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Motivation and Age related to Forwarding of Online

Content

Abstract:

Nowadays digitalization forms an integral part of society, with social media being embraced by all generations. With this rise, understanding the triggers in virality for online content is increasingly important for marketeers. This study aims to determine which motivation factors, including need to belong, individuation, altruism, and need for personal growth affect the frequency of content forwarding, and how this relationship is moderated by generation. This will be done by answering the research question: How does generation moderate the motivation to forward online content?

Individuation and altruism were hypothesized to have a positive correlation with content

forwarding, and generation was hypothesized to moderate the relationships between individuation and content forwarding, as well as altruism and content forwarding. To test these hypotheses an online survey was distributed to social media users between the ages of 9-56 years old. They were asked to state their age and frequency of content forwarding, and answer statements with regards to the motivation factors. Responses were analysed using regression in SPSS. Results confirmed individuation is positively correlated with forwarding, however altruism is not correlated with forwarding. Furthermore, generation didn’t moderate the relationship individuation and altruism had with forwarding, but interestingly generation did moderate the relationship between need for personal growth and content forwarding.

Keywords: content forwarding, need to belong, individuation, altruism, need for personal growth, generation

Author: Richard Schuurmans Student number: 11296461 Date of submission: 30/06/2021

Word Count: 6,065

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Statement of Originality

This document is written by Student Richard Schuurmans who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

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Table of Contents

1. Introduction ... 4

2. Theoretical Framework ... 5

2.1. Forwarding online content... 5

2.2. Motivations to forward online content ... 5

2.3. Generational effects and their moderating role ... 6

3. Data and Methods ... 7

3.1. Data Collection ... 7

3.2. Variables ... 8

3.3. Data preparation ... 10

4. Results ... 11

4.1. Correlations ... 11

4.2. Regression Assumptions ... 12

4.3. Hypothesis testing ... 13

4.4. Additional effects ... 16

5. Discussion ... 16

6. Conclusion ... 19

7. Reference List ... 21

8. Appendix: ... 22

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1. Introduction

Due to the increasing digitalization and embracing of social media by all generations, understanding social media usage and what causes content to become popular is becoming increasingly important for companies. Increasingly, marketeers are interested in

understanding why social media users forward online content (Jenkins, 2021). Forwarding is when someone shares electronic content, whether it be pictures, videos, or text and is an important means for content to spread and create benefits for companies through increasing virality, brand loyalty, and overall awareness.

Previous research has mainly looked at post characteristics to explain why certain posts go viral or become popular. For example, Sabate et al. (2014) studied the structural

characteristics of posts and found that images and videos caused the greatest level of engagement, with images being dominant. Furthermore, with regards to the length of posts, the number of characters used was positively correlated with the number of likes. When it comes to comments, they recommend using images in posts because this was positively correlated to number of comments. Moreover, the avoidance of links was found to cause more comments.

However, much less research has been done in examining if characteristics of social media users themselves also influence post forwarding. For example, Ho & Dempsey (2010) suggest that a person’s individuation is positively related to the forwarding of online content.

Further, the amount of online content consumed had a positive effect on forwarding of information. Moreover, they also suggest that altruism, or concern for others’ happiness is a positive predictor of forwarding. Additionally, Strutton et al. (2011) studied the differences between generation X and Y in online word-of-mouth and found differences in media used between the generations. While Y was more engaged in social networks, X shared more through email. That being said, both groups were found to be similar in their motivations, behaviours, and technology skills. Furthermore, Perrin (2015) studied the differences between age groups and found young adults (18-29) are the most likely to use social media.

Another study focused on the increased embracing of social media by older adults. This study by Pew Research Center (“Demographics of social media users and adoption in the United States”, 2021) found that between 2005-2021 there was an increase in social media usage by U.S. adults of almost 80 percent, and a strong increase in 50-64 year old users within the last decade. This gradual evening out of the age spectrum points out that new approaches to

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marketing will be necessary and understanding how motivations differ between age groups and the effect this has on the likelihood to forward online content, is an important topic for research.

However, these studies have so-far failed to incorporate generational differences. There may be a meaningful insight when exploring what motivations lead to the forwarding of online content, specifically with regards to generations. This would build on existing research, that focused on age and motivation to forward content separately, by exploring how age/

generation effects how different motivations lead to the increase of online content forwarding, and brings the research question:

How does generation moderate the motivation to forward online content?

2. Theoretical Framework

2.1. Forwarding online content

Forwarding in essence involves the spreading of content such as pictures, videos, or text to other users, and these users will then likely forward to other users. This exponential growth involved in the spread of content can lead to increased virality for companies and individuals alike. For example, you may see a post on Facebook of your favourite clothing brand and there is a sale happening, and you share this with a friend. Forwarding is not the same as liking or commenting on social media posts, it has a more social aspect to it. Therefore, the topic of forwarding is a good representation of post popularity on social media sites.

