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The interplay of social media use, social support, and self-regulation in adjusting to university Bond, Takara A. 2019

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  THE INTERPLAY OF SOCIAL MEDIA USE, SOCIAL SUPPORT, AND SELF-REGULATION IN ADJUSTING TO UNIVERSITY  by  Takara A. Bond  B.A., Saint Mary’s University, 2015  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF ARTS in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Human Development, Learning, and Culture)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)   February 2019  © Takara A. Bond, 2019   ii  The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, a thesis/dissertation entitled:  The interplay of social media use, social support, and self-regulation in adjusting to university  submitted by Takara A. Bond in partial fulfillment of the requirements for the degree of Master of Arts in Human Development, Learning, and Culture  Examining Committee: Dr. Jennifer D. Shapka Co-supervisor Dr. Danielle Law Co-supervisor  Dr. Kimberly Schonert-Reichl Supervisory Committee Member  Additional Examiner  Additional Supervisory Committee Members:  Supervisory Committee Member  Supervisory Committee Member  iii  Abstract  The current research examined the characteristics of post-secondary students who use social media, their motivation for using social media and its relationship to university adjustment, as well as the moderating role played by self-regulation, socio-demographic variables, and social support. A total of 403 undergraduate students from two Canadian universities participated in this research, answering questions pertaining to motivations for social media use, social and academic adjustment to university, self-regulation, social support, and socio-demographics. Results show that four motivations for social media use emerged: self-promotion, entertainment, socialization, and university-related. Motivations for social media use were similar across platforms, regardless of socio-demographics, social support, and self-regulation, highlighting the universality of social media. Further findings indicate that the relationship between self-promotion motivations for social media use and social adjustment was moderated by social support, self-regulation, academic performance, and age; whereas there was not relationship between self-promotion and academic adjustment to university. Socialization motivations for social media use was positively linked to social adjustment to university. Self-regulation moderated the relationship between academic adjustment and both socialization and university related motivations for social media use. Lastly, there were no significant relationships or interactions between entertainment motivations for social media sue and either social or academic adjustment to university. As we move into an increasingly technological world, it is important to understand the nuances of how and why emerging adults are using social media to ensure adaptive patterns of internet use, including successful adjustment to university. This work further points to the need to better understand motivations for social media use, in particular, self-promotion motivations, and their impact on the university experience.   iv  Lay Summary  Social media plays a key role in the lives of today’s undergraduate students, with 90% of students using at least one platform. This research aimed to understand why students use social media, and how this use impacts their adjustment to university. Findings show four motivations for social media use: self-promotion, entertainment, socialization, and university-related; which were similar across platforms and participants, though their impact on university adjustment differed. Entertainment motivations were not related to university adjustment; whereas both socialization and university-related motivations were related to decreased academic adjustment among high self-regulating students. Lastly, the relationship between self-promotion motivations and social adjustment changed based on social support, self-regulation, academic performance, and age. Findings indicate that social media interventions should target the population and hold specific goals, such as encouraging students to use social media for socialization for improved social adjustment, or to enhance their overall self-regulatory skills when using social media.   v  Preface  This dissertation is the original intellectual product of the author, Takara A. Bond. Data were collected as a joint endeavor between Master of Arts students Takara A. Bond and Natasha Parent, under the supervision of Dr. Jennifer D. Shapka. The two students developed independent research proposals and measures, and worked collaboratively to obtain ethical approval, recruit participants, co-collect data, and prepare data for analysis. Accordingly, some data presented in the current research is also presented in Natasha Parent’s Masters thesis, titled “Attachment to Mobile Phones: An Examination of University Students’ Mobile Phone Use within an Attachment Theory Framework” which was also completed at the University of British Columbia. Dr. Jennifer D. Shapka supported this work throughout the process, with contributions to this work including support with measure development, guidance in data analysis, and providing manuscript edits. All other aspects of the work presented, including but not limited to, conducting literature review, developing methods, selecting and developing measures, planning and conducting data analysis, and writing the current document, were undertaken by Takara A. Bond. All research presented were approved by the University of British Columbia’s Behavioural Research Ethics Board [certificate # H18-00752] and Wilfrid Laurier University’s Research Ethics Board [certificate # 5719].  vi  Table of Contents  Abstract ......................................................................................................................................... iii Lay Summary ............................................................................................................................... iv Preface .............................................................................................................................................v Table of Contents ......................................................................................................................... vi List of Tables ................................................................................................................................ ix List of Figures .................................................................................................................................x Acknowledgements ...................................................................................................................... xi Dedication .................................................................................................................................... xii Chapter 1: Introduction ................................................................................................................1 Chapter 2: Literature Review .......................................................................................................3 2.1 Social Media ................................................................................................................... 3 2.1.1 Why we use social media. ....................................................................................... 4 2.2 University Adjustment .................................................................................................... 6 2.2.1 Social support and university adjustment. .............................................................. 7 2.2.2 Self-regulation and university adjustment. ............................................................. 9 2.2.3 Social media and university adjustment. .............................................................. 11 2.2.3.1 Motivations for social media use and university adjustment. ........................... 12 2.3 The Current Study ......................................................................................................... 13 Chapter 3: Methods .....................................................................................................................15 3.1 Participants .................................................................................................................... 15 3.2 Procedure ...................................................................................................................... 16 3.2.1 Recruitment at the University of British Columbia. ............................................. 16  vii  3.2.2 Recruitment at Wilfrid Laurier University. .......................................................... 17 3.2.3 Completing the questionnaire and inclusion criteria. ........................................... 17 3.3 Measures ....................................................................................................................... 18 3.3.1 Demographics. ...................................................................................................... 18 3.3.2 Motivation for social media use measure. ............................................................ 19 3.3.3 Self-regulation. ...................................................................................................... 20 3.3.4 Loneliness. ............................................................................................................ 20 3.3.5 University adjustment. .......................................................................................... 21 Chapter 4: Results ........................................................................................................................22 4.1 RQ1: What social media platforms are university students using, and what motivates their use? ................................................................................................................................... 22 4.2 RQ2: How are motivations for social media use related to social adjustment to university, and does this relationship differ as a function of self-regulation, social support, loneliness and socio-demographic factors? .............................................................................. 26 4.3 RQ3: How are motivations for social media use related to academic adjustment to university, and does this relationship differ as a function of self-regulation, social support, loneliness and socio-demographic factors? .............................................................................. 34 Chapter 5: Discussion ..................................................................................................................39 5.1 Motivations for Social Media Use and Adjustment to University ................................ 40 5.1.1 Self-promotion motivations for social media use. ................................................ 40 5.1.2 Socialization motivations for social media use. .................................................... 42 5.1.3 University-related motivations for social media use. ........................................... 43 5.1.4 Entertainment motivations for social media use. .................................................. 44 Chapter 6: Conclusion .................................................................................................................45  viii  6.1 Limitations, Strengths, and Future Directions .............................................................. 45 6.2 Implications and Conclusion ......................................................................................... 47 References .....................................................................................................................................48 Appendices ....................................................................................................................................58 Appendix A Measures ............................................................................................................... 58 A.1 Demographics Survey ............................................................................................... 58 A.2 Social Media Motivations Measure .......................................................................... 61 A.3 Self-Regulation Scale ................................................................................................ 63 A.4 University of California - LA Loneliness Scale ....................................................... 64 A.5 Student Adaptation to College Questionnaire ........................................................... 65 Appendix B Informed Consent Form ....................................................................................... 66 Appendix C Participant Feedback Letter .................................................................................. 69   ix  List of Tables  Table 4.1.1 Descriptive statistics for the social media use measure. ............................................ 24 Table 4.1.2 Factor loadings and communalities based on a principal components analysis with varimax rotation for the full social media use measure, containing 12 items (N=375) ................ 25 Table 4.2.1 Summary of hierarchical regression examining the influence of socio-demographics, social support, and self-regulation on social adjustment to university ......................................... 28 Table 4.2.2 Summary of hierarchical regressions examining the influence of socio-demographics, social support, and self-regulation on socialization motivations for social media use and social adjustment to university ........................................................................................ 29 Table 4.2.3 Summary of hierarchical regressions examining the influence of socio-demographics, social support, and self-regulation on self-promotion motivations for social media use and social adjustment to university ........................................................................................ 30 Table 4.3.1 Summary of hierarchical regressions examining the influence of socio-demographics, social support, and self-regulation on academic adjustment to university ........... 35 Table 4.3.2 Summary of hierarchical regressions examining the influence of socio-demographics, social support, and self-regulation on socialization motivations for social media use and academic adjustment to university ................................................................................... 37 Table 4.3.3 Summary of hierarchical regressions examining the influence of socio-demographics, social support, and self-regulation on university related motivations for social media use and academic adjustment to university ........................................................................ 38   x  List of Figures  Figure 4.2.1 Graph displaying the interaction between self-promotion motivations for social media use and age in relation to social adjustment to university. ................................................. 31 Figure 4.2.2 Graph displaying the interaction between self-promotion motivations for social media use and overall grade in relation to social adjustment to university .................................. 32 Figure 4.2.3 Graph displaying the interaction between self-promotion motivations for social media use and satisfaction with social support in relation to social adjustment to university. ..... 