4.96 .008 .06 .06

Sentiment * Visual type 2 161 .25 .779 .00 .00

Sentiment * Humor style * 2 161 .67 .512 .01 .01

Visual type

Note: p-values in bold indicate a significant effect.

As can be seen, a significant effect has been found for sentiment and sentiment * humor style.

This indicates that different sentiments (e.g., negative, neutral or positive) have different effects on certain humor styles on reputation. More specifically, a neutral sentiment lead to a lower score on reputation (M = 3.78, SE = .10) compared to a negative sentiment (M = 4.52, SE = .11) or positive sentiment (M = 4.79, SE = .08).

As the design that was conducted is a mixed design, the subsequent ANOVAs that accompany the MANOVA are a within-subject two-way ANOVA and a between-subject two-way

ANOVA. According to Field (2013), if a MANOVA shows significant results, the subsequent

ANOVAs need to be interpreted as well.

The results of the within-subject ANOVA can be seen in Table 8. Mauchly’s test indicated that the assumption of sphericity was met (χ2(2) = .97, p =.066) thus the degrees of freedom did not need to be corrected. Moreover, the Levene’s test for homogeneity of variances showed non-significant results for negative sentiment (F (3,162) = 1.87, p = .138), neutral sentiment

(F (3,162) = .20, p = .895), and positive sentiment (F (3,162) = 2.03, p = .112). This indicates that the assumption of homogeneity was met.

Table 8

Main effects and interaction effect of within-subject ANOVA of sentiment, humor style and visual type - Reputation.

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Effect df1 df2 F p η²

Sentiment 2 324 60.71 <.001 .27

Sentiment * Humor style 2 324 5.72 .004 .03

Sentiment * Visual type 2 324 .29 .746 .00

Sentiment * Humor style * Visual type 2 324 .78 .458 .01

Note: p-values in bold indicate a significant effect.

Similar to the results of the MANOVA, a significant main effect was found for sentiment and sentiment * humor style. This validates the results of the MANOVA even more, meaning that sentiment has different effects on humor style. Table 9 shows the means and standard error of humor type per sentiment for reputation scores. As can be seen, affiliative humor combined with a neutral sentiment led to the lowest score of reputation and affiliative humor combined with a positive sentiment led to the highest score of reputation.

Table 9

Mean and Standard Error scores of humor style per sentiment of reputation scores.

Humor type Sentiment M SE

Affiliative Negative 4.46 .15

Affiliative Neutral 3.49 .14

Affiliative Positive 4.80 .11

Self-enhancing Negative 4.57 .15

Self-enhancing Neutral 4.07 .15

Self-enhancing Positive 4.77 .11

The mean and standard deviation scores of humor type per sentiment and visual type can be found in Appendix O.

Lastly, the results of the between-subjects ANOVA of the main factors humor style and visual type on reputation need to be reported. The univariate test showed that there were no

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS differences on reputation score between the different humor types, (F (1,162) = 1,96, p = .163, η²

= .01). This means that participants in the affiliative humor condition (M = 4.25, SE = .11) did not significantly differ in reputation score compared to participants in the self-enhancing condition (M

= 4.47, SE = .11). Moreover, the between-subjects ANOVA also indicated non-significant results of visual type on reputation, (F (1,162) = 1.39, p = .24, η² =.01). Participants did not rate significantly different scores in reputation in the GIF condition (M = 4.45, SE = .11) compared to the meme condition (M = 4.27, SE = .11). The interaction effect of humor style and visual type on reputation showed a highly non-significant result, (F (1,162) = .00, p = .962, η² <.01). This means there is no significant difference between affiliative humor combined with a GIF (M = 4.35, SE =

.15) or affiliative humor combined with a meme (M = 4.15, SE = .16) and self-enhancing humor combined with a GIF (M = 4.56, SE = .16), or affiliative humor combined with a meme (M =

4.38, SE = .16).

The same analyses have been done for perceived CHV with the same variables. The results of the mixed MANOVA can be found in Table 10

Table 10

Main effects and interaction effect of MANOVA of sentiment, humor style and visual type – Perceived CHV.

Effect df1 df2 F p Pillai’s Trace η²

Sentiment 2 161 63.99 <.001 .44 .44

Sentiment * Humor style 2 161 5.25 .006 .06 .06

Sentiment * Visual type 2 161 .00 .999 .00 .00

Sentiment * Humor style * 2 161 1.52 .221 .02 .02

Visual type

Note: p-values in bold indicate a significant effect.

The results show, similar to reputation, a significant result of sentiment and sentiment * humor style. This indicates that likewise for perceived CHV, there is a difference in score for

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS different sentiments and humor types. The ANOVA results of the within-subjects design test can be found in Table 11. Mauchly’s test indicated that the assumption of sphericity had been violated, χ2(2) = .94, p =.005, therefore Greenhouse-Geisser corrected test is reported for this condition (ε =.94). Moreover, the Levene’s test for homogeneity of variances showed non- significant results for negative sentiment (F (3,162) = 2.52, p = .06), neutral sentiment (F

(3,162) = .91, p = .435), and positive sentiment (F (3,162) = 2.49, p = .062). This indicates that the assumption of homogeneity was met.

Table 11

Main effects and interaction effect of within-subject ANOVA of sentiment, humor style and visual type -Perceived CHV.

Effect df1 df2 F p η²

Sentiment 1.88 304.77 68.44 <.001 .30

Sentiment * Humor style 1.88 304.77 6.60 .002 .04

Sentiment * Visual type 1.88 304.77 .00 .999 .00

Sentiment * Humor style * Visual type 1.88 304.77 1.87 .158 .01

Note: p-values in bold indicate a significant effect.

The ANOVA shows also a main significant effect for sentiment and humor style * sentiment, akin to the MANOVA. This confirms the finding of the MANOVA even more that different sentiments

(e.g., positive, neutral or negative) have different effects on CHV. The mean and standard error scores can be found in Table 12. This shows that a neutral sentiment leads to a lower score of

CHV for both affiliative humor style and self-enhancing humor style. A positive sentiment leads to the highest score on perceived CHV for both affiliative and self-enhancing humor.

Table 12.

Mean and Standard Error scores of humor style per sentiment of reputation scores.

Humor type Sentiment M SE

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Affiliative Negative 4.92 .14

Affiliative Neutral 3.97 .16

Affiliative Positive 5.42 .11

Self-enhancing Negative 4.97 .15

Self-enhancing Neutral 4.67 .16

Self-enhancing Positive 5.54 .11

The mean and standard deviations of humor and visual type per sentiment can be found in

Appendix N.

To conclude, the between-subjects ANOVA needs to be reported. The results of this

ANOVA show a non-significant effect for humor type on perceived CHV, (F (1,162) = 3.33, p =

.070, η² =.02). The participants in the affiliative humor condition (M = 4.77, SE = .11) did not score significantly different compared to the participants in the self-enhancing humor condition

(M = 5.06, SE = .11). The results for visual type on perceived CHV did show a significant result,

(F (1,162) = 4.01, p = .047, η² =.02). This seems to indicate that participants in the GIF condition rated a significantly higher score for perceived CHV (M = 5.07 SE = .11) compared to participants in the meme condition (M = 4.76, SE = .11). However, as three out of the seven assumptions of the mixed MANOVA were not met, this result might be biased and should be interpreted with extreme caution. Especially as the results are barely significant, it might be possible that we are dealing with a type I error. Possible implications are further discussed in the discussion.

The interaction effect of humor style and visual type on perceived CHV showed a non- significant result, (F (1,162) = .76, p = .386, η² =.01). This indicates that there is no significant difference between affiliative humor combined with a GIF (M = 5.00, SE = .15) or affiliative humor combined with a meme (M = 4.54, SE = .16) and self-enhancing humor combined with a

GIF (M = 5.15, SE = .16), or affiliative humor combined with a meme (M = 4.97, SE = .16).