2.2. Motivations to forward online content

When it comes to motivations involved in sharing information an article by Ho & Dempsey (2010) is very inciteful. Here they studied factors critical to viral growth on social media, and analysed internet users’ motivations to pass along content. In this study, a framework to explain why people communicate interpersonally by Schutz (1958) is used, which recognizes inclusion, affection, and control to be the three main motivations. Under inclusion, the need to belong and need to be unique were studied, and it was found that only need to be unique, or individuation, was positively related to the forwarding of online content. Individuation is a type of expression of yourself. C. G. Jung (1953) defined individuation as becoming an individual, and, in so far as “individuality” embraces our innermost, last, and incomparable uniqueness. It’s essentially the coming to selfhood. Individuation therefore is of particular interest with regards to content forwarding, as sharing content with others can be seen as a

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sign of individuation where you are expressing yourself to others. This is backed up by another study which investigated what motivates knowledge sharing in online knowledge forums. According to Lee & Jang (2010) those with higher public individuation were found to be more likely to contribute on online forums. This leads to the following hypothesis:

H1: Higher individuation, or need to be unique will be positively correlated with forwarding

While need to belong wasn’t found to be related, Ho & Dempsey (2010) noted that they researched email forwarding, and an individuals need to belong may be a more important motivation when it comes to social media. Therefore, need to belong could provide a meaningful insight to the differences in forwarding motivations between these mediums.

Moreover, for the motivation pertaining to affection, altruism, or the concern for others’

happiness, could best be described through a quote of the Dalai Lama; “The root of happiness is altruism – the wish to be of service to others”. A person with altruistic traits is seen as one who is concerned with the wellbeing of others and takes actions with others in consideration.

Ho & Dempsey (2010) found altruism to have a positive effect on forwarding. This is in line with an article by Wu et al. (2009) where knowledge sharing in the workplace was discussed, and employees’ altruism traits were found to increase knowledge sharing, forming the

following hypothesis:

H2: Altruism will be positively correlated with forwarding

Lastly, Ho & Dempsey (2010) also researched the control motivation, and under this the need for personal growth. Surprisingly, they found while significantly related to forwarding, the relation was negative. This is in contrast with a study done by Li & Ma (2014) where perceived need for growth between university students was measured and found to have a significant positive effect on online knowledge sharing behaviour.

2.3. Generational effects and their moderating role

So far only direct effects of motivation on content forwarding have been referred to, however for the following hypotheses a moderation variable is introduced. Age / generation is added as the moderation variable (Z) to test whether it influences the effect of X on Y, in this case being motivation on content forwarding. This forms the following conceptual model:

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With this model a researcher always assumes to find one of the following situations. The effect of the motivation on content forwarding is stronger because of a higher generation, being positive moderation. Or the researcher finds that the effect of the motivation on content forwarding is weaker because of a higher generation, which is negative moderation.

Moving on to the moderation hypotheses, an article by Mazor & Enright (1988) highlights the level of individuation among a range of age groups under 20 and found that individuation increases with age. Less is known about older age groups with regards to individuation, and this is a gap that will be researched in this paper. The following hypothesis relates age with individuation:

H3: Generation negatively moderates the relationship between individuation and content forwarding

A study by Freund & Blanchard-Fields (2014) studied the differences in altruism across adulthood and found that older adults have more of an aim to contribute to the good of others.

This together with Ho & Dempsey (2010) finding that altruism is a positive predictor of forwarding leads to the following hypothesis:

H4: Generation negatively moderates the relationship between altruism and content forwarding

3. Data and Methods

3.1. Data Collection

Data was collected on online media users of all ages. These users were grouped into sub-groups based on generation, being either X, Y, or Z, ranging from people born in 1965 to 2012. A total of 60 respondents were tested, 20 in each generation, and Google forms was used as the survey developing platform. The respondents filled in a survey pertaining to the four motivation variables, age, and frequency of content forwarding. The four motivation variables measure the respondents’ need to belong (NTB), individuation, altruism, and their need for personal growth.

X = Motivation (Need to belong, Individuation, Altruism, Need for

Personal Growth)

Y = Content Forwarding

Z = Generation

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3.2. Variables

Age will be measured in terms of generation. Users will be categorized into groups based on generation. For this, generation X includes individuals born from 1965-1980, Y is from 1981-1996, and Z being the latest generation born between 1997-2012 (“Boomers, Gen X, Gen Y, and Gen Z Explained”, 2021). Specifically, in the survey respondents will be asked to fill in basic information such as age and generation, where under generation the birth years are described, to avoid confusion.

Both of these are used together to act as a backup in case a respondent filled in a wrong answer or didn’t fill in either one. This was found to be quite useful as it was later found that a respondent failed to fill in generation in their response, but since the age was filled in the generation could be added.