33 Figure 4.3.1 Graph displaying the interaction between socialization motivations for social media use and self-regulation in relation to academic adjustment to university. .................................... 36   xi  Acknowledgements I first want to thank my thesis supervisor, Jenna Shapka, for her encouragement as this research project evolved. I am especially grateful for providing tremendously helpful feedback and guidance as I worked through this project, and for her support in taking a break yet meeting my goals in the face of losing my dearest friend weeks before the planned defense date. I am further grateful for my committee members, Danielle Law and Kimberly Schonert-Reichl, for their dedication to my success, in taking the time to read my work and provide meaningful, thought-provoking feedback. Gratitude is extended to the HDLC faculty for their encouragement and support in pursuing further graduate studies and advancing my skills. I would also like to thank my peers in in Human Development, Learning and Culture (HDLC), and the Developmental Change and Technology lab, both past and present, for providing a fantastic learning environment and supporting me through this project. Special thanks to Natasha Parent for collaborating on our data collection and future publications, and Omar Garcia-Naveretti for answering all of my social media questions. I am also grateful for the financial support provided by the University of British Columbia.  I would like to thank my family, Brad Bond, Jane Bond, and Gillian Clement for your listening ears as I phone with excitement over numbers, disappointment in technology, or ramblings about my never-ending to do list. In addition, this project and my studies would not be possible without the love and support of my partner, Tanyo Tanev. Your encouragement to try new things and steadfast support of my peculiar interests, along with our escapes from the city have made this a truly enjoyable experience. Thank you for continuing to support my dreams. Finally, I want to thank my dog friends, Daffodill and Maya. Your unconditional love for me and the beach has been a constant source of joy and anxiety relief as I venture through a cross-country move to pursue my educational dreams, then dive into the depths of graduate school.  xii  Dedication    In loving memory of Daffodill June 10, 2004 to November 8, 2018  You were by my side when I first ventured into the world of social media, beginning with MySpace and Piczo, then testing out new platforms as they came out. We spent countless hours connecting with friends across the globe on TinyChat, then documented life on DailyBooth and Twitter. We watched the rise and fall of Tumblr as it skyrocketed into popularity then was sold to Yahoo, and observed the evolution of Facebook, Instagram, and Twitter into what they are today. You were the first one I pitched my research ideas to, and always were my number one cheerleader. Much like the social media platforms of my adolescent years, all great things must come to an end. Your goofy faces and study snuggles will be forever missed.  1  Chapter 1: Introduction Today’s undergraduate university students, also known as millennials and digital natives, are the first generation to grow up online. They share intimate details online with close friends and strangers, spend class or work time browsing social media, and strive to achieve internet stardom. Social media plays a key role in their lives, as 90% of undergraduate university students have at least one online account (Perrin, 2015). Considering the ubiquitous presence of technology in the lives of today’s students, research has emerged linking social media with maladaptive outcomes. For example, Facebook has been negatively linked with academic performance (Junco, 2015; Rosen, Carrier, & Cheever, 2013; Wood et al., 2012), and time spent on Facebook has been negatively related to academic engagement (Junco, 2012). Previous research has examined the relationship between time spent on social media and university adjustment, academic engagement, and procrastination; indicating that both positive and negative relationships were present; however, the direction depended on the social media site in question (Bond, 2015). For instance, positive relationships surfaced between university adjustment and Twitter and Instagram use, but negative relationships have been found between university adjustment and Facebook and Tumblr use (Bond, 2015). Recent work corroborates this, showing that students were more engaged in their classes when using Twitter for course work (Junco, Heiberger, & Loken, 2011). This suggests that the impact of social media use is dependent on which platform is used, as well as how and why students are using the sites.  In line with these findings, the uses and gratifications theory (Blumler & Katz, 1974) suggests that we seek gratifications from social media, such as affection seeking, entertainment, and information sharing (Malik, Dhir, & Nieminen, 2016; Park & Goering, 2016). Further research has supported this theory, in that students who used Facebook to maintain social connections reported increased social adjustment to college (Yang & Brown, 2015). An  2  important use of social media among undergraduate students is for communicating with family (Hofer et al., 2009), as moving away from home for university often elicits feelings of loneliness and a struggle to belong (Dieter, 2016), both of which are associated with decreased adjustment to university (Crede & Niehorster, 2012). When engaging with social media and managing academic demands, one must employ self-regulation, or the adaptation of behaviour to obtain a goal (Baumeister & Vohs, 2004; Berger, 2011; Muraven & Baumeister, 2000). Previous research highlights the importance of self-regulation when engaging with social media, as self-regulation skills have been linked to healthy patterns of technology use among adults (Błachnio, Przepiórka, & Pantic, 2015; Błachnio & Przepiorka, 2016; Van Deursen, Bolle, Hegner, & Kommers, 2015).  To complement previous research, the current work aimed to understand how and why undergraduate students use social media, and how these specific uses impact their university adjustment. More specifically, this research examined the characteristics of post-secondary students who use social media, their motivation for using social media, its relationship to university adjustment, as well as the potential mediating role played by socio-demographic variables, self-regulation, and loneliness.  3  Chapter 2: Literature Review 2.1 Social Media The internet has been open to public access since the 1990’s, with the rise of social media websites beginning in 2003 with myspace (a social blogging website, primarily used to share music and concert dates). Early forms of social media were largely used for connecting strangers based on shared interests, views and activities, and were accessed via an internet browser on a computer (Boyd & Ellison, 2007). Over the years, social media websites have adapted to consumer demands with current websites focusing on user interactions through sharing and creating content (Rains & Brunner, 2015), with users often accessing social media through mobile devices. Indeed, a recent report from Facebook (a website to share photos, videos, articles, and opinions with friends and family; 2017) reported the majority of users now access a mobile version of the Facebook website on their smartphones. This trend is likely extended to other platforms, as Instagram (a public photo and video sharing platform) and Snapchat (a private, ephemeral photo and video sharing platform) have developed many mobile-based features. For the purposes of this study, social media is therefore defined as websites and mobile applications, hereafter referred to as platforms, where the user can create a profile, make connections, and share the connections with others to create more connections and networks (Boyd & Ellison, 2007; Joosten, 2012).  Undergraduate students are avid social media consumers, with 94% of university students using Facebook for at least an hour every day, primarily for a distraction from their studies (Wise, Skues, & Williams, 2011). Facebook users report spending less time studying compared to their classmates who do not use Facebook (Kirschner & Karpinski, 2010). Indeed, related work found that two-thirds of students reported using electronic media as a distraction while in class, studying, or completing coursework (Jacobsen & Forste, 2010). Finally, 12% of high  4  school students believe their social media use hurts their grades (Miah, Omar, & Allison-Golding, 2012), a percentage that is likely similar to that of undergraduate students. 2.1.1 Why we use social media. The uses and gratifications theory is often used to explain how and why we use social media, in part because social media use often provides a gratification or reward (Alhabash, Park, Kononova, Chiang, & Wise, 2012). The uses and gratifications theory is a person-focused theory that suggests we are active consumers of media (Blumler & Katz, 1974). This means we actively seek out forms of media that best meet our needs, then choose how to use the media to satisfy such needs (Blumler & Katz, 1974). This theory was developed in the 1970s by Blumler and Katz (1974), in an attempt to explain sociologist Paul Lazarsfeld’s observations of patterns in radio listeners (Lazarsfeld, 1940). Lazarsfeld found that people listen to radio programs with the motivation to receive various gratifications; e.g., companionship, to fill time, to change our mood, to counteract loneliness or boredom, to gather news, to participate vicariously in events, and to enhance social interactions (Lazarsfeld, 1940). The uses and gratifications theory focuses on identifying the psychological needs that motivate one’s use of a particular form of media, and the motivation for selecting one form of media over another. Accordingly, Katz and Blumer theorize that we use media: to match one’s wits against others, to get information and advice for daily living, to provide a framework for one’s day, to prepare oneself culturally for the demands of upward mobility, or to be reassured about the dignity and usefulness of one’s role. (1974, p. 20) Since the initial conception of the uses and gratifications theory, the theory has been applied to all forms of media in a variety of contexts, ranging from why children develop an interest in comic books (Wolfe & Fiske, 1949) to more recent studies looking at online game  5  addiction (Li, Liu, Xu, Heikkila, & van der Heijden, 2015), and studies focused on understanding motivations for Facebook use (Alhabash et al., 2012). The uses and gratifications theory suggests that we actively select the platform that will best meet our needs and use it to help fulfill our goals (Florenthal, 2015). This means that a person looking to explore their identity will be motivated to seek a platform that allows them to do so, such as Tumblr (a short-form blogging site that allows users to connect with others through reblogging (re-posting) each other’s content); whereas a person looking to keep in touch with their friends will be motivated to use platforms that focus on social communication, such as Facebook. Regarding social media use, frequently reported gratifications include: to gain affection, to gain attention, to disclose information about themselves, to entertain themselves, to pass time, to share information, to gain social influence, and to experience social interactions (Florenthal, 2015; Malik et al., 2016; Park & Goering, 2016; Smock, Ellison, Lampe, & Wohn, 2011). In line with the perspective that we select media to meet our needs, recent work has found that gratifications received from Facebook use appear to be dependent on how Facebook is used. For example, if the focus is on private communications with individuals, the gratifications surround social needs; whereas public communications on Facebook provide gratifications around information sharing (Smock et al., 2011). This work is supported by several studies investigating the psychosocial benefits of technology use that have found that the benefits differed based on the manner in which the medium is used (Valkenburg & Peter, 2009; Yang & Brown, 2015). For example, participants who reported using Facebook to maintain social connections also report increased social adjustment to college (Yang & Brown, 2015), whereas using Facebook as a procrastination tool is associated with higher academic stress (Meier, Reinecke, & Meltzer, 2016). Taken together, it appears that manner of social media use plays a significant role in the motivation for such use. Despite these compelling findings, much of the  6  work on gratifications from social media use has not distinguished which platform they focus on, or focused solely on Facebook, which is problematic as it is speculated that there are important distinctions between social media platforms (Rains & Brunner, 2015). The current study has addressed this gap by exploring multiple social media platforms and identifying differences and similarities across these platforms, as well as by exploring why individuals use different platforms. 2.2 University Adjustment Successful university students have adjusted to their university environment, as reflected in their attitudes towards their course of study, their engagement with class material, their study habits and their academic efforts (Baker & Siryk, 1989). Following a review of the research regarding university adjustment conducted prior to 1989, Baker and Siryk proposed that university adjustment can be broken down into four core elements: academic adjustment, social adjustment, personal-emotional adjustment, and university attachment (Baker & Siryk, 1989). Academic adjustment concerns a student’s success in managing educational demands and is evaluated through one’s satisfaction with their academic environment, as well as their motivation for, application to, and performance in academics. Social adjustment is the ability to meet social demands that accompany the university experience, such as participation in extracurricular activities and creating relationships with classmates and professors. Personal-emotional adjustment focuses on the psychological and physical well-being of students during their university experience. Finally, university attachment is indicative of students’ sense of belonging at their school and among classmates and professors. Since the development of a definition of university adjustment and the conceptualization of these four distinct categories, researchers have overwhelmingly adopted these four categories of university adjustment, with the ubiquitous  7  finding that university adjustment is positively correlated with academic performance (Baker & Siryk, 1989; Credé & Niehorster, 2012). A recent meta-analysis examining twenty years of university adjustment research found a positive correlation between overall university adjustment and conscientiousness, self-efficacy, and self-esteem (Credé & Niehorster, 2012). This study also indicated that students with positive emotionality, high optimism, low negative emotionality, low stress, and low depression are likely to experience better overall university adjustment (Credé & Niehorster, 2012). Alongside these individual differences, there are also gender and cultural differences in university adjustment. In particular, students who identify as an ethnic minority or as a woman tend to report more difficulties in adjusting to the university environment and have worse social experiences (Cabrera, Nora, Terenzini, Pascarella, & Hagedorn, 1999; Loeb, 2006; Nora & Cabrera, 1996). This is especially pronounced for women who study in programs with predominantly male instructors (e.g., math and sciences), as these women reported decreased academic success and university adjustment in comparison to their male classmates (Loeb, 2006). 