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

9.2. Covariates: MANCOVA and ANCOVA

Some demographic variables such as age, whether the participant knew what webcare was prior to the survey if the participant participated in webcare conversations before and if the participant used Facebook and Snapchat on a daily basis had an influence on the scores of both dependent variables. This indicates that these variables should be included as covariates.

However, it is a requirement that all covariates are measured at a continuous level, which is only the case for age. Therefore, this will be the only covariate used from the demographic variables. Moreover, it is interesting to see if a particular preference for humor style

(affiliative or self-enhancing) had an effect on the scores of reputation and perceived CHV.

The humor styles of each participant were measured using the Humor Style Questionnaire

(Martin, Puhlik-Doris, Larsen, Gray & Weir, 2003). Therefore, a MANCOVA analysis was performed to see if there is a difference in results of humor style and visual type on reputation and CHV when controlling for age and personal humor style (affiliative or self-enhancing).

However, these results are all non-significant indicating that the covariates of age and HSQ do not significantly account for a part of the variance of humor type and visual type on reputation and CHV. The full statistical analysis and description can be found in Appendix M.

To check whether education level could account for a part of the variance in humor type and visual type on CHV, a Factorial ANCOVA test was conducted. The Levene’s test was nonsignificant (F (3,160) = 2.24, p = .086) indicating that the assumption of homogeneity was met. The assumption of homogeneity of regression slopes is also met (F(3,157) = 1.26, p

=.289, η² = .02). The covariate of education level was significantly related to CHV, F(1,159) =

5.44, p =.021, η² = .03. However, there was no significant interaction found between humor type and visual type on CHV after controlling for the effect of education level, F(1,159) = .33, p

=.568, η² < .01.

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

10. Conclusion and Discussion

The goal of this study was to contribute to the existing line of literature on humor in a new context: webcare. In 2016, a survey held among Dutch webcare practitioners indicated that

29% of the Dutch webcare employees used humor and 62% sometimes used humor in webcare conversations (van Os, Hachmang, Derksen & Keurning, 2017). However, little research had been done on humor and visuals in webcare. Therefore, the aim of this investigation was to explore the usage of humor and humorous visuals in Dutch webcare conversations. More specifically, it was investigated which humor type by Martin et al.

(2003) was most employed and what kind of effect these humor types and visuals type had on reputation and perceived CHV. First, a corpus study was conducted in order to find out what type of humor and what type of visual was most commonly used in Dutch webcare conversations. Second, an experimental study was conducted to examine the effect of most used humor styles and visual types on reputation and perceived CHV. In this chapter, the conclusions are drawn and discussions and implications of the content analysis and experimental study are discussed.

The corpus study has identified self-enhancing humor and affiliative humor as the most used humor types in Dutch webcare conversations. In the sample, a little more than half of the investigated tweets contained a visual. When a visual was used, GIF or a meme was the most frequently used choice. The corpus study also provided insights into the type of joke

(e.g., pun) that were used in webcare conversations. The sample consisted mostly out of exaggeration jokes, puns or jokes that belonged in the other category. Because the category

‘other’ was quite large, a closer look was taken to see if another category could be identified within the ‘other’ category. Besides the category of ‘surprise’, no other category could be defined. The corpus study provided a basis for the experimental study because the most used humor and visual types were used in the experimental study.

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

The experimental study was a survey conducted to test the effect of the different types of humor (i.e., affiliative, and self-enhancing humor) and visual type (i.e., meme or GIF) on

CHV and reputation. Moreover, it was investigated whether sentiment of eWom yielded different scores for humor style and visual type on reputation and perceived CHV.

The analysis on the influence of sentiment on humor and visuals in webcare showed a significant main effect of sentiment and the interaction effect of humor and sentiment for both reputation and CHV. Nevertheless, the results of this investigation showed no significant differences for humor type on reputation and perceived CHV, nor an interaction effect.

Moreover, no significant main effect was found for visual type on reputation but there was a significant effect found between visual type and perceived CHV. Nevertheless, no interaction effect was found between humor or visual type and reputation and perceived CHV. There were also several checks done to see if other variables (e.g., humor preference and age) could explain or influenced these results, but these analyses were also non-significant. These results answer the second research question: do different humor styles and visual types in webcare conversations affect customers’ perceptions concerning corporate reputation and perceived conversational human voice?

The implications and limitations of both studies are discussed below.

10.1. Corpus study: Implications and future research

The fact that affiliative humor and self-enhancing humor were the most used humor styles in the corpus study make sense intuitively. Affiliative humor and self-enhancing humor are considered more tolerant, accepting and relationship enhancing, whereas aggressive and self- defeating humor is considered a more hostile form of humor designed to belittle oneself or others (Martin et al., 2003). One of the goals of webcare is customer service (van Noort,

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Willemse, Kerkhof & Verhoeven, 2014) so it does not seem likely that webcare employees will knowingly use forms of humor that make fun of customers in a malicious way.

The fact that only half of the sample consisted of visuals also raises questions towards the appropriateness of using visuals in webcare conversations. When visuals were used, GIFs were preferred next to memes or regular pictures. A reason why GIFs are employed most is perhaps that Twitter has a GIF search function, which makes it easier to search and select a

GIF than a meme or a picture. Another possible theoretical explanation could be grounded in

Media Richness Theory. Bakhshi and colleagues (2016) explained that due to numerous reasons (e.g., storytelling capabilities), GIFs are considered to be more engaging than other visuals used. Perhaps, these explanations are also the reason why webcare employees prefer to use GIFs over memes.

The corpus study also had some limitation. One limitation is that purposive sampling techniques were used which means that the results cannot be generalized to the population of humorous tweets. However, the objective of the corpus study was to get a better understanding what type of humor styles and humor types are used in webcare conversations.

Thus, it is not that important for the objective of the corpus study that the results are not generalizable. Another observation that was made, was that the ‘other’ group of the type of joke was quite big. When taking a closer look at the group, it appeared that the joke type

‘surprise’ as defined by Taecharungroj and Nueangjamnong (2015) could be distinguished.

Other groups weren’t clearly identified as previous literature has not clearly defined joke types. For example, the study of Buijzen and Valkenburg (2009) examined types of humor used in TV commercials by a list of 41 humor techniques of which seven categories arose.

Humor is subjective and ambiguous, but scholars do not seem to clearly agree on what really constitutes a joke. Therefore, it is important that a literature review will be conducted on

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS humor in several disciplines (e.g., advertising, work environment, interpersonal relations etc.) in order to clearly define what constitutes humor and defines a joke.

10.2. Experimental study: Implications and future research

While humor and visuals in a webcare context have not been studied before, to the author's knowledge, inferences were drawn from the broad line of research on humor in advertising when interpreting the results. Humor in advertising, for example, has been studied extensively and the majority of studies report positive effects of humor in advertising. For instance,

Einsend (2009) found that consumers were more positive towards the ad when humor was used. Strick, van Baaren, Holland and van Knippenberg (2009) showed that when using humor in advertisements, consumers will like the product more by association. This research indicates that humor styles (i.e., affiliative and self-enhancing humor) in webcare does not make a difference in terms of corporate reputation and perceived CHV.

The main analyses of sentiment on humor and visuals in webcare showed a significant main effect of sentiment and a significant interaction effect of humor and sentiment on reputation and perceived CHV. When the means of each sentiment of the webcare conversation was compared, it was revealed that for both reputation and perceived CHV, a neutral sentiment had a significantly lower score compared to positive and negative sentiment.