Further the basic demographic of gender will be included, as this may provide further insight in the results and discussion sections. Through the course of collecting respondents females have responded more often than males which lead to 62 percent of the respondents being female. This isn’t necessarily a problem as gender isn’t part of this research, however due to the disparity it must be proven later on in the data preparation section that females do not respond significantly different than males with regards to the variables of interest.

User Motivation was measured following the structure of Ho & Dempsey (2010) and involved questions and statements regarding an individuals’ need to belong, individuation, altruism, and need for personal growth.

The need to belong (NTB) questions followed the structure of the Leary et al. (2012) NTB scale. This involved 10 statements of which the answers are measured on a 5-point Likert scale, and respondents must indicate to what extent these statements are true to them. Answers range from 1 = not at all, 2 = slightly, 3 = moderately, 4 = very, 5 = extremely. The statements that have reverse in brackets are statements that are counter indicative and must be recoded in the data preparation phase. The statements include the following:

1. If other people don’t seem to accept me, I don’t let it bother me. (Reverse) 2. I try hard not to do things that will make other people avoid or reject me.

3. I seldom worry about whether other people care about me. (Reverse) 4. I need to feel that there are people I can turn to in times of need.

5. I want other people to accept me.

6. I do not like being alone.

7. Being apart from my friends for long periods of time does not bother me. (Reverse) 8. I have a strong “need to belong”.

9. It bothers me a great deal when I am not included in other people’s plans.

10. My feelings are easily hurt when I feel that others do not accept me.

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For measuring individuation, the individuation scale formed by Maslach et al. (1985) was used. This consists of 12 statements with which answers are measured on a 5-point Likert scale indicating how true the statement is to the respondent. Answers range from 1 = not at all willing to do this, 2 = not very willing, 3 = slightly willing, 4 = fairly willing, 5 = very much willing to do this. The statements include the following:

1. Give a lecture to a large audience.

2. Raise your hand to ask a question in a meeting or lecture.

3. Volunteer to head a committee for a group of people you do not know very well.

4. Tell a person that you like him/her.

5. Publicly challenge a speaker whose position clashes with your own.

6. Accept a nomination to be a leader of a group.

7. Present a personal opinion, on a controversial issue, to a group of strangers.

8. When asked to introduce yourself, say something more personal about yourself than just your name and occupation.

9. Give an informal talk in front of a small group of classmates or colleagues.

10. Speak up about your ideas even though you are uncertain of whether you are correct.

11. Perform on a stage before a large audience.

12. Give your opinion on a controversial issue, even though no one has asked for it.

Next, Altruism was measured by using the scale used by Price, Feick & Guskey (1995). Users must respond to 5 statements stating their level of importance on a 7-point Likert scale. Answers range from 1 = very important to 7 = very unimportant. The statements include the following:

1. To help other people.

2. To serve mankind.

3. To share what you have.

4. To give to others.

5. To be unselfish.

Lastly, a user’s need for personal growth was measured through the Personal Growth Initiative Scale (Robitschek, 1998). Here respondents state their level of agreement with 9 statements with answers on a 6-point Likert scale ranging from 1 = definitely disagree, 2 = mostly disagree, 3 = somewhat

disagree, 4 = somewhat agree, 5 = mostly agree, 6 = definitely agree. The statements contain the following:

1. I know how to change specific things that I want to change in my life.

2. I have a good sense of where I am headed in life.

3. If I want to change something in my life, I initiate the transition process.

4. I can choose the role that I want to have in a group.

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5. I know what I need to do to get started toward reaching my goals.

6. I have a specific action plan to help me reach my goals.

7. I take charge of my life.

8. I know what my unique contribution to the world might be.

9. I have a plan for making my life more balanced.

The variable content forwarding follows the structure of Ho & Dempsey (2010). Respondents will answer the amount of times they forward content per week by choosing one of six frequency brackets:

1) never, 2) 1-2 times, 3) 3-5 times, 4) 6-10 times, 5) 11-20 times, 6) more than 20 times

3.3. Data preparation

In order to get the data ready to perform analyses a number of steps need to be taken. To start off it’s important to check for any extreme outliers in the survey responses. This was done by using the Explore tab in SPSS and making a boxplot for the individual survey statement responses. Here no extreme outliers were found, so all the respondents’ answers can be kept.

Next, since the respondents answered with responses such as “Extremely Important” and

“Completely Agree”, these Likert scale responses needed to be recoded into numeric values.

For the 10 need to belong statements they were measured on a 5-point Likert scale and ask how true the statement is to the respondent. The response “Not at all” was changed to the numeric value 1, and the response “Extremely” was changed to 5, with the rest of the answers in-between taking on the corresponding values. The same approach was taken for the 12 Individuation statements, 5 altruism statements, and 9 need for personal growth statements.