2.2.1 Social support and university adjustment.  Though individual differences are present in university adjustment, various studies have identified social support as a protective factor (Hertel, 2002). For instance, previous research has shown that students who were more involved in extracurricular activities tended to report better social adjustment to their university (Baker & Siryk, 1989). This finding is supported in work showing that students who take advantage of university resources, such as orientation programs, are more adjusted to their environment (Abe, Talbot, & Gellhoed, 1998). In line with these findings, previous research suggests that this social support can be from parents, friends, and professors (Credé & Niehorster, 2012). In particular, parental support was found to be instrumental in academic adjustment, peer support was instrumental to social adjustment, and  8  faculty support was instrumental in all aspects of university adjustment except social adjustment (Credé & Niehorster, 2012).  The widespread benefit of social support suggests that support from a variety of sources is beneficial for university adjustment; however, undergraduate students often begin their university experience by moving to a new city. This involves developing new social relationships and separating from family, an experience which isolates students from their previous sources of social support and is associated with increased loneliness (Dieter, 2016). Previous studies found a negative association between moving away from home for university and university adjustment (Mooney, Sherman, & Presto, 1991). Recent work contrasts this finding, in that the distance from home community and family is not related to university adjustment (Golonka, 2013; Hofer, Souder, Kennedy, Fullman, & Hurd, 2009).  As much of the negative associations were produced prior to the widespread penetration of social media and smartphones, this differential finding may be related to the increased connectivity between university students and family and friends from home through social technology. Indeed, recent work shows that university students communicated with their family 53 times per week on average (Golonka, 2013), in contrast to the previous finding that students communicated with their family once a week or less (Hofer et al., 2009). Further research has examined students’ distance from home community, finding that students contacted their family at similar rates, regardless of their distance from home community (Hofer et al., 2009). In line with Credé and Niehorster’s (2012) meta-analysis, recent research suggests that students find it adaptive to continue to rely on their parents for support, as having a close relationship and frequent contact with parents was positively related to both social and academic adjustment to university (Golonka, 2013). Taken together, findings suggest that technology may mitigate the negative impact of the distance between university students and their family.  9  In line with this speculation and Hertel’s (2002) suggestion that social support aides in university adjustment, research indicated that support from high school friendships aides in overall university adjustment and is especially important during the first semester of university (Oswald & Clark, 2003). These friendships help to counter the negative effects of loneliness (Dieter, 2016); however, previous research found that the moderating role played by high school friends decreased throughout university and these relationships are replaced by university friendships (Oswald & Clark, 2003; Swenson, Nordstrom, & Hiester, 2008). Oswald and Clark (2003) suggest that increased communication with high school friends can moderate the negative impact of relocating for university, a finding that has recently been replicated in research examining social media communications with high school friends (Dieter, 2016). This work also found that friendship quality between high school friends and university friends were positively related to social and academic adjustment (Dieter, 2016), highlighting the importance of social support for university adjustment. As much of this research was conducted prior to the rise of social media, the current study extended this work by exploring how access and involvement with social media and connections to home was related to the four aspects of university adjustment identified by Baker and Siryk (1989). 2.2.2 Self-regulation and university adjustment. Self-regulation can be broadly defined as the conscious or unconscious adaptation of cognition, emotion, and behaviour to accomplish one’s goals or to adapt to the situational requirements (Baumeister & Vohs, 2004; Berger, 2011; Muraven & Baumeister, 2000). Individuals often have more than one desired outcome, goal, or gratification in mind at any given time, which requires an ability to prioritize goals (Simon, 1967). In fact, maladaptive self-regulation outcomes are often associated with poor goal prioritization (Berger, 2011), such as using social media while in class. Work by Bjornsen and Archer (2015) suggests that students  10  prioritize the goal of promoting themselves online over the goal of academic success, with the finding of a negative correlation between in-class social media use and academic performance (Bjornsen & Archer, 2015). Further studies have supported this relationship, in that self-regulation skills have been linked to healthy patterns of social media and smartphone use (Błachnio & Przepiorka, 2016;Van Deursen et al., 2015) and improved university adjustment (Crede & Niehorster, 2012); whereas poor self-regulation has been associated with increased in class cyberloafing, or internet procrastinating (Prasad, Lim, & Chen, 2010). Individuals with adaptive self-regulatory abilities tend to have improved social functioning, are more liked by their peers, perform better in groups, and are well adjusted (Calkins, Dedmon, Gill, Lomax, & Johnson, 2002; Eisenberg, 2000; Heatherton & Vohs, 1998; Lengua, 2002; Mischel et al., 2011). Further studies of self-regulation and education found connections between self-regulation and increased academic achievement (Blair & Diamond, 2008), as well as improved university adjustment; indicating that effective learners are self-regulating (Butler & Winne, 1995; Winne & Perry, 2000). These educational benefits of self-regulation may be due to the increased ability of self-regulators to reprioritize goals, allowing them to focus self-regulatory efforts toward the most important task (Simon, 1967).  Within the realm of social media, those who consume a lot of social media are often labeled as social media addicts with no self-control (Turel & Qahri-Saremi, 2016). Although we know university students spend a lot of time online (Perrin, 2015), a recent meta-analysis found that only between 1.6% and 8.6% of the population showed evidence of social media addiction, based on regulatory models of addiction (Andreassen, 2015). This highlights the importance of self-regulation in social media use and is supported by both the uses and gratifications theory and the social cognitive theory of self-regulation, as they both assume the individual is in control of their behaviour and focus on obtaining a goal or gratification. In combining these two theories,  11  one can speculate, as LaRose and Eastin (2004) have, that we regulate our media engagement in order to obtain a gratification; however, if that form of media is not allowing us to achieve our desired goal, we will utilize it in a different manner or select a different platform. Further studies revealed a link between self-regulation skills and healthy patterns of technology use among adults (Błachnio et al., 2015; Błachnio & Przepiorka, 2016; Van Deursen et al., 2015). As extant work has shown the importance of self-regulation in developing adaptive patterns of social media use, it is important to consider students’ self-regulatory abilities when using social media in their university environment. 2.2.3 Social media and university adjustment. Research has emerged outlining the impact of social media on academic outcomes for undergraduate students. For example, Whon & LaRose (2014) found that time spent using Facebook was negatively associated with grade point average. Similarly, a study that linked heavy-use of social media to poor mental health outcomes found that moderate levels of social media-use were linked to increased involvement in extra-curricular activities (Romer, Bagdasarov, & More, 2013). However, using a simplistic measure of time spent online often masks underlying, explanatory patterns, as 24% of adolescents ‘almost constantly’ access social media (Lenhart, 2015). When exploring the manner of using social media, the negative association between Facebook use and grade point average becomes more complex. For example, in a study by Junco (2012), it was found that using Facebook to collect and share information related to academics was positively associated with participant grade point average, but that using Facebook to communicate with friends was negatively associated with their grade point average. Finally, a study by Rutherford (2010) found that students who used social media to interact with classmates and teachers reported higher academic engagement in comparison to their peers. Thus, it appears that understanding how and why students are using social media  12  platforms will provide more insight into social media use than simply knowing how much time students spend using these platforms. Accordingly, the current study aimed to understand what motivates undergraduate students to use social media in addition to time spent on social media, to better understand the relationship between social media use and university adjustment. 2.2.3.1 Motivations for social media use and university adjustment. In addition to understanding how and why students use social media, it appears that the nature of the social media use also matters. For example, when Twitter (a text-based microblogging website) use was included as a part of the course curriculum, it was found that students reported increased academic engagement, higher grade point average, and an increased sense of community among participants, professors, and researchers (Junco et al., 2011). In contrast, qualitative interviews with undergraduate students indicate that students did not welcome social media, specifically Facebook, into the classroom (Talyor, Mulligan, & Ishida, 2012). These students reported a desire to maintain a separation between personal and professional identities and feared the consequences of Facebook being used in the classroom. Other research by Neier and Zayer (2015) supports this, as undergraduate students in their study did not to want social media tools in the classroom. Interestingly, Neier and Zayer (2015) found that these same students expressed positive views on universities that use social media in the educational environment, just not as an integral part of course curriculum. These findings suggest that using social media for completing course work is undesirable; unlike social and entertaining uses of social media. In looking at work that has explored the relationship between social media use and socio-emotional aspects of university adjustment, an overall positive socio-emotional impact of social media platforms is found (Gray, Vitak, Easton, & Ellison, 2013; Wise et al., 2011; Yang & Brown, 2015). For example, university students who used Facebook to maintain social  13  connections showed increased social adjustment to university (Yang & Brown, 2015). Wise and colleagues (2011) found that, in general, Facebook promoted social engagement over academic engagement. Namely, that it was used to make plans to attend school events, to meet with study groups, or to meet with friends, rather than learning about course material. Gray and colleagues’ (2013) research supported this, and found Facebook use to be instrumental in social adjustment to university. These studies, again, point to the need to explore more nuanced social media uses that go beyond a measure of time spent on social media. In addition, they suggest that social media use might impact different aspects of university adjustment in unique ways. Though there is a wide variety of research examining self-regulation and social media, there appears to be a dearth of work that examines the intersection among social media use, self-regulation, and university adjustment. The current study aimed to fill this gap; in addition, the current work explored the role of self-regulation in understanding how social media is related to university adjustment. 2.3 The Current Study Undergraduate students are using social media more now than any other time, with no decrease in sight. As previous research indicates that university students use Facebook primarily for a distraction from their studies (Wise, Skues, & Williams, 2011), an over-reliance and focus on social media for social purposes may be detrimental to academic adjustment (e.g., Junco & Cotten, 2012). In contrast, related work suggests that social media use might enhance more social emotional aspects of university adjustment by facilitating social support (Golonka, 2013). Further, Wise and colleagues (2011) found that, in general, Facebook promoted social engagement over academic engagement. Finally, previous work has primarily relied on simplistic assessments of social media use (e.g., time spent online); likewise, many studies have focused on individual platforms, with a strong emphasis on Facebook use (Rains & Brunner,  14  2015). Such studies largely find a negative association between time spent on Facebook and academic achievement (Whon & LaRose, 2013). To understand the underlying patterns that may be masked by simplistic assessments, recent research has begun to explore motivations for using social media. For example, participants who reported using Facebook to maintain social connections also report increased social adjustment to college (Yang & Brown, 2015), whereas using Facebook as a procrastination tool is associated with higher academic stress (Meier, Reinecke, & Meltzer, 2016). When exploring motivations of social media use and university adjustment, research has not yet explored the mediating effects of social support and self-regulation, though they are both important tools in adjusting to the university environment (Crede & Niehorster, 2012; Dieter, 2016; Hertel, 2002; Prasad, Lim, & Chen, 2010). Accordingly, the current research aimed to fill this gap, using a sample of students from two Canadian universities. Three research questions guided this work:  RQ1: What social media platforms are university students using, and what motivates their use? RQ2: How are motivations for social media use related to social adjustment to university, and does this relationship differ as a function of self-regulation, social support, loneliness and socio-demographic factors? RQ3: How are motivations for social media use related to academic adjustment to university, and does this relationship differ as a function of self-regulation, social support, loneliness and socio-demographic factors?  