Social Media Channel Differentiation
___________________________________________________________________________________________ ________________Brandon
Cchung
10800840
___________________________________________________________________________________________ ________________ Afstudeerproject BSc Informatiekunde Mentor: Dick Heinhuis_________________________________________________________________________________________________________
A
b s t r a c t
Inthisthesisanattemptwasmadeto differentiatepopularsocialnetworkingsitesforB2C-purposes forvaryingconsumertargetingintentionsofbusinesses.Asegmentationapproachwas used,inwhich segmentsofsocialmediaconsumerswerefirstidentifiedaccordingtodifferences inpersonality.For this,thebigfivepersonalityfactorswereused inconjunctionwiththepersonalitytraitnarcissism. Fivesocialmediaconsumersegmentswereidentified,each varyingbasedonhowhighlytheyscored onacertainpersonalityfactor.Inaddition,differentiatingaspectsofthe varioussocialnetworking siteswereidentifiedfromwhichthepresentstudy soughttoinferdifferencesinsocialnetworking sitesusageforeachconsumersegment.97socialmediauserscompleted asurveymeasuring differentaspectsoftheirpersonalityandsocial networkingsiteusage. Resultssuggestthat
segmentingsocialmediaconsumersbasedondifferencesin personalitydoesnotleadtoclearinsights fromwhichdifferentiationofsocialnetworkingsitesforvaryingconsumertargeting intentionscanbe achieved.
1.
Introduction
Nowadays it is mandatory for nearly every business to be active on social media as part of their multichannel strategy (Pulizzi, 2013). Since its introduction, social media has had a huge impact on the way businesses have to communicate with the outside world and execute marketing strategies (Mangold & Faulds, 2009; Kaplan & Haenlein, 2010; Kietzmann, et al. 2011). This is not without good reason, as social networking sites such as Facebook manage to attract over 1 billion monthly active users (Rohrs, 2013; Scott, 2015; Statista, 2017) and are thus an invaluable source of potential customers and information. Furthermore, competitors in every business sector are also active on social media and if customers are unable to find you on these platforms you are losing out considerably to the point where you simply become invisible to them.
Many of these businesses, however, simply use standard social media (eg. Facebook, Twitter) without paying attention to the specific strengths, weaknesses and capabilities of these channels (Weinberg & Pehlivan, 2011). In addition, some of these businesses see social media as an entirely ‘stand-alone’ part of their organisation (Blanchard, 2011) and feel that they are forced to be on social media because of their competitors and increasing customer demand and as a result often do not look further into how these channels can actually provide real value to their business (Cespedes, 2015). In such a case, the opportunity cost of using social media without really doing anything with it will only result in the loss of valuable time and resources (Blanchard, 2011).
What these businesses ultimately fail to realize is that an effective social media program actually functions as an integrated communications mechanism that amplifies the impact of every function within an organisation, by leveraging the power of human networks to complement all other forms of tactical communications that an organisation employs, such as advertising or PR (Blanchard, 2011). As such, social media utilization should not be seen as a nuisance, but as a genuine business
opportunity.
With that said, although an impressive amount of academic literature is devoted to social media usages, much less attention has been given to the extent in which businesses should differentiate between choosing which social media channels to be active on. The rules and utilities governing each social media channel greatly determine how and for what purpose they are to be used (Weinberg & Pehlivan, 2011) and all social media differ to some extent from one another (Bernoff & Li, 2008). In addition, being active on too many different social media channels can also severely hamper the overall effectiveness of a social media programme. For example, even the most successful
international businesses with large social media teams to their disposal, often only choose up to a maximum of four channels to be active on (Pulizzi, 2013), with two or three channels being a sweetspot for smaller businesses. This is not only because you want to make use of channels that actually fit the brand and activities of the business, but also because it takes an enormous amount of time to monitor customer activity, reply to comments, create new content suitable for each specific channel (Quesenberry, 2016) and make sure that the persona across each channel is aligned with regards to the overall social media marketing strategy.
