Marketing To Emerging Adults:
Is There A Relationship Between Their Psychological Needs And Their Social Media Marketing Preferences?
Mel issa Day an 592 139 2 Mas ter The sis Gra
duate School of Communication
Master's Programme Communication Science: Youth And Media Helen Vossen
29 – 01 – 2015
Abstract
The fact that emerging adults spend a significant time online has motivated companies to invest increasingly in online marketing strategies when targeting this age group. However, research on
how emerging adults perceive these strategies and which factors may contribute to what they find appealing, is scarce. This study aimed to find out which online marketing strategies are perceived as appealing by emerging adults and if emerging adults' psychological developmental needs of
autonomy, identity and intimacy could serve as predictors of their preferences in social media marketing strategies specifically. Data for analysis was derived through an online survey in which seventy-seven emerging adults, aged 18 to 25, participated. Results suggest that while social media marketing strategies are more appealing to emerging adults than traditional online marketing strategies, the three key developmental needs did not seem to be significantly related to the appeal of the social media marketing strategies associated with these needs.
Marketing to Emerging Adults:
Arnett (2000) distinguished a new developmental age group in modern, Western society, which he called “the emerging adults” (aged 18 to 25). Studies have shown that this particular age group uses more digital media than the generations before them, namely, generation X and the baby boomers. Emerging adults spend an average of twelve hours a day engaging with media, of which three-and-a-half hours are being spend with online media (Coyne, 2013). The fact that they spend a significant amount of time online and thus online media can be seen as their preferred medium, has motivated companies to depend heavily on their online marketing strategies when targeting this age group. This has lead to an increase in companies' spendings on online media marketing. Recent estimates show that while in 2008 the total percentage of the advertising budget spent on online media marketing by U.S. companies was around 10.5%, by 2016 is it predicted that companies will spend 24% of their advertising budget only on online advertising.
However, recent studies have shown that emerging adults might not have such a positive attitude toward online marketing strategies as one might assume (Smith, 2011; Tanyel, Stuart & Griffin, 2013). Since companies are increasingly spending more money on online marketing strategies, it is an important matter to gain insight into which strategies are then perceived as appealing by this age group and which are perceived not appealing.
A possible explanation as to why certain online marketing strategies may be perceived as negative, is that most marketing approaches used in the online environment are made for traditional media such as TV or print advertisements: these are called “pushed” approaches. Here, consumers are seen as passive consumers where advertisements are “pushed” onto: they have no control over it. However, since the internet in itself is a “pull” medium, these “pushed” strategies do not fit the medium and are consequently seen as intrusive (Smith, 2011; Tanyel et al., 2013). Examples of a “push” strategy used in an online environment are banners or pop-up ads for instance. Research has shown that “pull” strategies such as community fan pages are preferred in an online environment because it allows the consumer audience to choose what they view instead of content being forced
upon them: here, the consumers are not seen as passive, but as active.
With this in mind, it is important to look at possible explanations as to why emerging adults in particular would be opposed to “push” strategies and prefer “pull” strategies. This way, an indication can be made about which “pull” strategies specifically they might prefer. Developmental theories suggest that emerging adults have different psychological needs than other age groups, even though they are fully developed physiologically. And these developmental characteristics may help explain what type of content they prefer. The uses and gratifications theory then, suggests that people actively seek out certain media content to satisfy certain needs (Katz, Blumer & Gurevitch, 1973; Whiting & Williams, 2013). From this perspective, you might say that emerging adults are turning to media to gratify their developmental needs. While online media in general shows several possibilities for emerging adults to satisfy these needs, social network sites provide significant opportunities as well. When keeping in mind emerging adults' negative attitudes towards “push” methods that are used in digital marketing strategies, Serazio (2013) actually points out that social media platforms provide more opportunities that create a two-way interactive dialogue between emerging adults and marketers or brands that is “pulled” by them as an active audience. This is why it is expected that social media marketing strategies are preferred by emerging adults as opposed to other online marketing strategies.
Even though there has been research on online marketing strategies that are perceived negatively by emerging adults (Tanyel et al., 2013; Smith, 2011), these studies did not include social media marketing strategies. Similarly, other studies have looked at social network sites as preferred platforms for online marketing strategies, however these studies did not investigate this in relation to emerging adults and their perceptions of these strategies (Truong & Simmons, 2010). Since emerging adults are such an important age group for marketers, and companies are
increasingly spending more money on online advertising, it is highly important to find out which social media marketing strategies are then preferred by emerging adults: and more importantly, if
their developmental needs could be used to predict their preferences in marketing strategies. This way, implications can be made that could help marketers to find new ways to create a more positive relationship with emerging adults by designing a more effective digital marketing campaign that targets them. Therefore, this research first of all aims to find out which online marketing strategies are perceived as appealing and which as not appealing by emerging adults and second of all, if there is a relationship between emerging adults' developmental needs and their social media marketing preferences: to see if these needs might serve as factors that can help predict their preferences in social media marketing strategies.
Literature Overview Emerging Adults
When distinguishing the new developmental age group, the emerging adults, Arnett (2000) named several cultural changes that made the rise of emerging adulthood possible. For example, he posited that the rise of birth control and new sexual standards made it possible for people to engage in sexual relations without getting married and having children right away: this way they could remain in a more free developmental stage for a longer period. He also named the increase in pursuit for higher education as a cultural change that made the rise of emerging adulthood possible. While pursuing higher education, people found more access to other choices than marriage and having a family. This all resulted in new social norms regarding adulthood and adult roles, and made it possible for young adults to remain in a relative independent period for a longer amount of time.
This age group is part of a generational cohort what we today call Generation Y, Digital Natives or Millennials. Referring either to the cohort that followed Generation X or to the fact that they are born in a media-saturated world and therefore are native to the digital world, or to their birth years ranging from the early 1980s to the early 2000s. Throughout this paper I will maintain
the term emerging adults when referring to this age group of people between 18 and 25 years old. These cultural shifts allowed this age group to remain in an independent period for a longer time. Arnett (2000) points out several of their socio-economic characteristics. First of all, emerging adults are independent from their parents: most of them will live outside their parental home by the time they go to college. However, unlike the generations before them, they are less likely to be married and to have children in these younger years and this makes them have more leisure time on their hands. The fact that their responsibilities thus are very focused towards themselves (as
opposed to for example children or spouses), makes it possible for them to make independent decisions and decide how they desire to spend their leisure time and money. Another important socio-economic characteristic is that they remain semi-independent from their parents when it comes to finances: the fact that they're not completely financially independent makes it possible for them to have more disposable income than other age groups (compared to adolescents and adults).