For the generation variable, respondents were able to select X, Y, or Z. This was recoded to X = 1, Y = 2, and Z = 3. Lastly for the frequency of forwarding online content which were measured with 6 frequency brackets the same approach was taken where the lowest bracket

“Never” became the numeric value 1, and the highest bracket “More than 20 times” became value 6, and the rest of the brackets to the corresponding values.

Next, it’s important to recode any statements that may be reversely formulated. In particular among the 10 need to belong items there are 3 that are counter indicative. Specifically, statement one, three and seven. In SPSS these were recoded using the transform function, where it was recoded into the same variable. Since these items were measured on a 5-point Likert scale, responses with the lowest value 1 were recoded to the highest value 5, and all the values in between took their corresponding values.

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The next step in preparing the data is to do a reliability analysis to see if the statements have an acceptable correlation with one another and whether removing statements can increase the reliability that our statements are measuring the same thing. This is necessary in order to have confidence in the data once variables are formed out of the items by taking means. For this a test of Cronbach’s alpha was done in SPSS. For the 10 items under NTB there was an alpha of 0.753, which is reasonably good (See Appendix 2.1). If the 7th item is deleted the alpha will increase to 0.783 but this increase doesn’t justify losing 1/10 of the data, so all the items are kept. For the 12 Individuation items (See Appendix 2.2) there was an alpha of 0.919 which shows excellent reliability and the alpha couldn’t increase by deleting items, so all 12 items are kept. For the 5 Altruism items (See Appendix 2.3) there’s a Cronbach’s alpha of 0.790 which is also quite good and doesn’t increase by removing items, so all 5 items will be kept. Lastly, the 9 Personal Growth items received an alpha of 0.846 (See Appendix 2.4) which is very good and can only be increased slightly to 0.859 by removing the 4th item, but this is a small increase and doesn’t justify loosing 1/9th of the data, so all 9 items are kept.

Now that inter-item correlation has been proven to be acceptable the next step is to group these items together into one variable. This was done by taking the mean of all the scores by using the transform function in SPSS. The newly computed variables for “Need to belong”,

“Individuation”, “Altruism” and “Need for personal growth” will be used for the further analysis.

Lastly, as mentioned earlier, since females responded to the survey more than males with a decent margin, it’s important to see no significant correlation between gender and the

variables related to this study. It becomes clear when looking at the correlation table (Refer to Appendix 3.2) that there are no significant differences between males and females with regards to the four motivation variables, and the frequency of content forwarding, with the lowest p-value being 0.138 with the variable individuation, which proves no significance.

This concludes that the disparity in responses between men and women is of no importance and the data can be kept as is. Now all the data has been prepared statistical analysis can be done.

4. Results

4.1. Correlations

Table 1 has the means, standard deviation, and correlations of our variables. All the variables have insignificant correlations with one another except for three cases. Individuation has a

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significant positive correlation with content forwarding (r = 0.265, p < 0.05), need for personal growth has a significant positive correlation with individuation (r = 0.454, p <

0.001), and need for personal growth has a significant positive correlation with altruism (r = 0.299, p < 0.05). Further, the numbers in parenthesis represent the Cronbach’s alpha for the variables, which wasn’t measured for content forwarding and generation since they contain only one item. For need to belong the alpha was 0.753, for individuation it was 0.919, for altruism 0.790, and lastly for need for personal growth the Cronbach’s alpha was 0.846 which are all very good.

Table 1 (Formed from Appendix 3.1) Descriptive Statistics and Correlations

Variable M SD 1 2 3 4 5 6 7

Content forwarding 3.13 1.69 ()

Generation 2.00 0.82 -0.012 ()

Need to belong 3.08 0.56 0.243 0.076 (0.753)

Individuation 3.24 0.85 0.265 -0.090 0.077 (0.919)

Altruism 5.62 0.75 0.170 -0.071 0.057 0.236 (0.790)

Need for personal growth

4.18 0.70 0.220 -0.055 0.048 0.454 0.299 (0.846)

4.2. Regression Assumptions

In order to do a linear regression, the data must satisfy 5 assumptions. First, the independent variables must have a linear relationship with the dependent variable. For NTB, there is a very weak positive linear relationship (See Appendix 1.1.1). For Individuation, there is a weak positive linear relationship (See Appendix 1.1.2). For Altruism, there is also a weak positive linear relationship (See Appendix 1.1.3), and lastly for ‘need for personal growth’

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there is a weak positive linear relationship (See Appendix 1.1.4). Next, independence of residuals is essential, and since the survey followed a cross-sectional design, meaning the data was collected at one point in time, this assumption is also met. Further, to check for normality of residuals a P-Plot must be constructed. Taking a look at the P-plots for the 4 independent variables related with the dependent variable (See Appendix 1.2.1-1.2.4), all seem to be approximately normally distributed. Moreover, homoscedasticity of residuals must be satisfied, and this was analysed by means of scatterplot (See Appendix 1.3.1-1.3.4) where the results for all 4 independent variables were found to be equally variable. Lastly, outliers cannot be present in the data, and this is met, since in the data preparation section no extreme outliers were found.