Furthermore, businesses have all kinds of varying objectives for marketing activities on social media, including, but not limited to, increasing brand awareness (Hooley, et al. 2008; Pulizzi, 2013; Verhage, 2013; Quesenberry, 2016), increasing product sales through advertising (Hooley, et al. 2008;
Verhage, 2013; Scott, 2015), increased customer support (Hooley, et al. 2008; Verhage, 2013), improved customer relations (Verhage, 2013), improved customer retention (Blanchard, 2011; Pulizzi, 2013), increasing customer engagement (Hooley, et al. 2008), demand & lead generation (Blanchard, 2011; Pulizzi, 2013; Scott, 2015) and social listening (Fournier, et al. 2016). While traditional marketing goals are generally concerned with increasing direct sales, marketing experts and businesses who utilize social media effectively generally agree with the sentiment that social media is more suited for creating awareness (Cespedes, 2015) and connecting with (potential) customers (Coiné & Babbitt, 2014). Setting clear marketing objectives is crucial, as they not only contribute to achieving the organisational goals (Verhage, 2013), but also provide focus for what the business wants to do on social media (Blanchard, 2011).
However, setting objectives for social media is just the first step (Blanchard, 2011; Scott, 2015; Quesenberry, 2016). What businesses often seem to overlook when it comes to the content they produce on social media, is that they themselves are not the target (Pulizzi, 2013; Scott, 2015). Everything that a businesses puts out on social media, regardless of the objective that they want to achieve, begins and ends with the audience (Pulizzi, 2013). If the business does not have a clear picture of who they want to reach, where to reach them and what their specific characteristics, needs and interests are, then they will not be successful on social media (Pulizzi, 2013). This is
because a business can not listen to, and communicate with, everyone on social media (Quesenberry, 2016) and creating social media content that drives engagement requires clear insight on what the target consumer finds valuable or entertaining (Quesenberry, 2016). In addition, there are many different social media channels out there, each with different user bases, features and restrictions (Weinberg & Pehlivan, 2011). Having knowledge of which social media channels consumers are using and for what purposes can greatly benefit businesses in the process of selecting specific channels to be active on.
Although the introduction of web 2.0 applications and to a greater extent social media has made it much easier for businesses to target their market activities towards specific groups of consumers (Scott, 2015), academic literature has actually paid very little attention on how to differentiate between various social media channels with regards to these targeted marketing activities. As has been mentioned earlier, differentiation is important because businesses have limited time and resources and thus being able to focus marketing efforts on those social media channels that are most effective for specific consumer targeting purposes should provide the greatest results. As such, in this theses an attempt will be made to provide an oversight of the most popular social media channels for business-to-consumer (B2C) purposes suitable to specific consumer targeting intentions of businesses.
effectivewithregardstospecificconsumertargetingintentions?”
Furthermore, as we aim to guide businesses in the process of selecting social media channels based on specific consumer targeting intentions with regards to social media, we also need to be able to identify and profile distinct groups of consumers based on their differing characteristics, needs, wants or behavior (Blythe, 2005; Kotler & Armstrong, 2012; Scott, 2015; Kotler & Keller, 2016). Once clear differentiation between groups of consumers has been achieved, a business should not only be able to target the group of consumer most suited to their specific marketing objectives (Verhage, 2013; Kotler & Keller, 2016), but also be able to create relevant social media content that aligns with the interests of this specific consumer segment (Pulizzi, 2013; Scott, 2015; Quesenberry, 2016). Indeed, while there is no one way to segment a market (or in this case social media user base) and catering to the needs and wants of each individual is impossible (Kotler & Armstrong, 2012), knowing what kind of general person you are aiming your marketing efforts towards is essential for social media success (Pulizzi, 2013). As such, we first need to identify and select key consumer
segmentation variables upon which clear differentiation between groups of consumers on social media can be achieved.
Sub question 1 will be formulated as follows: “Whatarethekey consumersegmentationvariables thatallowforcleardifferentiationbetweenvarious potentialconsumergroups onsocialmedia?” In addition, each social media channel differs to some extent from one another (Bernoff & Li, 2008), otherwise there is no real reason for businesses to select a specific social media channel over the other to begin with. Because of this, it is necessary to distinguish these channels by taking into account the key differentiating aspects of these channels in terms of general motivations for use and relevant capabilities for each one. Once we have defined these key aspects, we can potentially determine the extent to which a specific social media channel is suited for specific consumer segment targeting purposes. As the scope of this thesis is limited to the most popular social media channels for B2C-purposes, we will only examine Facebook, Twitter, Instagram, Snapchat and Pinterest. This selection of social media channels is based on overall user popularity and business relevance worldwide (Statista, 2017) and in the Netherlands (ComScore, 2017). Furthermore, we also excluded B2B-oriented social networking sites such as LinkedIn, chat applications (eg. Whatsapp, Messenger) or national variations of social networking sites (eg. Weibo) from our selection. Sub question 2 will thus be formulated as follows:“Whatarethekeydifferentiatingaspectsof popularsocialmediachannelsforB2Cmarketingpurposes?”