The fact that because of these cultural shifts, this age group was able to remain in an independent period for a longer time, of course had consequences for their socio-emotional
development. Coyne (2013) identifies emerging adulthood as a very unstable period. Since they are not forced by socio-economic circumstances to have a steady job right away, most emerging adults tend to have jobs in service industries where they do not acquire many skills or gain very useful job-related experiences. They see a job more as a way to support their active leisure life (Arnett, 2000). Next to job-related instabilities, emerging adults are also trying to decide who they are in their love lives and world views. While identity development starts in their adolescent years, most identity exploration takes place in emerging adulthood. Since emerging adults do not have families at a young age and enjoy a longer period of independence, makes them to be very self-focused: they do have more responsibilities than adolescents, however these are still directed to themselves. And finally, emerging adults are identified as being very optimistic about future possibilities. Mainly because this period is rather care-free for them and because of the cultural shifts that this age group
enjoys they have more possibilities to find out what they want to do with their lives. However, maybe the most important characteristic that sets them apart from other
generations before them (generation X and the baby boomers) is that they use more digital media than other generations before them. They make use of a higher integration of interactive media such as mobile phones and traditional online tools such as PC's for example when seeking entertainment but also information. They also make more use of interactive media in purchase-related activities than other generations: they do extensive market research on a product or brand before their purchase. They do this mainly throughout blogs, e-mail and various mobile applications for example (Moore, 2012).
There are two main reasons why emerging adults are interesting for marketers. First of all, they make up a large segment of today's population: by 2013, young adults between the ages of 15 and 25 made up 11.5% of the entire population of Europe. Second of all, they have a relative large amount of disposable income and much freedom to spend their (leisure) time however they desire. This is due to several socio-economic characteristics of this generation.
Online Marketing & Social Media Marketing: Any Negative Attitudes?
Before we go further into possible predictors of emerging adults' preferences in terms of marketing strategies, first we need a clear idea of the difference between digital marketing and social media marketing and their strategies. Online media marketing or digital marketing, has been conceptualized as marketing that involves using digital/online technologies (such as the internet, e-mail services, databases, mobile devices etc.) to support marketing activities aimed at promoting products/services (Chaffey, 2009; Wymbs, 2011). Chaffey (2009) makes a distinction between several digital marketing tools, namely: search marketing (SEO and SEA1), affiliate marketing (online partnerships, sponsorships and co-branding), display advertising (banners, videos,
1 SEO stands for Search Engine Optimization and SEA stands for Search Engine Advertising. These two internet marketing strategies are typically used in a combination with each other to increase websites' visibility on search engine results and to gain visibility by inserting searched websites onto advertising platforms such as side-panels, banners, or pop-up screens.
interactive games) and email marketing (via house consumer lists, rented lists of email addresses or co-branded emails). Smith (2011) then, provides a more detailed distinction of online strategies that are used to specifically target emerging adults. These strategies relate to either online advertising or website features. For online advertising the strategies used to target emerging adults are: pop-up ads, side-panel ads, coupons, email updates, YouTube videos and advertisements in interactive games. Strategies that relate to the website features include: lay-out, graphics, personalization, incentives/rewards, interactivity, offering of free items, shipping possibilities, pricing and return policies.
Social media is conceptualized by Kaplan & Haelein (2009) as a group of online
applications that have build on the foundations of Web 2.0 and that allows its consumers to create and share user-generated content. Their study offers a clear and concise overview of six different types of social media wherein social media marketing strategies can (and according to the, should) be used. They make a distinction between blogs, Social Networking Sites (Facebook, Twitter etc.), collaborative projects (Wikipedia), content communities (YouTube, Flickr, Slideshare), virtual social worlds (Second Life) and virtual game worlds (World of Warcraft).
As we can seen here, even though social media can be seen as a part of online marketing strategies, they both have very different characteristics. Whereas general digital marketing strategies make more use of “pushed” marketing approaches that are somewhat forced upon the consumer, social media marketing strategies make more use of “pulled” marketing approaches where the consumer is more enabled and actively participates in a two-way dialogue between the consumer and the brand or product. A study carried out by Smith (2011) then, makes a clear distinction between “pushed” and “pulled” advertising methods. By “pushed” methods the author means methods that are used commonly for traditional media such as print or TV, where the audience is seen as passive consumers where ads are “pushed onto them: they have no choice in what they are watching. By “pulled” methods, the author means methods that consumers have
control over (they are seen as active here) and they typically choose the content that they watch. This distinction is important to keep in mind when looking more closely as to what kind of advertising is preferred by emerging adults.
Marketers depend heavily on online advertising because their target group spends so much time online and therefore it is perceived as the preferred medium to send out advertising messages. However, there are already studies that have shown that emerging adults actually do not have such a positive attitude towards these digital marketing strategies as one might think. The fact that they grew up in a more media-saturated and brand-conscious world than the generation before them, makes them respond to advertisements differently and even reject the brands that were popular with their parents (Smith, 2011).
Recent studies have shown that emerging adults actually have an increasingly negative attitude towards intrusive online advertising in particular (Tanyel et al., 2013). Much like how the baby boomers and generation X resent advertising on “their” main medium, the television,
emerging adults also resent advertising on their most used medium because it is perceived as intrusive. Is it important to keep in mind that emerging adults are spending even more time on “their” medium, the Internet, than the generations before them spend on “their” medium: the internet is not only a source of entertainment for emerging adults, they also get their news from the Internet, they use it for information purposes and they interact with their peers through the Internet. This makes emerging adults even more attached to the Internet than the generations before them were to television. Additionally, it is noted that internet use is more goal oriented (compared to television viewing) because it requires active participation of its users. This makes online
advertising even more intrusive because it interrupts a higher level of activity (high-involvement) amongst its users and this may lead to more negative attitudes. The study by Tanyel et al. (2013) found that emerging adults indeed hold negative attitudes toward online advertising: more than they do toward other forms of traditional advertising (out-of-home, print and television advertising).