The mean of positive sentiment was for humor type on reputation and perceived CHV the highest. These results indicate either two things. First, it could mean that it does matter what type of humor you use unless the sentiment of a webcare conversation is neutral. In that case, self-enhancing humor would score higher on reputation and perceived CHV. On the other hand, it could also indicate that the type of joke or the wording of the neutral sentiment was less successful compared to the others which resulted in a lower mean score. The neutral joke that was used was based on a real webcare conversation about a customer who lost his glasses

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS on the train which was adjusted to losing glasses in a plane for this study. The materials that were used were not pre-tested as it was based on real webcare conversations, ensuring the validity of the materials. But even in real webcare conversations, some jokes are not perceived as funny by everyone. A study by Zhang and Zinkhan (2006) argued that the audience to which the humor is communicated to matter as what some individuals perceive as funny, others do not. Therefore, future study should empirically investigate more whether webcare sentiment and humor have an effect on how a joke is perceived.

As for the following analyses, no reliable significant results have been found for the effects of humor on reputation and perceived CHV. Previous literature might explain why no significant results have been found. Besides humor in advertising, very limited research has been done on humor in customer care services, which concludes mixed results. The study by

Mathies, Chiew, and Kleinaltenkamp (2016) showed that customer experience was more likely to improve when employees used humor. However, the recent study by Söderlund,

Oikarinen, and Heikka (2017) revealed that the use of jokes reduced customer satisfaction.

Customer service is one of the main goals of webcare, therefore this study also contributes to the line of research on customer service and humor. While the style of humor does not significantly affect corporate reputation, it is interesting for future studies to find out what role humor in webcare has on customer experience and customer satisfaction.

Moreover, the way that affiliative humor and self-enhancing humor were operationalized and manipulated in our materials might have been too subtle. In contrast, the study of Kuiper and Leite (2010) on personality impressions based on the four humor styles of

Martin et al. (2003) did find a clear distinction between affiliative humor and self-enhancing humor compared to aggressive and self-defeating humor. In addition, Kuiper and Leite (2010) also concluded that affiliative humor led to more positive impressions compared to self- enhancing humor. The difference between the operationalization and implementation of

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS affiliative humor and self-enhancing humor in the study of Kuiper and Leite (2010) and the current study is that Kuiper and Leite (2010) gave the participants an applied definition of the different types of humor before filling out the questionnaire. Doing so, they might have primed the participants to focus on the different types of humor. This study did not give the participant any indication on the humor types that were investigated and even actively tried to hide that the survey was about humor in webcare by adding filler conversations that were non- humor related. Perhaps when the distinction is made more obvious between self-enhancing humor and affiliative humor in a webcare message, a different result may be found. However, it is not recommended to provide the participants with a definition of the different types of humor as they might be primed which makes the results not realistic.

A better suggestion to improve the way the study is conducted to add a control condition where no humor is used. The way the study is currently designed is to test if there are any differences in corporate reputation and perceived CHV for affiliative humor style and self-enhancing humor style. The current study is designed to test if there is a difference between affiliative humor and self-enhancing humor on corporate reputation and perceived

CHV. Consequently, it did not test if humor or no humor have different effects (i.e., does humor on its own have different effects on corporate reputation and CHV?) Therefore, this study is limited to draw conclusions on the effects of only two humor styles.

Regarding visuals, the use of humorous visuals such as GIFs or memes does not affect reputation but does have a small effect on perceived CHV. Thus, according to these results, it does not significantly matter for companies whether affiliative humor or self-enhancing humor is employed in webcare conversations. Moreover, it also does not matter whether a webcare employee uses a GIF or meme in a webcare message for the corporate reputation.

The only significant result of visual type on perceived CHV is somewhat debatable implications. On the one hand, media theories such as Media Richness Theory (Daft &

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Lengel, 1986) but more likely Social Influence Theory (Fulk, Schmitz & Steinfield, 1990) might explain why visuals such as GIFs and memes influence perceived CHV. Media

Richness theory postulates that media can be ‘rich’ or ‘poor’ and this has an influence on how well a task is performed (Daft & Lengel, 1986). However, this theory is more a theoretical concept as it has not been empirically proven (e.g., Dennis & Kinney, 1998; Vickery, Droge,

Stank, Goldsby & Markland, 2004). Social influence theory states that media richness is not part of a medium but a social construct that individuals perceive differently (Fulk, Schmitz &

Steinfield, 1990; Lee, 1994). A study on GIFs by Bakshi, Shamma, Kennedy, Song, de Juan, and Kaye, (2016) did not directly show media richness theory but did confirm that GIFs are valued because they can communicate certain cues such as emotions and gestures. Because

GIFs have this ‘richer’ ability, it might explain why they score higher on perceived CHV.

On the other hand, one might question the reliability of the significant finding of the effect of visual type on perceived CHV with a p-value of .047 and an effect size of η² = .02.

One advantage of conducting the multiple MANOVA analysis is that is controls for type I error compared to when conducting a mixed ANOVA. While this goes for the main analysis, the follow-up analysis still consists of multiple ANOVA’s and are thus more sensitive to type I error

(Field, 2013). Given the barely significant p-value and very small effect size, it is possible that the significant finding of visual type on perceived CHV is a type I error. Especially as three of the seven assumptions of the MANOVA were violated and could not be corrected. Therefore, it cannot be assumed that visual type has an effect on perceived CHV.

An alternative reason no significant results were found for both variables is the way the study was conducted. This study focused on humor and visuals in webcare conversations on

Twitter whereas most studies on online (humorous) visuals have been on platforms such as

Facebook (e.g., Taecharungroj & Nueangjamnong, 2015). In a Facebook webcare message, a customer and a webcare employee are not restricted to a certain amount of characters like on

Twitter. Therefore, Facebook webcare conversations might be longer, contain more

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS information or jokes. As there are no face-to-face cues that can be seen in webcare jokes, one must rely on additional words, visuals, and emoticons. Furthermore, recent numbers suggest that Twitter users are declining. Even in the current research, the number of participants active on Twitter was the lowest compared to the other social media forms.

10.3. Future research

Based on the findings and limitations of the current study, multiple suggestions for future research can be made. While the current study focused on humor in webcare, the situation of when no humor is used to respond to a webcare message has not been considered. It could very well be possible that in some situations, customers do not appreciate the ‘funny’ attempt of a webcare employee. An example of a situation could be when the customer has a serious complaint or does not start the humor in the conversation. Future research should conduct another study in which a no-humor condition is also considered and more questions should focus on identifying in what situations humor is appropriate in a webcare setting. This study also found significant results for the role of sentiment on humor types. Therefore, it is interesting to examine this effect more. Specifically, when compared to the non-humor condition.

As for visuals in webcare conversations, it is interesting to see if different platforms yield different effects. For example, because more people use Facebook compared to Twitter, it might be that consumers prefer visuals on Facebook better than Twitter. Another suggestion for future research is conducting the same research but with different variables. For example, type of company (e.g., governmental agency or a commercial company) might also have different results on the use of humor and visuals. A governmental institution might be considered more serious and therefore less appropriate to use humor and visuals compared to a commercial company.

