Arts and Social Sciences, Irving K. Barber Faculty of (Okanagan)
Psychology, Department of (Okanagan)
DSpace
UBCO
Wiebe, Sage Lynn
Master of Arts - MA
University of British Columbia
Social support can have wide-ranging positive effects on emotional well-being, particularly during times of stress. Digitally mediated communication has become a common means of connecting with supportive others, raising questions of whether digital support, such as texting, can match the effectiveness of in-person support. Conflicting theories and empirical results to date may be due to individual differences impacting one’s experience of digitally mediated communication and support. The current research investigated the role of social anxiety symptoms in users’ reported overarching attitudes, preferences, and tendencies surrounding digitally mediated support in daily life, and how this may relate to the effectiveness of in-person versus text-based support in two lab-based experiments. In Experiment 1, participants underwent a psychological stress task followed by receiving support either in-person or via texting from a research confederate. Experiment 2 followed a similar procedure but with support from a close friend. Participants' levels of positive (“happy,” “calm”) and negative (“anxious”) affect, along with satisfaction with support, were measured. Higher symptoms of social anxiety were associated with a greater preference for and utilization of asynchronous digital communication (such as texting) for support in daily life. However, findings from Experiment 1 indicated that those with higher social anxiety symptoms had significantly higher anxiety following text-based support, compared to in-person support. Otherwise, the effectiveness of in-person versus text-based support did not vary based on social anxiety symptoms. Thus, while digital communication may be preferred by more socially anxious individuals, this doesn’t seem to translate to better outcomes for mood and satisfaction with support. This research has important implications, suggesting the need for a more nuanced understanding of effective support for those with higher social anxiety levels in relational and therapeutic contexts.
IN-PERSON VS. TEXT-BASED SUPPORT: RELATIONSHIPS BETWEEN SOCIAL ANXIETY, COMMUNICATION ATTITUDES, AND SUPPORT EFFECTIVENESS by Sage Lynn Wiebe B.A. (Hons.), The University of British Columbia, 2021 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ARTS in THE COLLEGE OF GRADUATE STUDIES (Psychology) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) July 2024 © Sage Lynn Wiebe, 2024 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: IN-PERSON VS. TEXT-BASED SUPPORT: RELATIONSHIPS BETWEEN SOCIAL ANXIETY, COMMUNICATION ATTITUDES, AND SUPPORT EFFECTIVENESS submitted by Sage Lynn Wiebe in partial fulfillment of the requirements of the degree of Master of Arts . Dr. Susan Holtzman, Irving K. Barber Faculty of Arts and Social Sciences Supervisor Dr. Maya Libben, Irving K. Barber Faculty of Arts and Social Sciences Supervisory Committee Member Dr. Jessica Lougheed, Irving K. Barber Faculty of Arts and Social Sciences Supervisory Committee Member Dr. Kathy Rush, School of Nursing, Faculty of Health and Social Development University Examiner iii Abstract Social support can have wide-ranging positive effects on emotional well-being, particularly during times of stress. Digitally mediated communication has become a common means of connecting with supportive others, raising questions of whether digital support, such as texting, can match the effectiveness of in-person support. Conflicting theories and empirical results to date may be due to individual differences impacting one’s experience of digitally mediated communication and support. The current research investigated the role of social anxiety symptoms in users’ reported overarching attitudes, preferences, and tendencies surrounding digitally mediated support in daily life, and how this may relate to the effectiveness of in-person versus text-based support in two lab-based experiments. In Experiment 1, participants underwent a psychological stress task followed by receiving support either in-person or via texting from a research confederate. Experiment 2 followed a similar procedure but with support from a close friend. Participants' levels of positive (“happy,” “calm”) and negative (“anxious”) affect, along with satisfaction with support, were measured. Higher symptoms of social anxiety were associated with a greater preference for and utilization of asynchronous digital communication (such as texting) for support in daily life. However, findings from Experiment 1 indicated that those with higher social anxiety symptoms had significantly higher anxiety following text-based support, compared to in-person support. Otherwise, the effectiveness of in-person versus text-based support did not vary based on social anxiety symptoms. Thus, while digital communication may be preferred by more socially anxious individuals, this doesn’t seem to translate to better outcomes for mood and satisfaction with support. This research has important implications, suggesting the need for a more nuanced understanding of effective support for those with higher social anxiety levels in relational and therapeutic contexts. iv Lay Summary Social support has many benefits. Technological advances now allow us to get support digitally every day—including through texting. Debates