Finally, once we combine the results of the previous two sub questions and have identified the most relevant social media consumer segments in combination with the hypothesized differences in social networking sites use for each of these consumer segments, the validity of the hypotheses has to be tested.
The last sub question (3) is then: “Candifferencesinsocialnetworkingsite usagebeinferredfromthe identifiedsocialmediaconsumersegments?”
To be able to answer these questions, however, requires extensive knowledge of the subject and as such we first have to examine various related aspects. First, a literature review will be provided in which we will examine traditional marketing handbooks and journals to further explore the concept of segmentation, as well as find and evaluate commonly used segmentation bases and variables for marketing purposes. After this, we will examine journals and other literature that have specifically applied segmentation in social media to further identify and select relevant segmentation variables for social media consumer segmentation. Once we have identified the relevant segmentation variables we can apply these variables to define relevant consumer segments. For each identified consumer segment a brief description will be provided of their characteristics and motivations for social media use. Second, we will examine 5 of the most popular social network sites suitable for businesses, namely Facebook, Twitter, Instagram, Snapchat & Pinterest and provide key
differentiating aspects for each, which will be identified by examining general motivations as to why users make use of each specific channel, in addition to the main functions that are provided. Finally, once we have identified the relevant consumer segments and have provided differentiating aspects of the discussed social media channels, we can hypothesize differences in social networking site use for each consumer segment. To determine the validity of these hypothesized differences in social networking site use per consumer segment, a survey will be held under social networking site consumers in which we will attempt to determine the characteristics and social networking site uses of these consumers. With the follow-up analysis of the survey results we will determine if a general oversight of predefined consumer segments can be used effectively for the social media channel differentiation process. We will then discuss the various implications of this paper and conclude by highlighting potential future work.
Before we continue onto the literature review, a definition of social media will be provided as even now there exists some confusion as to what the term exactly means. When we refer to social media we will use the definition of David Meerman Scott (2015), who defines social media as follows: “Social media provide the way people share ideas, content, thoughts, and relationships online. Social media differ from so-called mainstream media in that anyone can create, comment on, and add to social media content. Social media can take the form of text, audio, video, images, and
communities.”
2.
Literature Review
2.1 Segmentation in Marketing
Smith (1956) was the first to develop the concept of segmentation, which is concerned with grouping consumers in terms of their differing characteristics, needs, wants or behavior (Blythe, 2005; Kotler & Armstrong, 2012; Scott, 2015; Kotler & Keller, 2016) that require separate marketing strategies or mixes (Kotler & Armstrong, 2012). One of the main appeals of segmentation is that it is very efficient, as it allows a business to focus their attention, strategy and resources on things they deliver best (Wyner, 2016). In traditional marketing practices, segmentation is mostly used to divide markets into smaller consumer segments (Blythe, 2005, Hooley, et al. 2008; Verhage, 2013; Kotler & Armstrong, 2012; Kotler & Keller, 2016). Through market segmentation, businesses are able to reach groups of
consumers more efficiently and effectively with products and services that match their unique needs (Kotler & Armstrong, 2012). To accurately define these market segments both external data such as market research, as well as internal data which flows from ongoing business operations might be required (McDonald & Wilson, 2011). However, businesses can basically segment a market according to any kind of segmentation variable, as long as the segments themselves are measurable,
substantial, accessible, actionable, differentiable and stable (Gavett, 2014), with the three key criteria being accessibility, substance and measurability (Kotler, 1991).
Although there are several different bases for the segmentation of consumers, the most common ones are Demographics, Geographics, Psychographics and Behavior (Blythe, 2005; Kotler & Armstrong, 2012; Verhage, 2013; Kotler & Keller, 2016;). Other less common bases include
generational segmentation (McCrindle, 2007) and cultural segmentation (Hassan & Katsanis, 1994). We will now examine each of the most common bases of segmentation in further detail.