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

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MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

12. Appendix

Appendix A: Table of Companies of Content Analysis

Table 13

Companies and search query information of content analysis

Name Industry Twitter Handle Search Query N Total

Tweets

Coolblue Electronics @Coolblue_NL site:twitter.com/Coolblue_ 1071

NL author:Coolblue_NL

Tele2 Telecommunications @Tele2Nederland site:twitter.com/Tele2Ned 1316

erland

author:Tele2Nederland

T-mobile Telecommunications @tmoble_webcare site:twitter.com/tmobile_w 2251

ebcare

author:tmobile_webcare

Vodafone Telecommunications @VodafoneNL Site: 1627

twitter.com/vodafoneNL

Author: VodafoneNL

ABN AMRO Financial services @ABNAMRO Site: 638

twitter.com/ABNAMRO

Author: ABNAMRO

Albert Heijn Supermarket Chain @albertheijn site: 1312

twitter.com/albertheijn

author: albertheijn

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Nationale Rail Transport @NS_Online site: 11290

Spoorwegen twitter.com/NS_online

author: NS_Online

Netflix Entertainment @NetflixNL Site: 58

twitter.com/NetflixNL

author: NetflixNL

Efteling Amusement Park @Efteling site: twitter.com/Efteling 934

author:Efteling

Ziggo Telecommunications @ZiggoWebcare site:twitter.com/ZiggoWeb 4595

care author:

ZiggoWebcare

Appendix B: Codebook Content Analysis Humor in Webcare Conversations

This codebook is intended for coding humorous webcare messages by the company itself (i.e., humorous responses of consumers are not taken into account) on Twitter. This means that only the first tweet of the company that contains humor needs to be coded. The guideline is to first read the webcare conversation completely and then start coding. For each code there is a specific guideline, so please read the guideline first and code according to the guideline.

Tweet number

Please number the tweets for reference. The sample consists of 100 tweets, so the first tweet that is coded needs to be coded as 1, the second tweet as 2 and so on.

Coder

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Please code the coder of the webcare conversation.

0 = EVDW

1 = VLD

Link Tweet

Paste the URL of the webcare conversation that will be coded.

Date Tweet

Mention the date of the webcare conversation. If the webcare conversation covers two or more days then give the date range (e.g., 10-03-2017 – 12-03-2017).

Company

If there is an interaction concerning two companies, code for the initial company.

1 = Coolblue

2 = Tele2

3 = T-Mobile NL

4 = Vodafone NL

5 = ABN AMRO

6 = Albert Heijn

7 = NS

8 = Netflix

9 = Efteling

10 = Ziggo

Sentiment of the beginning of the webcare conversation

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Indicate the sentiment of the cause of the tweets. So, indicate the sentiment for the reason why a consumer sends a tweet to the company. For example, if an individual sends a tweet with a complaint then the sentiment needs to be coded as negative.

0 = negative

1 = positive

2 = neutral

3 = no sentiment (when parts of the webcare conversation are missing and it starts with a humorous response of the company).

Humor started by the consumer

Indicate if the WOM already contains humor before the company implements humor. So, if the consumer starts with a joke then code for ’yes’ and if the consumer does not start with a joke then code for ‘no’.

0 = no

1 = yes

The moment of humor implementation

Indicate for each company when the humor is implemented in the webcare conversation. If it is in the first two messages then code 0, if it is in the middle of the conversation (everything between the first two and final two messages) then code 1, and if it is in the last two messages then code 2. If the company uses humor in more messages in the webcare conversation then only code for the first humorous message.

0 = in the first two messages

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

1 = middle (everything between the first two and final two messages)

2 = in the final two messages

Example: the example shows a webcare conversation that should be coded with “2” since the humor implemented by the company takes place in the final two messages.

Humor style

The coding of these different types of humor is based on the 2x2 model of Martin et al.

(2003). This model is based on the valence of the humor and the direction of the humor.

Therefore, it is asked to code the valence of the humor in the webcare conversation and the direction of the humor in the webcare conversation. A combination of the valence and the direction will lead to a specific type of humor.

Valence of the humor

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Indicate for each humorous message if the humor is either positive or negative. A combination of both is not allowed, so either code positive or negative. A negative valence means that the joke revolves around negative topics (in the example the humor about stalking, which is a negative topic). A positive valence means that the joke revolves around positive topics (in the example the humor is about the sarcastic reference to the ‘friend’ that is actually the person himself).

0 = negative (e.g., Nuon below)

1 = positive (e.g., Etos below)

Example:

Direction of the humor

Indicate for each humorous message if the humor is pointed towards the consumer or the company itself. A combination of both is not allowed, so either code that the humor is pointed towards the consumer or towards the company. The example of Nuon indicates that Nuon is the stalker, so the humor is about the company Nuon itself. In contrast, in the example of Etos the subject of the joke is the consumer because the ‘friend’ in the joke is the consumer himself. Therefore, the humor is aimed at the consumer.

0 = towards the company (e.g., Nuon below)

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

1 = towards the consumer (e.g., Etos below)

Example:

Type of joke

Indicate for each humorous message what type(s) of joke is present in the humorous webcare message. Either pick “present” or “not present” for each type of joke. The types of jokes that are distinguished are: pun, sarcasm, personification, exaggeration, and other. It is possible to have more than one type of joke in one humorous webcare message. In addition, when the types of jokes are not applicable for the humorous webcare message please indicate “1” in the

“other” row. The examples implemented below can also include more types of jokes.

0 = not present

1 = present

A short explanation and example of each type of joke is included below

Pun: features of language are used in order to create the humor, so language and the unusual use of language make the webcare message humorous (Taecharungroj and Nueangjamnong,

2015).

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Example:

Sarcasm: the use of sarcasm makes the webcare message humorous. This means that the polarity of a message is flipped (Kunneman et al., 2015), which means that the sender uses positive words or elements while the meaning of the message is negative and vice versa

(Taecharungroj & Nueangjamnong, 2015). So, the sender does not mean what he/she specifically says (Taecharungroj & Nueangjamnong, 2015).

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Example:

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Personification: features of humans are given to objects, plants, and animals. This means that these objects, plants, and/or animals are made human and that therefore the message is humorous (Taecharungroj & Nueangjamnong, 2015).

Example:

Exaggeration: something gets exaggerated and overdone, so the webcare message is humorous because the sender is exaggerating something (Taecharungroj & Nueangjamnong,

2015).

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Example:

Other: please code ‘other’ when none of these types of jokes are suited for the humorous webcare message.

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Gender of sender

Indicate for each company what the gender of the sender is who composed the humoristic webcare message. First, try to see if the webcare message by the company ends with the person’s name (full name since initials do not indicate someone’s gender). Second, see if the responder included a picture of him/herself. If the name and/or picture are doubtable or the sender uses initials then please code 2. However, when a name is doubtable please use the following link in order to see if it is a common name for either a boy or a girl: https://www.svb.nl/int/nl/kindernamen/. If it is a common name for either a boy or a girl the coder can still code either 0 or 1. If there is no signature at all then please code 3. Only code for 0 or 1 when the gender is obvious or when the name can be found by using the link. The example below shows that the message ends with ‘^Marlies’, this is not a common name for men so this should be coded as 1.

0 = male

1 = female

2 = use of initials and/or name/picture is doubtable

3 = no signature

Example:

Gender of the receiver

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Indicate for each receiver of the message if the gender of the receiver is male or female. The receiver is the person to whom the humor is addressed to. First, try to look at the person’s name in order to indicate the gender. Second, try to see if the picture is in line with the gender you wanted to code for. If the name and picture contradict each other please code 2. Please only code for 0 or 1 when the gender of the receiver is obvious or, in this case, when there is doubt the following link can be used as well: https://www.svb.nl/int/nl/kindernamen/. If it is a common name for either a boy or a girl according to the website the coder can still code either

0 or 1. When there is a case in which no name and picture are included please code 3. The example obviously shows (by picture and name ‘Rik’) that the receiver is a man so this should be coded as 0.

0 = male Example:

1 = female

2 = doubtable

3 = not traceable

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Visual

Indicate for each humorous webcare message if the company implements a visual. A profile picture and emoticon does not count as a visual. Pick “not present” when the webcare message of the company does not contain a visual and only text is implanted in the webcare message. Pick “present” when the webcare message of the company does contain a visual, even when it also contains text.

0 = not present

1 = present

Type visual

Indicate for each humorous webcare message if the visual is moving or not moving. If there is no visual in the humorous webcare message please indicate this again by coding 2.