A possible reason for why emerging adults have a negative attitude towards online marketing is given by Smith (2011). This study investigates explicit digital marketing strategies used to target emerging adults and which of these are found annoying by them. It is pointed out that the old “pushed” advertising approach is not effective with digital media because the Internet is more a “pull” medium where consumers choose the content that they view. Therefore, when “push” methods are used, consumers perceive them as distracting, disturbing, forced upon them and
interfering with their work. As a consequence of this, these advertising messages are not appreciated and are merely found annoying. This is why marketers are now trying to find new innovative methods to pull the consumer to their websites and into a long lasting relationship (Smith, 2011).
These findings indicate that marketers need to pull consumers into their brand narratives, within their online social network sites and brand communities and to create brand awareness and positive associations with the brand, the pushed approach will not work with social network sites (Simmons, 2008; Truong & Simmons, 2010). This is because the “pull” characteristic of the internet makes for increasingly empowered and digitally enabled consumers, and these consumers do not appreciate it when this characteristic is undermined and their control over the medium is taken away by intrusive messages. The internet as a medium is only appreciated when consumers have control over it (in what they chose to look at or search for online). With this in mind, social network sites were pointed out as typical “pull” platforms that are highly appreciated by consumers: mainly because they trusted brands portrayed positively by third party sources more than what advertisers tried to “push” onto them. More specifically, brand communities were mentioned as important platforms to engage with preferred brands and being informed. Brand communities were also seen as platforms that provided unbiased information and therefore generated more trust among their consumers.
Gratifications Theory
To help predict what emerging adults prefer in terms of media use and how they prefer being targeted by marketers, we need to take a look at several possibilities as to why social media
resonates so well with them and maybe this can help us to pinpoint which elements of online media they would prefer most. Besides the fact that emerging adults grew up in the digital age and are therefore natives to the digital language, there have been several studies that have pointed out that they have certain developmental characteristics that create needs that can be satisfied especially through the use of digital media. This helps to explain why online media resonates so well with them.
While emerging adults' physical development is complete, there is still a lot of development taking place in terms of autonomy, identity and intimacy (Coyne, 2013). The uses & gratifications theory posits that individuals seek out media based on if it helps to fulfil certain needs (Bartsch, 2010). Therefore, you could say that emerging adults are seeking out digital and social media to gratify their developmental needs of autonomy, identity and intimacy.
Even though digital media in general offers a lot of possibilities for emerging adults when it comes to satisfying these developmental needs, social media platforms might offer even more possibilities. For the development of autonomy, they may use social media as a way to practice their newly gained independence and freedom through making their own choices and engaging in content that they choose. They may do this through engagement on social media platforms in the form of participating in user-generated content. For the development of intimacy emerging adults may depend on social network sites to communicate with their friends or to keep in touch with them when they move away from their parental house or to other cities or even countries. Then, for the development of identity, social media platforms offer a range of possibilities to satisfy this need. Social network sites offer them various ways to express and explore their identity, which help emerging adults gratify the need for identity development, for example through postings, making
statements, likes, shares and following etc. (Coyne, 2013).
The uses & gratifications approach and psychological developmental theory provided insight as to which types of digital media might help emerging adults gratify their specific
developmental needs of autonomy, identity and intimacy. Now, we can look further into how these insights may help predict which elements of online media marketing and social media marketing in particular are preferred by emerging adults. More specifically, the present study aims to investigate how online marketing strategies are perceived by emerging adults and if there is a relationship between emerging adults' developmental needs and their online marketing preferences.
When keeping in mind emerging adults' negative attitudes towards “push” marketing methods that are commonly used in online marketing strategies, we can expect that traditional online marketing strategies will not be perceived as appealing by emerging adults. Since social media platforms provide opportunities that create a two-way interactive dialogue between emerging adults and marketers or brands that is “pulled” by them as an active audience, it is expected that social media marketing strategies are perceived more appealing by emerging adults than other online marketing strategies.
H1: Social media marketing strategies are more appealing to emerging adults than traditional online marketing strategies.
Then, to gain insight into which elements of social media to use when designing a campaign that creates a more positive relationship with emerging adults, it is important to understand why these elements of social media marketing may be appealing to them.
First of all, the engaging element of social media platforms is a feature that may serve emerging adults' need for autonomy. As said before, Serazio (2013) points out that emerging adults need to create something so that an interactive dialogue takes place between the consumer and
marketer. Several studies have shown that user-generated elements of social media prove to be important for emerging adults, because it allows the need of self-efficacy to be gratified through the use of it (Montgomery, 2009; Serazio, 2013; Sundar, 2013). Therefore, it is assumed that emerging adults who have a higher need for autonomy find user-generated content strategies more appealing. It is also expected that the appeal of user-generated content strategies is increased by how often emerging adults participate in these strategies. Which brings us to the next hypotheses.
H2a: Autonomy support is positively related to appeal of user-generated content strategies.
H2b: The relationship between need for autonomy support and appeal of user-generated content strategies is increased by how often they participate in these strategies.
Second of all, the personalization element of social media platforms is also a feature that may satisfy certain needs of emerging adults. This element is mostly related to the need for identity exploration and presentation because the possibility for them to personalize content gives them the opportunity to explore their own identity through trying out several identities that may fit them. Serazio (2013) points out that next to the need to “create” content, they also have the need to “share” this content with their friends. Montgomery (2009) shows that advertisements that are inserted into personalized media experiences have shown to be more effective. Again, it is expected that the appeal of personalization strategies is increased by how often emerging adults participate in these strategies. This brings us to the following hypotheses.
H3a: Identity support is positively related to appeal of social media marketing strategies that make use of personalization.
H3b: The relationship between need for identity support and appeal of social media
marketing strategies that make use of personalization is increased by how often they participate in these strategies.
Finally, the element of social interaction in social media platforms may satisfy needs that are related to the search for intimacy. Brands make use of this need by creating a sense of community that is enhanced throughout their social media platforms by creating possibilities of social
interactions among peers: something that is very important for this generation. This brings us to the final hypotheses of this research.
H4a: Intimacy support is positively related to appeal of social media marketing strategies that make use of communities.