15  Chapter 3: Methods 3.1 Participants A total of 403 undergraduate students from two Canadian universities participated in this research, with a mean age of 20.40 (sd = 1.75, range = 18 to 25). Participants were mostly in their second (25.3%) or third year (31.9%) of their undergraduate degree and were largely domestic students (88.1%). Participants indicated grades ranging from 50% to 100%, with a mean overall grade of 76.8% (sd = 8.65). The sample was mostly female (75.4%), and straight-identifying (10.2% of participants identified as LGBTQ), with 80.4% of participants indicating that they lived in Canada for over 10 years, and primarily identified as White (44.2 %), East Asian (27.5%), and South Asian (16.6%). Most participants moved to attend university, with 15.9% moving a long flight from home, 20.6% moving a long drive from home, and 29.8% moving a short drive from home.  Participants were recruited from two universities, Wilfrid Laurier University (WLU; n = 201) and The University of British Columbia (UBC; n = 202). As such, differences in their demographics were explored. There was a significant difference in mean age (t376.8= 2.048, p < .001) and year of university (t386.3= 3.483, p < .05) between the two universities, in that WLU students were younger and had attended university for fewer years. This is not surprising given that WLU participants were recruited through a university-based participant pool and needed to be enrolled in lower level undergraduate courses to obtain access to the pool. WLU presented significantly more South Asian students (t385.4= -2.056, p < .001) and White students (t398.7= 3.970, p < .001), whereas UBC presented a higher proportion of East Asian students (t343.6= 7.700, p < .001), both of which are similar to the larger population in these geographic areas (Statistics Canada, 2017). A further difference between the two universities was that WLU had a larger proportion of international students (t357.4= -2.942, p < .001), whereas UBC’s students  16  were newer to Canada (t265.3= 5.506, p < .001), and more had moved away from home to attend university (t337.6= 3.547, p < .001). 3.2 Procedure 3.2.1 Recruitment at the University of British Columbia.  Upon obtaining approval from UBC’s Behavioral Research Ethics Board, participants were recruited from campus by targeting various locations that are heavily populated with undergraduate students from May to July of 2018. Interested students were asked to complete an online self-report questionnaire on Qualtrics, which can be accessed through an internet browser on a computer, tablet, or mobile device. Participants were given the opportunity to enter a draw to win a $250 gift card to the UBC bookstore, both in the informed consent letter and in the feedback letter (Appendix B and Appendix C, respectively). Participants were informed that the researchers would complete the draw on July 15th, 2018, with the winner being selected through a random number generator. To complete the questionnaire, participants had the option of doing so immediately using an iPad mini which was preloaded with the online questionnaire; however, no participants were interested in this option. Alternatively, interested students were given a flyer containing a website linking to the online survey, and were instructed on how to access the survey.  As this recruitment method was fairly time consuming and recruited a limited number of participants due to the reduced number of students on campus during the months of May, June, and July, two other recruitment methods were also employed at UBC. First, posters were placed around UBC, in busy spaces such as the bus loops and washrooms in the student union building and libraries. The last recruitment method took place virtually and involved circulating information about the study on various UBC student-lead Facebook groups. This involved searching the term “UBC student” on Facebook, then joining all groups that were present in the  17  search results. Next, the researcher posted a short description of the study onto the discussion page of 38 groups, posting in each group up to three times over the three month period. As the same link was used for all recruitment methods, we are unaware of which was the most successful.  3.2.2 Recruitment at Wilfrid Laurier University.  Upon obtaining ethical approval from Wilfrid Laurier University's Research Ethics Board and the University of British Columbia, participants were recruited through WLU’s Psychology Research Experience Program (PREP) during the spring of 2018. Students who were enrolled in Psychology courses that included a PREP component received .25 course credit for participating in the research via the PREP website. Participants who enrolled this way accessed the online self-report questionnaire from a link on the PREP website, which directed them to the Qualtrics hosted survey.  3.2.3 Completing the questionnaire and inclusion criteria.  When accessing the study online, participants were initially taken through an informed consent process (Appendix B), ensuring that they are aware of the purpose of the study and of their rights as a study participant. Following consent, participants responded to four items regarding their student status, smart phone ownership, social media use, and age. These items appeared first as they determined if the participant was eligible to participate in the study. A total of 542 potential participants opened the online survey website, with 537 of those participants providing consent to participating in this research. Five potential participants declined consent and ten individuals did not respond to the consent item. Of the remaining 527 participants, 69 were not undergraduate students, therefore were not eligible to participate. All remaining participants reported owning a smartphone, but one did not use social media and was therefore excluded from participating. The final inclusion criteria removed 16 potential participants from  18  the data set, as six were under 18 years of age, seven were over 25 years of age, and three preferred not to answer. The 91 participants who indicated that they do not meet the inclusion criteria were automatically directed to the feedback letter ( as seen in Appendix C).  The eligible 451 participants were directed to complete the remainder of the questionnaire, which took approximately 30 minutes. At the end of the questionnaire, participants were reminded about their rights as a research participant and provided with information about counselling services in the event that responding to the questions elicited negative emotions. Of the 451 participants who met the inclusion criteria, 14 participants ceased participation mid-way through the inclusion items, and a further 34 participants did not continue past the demographic items. These 34 participants differed from the final sample in that there were significantly more females (t15.577= 1.082, p < .05), LGBTQ identifying individuals (t13.460= 1.369, p < .01), and people who do not identify as White (t15.106= -.852, p < .01). As these participants did not complete measures that are pertinent for the current study’s research questions, they were removed from participation, leaving a final sample of 403 participants.  3.3 Measures 3.3.1 Demographics. Demographic information included the four items used for the inclusion criteria, including student status, smart phone use, social media use, and age (Appendix A.1). Participants also responded to items regarding their gender, ethnicity, and sexual orientation, as well as their living situation, citizenship (international vs. domestic student), distance moved to attend university, length of time in Canada, and proximity to and communication with family and closest friends. Finally, participants responded to two items regarding their student status, outlining their current year of study and average overall grade.  19  3.3.2 Motivation for social media use measure. To measure motivations for social media use among undergraduate students, a 12-item measure was developed for the purpose of this study (Appendix A.2). Previous measures of social media use focused exclusively on frequency of use of social media, which were not specific enough for the current research. Accordingly, a new measure was created. To create this measure, a review of previously employed measures of social media and internet use was conducted, including work in psychology, education, and marketing. Upon examining previous measures and literature, three categories of social media use surfaced: to disclose information about themselves, to entertain themselves, and to experience social interactions (Alt, 2015; Chandler & Munday, 2016; Dolan, Conduit, Fahy, & Goodman, 2016; Florenthal, 2015; Kaye & Johnson, 2004; Malik et al., 2016; Park & Goering, 2016; Smock, Ellison, Lampe, & Wohn, 2011). Items were then developed to tap into each of these aspects of social media use. The final version of the measure included twelve questions that tapped into the self-promotional (four items), entertainment (three items), and relational (three items) uses of social media. The last two of the twelve items assessed academic uses of social media (e.g. “I use Facebook to explore class topics and university related updates (ex. Clubs, AMS)”). These items were exploratory, as previous research has not examined academic uses of social media in this manner.  Expert colleagues at UBC were consulted throughout the development of this measure, to anecdotally verify that the items would measure what they were intended to measure, and to ensure that vocabulary was void of jargon and reflective of current trends in social media consumption. As part of research question 1, the items for the measure were exploratory factor analyzed to confirm the subscales. Participants responded to these items for up to three separate social media platforms that they identified using, from a choice of six. Platforms included Facebook, Instagram, Twitter, Snapchat, which are the most commonly used platforms according  20  to a recent Pew Research Institute report (Smith & Anderson, 2018), as well as WeChat, and QQ, which are platforms commonly used by Asian and other international students.   3.3.3 Self-regulation. To measure self-regulation, the Self-Regulation Scale (SRS; Schwarzer et al., 1999) was employed (Appendix A.3). The SRS is a 10-item measure of self-regulation that focuses on the goal-pursuit stage of self-regulation. Responses were placed on a 4-point Likert scale, ranging from “not at all true” to “exactly true”. This measure focuses on attention regulation and emotion regulation, with items such as “I stay focused on my goal and don’t allow anything to distract me from my plan of action”, and “If an activity arouses my feelings too much, I can calm myself down so that I can continue with the activity soon”. Previous research demonstrates the reliability and validity of this measure, and evidence that the SRS is an appropriate measure of self-regulation among young adults (19 to 39 years) (Diehl, Semegon, & Schwarzer, 2006). As previous research has employed the SRS in health sciences (Cholowski & Cantwell, 2007), developmental psychology (Brose, Schmiedek, Lövdén, & Lindenberger, 2011), and educational psychology (Prasad et al., 2010), this was an appropriate measure for the current research (10 items; a = .892).   3.3.4 Loneliness. Loneliness was measured through the eight-item version of the University of California-Los Angeles Loneliness Scale (ULS-8; Hays & DiMatteo, 1987; Appendix A.4). This measure was initially developed as a long-form measure of loneliness with 20 items however, researchers sought a short-form of this measure to minimize respondent burden. The resulting eight item measure contains questions such as “I lack companionship” and “I feel isolated from others”, with participants responding to the items on a four-point Likert scale ranging from “I never feel this way” to “I often feel this way”. Previous research has validated that this eight-item measure  21  is representative of the full 20 item scale and is an accurate measure of loneliness among American college student populations (Hays & DiMatteo, 1987). Accordingly, the ULS was an appropriate measure for the current research (8 items; a = .861). 3.3.5 University adjustment. The Student Adaptation to College Questionnaire (SACQ; Baker & Siryk, 1989) is a 67-item questionnaire examining university adjustment, with responses ranging from “applies very closely to me” to “does not apply to me at all” on a 9-point Likert scale (Appendix A.5). This measure contains four sub-scales, each exploring key predictors of university success. Items in this measure examine academic adjustment (e.g., “My academic goals and purposes are well defined”), social adjustment (e.g., “I have several close social ties at college”), personal-emotional adjustment (e.g., “I haven’t been sleeping very well”), and university attachment (e.g., “I feel that I fit in well as part of the college environment”). As the current research is primarily concerned with social and academic adjustment, only these sub-scales were employed in the current study. Further, repeated or similar items were removed to minimize the measure length. In total, 35 items of the SACQ were employed in the current research, 20 of which concerning academic adjustment (a = .851), and 15 items concerning social adjustment (a = .879). As this measure was developed for use in college populations among western countries, the word “college” was replaced with “university”, to ensure that participants were responding with their current university in mind. Previous research demonstrates the SACQ is a reliable and valid measurement of college adjustment (Baker & Siryk, 1989; Dahmus, Bernardin, & Bernardin, 1992), with a recent meta-analysis verifying its validity and reliability as a measure of university adjustment in today’s university culture (Credé & Niehorster, 2012).   22  Chapter 4: Results 4.1 RQ1: What social media platforms are university students using, and what motivates their use? Regarding the most used platform, Instagram emerged as the most popular platform, with 75.9% of participants listing it as one of their top three platforms. Facebook and Snapchat were also fairly popular, with 70.7% and 70.0% of participants listing them as a top three platform, respectively. Twitter was less popular, with just 21.6% of participants indicating it as a top three platform, whereas WeChat and QQ were only indicated as a top three platform by 6% and 1.5% of participants, respectively. When asked “approximately how much time a day do you spend on social media?”, the majority of participants indicated that they spend “a moderate amount” or “a lot” of time on social media each day (59.9%). Based on t-tests, there were no differences in these responses based on socio-demographic factors, including age, gender, ethnicity, sexual orientation, international student status, time spent in Canada, distance moved for university, year of study, or overall grade.  