4.3. Hypothesis testing

Tables 2.1, 2.2, 2.3 and 2.4 contain the unstandardized betas, standard errors, p-values, and R2 for the 2 main effects and interaction effect for our independent variables need to belong, individuation, altruism, and need for personal growth. The unstandardized beta value states how much the dependent variable content forwarding changes when the independent variable increased by one unit. Standard error represents the accuracy to which our sample distribution represents the population, and the p-value shows whether the effect being measured is

significant with p < 0.05. R2 represents the proportion of variance in content forwarding that can be explained by this model.

Table 2.1 Need to belong (Formed from Appendix 5.1)

Y (Content Forwarding)

Antecedent Coeff. SE p

X (Need to Belong) 0.85 1.12 0.45

W (Generation) 0.1 1.53 0.95

X * W -0.05 0.5 0.91

Constant 0.64 3.44 0.85

Model Summary R2 = 0.06, F = 1.2, P=0.32

ΔR= 0.0002, F= 0.012, P= 0.91

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Table 2.2 Individuation (Formed from Appendix 5.2)

Y (Content Forwarding)

Antecedent Coeff. SE p

X (Individuation) 0.9 0.6 0.15

W (Generation) 0.75 1.1 0.5

X * W -0.23 0.34 0.5

Constant 0.19 2.05 0.93

Model Summary R2 =0.078, F= 1.57, P= 0.2

ΔR= 0.008 , F= 0.45 , P= 0.5

Table 2.3 Altruism (Formed from Appendix 5.3)

Y (Content Forwarding)

Antecedent Coeff. SE p

X (Altruism) 1.087 0.84 0.2

W (Generation) 2.01 2.25 0.38

X * W -0.36 0.4 0.37

Constant -3.01 4.79 0.53

Model Summary R2 = 0.043, F =0.83 , P= 0.48

ΔR= 0.014, F= 0.81, P= 0.37

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Table 2.4 Need for personal growth (Formed from appendix 5.4)

Y (Content Forwarding)

Antecedent Coeff. SE p

X (Personal Growth) 1.77 0.77 0.024

W (Generation) 2.8 1.6 0.086

X * W -0.67 0.38 0.082

Constant -4.36 3.3 0.192

Model Summary R2 =0.1, F = 2.05, P=0.117

ΔR= 0.051, F= 3.14, P= 0.082

Conditional Effect When W=1, Coeff.= 1.11, SE=0.45, P= 0.016, LLCI= 0.21, ULCI= 2.01 When W=2, Coeff.= 0.44, SE= 0.31, P= 0.16, LLCI= -0.18, ULC1= 1.06 When W=3, Coeff.= -0.22, SE= 0.52, P= 0.67, LLCI= -1.27, ULCI= 0.83

Now the moderation analysis has been done the hypotheses will be tested. To start off, hypothesis 1: ‘A higher individuation, or need to be unique will be positively correlated with forwarding’ will be tested. This was done by doing a linear regression in SPSS (Refer to Appendix 4.1). The output shows that individuation has a significant positive effect on content forwarding (Coeff.= 0.524, SE= 0.251, p < 0.05). This shows that with every 1 unit increase in individuation, content forwarding subsequentially increases by 0.524 units. In conclusion, hypothesis 1 is supported.

Moreover, looking at hypothesis 2: ‘Altruism will be positively correlated with forwarding’, was also tested by doing a linear regression (Refer to Appendix 4.2). The output shows that altruism has an insignificant positive effect on content forwarding (Coeff.= 0.38 , SE= 0.29, p= 0.19). While the relationship is positive it is insignificant due to the p-value being greater than 0.05, and therefore hypothesis 2 is not supported.

Next, diving into the interaction hypotheses, these were tested using the PROCESS macro v3.4 of Hayes (2018). To start with hypothesis 3: ‘Individuation will have a stronger effect on

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content forwarding among older online users, meaning generation negatively moderates the relationship between individuation and content forwarding’, was tested. For this Table 2.2 is referred to, where it’s clear that there is a non-significant negative interaction effect (Coeff.=

-0.23, SE= 0.34, p = 0.5). Hypothesis 3 can therefore not be supported.

Lastly hypothesis 4: ‘The motivation altruism will have a stronger effect on forwarding with older users, meaning generation negatively moderates the relationship between altruism and content forwarding’, was not found to have support. For this, Table 2.3 was referred to and there was found to be an insignificant negative interaction effect (Coeff.= -0.36, SE= 0.4, p=

0.37).