exist over whether in-person or digital support is more helpful. Research suggests both are effective, but findings are mixed, possibly due to individual differences. For instance, people with social anxiety might prefer digital support, but it is unclear if they get better support. Our research tested whether people with higher social anxiety levels prefer and use digital support more, and its impact on their relationships. We also tested the helpfulness of digital (specifically texting) versus in-person support. In two lab-based experiments, stressed participants received support via texting or in-person. We found that people with higher social anxiety levels are more likely to prefer communicating digitally (including texting) for support in daily life, but experimental results showed they benefit more from in-person support, informing our understanding of digital support and communication. v Preface This research was conducted at the University of British Columbia (Okanagan) and supervised by Dr. Susan Holtzman. For the present thesis, I was responsible for the conception and design of the specific investigation using existing data, data preparation and analyses, and the writing of every section of the thesis. Generative artificial intelligence was utilized solely to identify potential errors in syntax for relevant statistical programming code. Prior to original data collection, each experiment was reviewed and approved by the Behavioral Research Ethics Board of the University of British Columbia (Okanagan). Research hypotheses and analytic methods were registered with the Open Science Framework under the DOI 10.17605/OSF.IO/ J3WST. To date, the results of this research have not been published. vi Table of Contents Abstract ____________________________________________________________________ iii Lay Summary _______________________________________________________________ iv Preface _____________________________________________________________________ v Table of Contents ____________________________________________________________ vi List of Tables ________________________________________________________________ x List of Figures _______________________________________________________________ xi Acknowledgments ___________________________________________________________ xii Dedication _________________________________________________________________ xiii Chapter 1: Introduction _______________________________________________________ 1 1.1 Social Support and Well-Being ______________________________________________ 2 1.2 Social Support and Modes of Communication __________________________________ 3 1.3 What is Digitally Mediated Communication? __________________________________ 3 1.4 Theories Supporting In-Person Communication_________________________________ 5 1.5 Theories Supporting Digitally Mediated Communication _________________________ 7 1.6 Empirical Support for Differing Communication Modes __________________________ 9 1.7 The Impact of Individual Differences ________________________________________ 12 1.8 Social Anxiety __________________________________________________________ 13 1.9 Social Anxiety and Modes of Communication _________________________________ 16 1.10 The Current Study ______________________________________________________ 19 vii 1.10.1 Research Aims and Hypotheses ________________________________________ 20 1.10.1.1 Aim 1 ________________________________________________________ 20 1.10.1.2 Aim 2 ________________________________________________________ 21 Chapter 2: Experiment 1 _____________________________________________________ 23 2.1 Method _______________________________________________________________ 23 2.1.1 Participants _________________________________________________________ 23 2.1.2 Procedure __________________________________________________________ 23 2.1.3 Measures __________________________________________________________ 25 2.1.3.1 Demographics and Technology Use __________________________________ 25 2.1.3.2 Social Anxiety ___________________________________________________ 26 2.1.3.3 Positive and Negative Affect _______________________________________ 26 2.1.3.4 Satisfaction with Support __________________________________________ 27 2.1.4 Analytic Approach ___________________________________________________ 27 2.1.4.1. Data Characteristics & Preliminary Analyses __________________________ 27 2.1.4.2 Main Analyses __________________________________________________ 28 2.2 Results ________________________________________________________________ 29 2.2.1 Sample Characteristics ________________________________________________ 29 2.2.2 Preliminary Analyses _________________________________________________ 30 2.2.3 Hypothesis Testing ___________________________________________________ 31 2.2.3.1 Hypothesis 1 ____________________________________________________ 31 viii 2.2.3.2 Hypothesis 2 ____________________________________________________ 31 Chapter 3: Experiment 2 _____________________________________________________ 34 3.1 Method _______________________________________________________________ 34 3.1.1 Participants _________________________________________________________ 34 3.1.2. Procedure _________________________________________________________ 35 3.1.3 Measures __________________________________________________________ 36 3.1.3.1 Demographics and Technology Use __________________________________ 36 3.1.3.2 Social Anxiety ___________________________________________________ 37 3.1.3.3 Positive and Negative Affect _______________________________________ 37 3.1.3.4 Satisfaction with Support __________________________________________ 38 3.1.4 Analytic Approach ___________________________________________________ 38 3.1.4.1 Data Characteristics & Preliminary Analyses___________________________ 38 3.1.4.2 Main Analyses __________________________________________________ 39 3.2 Results ________________________________________________________________ 39 3.2.1 Sample Characteristics ________________________________________________ 39 3.2.2 Preliminary Analyses _________________________________________________ 40 3.2.3 Hypothesis Testing ___________________________________________________ 41 3.2.3.1 Hypothesis 1 ____________________________________________________ 41 3.2.3.2 Hypothesis 2 ____________________________________________________ 41 Chapter 4: Discussion ________________________________________________________ 44 ix 4.1 Social Anxiety and Attitudes Surrounding Communication _______________________ 44 4.2 Social Anxiety and Effectiveness of Communication Modes ______________________ 47 4.3 Practical and Clinical Implications __________________________________________ 53 4.4 Strengths, Limitations, and Future Directions _________________________________ 55 4.5 Conclusion ____________________________________________________________ 60 Bibliography _______________________________________________________________ 61 Tables _____________________________________________________________________ 87 Figures ____________________________________________________________________ 91 x List of Tables Table 1: Experiment 1: Means and Standard Deviations for Main Study Variables..................... 87 Table 2: Experiment 1: Moderated Regression Results for Post-Support Outcomes ................... 88 Table 3: Experiment 2: Means and Standard Deviations for Main Study Variables..................... 89 Table 4: Experiment 2: Moderated Regression Results for Post-Support Outcomes ................... 90 xi List of Figures Figure 1: Experiment 1: Visualization of SIAS-6 x Condition Interaction for “Anxious”.......... 91 xii Acknowledgments This research would not be possible without the support of many people. To my supervisor, Dr. Susan Holtzman, thank you so much for your kindness, support, mentorship, and confidence in me not only through this project but for all of the years we have researched together. To my committee members, Dr. Maya Libben and Dr. Jessica Lougheed, I am so grateful for your guidance and perspective throughout this process. To Dr. Charlie Taylor, thank you for your expertise and support in navigating this field of research. To Rebecca Godard, thank you for offering your time and support so willingly and for keeping me up to date on the latest studies. I would also like to thank the Social Sciences and Humanities Research Council of Canada (SSHRC) for the funding that enabled me to carry out this research. To my cohort mates, Toria and Jordy, thank you for all of the venting meetings, FaceTimes, and work sessions where we got no work done. I couldn't have asked for better friends to go through this process with. To Marcus and Amané, thank you for your friendship and vehicular support; my appreciation is immeasurable, except perhaps by knitwear and Skittles. Thank you to Max Verstappen and Josh Allen for inspiring me to pursue greatness. Thank you to all of my many pets for giving me moments of cuteness, love, and laughter. Dexter, you are lying beside me as I write this and you have been every single day while I worked. Thank you for your silent and sleepy support. To my amazing family, my favourite people in the world—Dad, Mom, Sara and Senna, thank you so much for believing me and supporting me every step of the way through this long journey. Everything I do is for you and because of you. Texting, calling, and visiting with you all has been such a light in my life throughout my education. I love you all so much. xiii Dedication To Mitch, Thank you for keeping me grounded and making me laugh. 1 Chapter 1: Introduction The emotional benefits of social support are well-established (e.g., Taylor, 2011). However, with technological advances permitting unprecedented access to others through digital means (e.g., texting, social media) and newer generations experiencing shifts in social norms regarding appropriate modes of communication, the landscape of receiving social support has transformed substantially (Colasante et al., 2022). This shift has triggered a proliferation of public discourse and scientific investigation into whether communication mode matters when it comes to giving and receiving support. There are numerous and conflicting theoretical perspectives regarding the superiority of both in-person communication and digitally mediated communication (e.g., Daft & Lengel, 1986; Dennis & Valacich, 1998). Related empirical findings also support both communication mediums in various contexts from fostering friendships to decreasing stress (e.g., Desjarlais & Willoughby, 2010; Holtzman et al., 2017). One possible reason for the mixed findings in this literature is that there may be individual differences impacting the experience and effectiveness of digital forms of communication (Navarro et al., 2020; Peter & Valkenburg, 2006). Social anxiety has been proposed to be one of these factors (Forgays & Schreiber, 