0 = not moving

1 = moving

2 = no visual present

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Text in visual

Indicate for each humorous webcare message if the visual contains text. If there is no visual at all in the humorous webcare message please indicate this again by coding 2. The example below shows a visual that contains text, which should be coded as 1.

0 = not present

1 = present

2 = no visual present

Example:

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Presence of the joke in either text and/or visual

Indicate where the humor is implemented in the humorous webcare message. Code 0 if the humor is only applied in the text, and code for 1 if the humor is only applied in the visual. If the humor is applied in the text AND in the visual (so the combination of both reinforces the humor) please code for 2. The example shows that the humor is only applied in the visual so this needs to be coded as 1.

0 = present in text

1 = present in visual

2 = combination of text and visual

Example:

Retweets and likes

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Indicate how many retweets and likes the humorous message received in numbers. The example below shows where the likes and retweets can be found. The heart indicates the likes of the tweet and the arrows indicate the retweets. For this example, the coder should code 424 for the retweet variable and 784 for the like’s variable.

Example:

Appendix C: Intercoder reliability

Table 14

Intercoder Reliability using Cohen’s Kappa.

Item κ-value p-value Judgement

Sentiment 1.00 <.001 Very good

Humor in eWOM 1.00 <.001 Very good

Valence .46 .02 Moderate

Valence* 1.00 <.001 Very good

Direction .63 .01 Good

Pun .61 .01 Good

Sarcasm .58 .004 Moderate

Sarcasm* .64 .002 Good

Personification 1.00 <.001 Very good

Exaggeration .88 <.001 Very good

Other .57 .01 Moderate

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Other* .79 <.001 Good

Gender company 1.00 <.001 Very good

Gender consumer .90 <.001 Very good

Visual 1.00 <.001 Very good

Type visual 1.00 <.001 Very good

Text visual .92 <.001 Very good

Where joke 1.00 <.001 Very good

Moment of humor 1.00 <.001 Very good

Appendix D: Frequencies tables of content study

Table 15

Frequencies of the sentiment of the webcare conversations

Sentiment N

Negative 30

Positive 37

Neutral 31

No sentiment 2

Note: No sentiment means that due to a glitch in Coosto, the webcare conversation prior to the joke could not be distributed.

Table 16

Frequencies of humor in eWOM

Humor in eWOM N

No 73

Yes 27

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Table 17

Frequencies of type of humor

Type of humor N

Self-enhancing 19

Affiliative 72

Aggressive 8

Self-defeating 1

Table 18

Frequencies of type of joke

Type of joke N

Pun 21

Sarcasm 18

Personification 7

Exaggeration 27

Other 42

Table 19

Frequencies of gender of consumer

Gender of consumer N

Male 60

Female 30

Doubtable 5

Not traceable 5

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Note: Doubtable means that the name could be either a boys or girls name and the gender was not clear on the picture. Not traceable means that no actual name was used and the gender was not clear on the picture.

Table 20

Frequencies of gender of company

Gender of company / employee N

Male 25

Female 10

Use of initial and/or doubtable name/picture 36

No signature 29

Table 21

Frequencies of how many times visuals were present.

Visual present N

Not present 44

Present 56

Table 22

Frequencies of type of visual

Type of visual N

Meme 11

GIF 36

Picture 9

No visual 44

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Table 23

Frequencies of moment when humor was used

Moment humor N

First two messages 60

Middle 14

Final two messages 26

Table 24

Frequencies of where humor was used

Place of humor N

Present in text 42

Present in visual 26

Combination text and visual 32

Appendix E: Contingency tables of joke type

Table 25

Contingency table of humor styles and pun

Pun

Humor style Not Present (N = 79) Present (N = 21)

Self-enhancing 12 (63%) 7 (37%)

Affiliative 58 (81%) 14 (19%)

Self-defeating 1 (100%) 0 (0%)

Aggressive 8 (100%) 0 (0%)

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Table 26

Contingency table of humor styles and sarcasm

Sarcasm

Humor style Not Present (N = 82) Present (N = 18)

Self-enhancing 18 (95%) 1 (5%)

Affiliative 62 (86%) 10 (19%)

Self-defeating 1 (100%) 0 (0%)

Aggressive 8 (100%) 0 (0%)

Appendix F: Stimuli of sentiment, humor style, and visual type.

The visuals were added below the manipulated tweets in Qualtrics. The visual style was linked to the sentiment of the tweet. As GIFs cannot be displayed in this document so an URL to each GIF is provided.

Memes

Figure 3. Meme for negative sentiment tweet in affiliative humor and self-enhancing humor condition.

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Figure 4. Meme for neutral sentiment tweet in affiliative humor and self-enhancing humor condition.

Figure 5. Meme for positive sentiment tweet in affiliative humor and self-enhancing humor condition.

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Figure 6. Meme for positive sentiment tweet in filler stimuli.

GIFs

Table 27

Table containing URLs of GIFs used in the survey.

Stimuli GIF URL

Negative sentiment – affiliative and self- http://gph.is/146qwpF

enhancing condition

Neutral sentiment – affiliative and self- http://gph.is/2cVKFMB

enhancing condition

Positive sentiment – affiliative and self- http://gph.is/1qdls3c

enhancing condition

Positive sentiment – filler condition http://gph.is/291u1MC

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Manipulated Tweets

Figure 7. Stimuli containing negative sentiment and in the affiliative humor condition.

Figure 8. Stimuli containing neutral sentiment and in the affiliative humor condition.

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Figure 9. Stimuli containing positive sentiment and in the affiliative humor condition.

Figure 10. Stimuli containing negative sentiment and in the self-enhancing humor condition.

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Figure 11. Stimuli containing neutral sentiment and in the self-enhancing humor condition.

Figure 12. Stimuli containing positive sentiment in the self-enhancing humor condition.

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Figure 13. Filler conversation with negative sentiment.

Figure 14. Filler conversation with the neutral sentiment.

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Figure 15. Filler conversation with positive sentiment.

Appendix G: Measures statements

The following scale is adopted from the reputation scale of Walsh, Mitchel, Jackson, and

Beaty (2009).

Items that measured the reputation of Pennairways

1. PennAirways behandelt zijn klanten op een eerlijke manier.

2. PennAirways is een sterk, betrouwbaar bedrijf.

3. Ik zou PennAirways aanraden aan vrienden en kennissen.

4. Pennairways lost problemen snel en goed op.

5. PennAirways neemt de rechten van de klant serieus.

6. De medewerkers van PennAirways zijn gefocust op de behoefte van de klant.

The following scale is adopted from the perceived CHV scale of Kelleher (2009).

Items that measured perceived CHV of Pennairways

1. Met deze reactie staat PennAirways open voor een dialoog.

2. Met deze reactie gebruikt PennAirways een gespreksstijl die je ook in een face-to-face

gesprek zou kunnen hebben.

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

3. Met deze reactie communiceert PennAirways op een menselijke manier.

4. Met deze reactie toont PennAirways interesse voor communicatie.

5. Met deze reactie gebruikt PennAirways humor in de communicatie.

6. PennAirways maakt de communicatie aangenaam.

The following scale is adopted from the Humor Style Questionnaire scale of Martin et al.,

(2003).

Items that measured the level of self-enhancing humor of the participants

1. Als ik mij depressief voel, kan ik mezelf vaak opvrolijken met humor.

2. De goede manier om met problemen om te gaan is te bedenken wat het meest grappige

aspect in een situatie is.

3. Ik hoef niet met andere mensen te zijn om me te amuseren. Ik kan vaak dingen vinden

om te lachen, zelfs als ik alleen ben.

Items that measured the level of affiliative humor of the participants.