DemographicSegmentation
The most popular base for segmentation, demographic segmentation distinguishes between people based on several easy to measure variables such as: age, gender, family size, family life cycle, income, social class, occupation, generation, education, religion, nationality and ethnicity (Kotler &
Armstrong, 2012; Kotler & Keller, 2016), with the most commonly used variable being age (Blythe, 2005). One reason that demographics segmentation is so popular, is that most of the relevant information can be gathered from government statistics (Blythe, 2005). A second reason is that consumer needs, wants and usage rates often vary closely with demographic variables (Kotler & Armstrong, 2012).
GeographicSegmentation
Another popular segmentation base in traditional marketing, geographic segmentation distinguishes between groups of consumers based on geographical criteria. Commonly used variables are country, region, population density, city and climate (Kotler & Armstrong, 2012; Kotler & Keller, 2016). As businesses are operating in an increasingly globalised environment (Jain, 1989), they may want to pay special attention to geographical differences in needs and wants and tailor their marketing activities accordingly (Kotler & Armstrong, 2012; Kotler & Keller, 2016).
PsychographicSegmentation
Somewhat of a less commonly used base for segmentation, psychographics is the science of using both psychology and demographics to better understand consumers (Kotler & Keller, 2016). Psychographic segmentation divides groups of consumers based on psychological characteristics (Verhage, 2013) such as personality traits, lifestyle or values (Kotler & Keller, 2016). This form of segmentation is used because consumers with similar demographics can have very different
psychographic characteristics (Kotler & Armstrong, 2012; Gavett, 2014). While research has provided evidence of a link between personality and consumer behavior (Lastovicka & Joachimsthaler, 1988), it can be difficult to measure consumers’ psychological traits on a large scale (Blythe, 2005) and thus this form of segmentation regularly fails on the grounds of accessibility (Blythe, 2005). If a business is
able to successfully profile groups of consumers based on psychographic variables, however, it is able to segment markets more accurately and effectively than with purely demographic variables
(Verhage, 2013).
BehavioralSegmentation
Finally, basing marketing activities on how consumers actually act, rather than on what they say how they are going to act, increases the effectivity of these marketing efforts and carries less overall risk for the business (Verhage, 2013). As such, many marketers agree that dividing groups of consumers based on behavioral variables, known as behavioral segmentation, yields better results than
demographic or geographic segmentation (Kotler & Armstrong, 2012; Verhage, 2013). Common behavioral variables for buying behavior include: occasions, benefit-sought, user status, usage rate, brand loyalty, readiness stage and attitude towards product or service (Kotler & Keller, 2016). Depending on the context or the tasks that can be performed, different behavior variables can be constructed.
As there are many different bases of segmentation and each of these bases also consist of many different variables, it can be difficult for businesses to select those specific segmentation variables that are most appropriate in determining the ideal consumer segment for its marketing objectives. In addition, not all segmentation variables will be appropriate to all markets (Blythe, 2005). Businesses can use single-variable segmentation, or multivariable segmentation to divide groups of consumers (Blythe, 2005). While the former is the easiest way to segment groups of consumers, it is also the most inaccurate (Blythe, 2005). The latter on the other hand, is more accurate and effective in identifying and differentiating groups of consumers, but these groups will also be smaller as a result (Blythe, 2005). Wyner (2016) mentions, however, that by using fewer, but concise variables one is able to create simple and clear segments which also increases the odds of effective targeting. 2.2 Segmentation in Social Media
Over the past decade many attempts have been made by researchers in marketing, psychology and social sciences to segment consumers of social media. While a plethora of general statistical
information is available to marketers about social media users, such as demographic and geographics (Greenwood, et al. 2016), these alone are not enough to sufficiently differentiate between groups of users on these channels. Although they are often used to provide additional insights, by themselves they do not help identify how or why specific users act the way they do on these channels. Most social media segmentation studies, therefore, have used consumer behavior as a way to differentiate between various groups of consumers and have applied demographic or geographic variables as covariates to further describe these groups of consumers.