H4b: The relationship between need for intimacy support and appeal of social media marketing strategies that make use of communities is increased by how often they participate in these strategies.
In short, this research aims to find out how online marketing strategies are perceived by emerging adults. Based on previous research, it is expected that social media marketing strategies are preferred by emerging adults as opposed to other online marketing strategies. Then, it is expected that certain needs that emerging adults have, help predict which of the social media marketing strategies they prefer. The used elements of user-generated content, the possibility for
personalization and for social interaction are expected to be found appealing to emerging adults based on which need (autonomy, identity or intimacy) is highest.
Method Sample & Design
For this study, a cross-sectional method was used in the form of an online survey. A total of seventy-seven emerging adults between the age of 18 and 25 participated in this study. The average age was 23 (SD = 2.06, N = 77). This target group was recruited via social media such as
Facebook, LinkedIn and Twitter and several online forums. The survey was online for two weeks, wherein the data could be collected. Before initiating the survey, written informed consent was obtained from all participants.
It was chosen to explain the involved strategies to the participants as clearly as possible, so that presenting them with examples was not needed. This way, it is made sure that their answers were not biased by prior knowledge or opinions about brands that would have been included in the stimuli presented to them as examples of the strategies.
Measures
Appeal of traditional online marketing strategies. To measure how appealing online marketing strategies are to emerging adults, participants were asked their opinion on commonly used online marketing strategies: banners, pop-up advertisements, search marketing and email marketing. These strategies were explained thoroughly to them first. Then, they were asked to rate how appealing they find these strategies on a 5-point Likert scale. The options for the 5-point Likert scale being (1) “I strongly dislike it”, (2) “I dislike it a little”, (3) “Neutral”, (4) “I like it” and (5) “I like it a lot”. Analysis suggests that the data for (only) this measure is not normally distributed. Therefore, it was chosen to perform a principal component analysis for this measure as opposed to a maximum likelihood factor analysis (Costello & Osborne, 2005). The principal components
analysis of these four items showed that two components have an eigenvalue above 1. When we look more closely at the items positively associated with the first component, we can see that these
two items seem to measure the appeal of traditional marketing strategies that are more personally targeted. These two items consist of the appeal of search marketing (factor loading .69) and the appeal of email marketing (factor loading .92). The two items that are positively associated with the second component, we can see that these two items seem to measure the appeal of traditional marketing strategies that appear on one's screen. These two items consist of the appeal of online pop-ups (factor loading .89) and the appeal of online banners (factor loading .62). A reliability test showed that the scale was not reliable, Cronbach's Alpha = .53. In the results of the principal components analysis, we have seen that this scale measures two separate sides of traditional online marketing strategies: strategies that are more personally targeted and strategies that appear on one's screen. It is highly likely that this is also the reason why the reliability is not high for this scale. Even though these are different sides of traditional online marketing strategies, they are still both part of traditional online marketing strategies. Therefore, it was chosen to not delete any of the items nor to separate the items into two different scales. For purposes of analysis, a composite variable was made out of these four items: “Appeal of traditional online marketing strategies”. This was done by calculating the average score of each participant over all of the four items. See Table 1 for descriptive statistics.
Appeal of social media marketing strategies. To measure how appealing the variables user-generated content, personalization and communities in social media marketing strategies are, the strategies were first explained to the participants. Then, they were again asked to rate the appeal of the strategies on a 5-point Likert scale. The responses being again (1) “I strongly dislike it”, (2) “I dislike it a little”, (3) “Neutral”, (4) “I like it” and (5) “I like it a lot”. This measurement
consisted out of five items. Since this measurement (and the following) proved to be relatively normally distributed, maximum likelihood factor analysis were chosen to conduct. A maximum likelihood factor analysis that was carried out for this construct shows that the five items form a single scale: only one component has an eigenvalue above 1 (eigenvalue 2.59). All items correlate
positively with this component, the variable “Appeal of community pages” has the strongest association (factor loading is .82). Reliability did prove to be good, Cronbach's Alpha = .77. Therefore, this scale seems to be a reliable measurement of the appeal of social media marketing strategies and a composite variable was created for analysis purposes out of these five items by calculating an average score over the five items for each participant: “Appeal of social media marketing strategies”.
Two of these five items were also used to measure another construct: “Appeal of community strategies”. A correlation analysis shows that the two items are significantly related, r = .47, p < 0.01. Therefore, the scores of the two items were averaged for each participant to create one construct out of the two items. The measurements for “Appeal of personalization strategies” and “Appeal of user-generated content strategies” merely consisted out of one item each and therefore no new construct needed to be calculated. See Table 1 for descriptive statistics.
Autonomy support. To measure the need for autonomy, statements derived from the Basic Psychological Needs Scale that is based on self-determination theory, were turned into questions (Deci & Ryan, 2000). This was done because several items became too obvious in the form of a statement and it was important to make sure that participants did not become inclined to answer every statement with “Strongly agree”. Therefore, questions were asked that allowed participants to indicate how important exactly certain items were for them. The response options for these
questions were (1) “Not at all important”, (2) “Slightly important”, (3) “Neutral”, (4) “Fairly important”, (5) “Very important”. The construct consisted out of seven items. A factor analysis showed that the seven items form a single scale: only one component had an eigenvalue above 1 (eigenvalue 3.91). All items correlate positively with this component, the variable “the importance of having many opportunities for decision making” has the strongest association (factor loading is .88). Reliability of the scale is very high, Cronbach's Alpha = .85. Therefore, it appears the scale is reliable in measuring autonomy support. Accordingly, one variable was constructed to measure
autonomy support. The average score was taken for each participant over all seven items. See Table 1 for descriptive statistics.