1. Ik lach en maak vaak grappen samen met mijn beste vriend(en).

2. Ik vind het leuk om andere mensen te laten lachen.

3. Ik maak grappen om de band met andere te versterken.

Appendix H: Survey screenshots

Beste Participant,

Hartelijk bedankt dat u mee wilt doen aan dit onderzoek. Dit onderzoek wordt gehouden als onderdeel van mijn afstudeertraject voor de Master Communicatie- en

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Informatiewetenschappen. Mijn onderzoek gaat over ‘Webcare’, oftewel, online gesprekken tussen consumenten en bedrijven. Het invullen van deze enquête duurt 7 - 10 minuten.

U krijgt zodadelijk zes Twitter-gesprekken te zien tussen een consument en het fictieve bedrijf PennAirways. Lees elk gesprek nauwkeurig door. We vragen u om na elk gesprek een aantal stellingen in te vullen. Er is geen goed of fout antwoord, u mag gewoon invullen wat u denkt dat juist is.

De enquête is geheel anoniem en uw gegevens zullen vertrouwelijk worden behandeld. Uw antwoorden en gegevens zullen enkel voor mijn afstudeeronderzoek worden gebruikt. U kunt altijd besluiten tijdens het onderzoek om te stoppen of later terugkeren om verder te gaan.

Mocht u nog vragen of opmerkingen hebben, schroom dan niet om een e-mail te sturen naar

Alvast bedankt.

Evelien van der Wel

Ik neem vrijwillig deel aan dit onderzoek en geef toestemming dat mijn gegevens anoniem worden gebruikt voor alleen academische doeleinden.

o Ja (1)

o Nee (2)

Skip To: End of Survey If Consent = Nee (2)

95 MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

U krijgt zo zes twittergesprekken te zien tussen een (potentiële) klant en het fictieve vluchtmaatschappij PennAirways. Lees elk gesprek alstublieft goed en geef dan aan wat u vindt van de reactie van PennAirways en uw indruk van PennAirways als bedrijf.

In sommige twitterreacties komen afbeeldingen of video's voor. Bij een aantal van de tweets die u zult zien is dat ook zo. Helaas kan in deze survey niet de oorspronkelijke tweet met afbeelding getoond worden. Daarom ziet u hieronder eerst de Tweet en vervolgens de afbeelding die normaliter in de Tweet is opgenomen. Soms kan het enkele seconden duren voordat de afbeelding is geladen.

Ik heb de bovenstaande tekst gelezen.

o Ja

Klik op de pijltjes >> om te beginnen.

Condtie: Affiliative Conditie: Self-enhancing

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

GIF LINK: http://gph.is/146qwpF

Geef alstublieft aan in hoeverre u het eens of oneens bent met de volgende stellingen.

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Conditie: Affiliative Conditie: Self-enhancing

GIF LINK: http://gph.is/2cVKFMB

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Geef alstublieft aan in hoeverre u het eens of oneens bent met de volgende stellingen.

100

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Conditie: Affiliative Conditie: Self-enhancing

GIF LINK: http://gph.is/1qdls3c

101

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Geef alstublieft aan in hoeverre u het eens of oneens bent met de volgende stellingen.

102

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Filler: Negative sentiment

Geef alstublieft aan in hoeverre u het eens of oneens bent met de volgende stellingen.

103

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Filler: Neutral Sentiment

Geef alstublieft aan in hoeverre u het eens of oneens bent met de volgende stellingen.

104

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Geef alstublieft aan in hoeverre u het eens of oneens bent met de volgende stellingen.

105

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Filler: Positive sentiment

GIF LINK: http://gph.is/291u1MC

Geef alstublieft aan in hoeverre u het eens of oneens bent met de volgende stellingen.

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Geef alstublieft aan in hoeverre u het eens of oneens bent met de volgende stellingen.

U bent bijna op het einde van deze enquête. Wilt u de onderstaande vragen over uzelf invullen? Deze informatie is volledig anoniem en blijft vertrouwelijk.

Wat is uw leeftijd? (in cijfers)

Wat is uw geslacht?

o Man (1)

o Vrouw (2)

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MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Wat is uw hoogst afgeronde opleiding of de opleiding waar u momenteel mee bezig bent?

o VMBO (1)

o HAVO (2)

o VWO (3)

o MBO (4)

o HBO Bachelor (5)

o HBO Master (6)

o WO Bachelor (7)

o WO Master (8)

o PhD (9)

o Anders: (10) ______

Was u bekend met wat 'Webcare' was voordat u begon aan deze enquête?

o Ja (1)

o Nee (2)

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Geef aan in uw eigen woorden wat u denkt dat 'Webcare' is.

Heeft u zelf weleens geparticipeerd aan webcare gesprekken?

o Ja (1)

o Nee (2)

Display This Question: Als Heeft u zelf weleens geparticipeerd aan webcare gesprekken? Ja is geselecteerd

Zo ja, kunt u kort beschrijven waarover deze webcare gesprekken gingen?

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Sommige beelden die u heeft gezien bevatten een tekst in het Engels. Kon u deze tekst goed begrijpen?

o Ja, ik begreep wat er stond (1)

o Nee, ik vond het lastig. (2)

o Anders: (3) ______

Geef alstublieft aan welke social media sites u dagelijks bezoekt. Er zijn meerdere antwoorden mogelijk.

▢ Facebook (1)

▢ Instagram (2)

▢ Twitter (3)

▢ Snapchat (4)

▢ Anders: (5) ______

Klik nu op >> om naar de volgende pagina te gaan.

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Heeft u nog vragen of opmerkingen over dit onderzoek? Vul ze hieronder in.

Dit is het einde van deze enquête, bedankt voor uw medewerking. Als u op de pijltjes (>>) onderaan klikt, verzendt u de vragenlijst naar de onderzoekers toe.

Appendix I: Normality tests of various variables

Table 28.

Kolomogorov-Smirnov normality test of reputation for independent variables

Variable df D p

Gender: Man 73 .07 .200

Gender: Female 93 .09 .084

Education level: 45 .07 .200

Low

Education level: 61 .07 .200

Middle

Education level: 58 .11 .066

High

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Familiar with 98 .07 .200

webcare: yes

Familiar with 68 .07 .200

webcare: no

Participation 55 .09 .200

webcare: yes

Participation 111 .07 .169

webcare: no

Understood English 164 .07 .051

Usage of Facebook 153 .06 .200

daily: yes

Usage of Facebook 13 .09 .200

daily: no

Usage of Instagram 91 .05 .200

daily: yes

Usage of Instagram 75 .11 .022

daily: no

Usage of Twitter 35 .17 .013

daily: yes

Usage of Twitter 131 .06 .200

daily: no

Usage of Snapchat 65 .07 .200

daily: yes

Usage of Snapchat 101 .10 .013

daily: no

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Affiliative humor 85 .06 .200

Self-enhancing 81 .09 .086

humor

GIF 86 .07 .200

Meme 80 .11 .025

Table 29.

Kolomogorov-Smirnov normality test of perceived CHV for independent variables

Variable df D p

Gender: Man 73 .090 .200

Gender: Female 93 .10 .016

Education level: 45 .12 .114

Low

Education level: 61 .09 .200

Middle

Education level: 58 .07 .200

High

Familiar with 98 .09 .033

webcare: yes

Familiar with 68 .08 .200

webcare: no

Participation 55 .10 .200

webcare: yes

Participation 111 .12 .001

webcare: no

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Understood English 164 .09 .001

Usage of Facebook 153 .09 .003

daily: yes

Usage of Facebook 13 .19 .200

daily: no

Usage of Instagram 91 .10 .024

daily: yes

Usage of Instagram 75 .09 .200

daily: no

Usage of Twitter 35 .14 .086

daily: yes

Usage of Twitter 131 .10 .003

daily: no

Usage of Snapchat 65 .09 .200

daily: yes

Usage of Snapchat 101 .09 .036

daily: no

Affiliative humor 85 .11 .021

Self-enhancing 81 .09 .100

humor

GIF 86 .09 .093

Meme 80 .11 .023

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Appendix J: Statistical checks of evenly distributed data

Before any statistical test were done, the scales for reputation and CHV were recoded and computed. Moreover, chi-squares and two-way ANOVA’s were conducted to check whether the participants were evenly distributed over the humor type and visual type conditions.