Li & Bernoff (2008) in their widely praised, albeit slightly dated book Groundswell, for example, examined several key social media activities, such as creating, sharing and commenting on content to segment a large group of social media consumers. They identified seven different groups of
consumers and called them part of a ‘social technographics ladder’, of which each step on the ladder represented a higher involvement with social media. Similarly, Ip and Wagner (2008) segmented
online bloggers based on their usage levels and Wiertz and de Ruyter (2007) segmented users that shared, or contributed to content based on benefits received.
Whereas most of the early research focussed on a single behavioral dimension (eg. use of, or interaction with, social media) to segment social media users, or even treated these users as an entirely homogenous group, Foster, et al. (2011) attempted to segment young adult social media users based on multiple behavioral dimensions. These dimensions included technical actions performed, how often a person used social media and the social aspect of actions performed on social media. They found four different segments of young adult social media users which were based on their differing participation in three distinct types of online activities: making or contributing content for other users to review, socializing and connecting with other users, and seeking information from content posted by other users for decision making purposes.
Campbell, et al. (2014) found that while much attention had been paid to identifying what motivates consumers to go online or interact (Taylor, et al. 2011), not nearly as much attention was given to how these consumers react to brand marketing activities in social networking sites. They identified five different social media user segments, namely ‘Passive’, ‘Talkers’, ‘Hesitant’, ‘Active’, and ‘Averse’ of which each segment differed in how they were impacted by, and responded to social network marketing efforts of brands. Users within the ‘Active’ segment, for example, were most likely to interact with brands, make purchasing decisions as a result of brand marketing efforts and spread information about content that brands produced (Word-of-Mouth). In addition, significant covariates were found for the prediction of segment membership, which included information search
motivation, convenience motivation, entertainment motivation and other demographic variables such as age and gender. In a similar vein, Dimitriu & Guesalaga (2017) also examined a wide scope of consumers’ social media brand behavior and classified these into four factors to segment users accordingly. In addition to these factors, which included ‘brand tacit engagement’, ‘brand exhibiting’, ‘brand patronizing’, and ‘brand deal seeking’, demographic and behavioral segmentation variables were used to further describe segments. Some examples of variables include age, gender, social media usage rate, like/follow ratio, brand loyalty and brand attachment. The researchers identified six different segments and each segment differed greatly in how and when they interacted with certain brands on social media. In addition, they aimed to find general motivators for specific brand-related activities of consumer segments.
Another recent study by Park, et al. (2015) applied social surveillance (keeping track of other’s behavior) and self-surveillance (one’s own control of behavior) in addition to demographics to segment social networking site users and gain further insight into their behavior. They concluded that there were 4 different types of users, each based on how well they scored on both social surveillance and self-surveillance. Each group of users differed quite substantially in how and for what purposes they used social networking sites, with some examples being product or brand-related sharing, social presence and purchases. Similarly, Hodis, et al. (2015) specifically studied Facebook users and segmented them into four different types of users, namely ‘attention seekers’, ‘devotees’, ‘connection seekers’ and ‘entertainment chasers’. These segments were based on three broad
categories: user behavior, attitudes towards marketing activities and general use on Facebook. Furthermore, activities were then further classified under content creation (eg. posting status updates, uploading pictures & commenting) or content consumption (eg. browsing & liking) and users were then identified based on how high they scored on both types of activities. They used these segments to build a framework that guides marketers by providing marketing engagement strategies for each specific type of consumer on Facebook.
Finally, some studies have attempted to further segment groups of consumers with specific interests that are active on social media. One example is the research by Chung et al. (2016), who aimed to segment supporters of social ventures on social media based on the creation of content, interaction with others and the amount of control over the user experience.
While many studies have used behavioral segmentation variables to segment consumers on social media and even though demographic and psychographic variables are commonly employed as covariates, psychographic variables in themselves have rarely been the subject for social media consumer segmentation. Recently, however, there has been a study that used psychographics for social media consumer segmentation.
In this study, Madi (2016) attempted to segment consumers of social networking sites based on values. Values were used to determine the behavior that consumers performed on these sites, their motivations for social networking sites’ use and level of engagement in marketing activities. Three different segments were identified, of which each segment differed in which values adhered to them. For example, values like self-respect, being well respected and security were exhibited by
self-conservers, while self-fulfillment and accomplishment were representative for achievers. The author concluded that even when consumers in different segments acted somewhat similarly on social networking sites, they differed quite substantially in their motivations. Furthermore, values proved to be more consistent in understanding online consumers than virtual behavior.