Identity support. The statements that were used for the measurement of identity support were derived from the self-concept clarity scale constructed by Campbell, Trapnell, Heine, Katz, Lavallee & Lehman (1996). The statements were translated into Dutch. To measure the needs for identity support, several statements were shown to the participants. They are then asked to give a rating as to how much they agree with the statement on a 5-point Likert scale. The options being (1) “Strongly disagree”, (2) “Disagree”, (3) “Neither agree nor disagree”, (4) “Agree”, (5) “Strongly agree”. The construct consisted out of twelve items. A factor analysis showed that the items do not form a singular scale: three components had an eigenvalue above 1. However, the reliability of the scale did prove to be very good, Cronbach's Alpha = .90. Therefore, it is important that we look more closely to the items. The first thing that is noticeable is that the two items that correlated positively with the first component (eigenvalue 5.32), are the only recoded items in analysis and therefore, this could be due to the recoding of the items. The second component (eigenvalue 1.36) consisted out of seven items and these seem to measure general thoughts about the participants' identity. The third component however (eigenvalue 1.03), consists out of three items and also seem to measure thoughts about participants' identity, although the phrasing is different: the three items all start with “if”. The fact that these items loaded on a different component than the rest may be due to the phrasing of the items since this is a clear similarity between them. Since the reliability test showed the scale to be highly reliable, no items will be deleted from the scale in further analyses. Therefore, one variable was constructed out of these twelve items to measure identity support. This was done by calculating an average score for each participant. See Table 1 for descriptive statistics.
Intimacy support. Also for the measurement of intimacy support, statements were shown to the participants whereafter they were asked to give a rating on a 5-point Likert scale. The options
being (1) “Strongly disagree”, (2) “Disagree”, (3) “Neither agree nor disagree”, (4) “Agree”, (5) “Strongly agree”. These statements were derived from the same Basic Psychological Needs Scale as the statements that measure autonomy support (Deci & Ryan, 2000). The statements were translated into Dutch. This construct consisted out of seven items. A factor analysis was carried out and it showed that two components had an eigenvalue above 1 (eigenvalue 2.75; 1.59). Two items correlated positively with the first component and five items correlated positively with the second component. A closer look at the items revealed that the first component was made up of items that were recoded. Therefore, it is most likely that was due to the recoding of the items. The reliability test showed that the scale was not very reliable, Cronbach's Alpha = .50. However, the test also showed that if the first item would be deleted, the reliability would be higher (and sufficient), Cronbach's Alpha = .65. Therefore, it was chosen to delete the first item of the scale: “I really like the people I interact with”. Accordingly, one variable was constructed out of the remaining six items by calculating an average score of the items for all participants. See Table 1 for descriptive statistics.
Exposure to traditional online marketing strategies. To measure the amount of participation in these strategies, participants were asked additional questions. For the traditional online marketing strategies, participants were asked how often they come across these strategies, since no participation is involved in these strategies. With these questions, participants were asked to rate their answers on a 5-point Likert scale. The options being (1) “Never”, (2) “Almost Never”, (3) “Sometimes”, (4) “Often”, (5) “Very Often”. See Table 1 for descriptive statistics.
Participation in social media marketing strategies. For the social media marketing strategies they were specifically asked how often they participate in such strategies. Since participation is inherent to these strategies, it was decided that the participants' participation was more important to find out rather than them just coming across such strategies. Again, they were able to rate this on a 5-point Likert scale using the response options (1) “Never”, (2) “Almost
Never”, (3) “Sometimes”, (4) “Often”, (5) “Very Often”. The questions in the survey regarding participation consisted out of five items. Two of these items were used to construct a new variable “Participation in personalization strategies” by calculating the average score of these two items for all participants. Then, two other items were used to construct a new variable “Participation in community strategies”. This was also done by calculating the average score of these two items. “Participation in user-generated content strategies” solely consisted out of one item. See Table 1 for descriptive statistics.
Social media use. There were also some general questions asked in the survey regarding exposure. First of all, participants were asked how much time they spend specifically on social media per day. The response options for this question consisted out of six options, being: (1) “0-1 hour a day”, (2) “1-2 hours a day”, (3) “3-4 hours a day”, (4) “5-6 hours a day”, (5) “7-8 hours a day”, (6) “more than 8 hours a day”. And secondly, the participants were asked on how many social media platforms they are actively online on. They were presented with a list of eleven social media platforms and could choose as many options as necessary. The list consisted of: “Facebook”, “Twitter”, “YouTube”, “Instagram”, “Tumblr”, “Reddit”, “Pinterest”, “LinkedIn”, “Google+”, “Flickr” and “Vine”.
It was chosen to also include these two questions as covariates in all models: “Amount of time spent on social media per day” and “Amount of social media platforms participants are actively online on”. The reason for this is first of all, because previous research on “the mere exposure effect” has shown that a relationship exists between mere exposure and attitude enhancement (Zajonc, 1968). This means that continuous exposure to a certain stimuli tends to increase people's liking of that stimuli. In this research, that stimuli is social media. The increase in positive attitudes towards the medium accomplished by overall use of social media may then also reflect on the appeal of the marketing strategies used on this medium. Secondly, other similar research on effects of mere exposure pose that the more familiar people are with a certain medium
(the more knowledge they have on a medium), the more favourable they start to feel toward that medium (Cha, 2009). This way, it is arguable that the more various platforms participants are used by participants, the more familiar they are with the medium as a whole: since it lets them gain knowledge on several aspects of the medium and not just one. Therefore, it was found also relevant to look at the amount of social media platforms participants are actually actively online on to see if it affects the relationship between the developmental needs of emerging adults and the appeal of the social media marketing strategies. See Table 1 for descriptive statistics.
Analytical approach
First, bivariate correlations were calculated between all variables. Then, for the first
hypothesis (H1), a paired samples t-test using SPSS was conducted to compare the means between “Appeal of traditional online marketing strategies” and “Appeal of social media marketing
strategies”. For the analysis of H2A and H2B, hierarchical multiple regressions were carried out. The variable “Participation in user-generated content strategies was included as a moderator in the model. Then, to analyse H3A and H3B, hierarchical multiple regressions were again used, however now the variable “Participation in personalization strategies” was included as a moderator in the model. Finally, to analyse H4A and H4B, the same analyses were conducted, however here the variable “Participation in community strategies” was included as a moderator in the model. Throughout the study Andrew Hayes' Process was used within SPSS to analyse the effects of the moderators.
Finally, as said before, the variables “Amount of time spent on social media per day” and “Amount of social media platforms participants are actively online on” were included as covariates in all models.