Overall, the participants were evenly distributed over the humor and visual type conditions

(χ2(1) = .09, p = .765). Furthermore, the results indicated that all participants were evenly distributed in the humor type condition: gender (χ2(1) = .1.88, p = .171), age (F(1, 162) =

1.48, p = .226, η2 = .009), education level (χ2(2) = 1.18, p = .555), if the participant knew what webcare was prior to the survey (χ2(1) = 1.01, p = .315), if the participant ever participated in webcare (χ2(1) = 2.90, p = .089), if the participant understood the English in the visuals (χ2(2) = 2.00, p = .367), if they used Facebook on a daily basis (χ2(1) = 1.834, p =

.176), if they used Instagram on a daily basis (χ2(1) = 2.84, p = .092), if they used Twitter on a daily basis (χ2(1) = .54, p = .464), if they used Snapchat on a daily basis (χ2(1) = 2.25, p =

.133) and if they used another type of social media on a daily basis (χ2(1) = 1.43, p = .231). In addition, all participants were evenly distributed in the visual type condition as well with one exception (gender (χ2(1) = .06, p = .798), age (F(1,162) = 1.52, p = .220, η2 = .009), education level (χ2(2) = 2.36, p = .307), if the participant knew what webcare was prior to the survey

(χ2(1) = .06, p = .808), if the participant ever participated in webcare (χ2(1) = 2.20, p = .138), if the participant understood the English in the visuals (χ2(2) = 2.18, p = .337), if they used

Facebook on a daily basis (χ2(1) = 1.834, p = .176), if they used Instagram on a daily basis

(χ2(1) = .07, p = .789), if they used Twitter on a daily basis (χ2(1) = .00, p = .960), if they used

Snapchat on a daily basis (χ2(1) = .55, p = .459) and if they used another type of social media on a daily basis (: χ2(1) = .04, p = .848). The only exception in the visual type distribution were the daily Facebook users whom were not evenly distributed (χ2(1) = 4.66, p = .031). In total, 92.2% of the participants used Facebook on a daily basis whereas 7.8% did not. The

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MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS odds of participants using Facebook on a daily basis are 3.95 times higher than participants not using Facebook on a daily basis.

Appendix K: Demographic Variables

Prior to the statistical testing of the hypotheses, several demographic variables such as gender, age, and education level were checked to see if these variables had an influence on the dependent variables of reputation and perceived CHV. The only variable that was not checked was whether the participants understood the English level of the visuals. There was not enough data available to do so (i.e., only one participant indicated that he or she didn’t understand the level of English and elaborated in the comments that he or she did so because the participant did not pay attention to this).

There were no effects found between reputation and gender, education level, whether participants used Instagram, Twitter or other social media on a daily basis: gender (Mdif = -

.15, t(164) = -.94, p = .350, r = .07), education level (F(2, 163) = 1.90, p = .153, r = .15), whether participants used Instagram on a daily basis (Mdif = .28, t(164) = 1.78, p = .077, Bca

95% CI [-.05, .60] r = .14), whether participants used Twitter on a daily basis (Mdif = .23, t(164) = 1.19, p = .237, Bca 95% CI [-.14, .55] r = .09) or whether participants used another social media on a daily basis (Mdif = -.40, t(164) = -1.79, p = .075, r = .14). There was a significant influence found between reputation and age, whether the participant knew what webcare was before the survey, whether the participant participated in a webcare conversation before, if the participant used Facebook and Snapchat on a daily basis: age (b = -.01, β = -.17, t(164) = -2.24, p = .026, r2 = .03, F (1, 164) = 5.08), whether the participant knew what webcare was before the survey (Mdif = .43, t(164) = 2.72, p = .007, r = .21), whether participants participated in a webcare conversation before the survey (Mdif = .45, t(164) =

2.72, p = .007, r = .21), if the participant used Facebook on a daily basis (Mdif = .99, t(164) =

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3.48 p = .001, , r = .26), and if the participant used Snapchat on a daily basis (Mdif = .46, t(164) = 2.81, p = .006, Bca 95% CI [.14, .77], r = .14). This indicates that the groups in these variables scored significantly different on the reputation scale and thus have an influence on reputation. Therefore, these variables should be taken into account as covariables in the main analysis of reputation. However, a requirement for covariables are that the variables are measured on interval or ratio level. This is only the case for age, therefore only age will be concluded as covariable.

The demographic variables were also checked against perceived CHV. The variables of gender, participants who used Instagram, Twitter and other social media on a daily basis had a non-significant effect on perceived CHV: gender (Mdif = -.09, t(164) = -.57, p = .568,

Bca 95% CI [-.40, .20], r = .04), if participants used Instagram on a daily basis (Mdif = .26, t(164) = 1.63, p = .106, Bca 95% CI [-.07, .59], r = .13), if participants used Twitter on a daily basis (Mdif = .29, t(164) = 1.47, p = .143, Bca 95% CI [-.06, .63], r = .11), and if participants used other social media on a daily basis (Mdif = -.43, t(164) = -1.91, p = .058,

Bca 95% CI [-.97, .06], r = .15). For the variables of education level, age, if participants knew what webcare was before the survey, if participants participated in webcare conversations before and if participants used Facebook and Snapchat on a daily basis, a significant influence was found: education level (Welch’s F(2, 96.04) = 5.16, p = .007, r =

.22), age (b = -.02, β = -.24, t(164) = -3.15, r2 = .06, F (1, 164) = 9.94, p = .002), if participants knew what webcare was prior to the survey (Mdif = .40, t(121.51) = 2.37, p =

.019, Bca 95% CI[.08, .73], r = .21), if the participants participated in webcare conversations before (Mdif = .41, t(164) = 2.41, p = .017, Bca 95% CI [.11, .71], r = .18), if the participants used Facebook on a daily basis (Mdif = 1.33, t(164) = 4.73, p < .001, Bca 95% CI [.70, 1.99], r = .35) and if the participants used Snapchat on a daily basis (Mdif = .41, t(161.17) = 2.71, p

= .007, Bca 95% CI [.09, .74], r = .21). This indicates that for these variables, the groups

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MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS within these variables scored significantly different from each other which means that these variables have different effects on perceived CHV. For this reason, these variables need to be taken into account as covariables in the main analysis of perceived CHV. However, as a requirement is that covariables are measured at a continuous level, only education level and age are taken into account as covariables.

Appendix L: Assumptions of a mixed MANOVA

In order to test the hypotheses, two mixed MANOVAs and factorial MANCOVA was performed in which the within-subjects variable of sentiment (negative, neutral or positive) and the between-subjects variables of humor style (affiliative or self-enhancing) and visual type (Meme or GIF) were measured against reputation and perceived CHV. The statistical test of MANOVA was specifically chosen instead of a mixed ANOVA because when conducting multiple tests on the same data increases the chances of Type I errors. Moreover, with a

MANOVA test, the possible relationship between the dependent variables of reputation and perceived CHV are also investigated (Field, 2013). Before these tests were performed, several assumptions had to be checked which are discussed here. First, it was checked whether the dependent variables of reputation and perceived CHV were correlated as this is a requirement if you can perform the MANOVA. A bootstrapped correlation analysis showed that reputation and perceived CHV are strongly correlated, r = -.84, 95%CI [.79, .88], p < .001.