2014; Reid & Reid, 2010) as it can increase individuals’ comfort with, and preference for, digital communication (Reid & Reid, 2007). Preliminary evidence suggests that experiences of digital communication and support (e.g., how often it is received, or how it influences emotional/relational outcomes) may also differ for those with symptoms of social anxiety (Colasante et al., 2022; Lundy & Drouin, 2016; Reid & Reid, 2010). The current research thus had two overarching goals. The first was to test whether individuals higher in social anxiety symptoms are more likely to prefer and receive social support via digitally mediated communication, and to assess its perceived impact on their 2 relationships. The second was to investigate whether the effectiveness of social support via in-person and digital communication varies based on levels of social anxiety symptoms. 1.1 Social Support and Well-Being Perceived social support has been historically defined as one’s perception of being loved and cared for, valued and esteemed, and having access to a social network of individuals who are invested in and concerned about one’s well-being (Pearson, 1986; Taylor, 2011). The concept of received support similarly refers to specific supportive behaviours from individuals within one’s social network (Demaray & Malecki, 2002). While these definitions of social support seem to necessitate the involvement of individuals from the “inner ring” of one’s social network, this need not be the case. Indeed, impactful social support can be provided by friends and family, but also by acquaintances, colleagues, and even strangers (Van Lange & Columbus, 2021). While there is mixed evidence as to whether close or more distant relations provide more impactful support (Wang et al., 2021), each form and source of social support has been demonstrated as impactful, suggesting that individual and dyadic characteristics both play unique and important roles (Cutrona & Suhr, 1992; Taylor, 2011; Watson et al., 2019). Social support has been shown to be extremely beneficial and capable of eliciting a myriad of positive outcomes. Social support protects against physical illness and cognitive decline, enhances longevity, and decreases mortality risk (Holt-Lunstad et al., 2010; Taylor, 2011; Uchino, 2006). Those with greater support also report greater happiness (Wang et al., 2021), enhanced life satisfaction (Oh et al., 2014), increased positive affect, and decreased negative affect (Liu et al., 2016). Social support reduces stress (Yıldırım & Green, 2023) and buffers individuals from adverse outcomes related to distressing life events while similarly reducing the development of stress-related psychopathology (Demaray & Malecki, 2002; 3 Lewandowski et al., 2011; Taylor, 2011). Social support even helps to increase resilience in the face of future stresses (Ozbay et al., 2007). 1.2 Social Support and Modes of Communication When social support was first conceptualized, and throughout much of its growth and substantiation as a concept, the focus of this research was on support delivered through in-person communication (Cutrona & Suhr, 1992; Hobfoll & Stokes, 1988; Pearson, 1986). However, with the rising popularity of the internet and more widespread adoption of mobile devices, digital mediums for giving and receiving social support have risen to the forefront. Indeed, over the past 20 years, there has been a dramatic shift towards connecting with one another online, through means such as video chat, social media, online forums, and text messaging (Wang et al., 2021). Digital communication has become ubiquitous. As of January 2023, approximately 36.89 million Canadians, or 94% of Canada’s population, were internet users (Petrosyan, 2023). This number represents an increase from 92% in 2020 (likely impacted by COVID-19; Juvonen et al., 2021; Statistics Canada, 2021), and is projected to reach 99% by the end of 2024 (Bush, 2023). The digital sphere now represents a vehicle through which approximately three quarters of Canadians message one another, make voice and video calls, and access their social networks (Statistics Canada, 2021). This is particularly the case for young adults, 71% of whom engage with their smartphone at least every 30 minutes (Statistics Canada, 2021). These drastic developments indicate a significant transformation in the method through which social support can be received. 1.3 What is Digitally Mediated Communication? The dynamic landscape of what does—and does not—constitute digitally mediated communication requires consideration. Many of the early, foundational theories in this realm used the naming convention of “computer-mediated communication” (e.g., Walther, 1992), 4 reflecting the then-truth that this form of communication often took place by chatting on a traditional, often stationary, computer (Campbell, 2020). At present, however, technology has become far more mobile, and it is now common to see computers in pockets and on wrists; furthermore, computer-mediated communication has developed capabilities to convey audio and video (Xu & Liao, 2020). Thus, the “computer” component of computer-mediated communication is no longer as suitable a representation of the state of communication through virtual means. To address this, scholars have recommended shifting towards the term “digitally mediated communication” to more aptly account for the increasingly diverse ways in which individuals can