Madi (2016) raises an interesting point with his conclusion. While behavioral segmentation tells us what people do and are then often paired with demographics to gain additional insights, this alone is often not enough to fully understand what actually motivates people to act the way they do. After all, consumers with similar demographic characteristics who behave in the same way might do so for completely different reasons (Gavett, 2014).
Similarly, Samuel (2016) recently stated that aside from demographics, psychographics are just as important, if not more, important for marketers when it comes to understanding a group of
consumers. While demographics variables such as age, income and gender give valuable information about how different groups of consumers use different things, or make different purchases and should therefore always be taken into account, the actual reasoning behind these actions which can be gathered from psychographics can be much more insightful (Samuel, 2016). In addition she mentions that especially with the advent of the internet, differences in psychographics between consumers has become more relevant than ever for marketers, as online marketing tools such as those used on social media allow for much more precise consumer targeting, which means that
insights gathered from these psychographics can now be made much more actionable (Samuel, 2016).
The majority of the literature that have applied psychographic variables in social media, however, have done so by employing the psychographic variable personality to understand why and how consumers use social networking sites. Personality is a powerful tool in that it can help uncover what attracts different kinds of consumers to social networking sites, as well as which aspects of social networking sites appeal them. This is also because consumers are naturally goal-oriented in their use of media and specifically choose those media channels and complementing functions that satisfy their psychological and motivational needs (Blumler & Katz, 1974).
Because psychographics can be beneficial in profiling and segmenting groups of consumers in terms of accuracy and efficacy (Verhage, 2013) and the majority of studies that have used psychographic variables in social media have done so by using personality, it might be worthwhile to identify differentiating aspects of consumer’s personality that could allow us to segment groups of consumers on social media. As has been mentioned before, while a plethora of research has used consumer behavior to segment groups of consumers on social media, psychographics have been left mostly unexplored. Furthermore, Madi (2016) mentions that while behavioral segments such as ‘passive’ users (Campbell, et al. 2014) can be problematic in that they are very difficult to target for marketers, segments based on varying psychographic differences make it easier for marketers to understand what the motivations are of each segment, as well as understand what is considered valuable to them. This way, it not only allows marketers to tailor their communication to specific consumer segments on social networking sites much more effectively, but it also makes each segment potentially valuable to marketers (Madi, 2016).
Building forth on these motivations, we will now examine various studies that have applied personality for social media consumer understanding in an attempt to find relevant differentiating aspects of personality amongst social media consumers. Once we have identified the key differences in personality between groups of social media consumers, we should be able to create distinct and relevant consumer segments.
2.3 Personality in Social Media
Before we continue on, it is important to note that nearly all literature that have applied personality for social media consumer understanding have done so by utilizing the personality factors from the Big Five Model. The big five model is a model of personality that captures broad individual
differences in social and emotional life (McAdams & Pals, 2006) by categorising these into five different factors. These factors are extraversion, neuroticism, conscientiousness, agreeableness and opennesstoexperience and each of these factors also have a bipolar counterpart, such as
introversion vs extraversion. Extraversion is related to being assertive, gregarious and seeking excitement, neuroticism relates to a person’s level of emotional control (with a lower score being more in control), conscientiousness is related to a person’s work ethic, orderliness and tendency to act dutifully, agreeableness is about being cooperative, sympathetic or trusting and, finally, openness
to experience relates to a person’s willingness to broaden their interests, be intellectually challenged, take different approaches, be adventurous or generally step outside their comfort zone. As can be noted, each factor broadly encaptures several abstract personality aspects, which can then be further related to certain personality traits. According to the big five model, most differences in personality can be explained based on these factors (Gosling, et al. 2003) and because of this, this model has been very popular in the previous decade for studying the personality of internet users (McCrae & Costa, 1997) and also recently with the rise of social networking sites. However, it should be noted that the model is certainly not without criticism (eg. Block, 1995; Eysenck, 1992). Some studies have for example shown that it might not be universally applicable to all societies (Gurven, et al. 2013). Most early attempts at using personality in social media have been for the prediction of social media adoption amongst consumers. Correa, et al. (2010), for example, examined the relation between specific personality factors of the Big Five model (extraversion, neuroticism & openness to experiences) and consumers’ level of social media usage. Both extraversion and openness to experiences were found to be positively related to a person’s level of social media usage, but the results varied somewhat depending on both age and gender. For example, while both extraverted men and women were likely to be frequent users of social media, amongst men and women that scored high on neuroticism, only men were frequent users of social media. Another research by Gosling, et al. (2011) examined the personality factors of the Big Five model in an attempt to find manifestations of personality in both self-reported behavior and observable information (eg. amount of pictures, friends, etc.) of users on Facebook. They found that extraversion was not only a strong predictor of social media usage frequency, but also of social media engagement, which was associated with higher levels of activity. In addition, they concluded that a user's manifestation of personality in their offline lives also extended further into social networking sites.