Results Descriptive Statistics
As mentioned before, Table 1 in the appendix shows the descriptive statistics for all
variables. When we look a the mean scores of the dependent variables, it is first of all interesting to note that the mean for the appeal of all social media marketing strategies together is clearly higher (M = 2.93, SD = .64) than the mean for the appeal of traditional online marketing strategies (M = 1.86, SD = .53). Second of all, it is interesting to see that out of all three social media marketing strategies, appeal of community strategies seems to score highest in the sample (M = 3.17, SD = .78), followed by appeal of user-generated content strategies ( M = 2.82, SD = .93) and finally by appeal of personalization strategies ( M = 2.74, SD = .65).
Table 2 in the appendix shows the bivariate correlations that were calculated between all variables. There are a few significant correlations that are interesting to keep in mind. Table 2 shows us that appeal of social media marketing strategies correlates moderately with the appeal of traditional online marketing strategies. We also see that the latter is actually negatively correlated with autonomy support. Furthermore, participation in community strategies is correlated
moderately with a number of variables: appeal of social media marketing strategies, appeal of community strategies and intimacy support. While identity support is correlated moderately with appeal of personalization strategies as well as appeal of community strategies. Finally, participation in user-generated content strategies is correlated moderately with participation in personalization strategies.
The appeal of social media marketing strategies versus the appeal of traditional marketing strategies.
To test the first hypothesis, a paired samples t-test was conducted using SPSS. There was a significant difference found in the scores of the appeal of traditional marketing strategies (M = 1.86, SD = 0.53) and the appeal of social media marketing strategies (M = 2.93, SD = .64); t(76) = -13.15, p = < . 001. These results show that social media marketing strategies significantly appealed more to emerging adults than traditional marketing strategies. Therefore, hypothesis H1 is accepted.
Appeal of user-generated content strategies and Autonomy Support
To test hypothesis H2A, a hierarchical multiple regression was conducted. In the first step of this model, two control variables were included: “Amount of time spend on social media per day” and “Amount of social media platforms participants are actively online on”. The results show that this model was not statistically significant (F (2, 74) = 1.59; p = .212) and explained 4.1% of the variance in the appeal of user-generated content strategies. After including “Autonomy support” as a predictor, the model explained the same percentage of variance (F (3, 73) = 1.04; p = .379). This shows that the addition of “Autonomy support” did not cause an increase in the amount of variance explained in the appeal of user-generated content strategies after having controlled for participants' amount of time spent on social media and the number of social media platforms they are actively online on (R² change = .000). This shows that autonomy support was not a significant contributor when it comes to predicting the appeal of user-generated content strategies; b* = .005, t = 0.4, p = .967, 95 % CI [-0.37, 0.39]. And neither were “Amount of time spent on social media”; b* = .21, t = 1.74, p = .086, 95 % CI [0.03, 0.45] or “Amount of social media platforms”; b* = 0.02, t = -0.19, p = .086, 95 % CI [-0.03, 0.45]. Therefore, hypothesis H2A was rejected.
Then, the amount of participation in user-generated content strategies was added as a moderator between the need for autonomy and the appeal of user-generated content strategies. To test this, Andrew Hayes' Process analysis was used in SPSS. For hypothesis H2B, the amount of participation in user-generated content strategies was not a significant moderator between the need for autonomy support and the appeal of user-generated content strategies; p = .964, 95% CI [-2.11, 2.20]. Therefore, hypothesis H2B was rejected.
Appeal of personalization strategies and Identity Support
To test hypothesis H3A a hierarchical multiple regression was conducted as well. The results show that when merely the two control variables were included in the model, the model explained 1.9 % of variance in the appeal of personalization strategies and the model was not statistically
significant (F (2, 74) = .72; p = .490). After including the predictor “Identity support” in the model, the total variance explained by the model as a whole was 5.2 % (R² Change = .033; F (3, 73) = 1.34; p = .270). This means that the introduction of “Identity support” explained and additional 3.3 % of variance in the appeal of personalization strategies, after controlling for the amount of time participants spend on social media and the amount of platforms they are online on. These results show that identity support was not a significant contributor when it comes to predicting the appeal of personalization strategies; b* = .18, t = 1.59, p = .116, 95 % CI [ -0.40, 0.36]. And neither did the covariates “Amount of time spent on social media”; b* = .11, t = .93, p = .357, 95 % CI [ -0.09, 0.25] or “Amount of social media platforms”; b* = -0.01, t = -0.8, p = .937, 95 % CI [ -0.11, 0.10]. Therefore, hypothesis H3A was rejected.
Here, the amount of participation in personalization strategies was added as well as a moderator. The results of the test for hypothesis H3B also showed that the amount of participation in personalization strategies was not a significant moderator between the need for identity support and the appeal of personalization strategies; p = .35, 95% CI [-.29, .82]. Therefore hypothesis H3B was rejected.
Appeal of community strategies and Intimacy Support
Also for hypothesis H4A a hierarchical multiple regression was conducted. In the first step, the same two control variables were entered in the model. This model showed to be statistically significant (F (2, 74) = 5.27; p = .007) and explained 12.5 % of the variance in appeal of
community strategies. Then, the variable “Intimacy support” was added to the model and the results show that the total variance explained by the model as a whole was 16.3 % in the appeal of
community strategies while controlling for the amount of time participants spend on social media and the amount of social media platforms they are online on (R² Change = .039; F (3, 73) = 4.75; p = .004). These results indicate that the model as a whole can be used to predict the appeal of
model are significant. The results suggest that only “Amount of time spent on social media” seems to contribute significantly to the prediction of “Appeal of community strategies”. The strength of this predictor in the model is moderate; b* = .36, t = 3.26, p = .002, 95 % CI [0.12, 0.50]. “Amount of social media platforms” did not contribute significantly to the multiple regression model; b* = -0.05, t = -0.48, p = .632, 95 % CI [-0.15, 0.09] and neither did “Intimacy support”; b* = .20, t = 1.84, p = .070, 95 % CI [-0.02, 0.55]. Therefore hypothesis H4A was only accepted for the “Amount of time spent on social media” as a significant predictor for “Appeal of community strategies” and not for the covariate “Amount of social media platforms” and neither for the expected predictor “Intimacy support”.
Again, the amount of participation in community strategies was added as a moderator in the relationship between the need for intimacy and the appeal of community strategies. For hypothesis H4B the results show that the amount of participation in community strategies was not a significant moderator between the need for intimacy support and the appeal of community strategies; p = .600, 95% CI [-.23, .39]. Therefore hypothesis H4B was rejected.