To understand what motivates social media use, the motivation for social media use measure was analyzed through a principal components factor analysis. For the initial analysis, items were collapsed across all six social media platforms by creating a mean for each item (see item means in Table 4.1.1). Where sample size afforded it, subsequent analyses explored factor loadings for each platform. Correlations of the 12 collapsed items indicated that most variables were related, suggesting high levels of factorability. As the Kaiser-Meyer-Olkin measure of sampling adequacy was .78, above the recommended value of .6 (Cerny & Kaiser, 1977), and Bartlett’s test of sphericity was significant, (c2 (66) = 1611.62, p < .01), a factor analysis was conducted on the 12 items. The initial principal components factor analysis with varimax rotation  23  unveiled four different factors, which explained 70.0% of variance. The pattern of findings indicated that participants were motivated to use social media for: self-promotion (a = .843), entertainment (a = .743), socialization (a = .801), and university related tasks (a = .539) (Table 4.1.2). As item number 12, “I use social media to interact with family” presented factor loading below .7, it was removed from the socialization construct (Thompson, 2004). Subsequent factor analyses were conducted to determine if the findings hold true across Instagram, Snapchat, and Facebook. In general, the pattern of findings was similar across all platforms. For Instagram, all 12 items loaded on the same factors as the initial analysis. Snapchat’s factor analysis had one difference, in that item 1, “I use snapchat to get likes and comments” loaded on the university related factor. This is likely due to poor wording of the item, as there are no likes or comments on the ephemeral photo and messaging app; accordingly, this item was omitted in further analyses of Snapchat. The factor analysis examining Facebook responses had two differences from the first factor analysis, the first being that item 9, “I use Facebook to follow university related accounts (ex. Clubs, AMS, Santa Ono).”, appeared in the socialization factor. This may be because clubs often spread word about their events through Facebook and AMS (the Alma Mater Society) frequently posts about university wide events, allowing students to coordinate social events through the popular platform. Another difference was that item 12, “I use Facebook to interact with family.”, loaded onto the university related factor with a negative loading. Given the remarkable similarities in item loadings across platforms, composite variables were made based on the factor loadings collapsed for all social media platforms. These were the variables used for all subsequent research questions. To make the composite variables for each identified factor, the mean of all the items was taken to create four variables representing: self-promotion, entertainment, socialization, and university related motivations for social media use (see Table 4.1.2 for factor loadings and alphas).  24     Table 4.1.1 Descriptive statistics for the social media use measure. Item Mean Standard Deviation Skewness Kurtosis 1. I use social media to get likes and comments. 2.299 .902 .451 -.116 2. I use social media to share content about myself. 2.865 .950 -.103 -.470 3. I use social media to promote my profile. 2.098 1.009 .601 -.542 4. I use social media to create or share entertaining content. 2.741 1.007 -.016 -.601 5. I use social media to watch entertaining content from others. 3.763 .845 -.913 1.154 6. I use social media to get away from what I’m doing. 3.420 .910 -.470 .228 7. I use social media to explore a topic from class 1.980 .910 .941 .500 8. I use social media to browse content related to my interests. 3.179 .916 -.164 -.154 9. I use social media to follow university related accounts (ex. Clubs, AMS, Santa Ono). 2.748 .960 .193 -.421 10. I use social media to interact with friends from high school. 3.415 .996 -.277 -.370 11. I use social media to interact with friends on campus. 3.512 .967 -.415 -.228 12. I use social media to interact with family. 2.587 1.034 .247 -.561 Note. Responses were indicated on a Likert scale ranging from 1 to 5, with responses indicated as never (1), rarely (2), sometimes (3), often (4), and always (5).  25  Table 4.1.2 Factor loadings and communalities based on a principal components analysis with varimax rotation for the full social media use measure, containing 12 items (N=375) Item All Platforms (N=375) Facebook (N=280) Instagram (N=300) Snapchat (N=278)  SP EN SO UR SP EN SO UR SP EN SO UR SP EN SO UR Percentage of variance: 34.4 13.2 11.9 10.2 27.8 15.4 12.1 8.9 34.3 13.5 12.7 8.9 17.2 10.7 9.4 29.3 Alpha .842 .741 .689 .538 .813 .752 .558 .269 .856 .686 .566 .740 .695 .609 .703 .536 I use (platform) to…                 1. get likes and comments. .717   .412 .678   .306 .829    .269   .764 2. share content about myself. .837    .856    .820    .828    3. promote my profile. .780   .324 .810    .851    .578   .554 4. create or share entertaining content. .801    .759    .752    .759    5. watch entertaining content from others.  .822    .806    .854   .389 .680   6. get away from what I’m doing.  .789    .754    .712    .727   8. browse content related to my interests.  .728  .329  .826    .744    .704  .456 7. explore a topic from class.    .785 .325   .665    .864    .816 9. follow university related accounts (ex. Clubs, AMS, Santa Ono).    .715   .548    .345 .629    .740 10. interact with friends from high school.   .864    .796    .835    .826  11. interact with friends on campus.   .866    .858    .854    .849  12. interact with family.   .400 .462    -.569   .439 .613   .321 .578 Note. Factor loadings < .3 are suppressed; SP = Self Promotion; EN = Entertainment; SO = Socialization; UR = University Related; red font indicates that the specific item is deviant from other platforms.   26  4.2 RQ2: How are motivations for social media use related to social adjustment to university, and does this relationship differ as a function of self-regulation, social support, loneliness and socio-demographic factors? Preliminary correlation analyses exploring the four motivations of social media use and social adjustment to university indicated that only socialization motivations for social media use was significantly correlated with social adjustment to university (r = .336, n = 369, p = .01). However, to explore further the relationship between the four motivations for social media use and social adjustment, hierarchical linear regressions with ordinary least squares (OLS) analysis was used. More specifically, for each of the four motivations, a regression was run where demographic variables were entered in Block 1, psychosocial variables representing loneliness, self-regulation, and satisfaction with social support were entered in Block 2, one of the four motivations for social media was entered in Block 3, and finally, the interactions of the motivation for social media and each of the demographic (Block 1) and psycho-social (Block 2) variables were entered in Block 4 and 5 of the model.  Demographic variables were selected based on preliminary correlations, which indicate that both age and gender are significantly correlated with social adjustment to university (r = -.139, n = 380, p = .01; r = -.139, n = 376, p = .01, respectively). The only other demographic variable to be significantly correlated with social adjustment was ethnicity, represented as white vs else (r = .110, n = 380, p = .05); however, it had no contributions to the model above and it is not often considered in such research. Accordingly, ethnicity was not included in the final model. Though overall grade was not significantly correlated with social adjustment to university (r = .082, n = 337, p = .133), it was included in the model as previous research suggests a link between academic performance and social adjustment to university (Credé & Niehorster, 2012).   27  To calculate the interaction terms, the social media motivation was multiplied by the dependent variable (e.g., socialization motivations for social media use * age). Graphic representations of the interactions were developed by calculating the mean for the variables, then transforming the variable into a dummy variable representing high and low (e.g., the mean age was 20.4, therefore responses below 20.4 were coded as 0, whereas responses above 20.5 were coded as 1).  The dummy variables were subsequently graphed to display the interactions seen in Figures 4.2.1 through 4.2.3.  Prior to conducting the analyses, assumptions of linear regression were checked. First a plot of standardized residuals was examined, confirming that the residuals were normally distributed. Second, the assumptions for homoscedasticity were tested through a scatter plot. Finally, multicollinearity was examined through Variance Inflation Factors (VIF) with values range from 1.012 to 1.410, indicating a low risk of multicollinearity. As can be seen in Table 4.2.1 representing Block 1 and 2 of the regression models, all variables but overall grade were significantly associated with social adjustment. Note that Block 1 and 2 were the same for all regressions, so these are presented separately to avoid repetition.  Blocks 3, 4, and 5 of the regression models for each of the motivation for social media use variables are described below and presented in Tables 4.2.2 to 4.2.3. For both entertainment and university-related motivations for social media use there were no main effects, nor were any interactions significant. As such, these regressions are not reported here. For socialization motivations, as can be seen in Table 4.2.2, findings indicate that socialization motivations for social media use was significantly positively related to social adjustment to university above and beyond the effects of gender, age, overall grade, loneliness, self-regulation, and satisfaction with social support (Block 3; b  = .141, p < .001; DR2 = .525, p < .001). There were no significant interactions between socialization motivations for social media use and the control variables.   28  Table 4.2.1 Summary of hierarchical regression examining the influence of socio-demographics, social support, and self-regulation on social adjustment to university  B SE B b R2 DR2 Block 1    .054 .045*** Gender (Female = 1, Male = 0) -.357 .176 -.111*   Age (In Years) -.144 .044 -.181***   Overall Grade (Percentage) .017 .009 .106   Block 2    .518 .508*** Gender (Female = 1, Male = 0) -.259 .127 -.080*   Age (In Years) -.111 .032 -.139***   Overall Grade (Percentage) .019 .006 .122**   Satisfaction with Social Support .292 .059 .219***   Loneliness -1.138 .104 -.507***   Self-Regulation .342 .128 .114**   Note. * p < .05; ** p < .01; *** p < .001.                 29  Table 4.2.2 Summary of hierarchical regressions examining the influence of socio-demographics, social support, and self-regulation on socialization motivations for social media use and social adjustment to university  B SE B b R2 DR2 Block 3    .535 .525*** Gender (Female = 1, Male = 0) -.250 .125 -.077*   Age (In Years) -.084 .032 -.106**   Overall Grade (Percentage) .020 .006 .123**   Satisfaction with Social Support .276 .058 .207***   Loneliness -1.083 .104 -.483***   Self-Regulation .309 .126 .103**   Motivation for Social Media Use: Socialization .215 .063 .141***   Block 4    .540 .525*** Gender (Female = 1, Male = 0) -.238 .588 -.074   Age (In Years) .043 .092 .054   Overall Grade (Percentage) -.003 .023 -.020   Satisfaction with Social Support .277 .058 .208***   Loneliness -1.083 .104 -.482***   Self-Regulation .323 .126 .108*   MSM: Socialization .405 .142 .266**   MSM: Socialization X Gender  .005 .182 .006   MSM: Socialization X Age -.037 .026 -.427   MSM: Socialization X Overall Grade .007 .007 .308   Block 5    .545 .526*** Gender (Female = 1, Male = 0) -.058 .599 -.018   Age (In Years) .041 .109 .052   Overall Grade (Percentage) -.010 .025 -.064   Satisfaction with Social Support .517 .226 .388   Loneliness -1.347 .385 -.600***   Self-Regulation .440 .456 .147   MSM: Socialization .394 .142 .259**   MSM: Socialization X Gender  -.049 .185 -.054   MSM: Socialization X Age -.036 .032 -.418   MSM: Socialization X Overall Grade  .009 .007 .403   MSM: Socialization X Satisfaction with Social Support -.079 .070 -.257   MSM: Socialization X Loneliness -.031 .137 -.062   MSM: Socialization X Self-Regulation .091 .117 .159   Note. * p < .05; ** p < .01; *** p < .001; MSM = motivation for social media use; Blocks 1 and 2 are presented in Table 4.2.1.    30  Table 4.2.3 Summary of hierarchical regressions examining the influence of socio-demographics, social support, and self-regulation on self-promotion motivations for social media use and social adjustment to university  B SE B b R2 DR2 Block 3    .518 .507*** Gender (Female = 1, Male = 0) -.259 .127 -.080*   Age (In Years) -.111 .032 -.139***   Overall Grade (Percentage) .019 .006 .123**   Satisfaction with Social Support .292 .059 .219***   Loneliness -1.137 .104 -.506***   Self-Regulation .343 .128 .114**   Motivation for Social Media Use: Self-Promotion .010 .068 .006   Block 4    .519 .504*** Gender (Female = 1, Male = 0) -.510 .418 -.158   Age (In Years) -.033 .110 -.041   Overall Grade (Percentage) .024 .022 .153   Satisfaction with Social Support .291 .059 .218***   Loneliness -1.144 .105 -.509***   Self-Regulation .319 .131 .106*   Motivation for Social Media Use: Self-Promotion .726 .946 .419   MSM: Self-Promotion X Gender  .100 .159 .092   MSM: Self-Promotion X Age -.033 .044 -.388   MSM: Self-Promotion X Overall Grade -.002 .008 -.081   Block 5    .524 .504*** Gender (Female = 1, Male = 0) -.403 .425 -.125   Age (In Years) -.013 .113 -.016   Overall Grade (Percentage) .019 .023 .122   Satisfaction with Social Support .521 .187 .390   Loneliness -1.262 .308 -.562   Self-Regulation .484 .417 .161   Motivation for Social Media Use: Self-Promotion 1.133 1.168 .653**   MSM: Self-Promotion X Gender  .069 .161 .064   MSM: Self-Promotion X Age -.040 .045 -.474**   MSM: Self-Promotion X Overall Grade  -.001 .009 -.003**   MSM: Self-Promotion X Satisfaction with Social Support -.091 .070 -.255*   MSM: Self-Promotion X Loneliness .055 .119 .094*   MSM: Self-Promotion X Self-Regulation -.071 .164 -.125*   Note. * p < .05; ** p < .01; *** p < .001; MSM = motivation for social media use; Blocks 1 and 2 are presented in Table 4.2.1.   31  As can be seen in Table 4.2.3, after controlling for the demographic and psycho-social variables, self-promotion was not related to social adjustment to university; however, when the interactions between self-promotion motivations for social media use were explored in Blocks 4 and 5, there were several significant interaction effects. First, as seen in Figure 4.2.1, the benefits of social adjustment were more pronounced for younger participants, and significantly so for younger participants who were motivated to self-promote themselves on social media (b  = -.474, p < .01; DR2 = .504, p < .001).    Figure 4.2.1 Graph displaying the interaction between self-promotion motivations for social media use and age in relation to social adjustment to university.     32  Second, a significant interaction was present between self-promotion motivations for social media use and overall grade point average (b  = -.003, p < .05; DR2 = .504, p < .001). As seen in Figure 4.2.2, for students with higher grades, social adjustment to university was higher on average, and self-promotion did not have much of an impact on it. However, for students whose grades were lower, high levels of self-promotion was related to significantly higher levels of social adjustment.   