Assumptions of reputation

A mixed MANOVA has the same assumptions as a factorial ANOVA and a few additional ones. The assumption of normality was tested for all within-subjects and between-subjects variables. The results are displayed in Table 30.

Table 30

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Kolmogorov-Smirnov Normality tests for all within-subjects and between-subjects variables of reputation.

Variable df D p

Humor style: 85 .06 .200

Affliative

Humor style: Self- 81 .09 .086

enhancing

Visual type: GIF 86 .07 200

Visual type: Meme 80 .11 .025

Sentiment: Negative 166 .08 .006

Sentiment: Neutral 166 .06 .200

Sentiment: Positive 166 .09 .005

As can be seen, the assumption of normality was met for humor style. Nevertheless, the variables of visual type and sentiment all violated the assumption of normality. However, similar to an ANOVA, a MANOVA is fairly robust against the violation of normality if the sample sizes are roughly equal, which is the case. Nevertheless, the results of the MANOVA should be interpreted with caution. The assumption of independent observations was also met because the participants took the survey on their own device in their own time. The assumption of the MANOVA that the data must be randomly sampled at least at an interval level was also met because participants were randomly sorted into one of the four conditions.

The assumption of multivariate normality was checked by assessing at the highest

Mahalanobis distance. The Mahalanobis distance was 19.60 which is below the critical value of 20.52, indicating that this assumption is met. The assumption of equal covariance matrices was checked using Box’s M test. Box’s M had a significant value of 35.09 which was

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MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS combined with a p-value of .013 which indicates that the assumption of equal covariance matrices is not met. However, the group sizes are roughly equal thus it can be assumed that the Pillai statistics are robust. But caution with the interpretation is advised. In sum, all assumptions for reputation of a MANOVA are met except for the assumption of normality and the assumption of equal covariance matrices.

Assumption of perceived CHV

The assumption of independent observations and the assumption of random sampling were the same as for reputation (i.e., these assumptions are met). The assumption of normality was tested via the Kolmogorov-Smirnov test and displayed in Table 31. As can be seen, the assumption of normality is violated for humor style, visual type, and sentiment.

Table 31.

Kolmogorov-Smirnov Normality tests for all within-subjects and between-subjects variables of perceived CHV.

Variable df D p

Humor style: 85 .11 .021

Affliative

Humor style: Self- 81 .09 .100

enhancing

Visual type: GIF 86 .09 .093

Visual type: Meme 80 .107 .023

Sentiment: Negative 166 .15 <.001

Sentiment: Neutral 166 .09 .002

Sentiment: Positive 166 .15 <.001

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The assumption of multivariate normality was assessed by checking the highest

Mahalanobis distance. The Mahalanobis distance was 20.63 which is a little higher than the critical value of 20.52. This means that the assumption of multivariate normality was not met for the mixed MANOVA of perceived CHV. Box M’s test was used to assess the assumption of equal covariance matrices, which showed a significant value of 44.45 combined with a p- value of .001. This indicates that the assumption of equal covariance matrices is not met either. All in all, three assumptions were violated which means that the results of this test should be interpreted with extreme caution.

Appendix M: MANCOVA analysis

The Levene’s test for reputation (F (3,162) = .761, p = .517 and CHV (F (3,162) =

1.25, p = .294) both show a non-significant result, indicating that the assumption of homogeneity is met. The main effects and interaction effects of the independent variables humor style and visual style and covariates age and HSQ on the dependent variables of reputation and perceived CHV of the multivariate test are displayed in Table 29.

Table 32

Main effects and interaction effect of MANCOVA of age, HSQ self-enhancing, HSQ affiliative, humor type and visual type.

Effect df1 df2 F p Pillai’s Trace η²

Age 2 158 3.52 .032 .04 .04

HSQ Self-enhancing 2 158 1.99 .140 .03 .03

HSQ Affiliative 2 158 7.61 .001 .09 .09

Humor Type 2 158 2.93 .056 .04 .04

Visual type 2 158 2.22 .112 .03 .03

Humor type * Visual Type 2 158 1.70 .187 .02 .02

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Note: p-values in bold indicate a significant effect.

The result of the interaction effect as described in Table 32 are non-significant which indicates that the covariates of age and HSQ do not significantly account for a part of the variance of humor type and visual type on reputation and CHV.

Appendix N: Correlation analysis of humor style and sentiment

Table 33

Correlation analysis of the different sentiments and different humor styles – Reputation.

Affiliative Negative Neutral

r (p) r (p)

Negative 1

Neutral .47 (<.001) 1

Positive .54 (<.001) .24 (.029)

Self-enhancing

Negative 1

Neutral .63 (<.001) 1

Positive .68 (<.001) .61 (<.001)

Note: all p-values in bold are significant

Table 34

Correlation analysis of the different sentiments and different humor styles – CHV.

Affiliative Negative Neutral

r (p) r (p)

Negative 1

Neutral .41 (<.001) 1

Positive .52 (<.001) .40 (.029)

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Self-enhancing

Negative 1

Neutral .64 (<.001) 1

Positive .64 (<.001) .52 (<.001)

Note: all p-values in bold are significant

The results indicate that the sentiments for both self-enhancing and affiliative humor for reputation and CHV are not very highly correlated but very significant. The means and standard deviations are displayed in Table 32.

Table 35.

Means and standard deviations for reputation.

Variable N M SD

Affiliative – Negative – Reputation 85 4.46 1.39

Affiliative – Neutral – Reputation 85 3.50 1.30

Affiliative – Positive – Reputation 85 4.81 .93

Self-enhancing – Negative - Reputation 81 4.58 1.33

Self-enhancing – neutral – reputation 81 4.07 1.34

Self-enhancing – positive – reputation 81 4.77 1.05

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Appendix O: Means and Standard Deviation of humor type per sentiment and visual type for reputation.

Table 36.

Means and SD of humor type per sentiment and visual type for reputation.

Sentiment Humor type Visual type N M SD

Negative Affiliative GIF 45 4.54 1.43

Neutral Affiliative GIF 45 3.61 1.33

Positive Affiliative GIF 45 4.89 .75

Negative Affiliative Meme 40 4.38 1.35

Neutral Affiliative Meme 40 3.37 1.26

Positive Affiliative Meme 40 4.71 1.10

Negative Self- GIF 41 4.74 1.08

enhancing

Neutral Self- GIF 41 4.05 1.25

enhancing

Positive Self- GIF 41 4.89 .89

enhancing

Negative Self- Meme 40 4.40 1.55

enhancing

Neutral Self- Meme 40 4.09 1.45

enhancing

Positive Self- Meme 40 4.65 1.18

enhancing

124

MASTER THESIS VISUAL HUMOR IN WEBCARE CONVERSATIONS

Appendix N: Means and Standard Deviation of humor type per sentiment and visual type for CHV.

Table 37.

Means and SD of humor type per sentiment and visual type for CHV.

Sentiment Humor type Visual type N M SD

Negative Affiliative GIF 45 5.11 1.34

Neutral Affiliative GIF 45 4.31 1.49

Positive Affiliative GIF 45 5.58 .80

Negative Affiliative Meme 40 4.73 1.36

Neutral Affiliative Meme 40 3.63 1.50

Positive Affiliative Meme 40 5.27 1.29

Negative Self- GIF 41 5.10 .95

enhancing

Neutral Self- GIF 41 4.65 1.27

enhancing

Positive Self- GIF 41 5.70 .82

enhancing

Negative Self- Meme 40 4.84 1.50

enhancing

Neutral Self- Meme 40 4.69 1.46

enhancing

Positive Self- Meme 40 5.38 1.03

enhancing

125