digitally communicate (Campbell, 2020; Xu & Liao, 2020). In line with expanding how digitally mediated communication is defined, scholars have also recommended refining how this form of contact is theorized, suggesting a shift in focus towards its “mediated” aspect (Carr, 2020). This not only allows room for future growth in what constitutes digitally mediated communication, but also allows researchers to focus on what is arguably its most central aspect: the differential presentation of communicative cues (Xu & Liao, 2020). It is argued that concentrating on discrepancies in how various forms of communication present cues (i.e., text-based vs. in-person communication) preserves the utility of theories established in the context of earlier technological norms (Walther, 2011) and legitimizes their continued use. However, this field of research does not only endeavour to establish the boundaries of in-person and digitally facilitated communication and logistical disparities between the two. A substantial part of this discussion also revolves around determining which modality best facilitates interpersonal communication and social connection, and the unique affordances of different modes of communication. With this objective in mind, various theoretical perspectives 5 exist that advocate for the efficacy of both in-person and digitally mediated communication in terms of their impact on connection. Each will be discussed in turn. 1.4 Theories Supporting In-Person Communication The argument for in-person communication methods being better suited for social interaction has come from a variety of disciplines such as computer science, business and communication studies, health studies, and psychology. A cornerstone of this argument centers on socioemotional cues delivered verbally (e.g., vocal tone, inflection, volume), nonverbally (e.g., posture, eye contact, facial expressions, gestures), and even through tactile means (e.g., touching one’s arm or shoulder) to facilitate support and connection (Grondin et al., 2019). Theories arguing that digitally mediated communication (especially text-based communication) has a diminished ability to convey these cues are referred to as “cues-filtered-out” theories, and propose that since these cues are not adequately portrayed, individuals are prevented from accomplishing social functions (e.g., empathy, support, truthfulness) and the quality of social interaction suffers as a result (Culnan & Markus, 1987; Daft & Lengel, 1986). One of the most popular cues-filtered-out theories, media richness theory (Daft & Lengel, 1986), places different methods of communication on a continuum from low richness (or “lean” media) to high richness (or “rich” media; Grondin et al., 2019, Rhoads, 2010) based upon the number and quality of cues able to be portrayed as well as the synchronicity or bidirectionality of feedback (Walther, 2011). With this rubric, in-person communication is often placed at the highest end of this continuum and thought of as the optimal communication method as it is synchronous, bidirectional, and capable of transmitting verbal, nonverbal, and tactile cues. On the other hand, text-based communication is situated at the leanest end as typically asynchronous with a reduced ability to convey tactile, verbal, and nonverbal signals (Grondin et al., 2019). 6 Therefore, it is argued that in-person communication can better facilitate connection, empathy, and social support by avoiding the filtering of cues from conversation. Social presence theory (Short et al., 1976) similarly argues in favour of in-person communication’s richness and ability to facilitate connection in a way that digitally mediated communication cannot. This theory argues that different media differ in their capacity to elicit one’s perception that the other individual involved in a given conversation is physically present. Due to the differing capabilities of communication methods to transmit verbal and nonverbal cues, their abilities to convey social presence may be limited. For instance, in in-person settings, an individual is unequivocally able to sense the other person’s presence as they communicate, whereas in typical text-based settings, their only indicators as to the other individual’s presence are words, symbols, or emojis. Therefore, proponents of social presence theory argue that in-person interaction is more effective for the purposes of satisfactory relational communication as opposed to text-based media due to this imbalance of cues suggesting physical presence (Short et al., 1976). This theory is particularly important with regard to communication for the purpose of social support, as sensing that a communication partner is “there with you” can often translate to the sense that said individual is “there for you” (Grondin et al., 2019). Finally, common ground theory (Clark & Brennan, 1991) argues that communication necessitates not only the presentation of an idea, thought, or disclosure from the sender, but also the acceptance, acknowledgment, or “grounding” of this presentation by the recipient. This grounding process is imperative for the development of affiliation and empathy (Grondin et al., 2019). This theory posits that without this sense of grounding (i.e., without the receiver acknowledging your message or idea), the effectiveness of the communication is impaired from the sender’s perspective. In the context of text-based communication, since there are fewer 7 opportunities through which grounding (e.g., smiling, nodding, or exhibiting mimicry from the other involved in the conversation) can occur, this medium is more likely to portray ambiguity and less likely to benefit the sender. In fact, text messaging with an unresponsive individual can elicit feelings of ostracization and psychological distress (Smith & Williams, 2004). Taken together, there is a sizable repository of theory that argues for the superiority of in-person communication for high-quality, supportive communication. Despite this, there also exist several theories, both historical and more recent, that argue in favour of digitally mediated communication’s ability to meet, if not exceed, the effectiveness of in-person communication. 