4.4. Additional effects

Apart from interaction effects there are a number of main effects of the independent variables on content forwarding. Referring to Table 2.1-2.4, all main effects were found to be

insignificant except the main effect of ‘need for personal growth’ on content forwarding (Coeff.= 1.77, SE= 0.77, p > 0.05) (Table 2.4) Here there is a significant positive effect of need for personal growth on content forwarding, when the moderator generation is at the mean. Further, while the main interaction effect between personal growth and content forwarding is insignificant, by analysing the conditional effects (See Table 2.4), there is a significant positive effect of ‘need for personal growth’ on content forwarding when W=1 (Gen X).

5. Discussion

This research aimed to grasp a better understanding of the underlying causes of post virality on social media. In particular, how user motivation effects content forwarding and how this effect is moderated by generation. The output supports the first hypothesis, in that

individuation has a positive effect on content forwarding. Meaning when user individuation is higher content forwarding is higher as well. Moreover, the second hypothesis was not

supported and suggests altruism didn’t have a significant correlation with content forwarding.

The third hypothesis was also not supported, and points towards no significant interaction effect. This means that generation doesn’t seem to moderate the relationship between individuation and content forwarding. Lastly, the output shows the fourth hypothesis to be unsupported as well. There was a small negative moderation of generation on altruisms effect with content forwarding, however this was not found to be statistically significant.

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In relation to previous findings (Ho & Dempsey, 2010; Lee & Jang, 2010) it was found that those with higher individuation were more likely to share information. Our study confirms these findings. However, in contrast to findings (Ho & Dempsey, 2010; Wu et al. 2009) our study was not able to confirm that higher altruism is related to higher content forwarding and found no significant results with regards to this motivation. Furthermore, previous findings on the effect of ones need for personal growth on their sharing behaviour show contradictory results (Ho & Dempsey, 2010; Li & Ma, 2014). With Ho & Dempsey finding a negative relationship, and Li & Ma finding a positive one, this was an interesting variable to include in the study. Interestingly, while this study found no significant relationship between personal growth and content forwarding when analysing the linear regression, in the moderation analysis there was a relationship found. Specifically, there is positive conditional effect of personal growth on content forwarding when W=1, which in this case is generation X (See table 2.4). This means with respondents in generation X, who are born from 1965-1980, their level of need for personal growth has a positive relationship with content forwarding, so while we can’t infer a relationship for the other generations, generation X seems to be in line with the findings by Li & Ma (2014). This effect can be seen in a visualized manner in appendix 6. When viewing the graph there’s a visible difference in the slope of the curves at the three different levels of generation, where 1 equals generation X, 2 is Y, and 3 is Z. This difference in slope indicates an interaction effect occurring, as what that means is that the change in content forwarding when ‘need for personal growth’ is higher is different between the generations, and same when personal growth is lower. This graph shows a strong positive relationship between ones need for personal growth and how often they forward online content when they are between 41-56 years old. Further compared to the findings of Mazor &

Enright (1988), where they studied a range of age groups under 20 and found that

individuation increases with age, this together with the findings (Ho & Dempsey, 2010; Lee

& Jang, 2010) suggest that age moderates the relationship between individuation and content forwarding. However, no statistical significance was found in our results. This could be due to the ages used in this analysis, a much broader scope of age was taken, which suggests that it’s possible individuation grows with age up until a point where it no longer increases drastically. Moreover, when analysing the moderation results with motivation altruism, our output doesn’t support the increase in altruism with age found by Freund & Blanchard-Fields (2014). On top of this the main effect of altruism on content forwarding, found to be

significant by Ho & Dempsey (2010), was found to not be the case for this study.

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To critique this study and hopefully to some extent explain why expected results did not occur a number of factors are analysed. To start off, ultimately 20 respondents from each generation, with a total of 60 answered the surveys, and while this is a substantial amount if this amount were more, statistical significance could have been more likely. Furthermore, another critique with regards to the respondents would be the disparity in age groups. Due to the valid ages that fall into the three generations analysed in this study ranging from 9 to 56 years old (1965-2012), it was challenging to get a good spread in age, particularly for

generation Z as it’s difficult to get respondents of young age. When referring to Appendix 7, the disparity becomes clear. A large majority of the respondents fell between the ages of 21 and 28, and this isn’t necessarily surprising as students were by far the easiest age group to find when it comes to respondents, and on its own isn’t shocking. However when coupled with the age border between generation Y and Z of 1997, meaning those born before are in Y and those after in Z, that means that around 24 years old is the cut-off point, and this is worrying as now many respondents with similar age are split into these groups, while in reality they aren’t very different in terms of age. Further, due to this clustering at ages 21 to 28, there are gaps in other areas, for instance there are no respondents between 31 and 38.