Discussion
This study first of all aimed to find out which online marketing strategies are perceived as appealing and which as not appealing by emerging adults. Then, it also aimed to find out if there is a relationship between emerging adults' developmental needs and their social media marketing preferences. The study was carried out from the point of view that emerging adults seek out digital and social media to gratify their developmental needs of autonomy, identity and intimacy.
Therefore, this study looked at the specific elements of social media marketing that make use of the fact that emerging adults have these key developmental needs: respectively, the creation of user-generated content, marketing strategies that make use of personalization and finally, marketing strategies that make use of online communities. Results suggest that while social media marketing
strategies are indeed more appealing to emerging adults than traditional online marketing strategies (H1), the three main developmental needs were not significantly related to the appeal of specific social media marketing strategies studied in this research.
Following the idea that emerging adults have negative attitudes towards “push” marketing methods (methods commonly used in traditional online marketing strategies and not in social media marketing strategies that use more “pull” methods), it was hypothesized that emerging adults would show a preference of social media marketing strategies such as the creation of user-generated content, personalization- and community strategies as opposed to banners, pop-ups, email- and search marketing (H1). The results derived from the data supported this hypothesis: social media marketing strategies were found significantly more appealing to emerging adults than traditional online marketing strategies.
This study continued investigating whether three key developmental needs (autonomy support, identity support and intimacy support) could be significant predictors of the appeal of social media marketing strategies (respectively: user-generated content strategies, personalization strategies and community strategies). First, this study hypothesized that the appeal of
user-generated content strategies could be predicted by a higher need for autonomy support among emerging adults (H2A). The results of the study showed that there was no association found between those variables. Then, this study also hypothesized that a higher need for identity support among emerging adults could contribute to the prediction of the appeal of personalization strategies (H3A). However, also this expectation did not find support in the findings from the data. However it is interesting to keep in mind that Table 2 in the appendix does show a significant correlation between identity support and the appeal of personalization strategies. Of course, no causal inferences can be derived from this, however it does indicate that even though identity support might not be significant in predicting the appeal of personalization strategies, there is a relationship present between these two variables.
Finally, it was hypothesized that a higher need for intimacy support could make a significant contribution to predicting the appeal of community strategies (H4A). The results of this analysis suggested that although the model as a whole could indeed be used to predict the appeal of community strategies, intimacy support did not prove to a be significant predictor in this: the amount of time participants spend on social media per day actually did prove to be a significant predictor.
Next to the direct effects, moderations were tested as well. It was hypothesized that the amount of (prior) participation in these three strategies specifically would show a moderating effect between the needs and the associated strategies. In that the expectation was that the amount of participation would increase the appeal of these strategies (H2B, H3B and H4B). However, results indicate that the amount of participation was not a significant moderator in neither of the three relationships.
Furthermore, it is important to note that participation in community strategies did show a
significant correlation with the appeal of community strategies as well as with intimacy support. Again, even though no causal inferences can be made with this finding, it does indicate that a relationship exists between these variables.
While this study failed to draw any conclusions on that emerging adults' developmental needs might be a significant factor in determining which social media marketing strategies are preferred by them, the study did reveal that social media marketing strategies in general are found more appealing by emerging adults than traditional online marketing strategies.
The fact that the results derived from the data did not support most of the hypotheses, can mean that the effects do not exist in the population, but it can also mean that this research was not able to retrieve the effects that may actually exist. This may have had several reasons.
Limitations & Directions for Future Research
scales were constructed, some analyses were run using merely one item as a measurement. The measurements that consisted solely out of one scale include the appeal of user-generated content strategies and the appeal of personalization strategies. Since they consisted out of solely one item, these measurements could not be tested for reliability. A solution might be to construct reliable scales with multiple items via a pilot study beforehand and to use this in the study. However, it is important to keep in mind that making the survey any longer, does not make participating in the survey more attractive and could subsequently lower the amount of participants.
Another weakness of the study might be that although the different sorts of strategies were explained thoroughly to the participants, they had no visual examples to make sure the participants knew which strategies were being discussed. There were also no questions included to find out if the participants were familiar with these strategies. Including visual examples of the strategies might exclude the possibility that some participants did not think of the targeted strategies and therefore answered wrongly to the questions that followed the explanations. However, I do believe it is important to choose these examples of the strategies through the use of a pilot study. This way, questions can be asked regarding participants' familiarity with and attitude towards the strategies to measure if biases by prior knowledge or opinions exist.
This study reasons from the idea that emerging adults' developmental needs might influence what they prefer in their media use (Coyne, 2013; Serazio, 2013). Therefore, in the analyses, developmental needs were used as predictors for the appeal of distinct social media marketing strategies. However, since the data did not yield any significant results in this matter, it is important to consider that possible predictors might be very different than the developmental needs
distinguished for this study. An exploratory qualitative research might offer more insight into the underlying reasons for emerging adults' preferences. This could then be a start in understanding what predictors exist when trying to determine which factors influence which social media marketing strategies emerging adults find appealing. When relevant predictors are found, specific
implications can be made as to how certain elements of social media marketing can best be used when targeting emerging adults.
Conclusion
Although this study did not provide evidence that emerging adults' developmental needs might be predicting factors in which social media marketing strategies they prefer, it did establish that emerging adults prefer social media marketing strategies above traditional online marketing strategies. It is important to keep in mind that just the fact that no significant results were found in this matter, that it does not mean that they are not there. Several reasons may have lead to the fact that the developmental needs were not found to be contributing factors in predicting emerging adults' preferences. Therefore, this study suggest that further and more thorough research is needed to assess whether there is an effect present.
References
Arnett, J. J. (2000). Emerging adulthood: A theory of development from the late teens through the twenties. American psychologist, 55(5), 469.
Campbell, J. D., Trapnell, P. D., Heine, S. J., Katz, I. M., Lavallee, L. F., & Lehman, D. R. (1996). Self-concept clarity: Measurement, personality correlates, and cultural boundaries. Journal of Personality and Social Psychology, 70, 141–156.