Figure 4.2.2 Graph displaying the interaction between self-promotion motivations for social media use and overall grade in relation to social adjustment to university  There were also significant findings for the interactions of self-promotion by satisfaction with social support (b  = -.255, p < .05; DR2 = .504, p < .001), self-promotion by self-regulation (b  = -.125, p < .05; DR2 = .504, p < .001), and self-promotion by loneliness (b  = .094, p < .05;  33  DR2 = .504, p < .001). The pattern of interaction is the same for each of these interactions and is represented in Figure 4.2.3, which shows the interaction for self-promotion by satisfaction with social support. As can be seen from Figure 4.2.3, self-promotion functions differently depending on whether students reported high versus low levels of satisfaction with social support. For low levels of this construct, higher levels of self-promotion enhanced social adjustment. In contrast, when students reported higher levels of satisfaction with social support, higher self-promotion was associated with a reduction in social adjustment to university. This same pattern was found for self-regulation and loneliness, although reversed for loneliness, such that higher levels of loneliness was associated with the compensatory benefits of high levels of self-promotion and lower levels were associated with reduced social adjustment for high levels of self-promotion.    Figure 4.2.3 Graph displaying the interaction between self-promotion motivations for social media use and satisfaction with social support in relation to social adjustment to university.    34  4.3 RQ3: How are motivations for social media use related to academic adjustment to university, and does this relationship differ as a function of self-regulation, social support, loneliness and socio-demographic factors? Preliminary correlation analyses exploring the four motivations of social media use and academic adjustment to university indicate that there were no significant correlations between motivations for social media use and academic adjustment. However, similar to Research Question 2, a series of hierarchical linear regression with ordinary least squares (OLS) analysis was used to test the relationship more formally between each of the motivations for social media use and academic adjustment, after controlling for demographic and psychosocial variables. More specifically, gender, age, and overall grade were entered into Block 1 of the regression model. In Block 2 the composites for loneliness, self-regulation, and satisfaction with social support were added. In Block 3 the four composites for motivations for social media use were added. In Block 4 the interactions between motivation for social media use and the variables in Block 1 were entered. Finally, in Block 5 the interactions between motivation for social media use and the variables in Block 2 were entered.  Demographic variables were selected based on preliminary correlations, which indicate that overall grade was significantly correlated with social adjustment to university (r = .353, n = 337, p = .001). As with RQ1, The only other demographic variable to be significantly correlated with social adjustment was ethnicity, represented as white vs else (r = .110, n = 380, p = .05); however, it had no contributions to the model above and it is not often considered in such research. Accordingly, ethnicity was not included in the final model. Lastly, both age and gender were included in the model despite their lack of significant correlation (r = .092, n = 380, p = .07; r = -.031, n = 376, p = .5, respectively) as they were used in the research question two model.   35  In a similar fashion research question two, interaction terms were calculated by multiplying the social media motivation another variable (e.g., socialization motivations for social media use * age). Graphic representations of the interactions were developed by calculating the mean for the variables, then transforming the variable into a dummy variable representing high and low (e.g., the mean age was 20.4, therefore responses below 20.4 were coded as 0, whereas responses above 20.5 were coded as 1).  The dummy variables were subsequently graphed to display the interactions seen in Figures 4.2.1 through 4.2.3.  Again, for each regression model Blocks 1 and 2 were identical. As can be seen in Table 4.3.1, all but gender and age were significantly associated with academic adjustment. For both entertainment and self-promotion motivations for social media use there were no main effects, nor were any interactions significant. As such, these regressions are not reported here.  Table 4.3.1 Summary of hierarchical regressions examining the influence of socio-demographics, social support, and self-regulation on academic adjustment to university  B SE B b R2 DR2 Block 1    .107 .098*** Gender (Female = 1, Male = 0) .034 .128 .014   Age (In Years) .013 .032 .021   Overall Grade (Percentage) .038 .006 .324***   Block 2    .358 .345*** Gender (Female = 1, Male = 0) .129 .110 .053   Age (In Years) .032 .027 .053   Overall Grade (Percentage) .035 .006 .293***   Satisfaction with Social Support .181 .051 .181***   Loneliness -.250 .090 -.149**   Self-Regulation .768 .110 .342***   Note. * p < .05; ** p < .01; *** p < .001.  In the two models exploring socialization and university-related motivations for social media use, respectively, the main effect for these variables was not significant. However, for both models, as can be seen in Tables 4.3.2 and 4.3.3, the interaction between the motivation for  36  social media use and self-regulation was significant, b  = -.708, p < .05; DR2 = .361, p < .001 and b  = -.759, p < .05; DR2 = .356, p < .001 for socialization and university-related motivations, respectively. As displayed in Figure 4.3.1, for participants who reported higher level of self-regulation, being highly motivated to socialize on social media is associated with compromised academic university adjustment. In contrast, for those who reported lower self-regulation being motivated to socialize online was related to higher levels of academic adjustment. This pattern was the same for the interaction between university-related motivations for social media use and self-regulation in relation to academic adjustment to university; accordingly, Figure 4.3.1 is representative of both interactions.   Figure 4.3.1 Graph displaying the interaction between socialization motivations for social media use and self-regulation in relation to academic adjustment to university.    37  Table 4.3.2 Summary of hierarchical regressions examining the influence of socio-demographics, social support, and self-regulation on socialization motivations for social media use and academic adjustment to university  B SE B b R2 DR2 Block 3    .364 .350*** Gender (Female = 1, Male = 0) .125 .109 .052   Age (In Years) .020 .028 .034   Overall Grade (Percentage) .035 .005 .293***   Satisfaction with Social Support .188 .051 .188***   Loneliness -.274 .091 -.163**   Self-Regulation .782 .110 .349***   Motivation for Social Media Use: Socialization -.095 .055 -.084   Block 4    .369 .349*** Gender (Female = 1, Male = 0) .398 .515 .165   Age (In Years) .121 .08 .204   Overall Grade (Percentage) .004 .02 .032   Satisfaction with Social Support .186 .051 .187***   Loneliness -.276 .091 -.164**   Self-Regulation .791 .11 .352***   Motivation for Social Media Use: Socialization -.114 .124 -.100   MSM: Socialization X Gender  -.087 .159 -.129   MSM: Socialization X Age -.031 .023 -.480   MSM: Socialization X Overall Grade .010 .006 .593   Block 5    .368 .362*** Gender (Female = 1, Male = 0) .636 .519 .264   Age (In Years) .098 .095 .166   Overall Grade (Percentage) -.009 .021 -.079   Satisfaction with Social Support .129 .196 .130   Loneliness -.712 .334 -.424   Self-Regulation 1.679 .396 .748***   Motivation for Social Media use: Socialization -.127 .124 -.112   MSM: Socialization X Gender  -.160 .161 -.239   MSM: Socialization X Age -.024 .028 -.374   MSM: Socialization X Overall Grade  .013 .006 .815   MSM: Socialization X Satisfaction with Social Support .017 .061 .073   MSM: Socialization X Loneliness .147 .101 .344   MSM: Socialization X Self-Regulation -.282 .119 -.748*   Note. * p < .05; ** p < .01; *** p < .001; MSM = motivation for social media use; Blocks 1 and 2 are presented in Table 4.3.1.     38  Table 4.3.3 Summary of hierarchical regressions examining the influence of socio-demographics, social support, and self-regulation on university related motivations for social media use and academic adjustment to university  B SE B b R2 DR2 Block 3    .362 .348*** Gender (Female = 1, Male = 0) .144 .110 .060   Age (In Years) .030 .027 .051   Overall Grade (Percentage) .035 .005 .292***   Satisfaction with Social Support .184 .050 .185***   Loneliness -.240 .090 -.143**   Self-Regulation .787 .111 .351***   Motivation for Social Media Use: University-Related -.091 .059 -.070   Block 4    .366 .345*** Gender (Female = 1, Male = 0) -.216 .378 -.089   Age (In Years) -.002 .086 -.003   Overall Grade (Percentage) .043 .017 .361**   Satisfaction with Social Support .182 .051 .183***   Loneliness -.240 .090 -.143**   Self-Regulation .781 .113 .348***   Motivation for Social Media Use: University-Related -.273 .816 -.210   MSM: University-Related X Gender  .156 .159 .191   MSM: University-Related X Age .015 .035 .234   MSM: University-Related X Overall Grade -.003 .007 -.200   Block 5    .381 .356*** Gender (Female = 1, Male = 0) -.141 .377 -.059   Age (In Years) .007 .085 .012   Overall Grade (Percentage) .042 .017 .352**   Satisfaction with Social Support .031 .168 .031   Loneliness .120 .285 .072   Self-Regulation 1.496 .333 .667***   Motivation for Social Media Use: University-Related .938 1.030 .721   MSM: University-Related X Gender  .118 .158 .144   MSM: University-Related X Age .008 .034 .131   MSM: University-Related X Overall Grade  -.004 .007 -.224   MSM: University-Related X Satisfaction with Social Support .062 .066 .228   MSM: University-Related X Loneliness -.156 .119 -.365   MSM: University-Related X Self-Regulation -.312 .138 -.759*   Note. * p < .05; ** p < .01; *** p < .001; MSM = motivation for social media use; Blocks 1 and 2 are presented in Table 4.3.1.    39  Chapter 5: Discussion The aim of the current research was to understand how and why undergraduate students use social media, and how the specific uses impacted their social and academic adjustment to university. Findings indicated that undergraduate students used social media in similar ways, regardless of age, gender, ethnicity, sexual orientation, international student status, time spent in Canada, distance moved for university, year of study, or overall grade. Regarding motivations for social media use, previous research utilizing the uses and gratifications theory (Blumler & Katz, 1974) has largely shown that we use social media for affection seeking, entertainment, information sharing (Malik, Dhir, & Nieminen, 2016; Park & Goering, 2016), and to maintain social connections (Yang & Brown, 2015). The findings from the current study support this. More specifically, it was found that university students reported using social media for entertainment, self-promotion, socialization, and university-related information seeking. Again, this finding was true across all participants, regardless of socio-demographics; reinforcing the pervasiveness of social media use and providing further support for the uses and gratifications theory.  In response to a recent call for research put forth by Rains and Brunner (2015), the current study also examined multiple social media platforms to determine if the motivations for using social media differed based on platforms. This study was the first to empirically explore motivations for using different social media platforms. In opposition to what was expected, findings indicated that motivations for going online were remarkably similar for Facebook, Instagram, and Snapchat. Thus, despite the fact that each of these platforms are structurally different and offers different functionality, young adults appear to be motivated for the same reasons to use social media. This has the potential to greatly simplify future research because the  40  findings from this work suggest that social media use can be explored as a singular construct, rather than exploring platforms independently. 5.1 Motivations for Social Media Use and Adjustment to University       Regarding the connection between social media use and university adjustment, previous studies find general Facebook use to be instrumental in social adjustment to university (Gray et al., 2013; Yang & Brown, 2015), and to promote social engagement over academic engagement (Wise et al., 2011). Research has not yet directly examined the impact of social media use on academic adjustment to university. However, it is known that the impact of social media use is not clear cut, as previous work finds conflicting results regarding the relationship between social media use and academic performance (Bjornsen & Archer, 2015; Junco, 2012; Junco & Cotton, 2012; Junco, 2015; Rosen, Carrier, & Cheever, 2013; Wohn & LaRose, 2014; Wood et al., 2012). Below the findings for the current study are discussed according to the different motivations for using social media. 5.1.1 Self-promotion motivations for social media use. Self-promotion via social media is a developing field of research, with much of the literature pointing to a link between self-promotion and narcissism (Abell & Brewer, 2014; Moon, Lee, Lee, Choi, & Sung, 2016). Bjornsen and Archer speculate that students prioritize the goal of promoting themselves online over the goal of academic success, though their work did not directly examine self-promotion (2015). As such, the current study provides insight into self-promotion motivations for social media use, such as seeking likes, promoting one’s profile, and sharing content about oneself on social media.  First, findings indicate that there was a moderating effect of social support on the relationship between self-promotion and social adjustment to university. This is shown through both satisfaction with social support and loneliness. In particular, low levels of satisfaction with  41  social support and higher levels of self-promotion enhanced social adjustment. In contrast, when students reported higher levels of satisfaction with social support, higher self-promotion was associated with a reduction in social adjustment to university. For loneliness, this pattern is reversed in that higher levels were associated with compensatory benefits of high levels of self-promotion, whereas lower levels of loneliness were associated with reduced social adjustment for high levels of self-promotion. Taken together, findings provide support for previous research that show a positive relationship between social support and social adjustment (Abe, Talbot, & Gellhoed, 1998; Baker & Siryk, 1989; Credé & Niehorster, 2012; Hertel, 2002). Further, findings indicate that promoting one’s self via social media may provide similar benefits as peer support, which may be due to connections fostered through this self-promotional behaviour.  