This problem could in future research be fixed by being more intentional with capturing all the age groups, instead of just focusing on getting an equal number of respondents in each generation. Additionally, a critique geared more towards the choice of regression involves the use of three levels in the generation variable. As mentioned in the data preparation section X was recoded to 1, Y to 2, and Z to 3. This recoding allowed regression analysis to be

performed, and while this isn’t necessarily inaccurate, the results could have possibly been more reliable if only two categories were used for generation. Ultimately the decision to go forward with regression analysis was based on the equal dispersion of years in each

generation. Specifically, all three generations were made up of 15 ages, and this equal proportion of years per generation made regression an acceptable method of analysis.

Alternatively, dummy variables could also have been made but this would have made the results process very tedious as multiple regressions per motivation variable would have to be performed. Lastly, some more general critique geared towards the survey formulation itself includes not making it mandatory to fill in each question, this was not a default setting in Google Forms and it was assumed when making the survey that this would be a requirement, however this didn’t end up being the case and due to this a few respondents didn’t fill in their gender (even when given a ‘prefer not to answer’ option) and generation. However, due to the backup age question on generation, which was filled out by all respondents, and the lack

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of importance of gender in this study the seriousness of this was small. Additionally, it was difficult to determine the reliability of the responses, and this could have been improved by adding a statement in the middle of the survey where respondents are asked to fill in a specific answer which indicates that they are paying attention and not just skimming through putting random answers.

6. Conclusion

In order to answer the research question: ‘How does generation moderate the motivation to forward online content?’, a broader look at the findings is taken. While the generation in which an individual is born wasn’t found to affect the relationship between the motivation and content forwarding for most of the types of motivation involved in this study, there was one meaningful relationship. This being that for generation X there was a positive

relationship between their need for personal growth and the frequency of content forwarding.

This effect was not significant for the other generations and concludes that the relationship between this motivation and content forwarding, is moderated by generation. Therefore, to answer the research question, generation moderates the motivation to forward online content only for the motivation factor ‘Need for personal growth’. Further, to summarize the findings, individuals with higher individuation were found to share more content. However,

individuals with higher altruistic traits weren’t found to forward more content, unlike previous findings. With regards to the interaction effects both hypothesized relationships weren’t supported. This being that generation was not found to moderate the relationship between individuation and content forwarding, and that generation did not moderate the relationship between altruism and content forwarding. Noticeably however, generation did moderate the relationship between the need for personal growth and content forwarding, in the sense that for generation X there was a strong positive relationship between the need for personal growth and content forwarding, while for generation Y and Z there was not.

Future research could focus on analysing these effects in further detail by involving different forms of media instead of grouping it as one, which was done for this study. This could shed light on which media sites and apps are being used pre-dominantly by certain generations, and how different considerations could be taken into account with regards to how you make a post appealing to a certain audience. Furthermore, since this study failed to produce

significant results for a number of relationships that were expected to be significant in relation to previous studies, future research could also revisit these relationships and make sure to have a bigger sample and better dispersed age groups, as mentioned in the critique of

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this study. To achieve this, researchers should create a plan on how to find respondents of varying ages and create clear boundaries for acceptable proportions of each age group. This integration of planning and targeted sampling would allow researchers more control over their data and suit their data to their research model, which would result in more accurate results. Moreover, as an interaction effect was found between generation and need for personal growth, this could hint to more meaningful relationships with regards to the other motivation variables, and future studies should revisit these.

On top of confirming a number of results from previous studies this research contributed to new knowledge in the sense that an individuals need for personal growth was only found to have an effect on their frequency of content forwarding for respondents between the age of 41-56 years old, meaning that generation did moderate this relationship. This indicates that marketeers should take age into account when choosing how to appeal to them on social media. They would profit from targeting generation X individuals with content that appeals to their need for personal growth, and by doing this would increase their chances of achieving virality. Furthermore, this research has confirmed that individuation traits are positively related to content forwarding, and marketeers can therefore be sure that incorporating this into the content they release will have positive impacts on virality within these three generations. All in all, researchers have only scratched the surface of the underlying

components of social media use and what drives people to use social media in various ways.

This study aimed to broaden that knowledge and function as a basis for future researchers to continue the effort of understanding what causes online content to become popular and go viral, and how consideration of individual characteristics of users play a key role on social media.

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7. Reference List

Ho, J. Y., & Dempsey, M. (2010). Viral marketing: Motivations to forward online content. Journal of Business research, 63(9-10), 1000-1006.

Strutton, D., Taylor, D. G., & Thompson, K. (2011). Investigating generational differences in e-WOM behaviours: for advertising purposes, does X= Y?. International Journal of Advertising, 30(4), 559- 586.

Sabate, F., Berbegal-Mirabent, J., Cañabate, A., & Lebherz, P. R. (2014). Factors influencing popularity of branded content in Facebook fan pages. European management journal, 32(6), 1001- 1011.