Hughes, et al. (2012) also looked at the big five model personality factors on social media, but in particular with how it relates to how people use Facebook and Twitter for social and informational purposes. Personality was found to be related to online socializing and information search &
exchange, with age being the strongest predictive variable. In addition, the authors concluded that a person’s preferences for Facebook or Twitter also differed based on personality. For example, people who scored highly on sociability, neuroticism and extraversion had a preference for Facebook, while those who scored high on need for cognition had a preference for Twitter. Furthermore, the authors linked the differences in social networking site preferences to the inherent ‘styles’ of each social networking site.
Muscanell & Guadagno (2011) looked at both gender and the five personality factors as differentiators for social networking site usage motivations. According to the results of their research, male users were more likely to use social networking sites to establish new relationships, while female users were more likely to use them for maintaining relationships. In addition, male users that scored low on openness to experience were more likely to play games on these sites than men that scored high on this factor, while female users with low agreeableness were more likely to make use of direct messaging functionalities than female users with high agreeableness. Bergman, et
al. (2010) examined if narcissism had an effect on social networking site usage and motivations amongst millennials and if there were differences between narcissists and non-narcissists. They found that while there were no real differences in frequency of usage, the motivations for social networking site use were different for narcissists. General motivations were having as many connections as possible, making themselves look good or the belief that others were interested in what they displayed about themselves. In addition, narcissists were also more likely to post self-focussed pictures.
Seidman (2013) examined the relation between personality factors and how they relate to fulfillment of self-presentational and belonging needs of users on Facebook. She found that agreeableness and neuroticism were strong predictors for belongingness-related motivations and behaviors, while self-presentational motivations and behaviors were more likely to occur with low conscientiousness and also high neuroticism. Furthermore, she found that people scoring high on extraversion were more likely to use Facebook for communicative reasons and people with high neuroticism had a tendency to express hidden or ideal aspects of themselves. In addition, users with high agreeableness and extraversion were more likely to portray themselves honestly on these channels. Similarly, Michikyan, et al. (2014) also found that users with high neuroticism were more likely to present ideal or fake aspects of themselves.
Some studies have also focussed on personality and general motivations for the use of specific functions by consumers on social media channels. Wang, et al. (2012), for example, explored the relationship between the big five personality factors, in addition to narcissism, self-esteem and sensation-seeking, for the use of specific features on social networking sites by Chinese students. Similar to other studies, they too found that personality factors had a big influence on how social networking sites were used. For example, students scoring high on extraversion were more likely to make use of communicative or broadcasting functions, such as placing status updates and comments, but also adding friends. Neurotic students also were more likely to place status updates, but often for self-expression purposes. Agreeableness and self-esteem was also positively related with placing comments on other’s pages or content. Furthermore, narcissistic students were concerned with social networking functions to enhance self-presentation, such as posting pictures or status updates that showcased themselves in an attractive manner. Finally, the researchers found that gender also played a significant role in how students used function on social networking sites. Female students, for example, were more likely to upload pictures or status updates. Furthermore, in a different study, Wang, et al. (2015) found that attitudes, social & entertainment motivations, sociability and
self-efficacy were positively related to a person’s use of social functions on social networking sites. In addition, shyness, attitudes, entertainment motivation and self-efficacy were the strongest
predictors for the use of recreational functions on social networking sites.
Finally, a small pool of studies have taken a different approach in understanding how personality affects social networking site behavior and use. One such study by Kosinski, et al. (2014) applied machine learning to understand how personality factors affect online user behavior by examining website choices and Facebook profile features of consumers. Some results they found were that a