Second, the current study found a moderating effect of self-regulation on the relationship between self-promotion and social adjustment to university. Specifically, lower levels of self-regulation and higher levels of self-promotion enhanced social adjustment, whereas higher levels of self-regulation and higher self-promotion reduced social adjustment to university. Though work has not yet examined the role of self-regulation in social adjustment to university, there is evidence for links between self-regulation and improved social technology use (Błachnio & Przepiorka, 2016; Van Deursen et al., 2015), university adjustment (Crede & Niehorster, 2012) and improved social functioning (Calkins, Dedmon, Gill, Lomax, & Johnson, 2002; Eisenberg, 2000; Heatherton & Vohs, 1998; Lengua, 2002; Mischel et al., 2011). Accordingly, the current research suggests that self-promotion on social media is different to general technology use and should be further examined.  Third, findings point to a moderating effect of academic performance on the relationship between self-promotion motivations for social media use and social adjustment. For students with higher grades, social adjustment to university was higher on average, and self-promotion  42  did not have much of an impact on it. However, for students whose grades were lower, high levels of self-promotion was related to significantly higher levels of social adjustment. This suggests that self-promotion may be compensating for poor academic performance. Finally, benefits to using social media for self-promotion were more pronounced among younger participants, especially those who were motivated to promote themselves online. One potential explanation for this interaction is that younger participants are newer to their university environment and accordingly engage in self-promotion via social media to enhance their social circle in their new university setting. As this was not yet explored, further studies are needed to determine the reasoning behind this interaction.   5.1.2 Socialization motivations for social media use. When exploring motivations for social media use in relation to social adjustment to university, the current study found socialization motivations to be positively linked to social adjustment to university. This was apparent above and beyond the effects of gender, age, overall grade, loneliness, self-regulation, and satisfaction with social support, suggesting that using social media to enhance social connections is a universal task among undergraduate students. While there were no differences in the above relationship based on socio-demographics, social support, or self-regulation, there were also no interaction effects present. This reinforces the notion that the impact of socialization motivations of social media use on social adjustment is independent from other variables examined in the current study and emphasizes the importance of social media as a socialization tool for today’s undergraduate students.  When considering academic adjustment, the current study found that the relationship between socialization motivations for social media use and academic adjustment to university was moderated by self-regulation. More specifically, for participants who reported higher level of self-regulation, being highly motivated to socialize on social media is associated with  43  compromised academic university adjustment. In contrast, for those who reported lower self-regulation, being motivated to socialize online was related to higher levels of academic adjustment. These findings are in contrast to previous research indicating a negative relationship between using social media to socialize and academic outcomes (Rutherford, 2010). This highlights the importance of self-regulation in adjusting to university. Further, it is supported by previous work which indicates that self-regulation skills are linked to healthy patterns of social technology use (Błachnio & Przepiorka, 2016; Van Deursen et al., 2015) and improved university adjustment (Crede & Niehorster, 2012), whereas poor self-regulation has been associated with increased in class internet procrastinating (Prasad, Lim, & Chen, 2010). Taken together, these conflicting findings in previous research and the current study suggest that a deeper exploration of the impact of socialization motivations for social media use on academic adjustment to university is needed. 5.1.3 University-related motivations for social media use. When exploring the relationship between university-related motivations for social media use and academic adjustment to university, findings showed that students who reported increased self-regulation also reported higher overall academic adjustment, though those who also reported increased university-related motivations for social media use had decreased adjustment in comparison to their high self-regulating peers. These findings, again, highlight the importance of self-regulation in social media use and university adjustment. As both socialization and university-related motivations for social media use were impacted by self-regulation, this points to the need for further studies of the interplay of motivations for social media use, self-regulation, and academic adjustment to university. For social adjustment, no significant relationship was found. This may be due to poor wording of the questions, as the questions may not be relevant to all students. For example, it is  44  possible that students do not frequently use social media for university-related tasks, as previous research indicates that students desire to maintain a separation between personal and professional identities and feared the consequences of Facebook being used for academic purposes (Talyor, Mulligan, & Ishida, 2012). As very little research has explored academic uses of social media, further studies are needed to fully understand this behaviour. 5.1.4 Entertainment motivations for social media use. Findings for exploring the relationship between social media for entertainment and social and academic adjustment to university indicated that there was no significant relationship. This finding is likely due to the nature of seeking entertainment from social media, as it is generally fairly solitary and used as an escape from other aspects of life. We watch videos, read articles, scroll through endless streams of photos, and take Buzzfeed quizzes to determine what kind of sandwich we are (Misener, 2014). These forms of entertainment may become social by sharing things to our own profiles or engaging in conversations in the comments of a photo; however, when this happens it is possible that the motivation shifts from entertainment to socialization. As little is currently known about shifting motivations for social media use and using social media primarily for entertainment, further studies are required to understand the nature of seeking entertainment on social media.  45  Chapter 6: Conclusion 6.1 Limitations, Strengths, and Future Directions Though this work is novel in the field for exploring social media motivations in relation to university adjustment, it is not without limitations. First, the current measure of social media motivations was developed for the purposes of this research, with precautions taken to ensure its validity, such as consulting expert colleagues and social media users. Since this measure was not piloted prior to utilizing, it is difficult to state whether it will produce similar results in other populations. Future directions for the measure of social media motivations should involve re-working some items, such as “I use social media to get likes and comments”, as these rewards are platform specific. Future related research can work towards eradicating this limitation by conducting qualitative interviews with undergraduate student to understand their purposes of using social media in language that is commonly used in the population. These qualitative interviews can then be used to inform and update to the current measure of social media motivations, which would be then piloted and revised prior to use in further studies. As there is currently no universal measure of social media motivations, this is important work that should be undertaken in the near future. A further limitation in the current research is with the sample. Participants were recruited at the University of British Columbia and Wilfrid Laurier University during the spring semester, with inclusion criteria restricting participants to current undergraduate students aged 18 to 25 who own a smartphone and use social media. While this is a common practice in student-led research, it makes generalizing to populations outside of the Canadian undergraduate student demographic difficult, as it is unknown if social media practices are similar across countries and level of university sought. Future research should examine other populations to determine if the results hold true in other countries, and to determine if social media plays a similar role in  46  different levels of education, ranging from high school to post-doctoral studies. In addition to the limited sample, data collected for the current study was cross-sectional, which provides correlational, not causal, findings. Future studies can remedy this limitation by conducting longitudinal analyses, beginning at the transition from high school to university and ending at the final year of studies. Such work would determine the benefits and detriments of social media use across the university experience and provide causal statements regarding such use.  Limitations aside, the current work is an important step in better understanding the role of social media in university adjustment. As previous research has widely employed a measure of time spent online or focused on one platform, the finding that the motivations for social media use were not distinctly different across platforms allows for simplification in our measurement of social media use. Instead of exploring social media use through time spent online or examining differences across all existing platforms, future studies can instead utilize an updated version of the motivations for social media use measure developed for the current study.  The current research also points to future research, specifically surrounding motivations for social media use. First, an important future direction is to examine the contexts in which students act on these motivations. As previous work indicates that two-thirds of students reported using electronic media as a distraction while in class, studying, or completing coursework (Jacobsen & Forste, 2010), it is important to understand where these motivations are acted upon. Second, individual motivations for social media use should be further examined to fully understand their impact on undergraduate students. This will better support university engagement and marketing professionals to understand the best ways to engage with students on social media. It will also provide insight into social media interventions and methods for targeting individuals who may be in need of such interventions.   47  6.2  Implications and Conclusion The outcomes of the current research can be used by post-secondary educators, to assist in better understanding how undergraduate students use social media and how it is related to university adjustment. Further, findings can aid student support services in developing their social media presence and supporting students who are struggling with adjusting to their university environment. As the current study found that social media use was not significantly different across gender, age, ethnicity or academic performance, it points to the need to target interventions beyond demographics. Accordingly, university staff who focus on enhancing campus engagement may target specific social media motivations to encourage students to use social media for socialization for improved social adjustment to university, or to enhance their overall self-regulatory skills when using social media. Further, the current findings contribute to the current body of knowledge on university adjustment, socialization, self-regulation, and social media use and can be considered by parents and educators at all age levels to ensure that adaptive patterns of social media use and strong self-regulatory skills are adopted at a young age.  As we move into an increasingly technological world, it is important to understand the nuances of how and why emerging adults are using social media to ensure adaptive patterns of internet use, including successful adjustment to university. Findings from the current study highlight the benefits of social support and self-regulation in adjusting to university. 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What is your age (in years)? (Forced response for inclusion criteria) a. Under 18 b. 18 c. 19 d. 20 e. 21 f. 22 g. 23 h. 24 i. 25 j. Over 25 k. Prefer not to answer 5. What is your gender? a. Male b. Female c. Other d. Prefer not to answer 6. Do you identify as LGBTQ? a. Yes b. No c. I don’t know d. Prefer not to answer       (continued on next page)   59  7. Which ethnic group(s) do you identify with? (select more than one if needed) a. Indigenous (e.g., First Nations, Inuit, Metis) b. South Asian (e.g., East Indian, Sri Lankan, Pakistani) c. East Asian (e.g., Chinese, Japanese, Korean) d. Southeast Asian origins (e.g., Filipino, Thai, Vietnamese, Malaysian) e. West Asian (e.g., Iranian, Afghan) f. White (e.g., Caucasian, European) g. Black (e.g., Haitian, Trinidadian, Caribbean, African) h. Latin American (e.g., Mexican, Brazilian, Colombian) i. I don’t know j. Other (please list):  k. Prefer not to answer 8. Are you an international student? a. Yes b. No 9. How long have you lived in Canada? a. Over 10 years b. Over 5 years c. Under 3 years d. Less than a year 10. Did you move to attend university? a. I did not move b. I moved a short drive away c. I moved a long drive away d. I moved a short flight away e. I moved a long flight away 11. Are you satisfied with how often you get to see your family in person? a. Not at all satisfied b. Slightly dissatisfied c. Neutral d. Slightly satisfied e. Very satisfied 12. Are you satisfied with how often you talk with your family?  a. Not at all satisfied b. Slightly dissatisfied c. Neutral d. Slightly satisfied e. Very satisfied     60  13. Are you satisfied with how supported you feel by your family? a. Not at all satisfied b. Slightly dissatisfied c. Neutral d. Slightly satisfied e. Very satisfied 14. Are you satisfied with how often you get to see your close friends in person? a. Not at all satisfied b. Slightly dissatisfied c. Neutral d. Slightly satisfied e. Very satisfied 15. Are you satisfied with how often you talk with your close friends?  a. Not at all satisfied b. Slightly dissatisfied c. Neutral d. Slightly satisfied e. Very satisfied 16. Are you satisfied with how supported you feel by your close friends? a. Not at all satisfied b. Slightly dissatisfied c. Neutral d. Slightly satisfied e. Very satisfied 17. What year of university are you in? a. First b. Second c. Third d. Fourth e. Fifth or Higher 18. What is your current overall grade average? a. __ % (fill in the blank with percentage) b. (drop down menu containing letter grades) i. A+  ii. A  iii. A-  iv. B+ v. B vi. B-  vii. C+ viii. C ix. C- x. D xi. F   61  A.2 Social Media Motivations Measure Please answer the following questions:  1. Approximately how much time a day do you spend on your smartphone?  a. Almost never b. Not very much c. A little d. A moderate amount e. A lot f. Very much  g. Almost always 2. Approximately how much time a day do you spend on social media? a. Almost never b. Not very much c. A little d. A moderate amount e. A lot f. Very much  g. Almost always 3. Though it is difficult to estimate, approximately how much time do you spend on your smartphone daily?  