Perrin, A. (2015). Social media usage. Pew research center, 125, 52-68.

Jenkins, L. (2021). Social Media Marketing Trends for 2021: Predictions From the Pros : Social Media Examiner. Retrieved 31 March 2021, from https://www.socialmediaexaminer.com/social- media-marketing-trends-2021-predictions-from-pros/

Boomers, Gen X, Gen Y, and Gen Z Explained. (2021). Retrieved 31 March 2021, from https://www.kasasa.com/articles/generations/gen-x-gen-y-gen-z

Paletz, S. B., Auxier, B. E., & Golonka, E. M. (2019). Motivation to Share. In A Multidisciplinary Framework of Information Propagation Online (pp. 37-45). Springer, Cham.

Demographics of Social Media Users and Adoption in the United States. (2021). Retrieved 30 April 2021, from https://www.pewresearch.org/internet/fact-sheet/social-media/

Schutz, W. C. (1958). FIRO: A three-dimensional theory of interpersonal behavior.

Lee, E. J., & Jang, J. W. (2010). Profiling good Samaritans in online knowledge forums: Effects of affiliative tendency, self-esteem, and public individuation on knowledge sharing. Computers in Human Behavior, 26(6), 1336-1344.

Mazor, A., & Enright, R. D. (1988). The development of the individuation process from a social- cognitive perspective. Journal of adolescence, 11(1), 29-47.

Wu, W. L., Lin, C. H., Hsu, B. F., & Yeh, R. S. (2009). Interpersonal trust and knowledge sharing:

Moderating effects of individual altruism and a social interaction environment. Social Behavior and Personality: an international journal, 37(1), 83-93.

Freund, A. M., & Blanchard-Fields, F. (2014). Age-related differences in altruism across adulthood:

Making personal financial gain versus contributing to the public good. Developmental psychology, 50(4), 1125.

Li, S. M., & Ma, W. W. (2014, August). Exploring interpersonal relationship and growth need strength on knowledge sharing in social media. In International Conference on Hybrid Learning and Continuing Education (pp. 288-299). Springer, Cham.

Inceoglu, I., Segers, J., & Bartram, D. (2012). Age‐related differences in work motivation. Journal of Occupational and Organizational Psychology, 85(2), 300-329.

Leary, M., Kelly, K., Cottrell, C., & Schreindorfer, L. (2012). Individual Differences in the Need to Belong: Mapping the Nomological Network.

Maslach, C., Stapp, J., & Santee, R. T. (1985). Individuation: Conceptual analysis and assessment. Journal of Personality and Social Psychology, 49(3), 729.

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Price, L. L., Feick, L. F., & Guskey, A. (1995). Everyday market helping behavior. Journal of Public Policy & Marketing, 14(2), 255-266.

Robitschek, C. (1998). Personal growth initiative: The construct and its measure. Measurement and evaluation in counseling and development, 30(4), 183-198.

Jung, C. G. (1953). Two essays on analytical psychology.(Bollingen Series 20)

8. Appendix:

Appendix 1.1.1

Linearity of “Need to belong” with Dependent variable

Appendix 1.1.2

Linearity of “Individuation” with Dependent variable

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Appendix 1.1.3

Linearity of “Altruism” with Dependent variable

Appendix 1.1.4

Linearity of “Need for personal growth” with Dependent variable

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Appendix 1.2.1

Normality of residuals “Need to belong”

Appendix 1.2.2

Normality of residuals “Individuation”

Appendix 1.2.3

Normality of residuals “Altruism”

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Appendix 1.2.4

Normality of residuals “Need for personal growth”

Appendix 1.3.1

Homoscedasticity of residuals “Need to belong”

Appendix 1.3.2

Homoscedasticity of residuals “Individuation”

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Appendix 1.3.3

Homoscedasticity of residuals “Altruism”

Appendix 1.3.4

Homoscedasticity of residuals “Need for personal growth”

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Appendix 2.1

Reliability analysis of items under variable “Need to belong”

Appendix 2.2

Reliability analysis of items under variable “Individuation”

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Appendix 2.3

Reliability analysis of items under variable “Altruism”

Appendix 2.4

Reliability analysis of items under variable “Need for personal growth”

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Appendix 3.1

Correlations between variables

Appendix 3.2

Correlation between gender and variables

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Appendix 4.1

Linear Regression of Individuation on Dependent variable

Appendix 4.2

Linear Regression of Altruism on Dependent variable

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Appendix 5.1

Moderation analysis “Need to belong”

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Appendix 5.2

Moderation analysis “Individuation”

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Appendix 5.3

Moderation analysis “Altruism”

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Appendix 5.4

Moderation analysis “Need for personal growth”

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Appendix 6

Effect of “Need for personal growth” on content forwarding at levels of the moderator

Appendix 7

Frequency bar chart of age

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