Cha, J. (2009). Shopping on Social Networking Web Sites. Journal of Interactive Advertising, 10(1), pp.77-93
Chaffey, D.; Chadwick, F.E.; Johnston, K.; Mayer, R. 2009. Internet marketing: strategy, implementation and practice. 4th edition. Pearson Education: Pren- tice Hall 705 p. Costello, Anna B. & Jason Osborne (2005). Best practices in exploratory factor analysis: four
recommendations for getting the most from your analysis. Practical Assessment Research & Evaluation, 10(7).
Coyne, S. M., Padilla-Walker, L. M., & Howard, E. (2013). Emerging in a Digital World A Decade Review of Media Use, Effects, and Gratifications in Emerging Adulthood. Emerging Adulthood, 1(2), 125-137.
Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the selfdetermination of behavior. Psychological Inquiry, 11, 227-268.
Ellison, N. B., Steinfield, C., & Lampe, C. (2007). The benefits of Facebook “friends:” Social capital and college students’ use of online social network sites. Journal of Computer‐ Mediated Communication, 12(4), 1143-1168.
Ellison, N. B., Steinfield, C., & Lampe, C. (2011). Connection strategies: Social capital implications of Facebook-enabled communication practices. New Media & Society, 1461444810385389.
Gross, E. F., Juvonen, J., & Gable, S. L. (2002). Internet use and well‐ being in adolescence. Journal of Social Issues, 58(1), 75-90.
Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social Media. Business horizons, 53(1), 59-68.
Katz, E., Blumler, J. G., & Gurevitch, M. (1973). Uses and gratifications research. Public opinion quarterly, 509-523.
Killian, T., Hennigs, N., & Langner, S. (2012). Do Millennials read books or blogs? Introducing a media usage typology of the internet generation. Journal of Consumer Marketing, 29(2), 114-124.
Mangold, W. G., & Faulds, D. J. (2009). Social media: The new hybrid element of the promotion mix. Business horizons, 52(4), 357-365.
Moore, M. (2012). Interactive media usage among millennial consumers. Journal of Consumer Marketing, 29(6), 436-444.
Population of Europe by age group (2014). Retrieved from
http://ec.europa.eu/eurostat/tgm/refreshTableAction.dotab=table&plugin=1&pcode =tps00010&language=en
van Noort, G., Antheunis, M. L., & van Reijmersdal, E. A. (2012). Social connections and the persuasiveness of viral campaigns in social network sites: Persuasive intent as the underlying mechanism. Journal of Marketing Communications, 18(1), 39-53.
Van Noort, G., Voorveld, H. A., & van Reijmersdal, E. A. (2012). Interactivity in brand web sites: cognitive, affective, and behavioral responses explained by consumers' online flow
experience. Journal of Interactive Marketing, 26(4), 223-234.
Serazio, M. (2013). Selling (Digital) Millennials: The Social Construction and Technological Bias of a Consumer Generation. Television & New Media, 1527476413491015.
European Journal of Marketing, 42(3/4), 299-310.
Smith, K. T. (2011). Digital marketing strategies that Millennials find appealing, motivating, or just annoying. Journal of Strategic Marketing, 19(6), 489-499.
Smith, K. T. (2012). Longitudinal study of digital marketing strategies targeting Millennials. Journal of Consumer Marketing, 29(2), 86-92.
Tanyel, F., Stuart, E. W., & Griffin, J. (2013). Have “Millennials” Embraced Digital Advertising as They Have Embraced Digital Media?. Journal of Promotion Management, 19(5), 652-673. Whiting, A., & Williams, D. (2013). Why people use social media: A uses and gratifications
approach. Qualitative Market Research: An International Journal, 16(4), 362-369.
Wymbs, C. (2011). Digital marketing: The time for a new “academic major” has arrived. Journal of Marketing Education, 0273475310392544.
Zajonc, R. B. (1968). Attitudinal effects of mere exposure. Journal of personality and social psychology, 9(2p2), 1.
APPENDIX Table 1
Descriptive Statistics (N=77)
Variable Mean Standard Deviation
ATMS 1.86 .53 ASMMS 2.93 .64 AUGC 2.82 .93 APERS 2.74 .65 ACOM 3.17 .78 AUS 4.32 .56 IDS 2.76 .75 INS 3.85 .59 PUGC 1.22 .50 PPERS 1.40 .53 PCOM 2.88 1.11 TSM 2.21 .91 PSM 3.10 1.50
Note: ATMS = Appeal traditional marketing strategies; ASMMS = Appeal social media marketing strategies; AUGC = Appeal user-generated content strategies; APERS = Appeal personalization strategies; ACOM = Appeal community strategies; AUS = Autonomy Support; IDS = Identity Support; INS = Intimacy Support; PUGC = Participation in user-generated content strategies; PPERS = Participation in personalization strategies; PCOM = Participation in community strategies; TSM = Amount of time spent on social media per day; PSM = Amount of platforms participants are online on.
Table 2
Bivariate correlations between all variables. Measur
e
Atms Asmms Augc Apers Acom AuS IdS InS Pugc Ppers Pcom TSM PSM
Atm Asmms .27* Augc .21 .73*** Apers .23* .66*** .35** Acom .21 .88*** .44*** .48*** AuS –.29* .13 .02 .05 .17 IdS –.10 .22 –.04 .25* .31** .06 InS –.08 .15 .01 –.00 .20 .17 .21 Pugc .14 .01 .00 .11 –.05 .07 .08 –.08 Ppers .07 –.07 –.16 .15 –.10 .04 .04 –.24* .33** Pcom –.03 .37** .20 .13 .44*** .16 .17 .28* –.10 .08 TSM .10 .29** .20 .07 .35** .07 .16 –.001 .01 .03 .27* PSM .27* .04 .03 .01 .03 .11 .05 –.06 .11 –.07 .06 .27* Note: *p < .05, **p < .01, **p < .001
Note: Atms = Appeal traditional marketing strategies; Asmms = Appeal social media marketing strategies; Augc = Appeal user-generated content strategies; Apers = Appeal personalization strategies; Acom = Appeal community strategies; AuS = Autonomy Support; IdS = Identity
Support; InS = Intimacy Support; Pugc = Participation in user-generated content strategies; Ppers = Participation in personalization strategies; Pcom = Participation in community strategies; TSM = Amount of time participants spend on social media; PSM = Amount of platforms participants are online on.