a. ____ hours  4. Though it is difficult to estimate, approximately how much time do you spend connected to social media (on a computer, mobile phone, or tablet) daily? a. ____ hours  5. Please indicate up to three social media platforms that you often use? a. (platform 1) b. (platform 2) c. (platform 3) i. Facebook ii. Instagram iii. Snapchat iv. Twitter v. WeChat vi. QQ vii. Other, please indicate:     (continued on next page)  62  Response format for items 6 to 41:  (1) Never (2) Rarely, (3) Sometimes, (4) Often, (5) Always  6. I use (platform 1) to get likes and comments. 7. I use (platform 1) to share content about myself. 8. I use (platform 1) to promote my profile. 9. I use (platform 1) to create or share entertaining content. 10. I use (platform 1) to watch entertaining content from others. 11. I use (platform 1) to get away from what I’m doing. 12. I use (platform 1) to explore a topic from class. 13. I use (platform 1) to browse content related to my interests. 14. I use (platform 1) to follow university related accounts (ex. Clubs, AMS, Santa Ono). 15. I use (platform 1) to interact with friends from high school. 16. I use (platform 1) to interact with friends on campus. 17. I use (platform 1) to interact with family.  18. I use (platform 2) to get likes and comments. 19. I use (platform 2) to share content about myself. 20. I use (platform 2) to promote my profile. 21. I use (platform 2) to create or share entertaining content. 22. I use (platform 2) to watch entertaining content from others. 23. I use (platform 2) to get away from what I’m doing. 24. I use (platform 2) to explore a topic from class. 25. I use (platform 2) to browse content related to my interests. 26. I use (platform 2) to follow university related accounts (ex. Clubs, AMS, Santa Ono). 27. I use (platform 2) to interact with friends from high school. 28. I use (platform 2) to interact with friends on campus. 29. I use (platform 2) to interact with family.  30. I use (platform 3) to get likes and comments. 31. I use (platform 3) to share content about myself. 32. I use (platform 3) to promote my profile. 33. I use (platform 3) to create or share entertaining content. 34. I use (platform 3) to watch entertaining content from others. 35. I use (platform 3) to get away from what I’m doing. 36. I use (platform 3) to explore a topic from class. 37. I use (platform 3) to browse content related to my interests. 38. I use (platform 3) to follow university related accounts (ex. Clubs, AMS, Santa Ono). 39. I use (platform 3) to interact with friends from high school. 40. I use (platform 3) to interact with friends on campus. 41. I use (platform 3) to interact with family.   63  A.3 Self-Regulation Scale  1. I can concentrate on one activity for a long time, if necessary.  2. If I am distracted from an activity, I don't have any problem coming back to the topic quickly.  3. If an activity arouses my feelings too much, I can calm myself down so that I can continue with the activity soon.  4. If an activity requires a problem-oriented attitude, I can control my feelings.  5. It is difficult for me to suppress thoughts that interfere with what I need to do. (–)  6. I can control my thoughts from distracting me from the task at hand.  7. When I worry about something, I cannot concentrate on an activity. (–) 8. After an interruption, I don't have any problem resuming my concentrated style of working.  9. I have a whole bunch of thoughts and feelings that interfere with my ability to work in a focused way. (–)  10. I stay focused on my goal and don’t allow anything to distract me from my plan of action.  Note: (–) indicates the item has to be reversed. Response format: (1) not at all true, (2) barely true, (3) moderately true, (4) exactly true    64  A.4 University of California - LA Loneliness Scale Indicate how often each of the statements below is descriptive of you.  1. I lack companionship. 2. There is no one I can turn to. 3. I am an outgoing person. (–)  4. I feel left out.  5. I feel isolated from others. 6. I can find companionship when I want it. (–)  7. I am unhappy being so withdrawn. 8. People are around me but not with me.  Note: (–) indicates the item has to be reversed.  Response format: (1) I never feel this way, (2) I rarely feel this way, (3) I sometimes feel this way, (4) I often feel this way.    65  A.5 Student Adaptation to College Questionnaire These questions relate to your experiences at university. Read each one and decide how well it applies to you at the present time.  1. I feel that I fit in well as part of the university environment. 2. I have been keeping u to date on my academic work.  3. I am meeting as many people and making as many friends as I would like at university. 4. I know why I’m at university and what I want out of it. 5. I am finding academic work at university difficult. (–) 6. I am very involved with social activities at university. 7. I have not been functioning well during examinations. (–) 8. I am satisfied with the level at which I am performing academically.  9. I’m not working as hard as I should at my course work. (–) 10. I have several close social ties at university. 11. I’m not really smart enough for the academic work I am expected to be doing now. (–) 12. Lonesomeness for home is a source of difficulty for me now. (–) 13. Getting a university degree is very important to me. 14. I haven’t been very efficient in the use of study time lately. (–) 15. I really haven’t had much motivation for studying lately. (–) 16. I am satisfied with the extracurricular activities available at university. 17. Lately I have been having doubts regarding the value of a university education. (–) 18. I am satisfied with the number and variety of courses available at university. 19. I feel that I have enough social skills to get along well at university. 20. Recently I have had trouble concentrating when I try to study. (–) 21. I’m not doing well enough academically for the amount of work I put in. (–) 22. I am having difficulty feeling at ease with other people at university. (–) 23. I am satisfied with the quality or the caliber of a course available at university. 24. I am attending classes regularly. 25. I am satisfied with the extent to which I am participating in social activities at university. 26. I haven’t been mixing too well with the opposite sex lately. (–) 27. I am enjoying my academic work at university. 28. I have been feeling lonely a lot at university lately. (–) 29. I feel I am very different from other students at university in ways that I don’t like.  (–) 30. On balance, I would rather be home than here. (–) 31. Most of the things I am interested in are not related to any of my course work at university. (–) 32. I am very satisfied with the professors I have now in my courses. 33. I have some good friends or acquaintances at university with whom I can talk about any problems I may have. 34. I am quite satisfied with my social life at university. 35. I’m quite satisfied with my academic situation at university.  Note:  (–) indicates the item has to be reversed. Response Format: (1) Applies very closely to me (9) Doesn’t apply to me at all   66  Appendix B  Informed Consent Form          INFORMED CONSENT FORM   Title of Study: Smartphones and Social Media: An Examination of College Students’ Use of Smartphones and Social Media  Principal Investigators:   Takara Bond and Natasha Parent are conducting this research for their Master of Arts in Human Development Learning and Culture, in the Department of Educational and Counselling Psychology and Special Education at the University of British Columbia, under the supervision of Dr. Jennifer Shapka (phone number, email address) and Dr. Danielle Law (phone number, email address). If you have any questions you can contact the principal investigators by emailing (email address) and (email address).   This Study: You invited to take part in this research study if you are a university student between the ages of 18 years old to 25 years old, own a smartphone, and have at least one social media account. Your participation is voluntary. If you wish to participate in this study, you will be asked to sign this form. Before you decide, it is important for you to understand what the research involves. This consent letter will tell you about the study, why the research is being done, and what is involved with being part of the study.   What’s the purpose?   The purpose of this study is to learn about how university students’ use smartphones and social media. From past studies, we know that university students are the highest users of smartphones and social media both in terms of prevalence and frequency. As such, this research aims to examine UBC students’ relationships with their smartphones and use of social media.  What happens in this study?  To participate in this study, you can complete the survey on an iPad provided by the researchers, or through a personal device by accessing a secure survey website. The survey will take Department of Educational and Counselling Psychology, and Special Education The University of British Columbia Faculty of Education  2125 Main Mall Vancouver BC Canada V6T 1Z4 Tel  604-822-0242  Fax  604-822-3302 www.ecps.educ.ubc.ca  67  approximately 30 minutes. No further participation will be required upon completion of the survey.  What are the benefits of participating in this study?  By participating in this study, you will be contributing to the scientific literature on university students’ use of smartphones and social media. This information may contribute to the development of student support services, educational programming, and marketing campaigns promoting healthy social media and smartphone use.  You are invited to participate in a draw for a $250 gift card to the UBC Bookstore. To enter this draw, please click here or email (email address) with the subject line “Smartphones and Social Media Study” and include your name and email. The winner will be contacted on July 15th, 2018.  What are the risks of participating in this study?  There are no serious risks to this study, and you may withdraw from this study at any point with no penalty or consequence. If you would like to discuss anything about this study, you may contact the researchers via the contact information at the top of this page.  If you feel any discomfort as a result of this study, please consult the resources listed below:  -University Counselling Services (on campus counselling services)  -UBC Counselling Services: https://students.ubc.ca/health-wellness/counselling-services -Help Lines (online and phone counselling)   -BC Crisis Centre https://crisiscentre.bc.ca  What will happen with my information?  The results of this study will be reported in two students’ graduate thesis and may also be published in academic journal articles and conference presentations. If you are interested in receiving these results, please contact the researchers at the email addresses listed above.   Is my information private and confidential?  No information or records that disclose your identity will be published. You, like all participants in this study, will not be identified by name in any reports of the completed study. To make sure this is the case, you will be assigned a unique study number as a participant in this study. Only this number will be used on any research-related information collected about you during the course of this study, so that your identity as a participant in this study will be kept confidential. All of the information we collect will be securely stored on a secure computer server at UBC. We will be using a UBC Survey Tool provided by Qualtrics to collect data, which complies with the BC and Ontario Freedom of Information and Protection of Privacy Act since the survey data is kept secure and is stored and backed up in Canada. This data server is located in Canada and subject to Canadian laws. If you choose to participate in the survey, you understand that your responses to the survey questions will be stored and accessed in Canada and that your rights to  68  privacy are legally protected by federal and provincial laws that require safeguards to ensure that your privacy is respected.    How can I withdraw from this study?  Participation in this study is completely voluntary and you are free to withdraw at any time for any reason, without any negative impact. If there are certain questions that you do not want to answer that is okay – you don’t have to answer them. You do not have to give a reason for not answering questions or withdrawing from the study. Please contact the researchers if you decide to withdraw after submitting your survey responses.   What if I have questions about the study?  If you have any questions or would like further information with respect to this study, you may contact, Takara Bond (email address) and/or Natasha Parent (email address).  Who can you contact if you have complaints or concerns about the study?  If you have any concerns or complaints about your rights as a research participant and/or your experiences while participating in this study, contact the Research Participant Complaint Line in the UBC Office of Research Ethics at 604-822-8598 or e-mail RSIL@ors.ubc.ca or call toll free 1-877-822-8598. The UBC Behavioural Research Ethics Board has issued certificate H18-00752 for this study.  I have read the contents of this form and understand what participation in this study involves. I have been provided the opportunity to ask any questions related to my participation and have had them answered to my satisfaction. I understand that participation in this study is completely voluntary and that I have the right to refuse to participate in this study or withdraw at any point, without any negative impact.   Please check one of the following:  o I consent to participate in the study by completing the questionnaire  o I do not consent to participating in any part of this study      69  Appendix C  Participant Feedback Letter  Thank you for participating in this study!   If you have any questions or would like more information about this study, please contact Takara Bond (email address) and/or Natasha Parent (email address).   Participation in this study is completely voluntary and you are free to withdraw at any time for any reason. Please contact the researchers if you decide to withdraw.   By participating in this study, you are contributing to the scientific literature on college students’ use of smartphones and social media. If you would like to receive information about the results of this study, please contact the researchers.   If you feel any discomfort as a result of this study, please consult the resources listed below:  -UBC Counselling Services, drop in to their office at Room 1040, Brock Hall or visit https://students.ubc.ca/health-wellness/counselling-services for access to resources and further support.  -BC Crisis Centre provides online and phone counselling at https://crisiscentre.bc.ca -Empower Me is an online mental health service, operated through student care http://www.studentcare.ca/rte/en/UniversityofBritishColumbiaAMSGSS_EmpowerMe_EmpowerMe  If you have any concerns or complaints about your rights as a research participant and/or your experiences while participating in this study, contact the Research Participant Complaint Line in the UBC Office of Research Ethics at 604-822-8598 or if long distance e-mail RSIL@ors.ubc.ca or call toll free 1-877-822-8598. The UBC Behavioural Research Ethics Board has issued certificate H18-00752 for this study. 

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