1.5 Theories Supporting Digitally Mediated Communication As one of the key theories in support of digitally mediated communication, social information processing theory (Walther, 1992) acknowledges that this form of communication does often lack the nonverbal communicative cues inherent in in-person contact. However, this theory argues that a lack of nonverbal cues is not a detriment, and need not necessarily impede connection through digital means—instead, our human propensity to connect with one another and to build relationships drives us to make adaptations in order to execute this fundamental social process through whatever means possible, including digitally mediated communication. Per this theory, individuals adapt to communication media with decreased cues by capitalizing upon the available cues that remain (e.g., utilizing punctuation and emojis, adopting certain language styles; Reynolds et al., 2017; Walther & D’Addario, 2001) in order to create value and shared understanding through a traditionally “leaner” medium. This theory insinuates that the effectiveness of any given communication medium does not depend entirely upon the medium itself, but also upon the individuals utilizing it. 8 Channel expansion theory (Carlson & Zmud, 1999) similarly argues that with increased familiarization and continued use over time, the effectiveness of digitally mediated communication has the capacity to reach—if not surpass—that of in-person communication. Under this model, as one gains experience with a digital communication medium such as text messaging, their comfort with this medium is likely to grow, which can lead to greater benefit from its utilization. This theory also proposes that as one becomes closer to the individual with which they are communicating, traditionally “leaner” media may be perceived as richer (Carlson & Zmud, 1999). Thus, for populations such as young adults who are often referred to as “digital natives” having utilized technology for much of their lives (Colasante et al., 2022; Sherman et al., 2013), digitally mediated communication can be equally as effective as in-person communication. The hyperpersonal model of computer-mediated communication (Walther, 1996) goes beyond arguing that digitally mediated communication can simply “measure up” to in-person communication; instead, it argues that the former is able to facilitate more intimacy, self-disclosure, and satisfaction than the latter due to the same lack of cues that the previously discussed theories classified as a significant flaw. Walther proposes that in the absence of other cues, the receiver of digitally mediated communication may “fill in the blanks” with extensively positive perceptions of the sender (e.g., idealization, perception of similarity and attraction; Walther, 1996). Furthermore, the author argues that the often asynchronous nature of text-based communication allows for the maintenance of self-presentation in the form of crafting and editing messages that emphasize positive attributes and facilitate greater intimacy, support, and connection (Walther, 1996). Therefore, this theory argues that the asynchronous, cue-scarce nature of digitally mediated communication (such as text-based communication) may indeed be 9 ideal for intimate, supportive interaction. Other theorists have also highlighted the potential benefits of text-based communication insofar as it reduces the cognitive fatigue that can come along with cue-rich communication (Bailenson, 2021). 1.6 Empirical Support for Differing Communication Modes After discussing several competing theories, there is an evident lack of clarity as to which communication medium, in practice, should prove as more effective for communicating and conveying support. When both in-person and digitally mediated communication are taken to task, empirical findings appear to swing in favour of in-person communication, but intriguingly, the results are not unanimous. In terms of cross-sectional data, one comparative study found that participants who more often utilized in-person communication for social support following disruptive life events felt significantly better than participants who utilized electronic means such as the telephone or Internet (Lewandowski et al., 2011). However, substantial survey research highlights the potential benefit of digitally mediated support through instant messaging, social media, blogs, and even online gaming for perceived availability and receipt of social support—even if not directly comparing these outcomes with in-person communication (Hampton et al., 2011; Lin & Bhattacherjee, 2009; Rains & Wright, 2016). For example, digitally mediated supportive interactions with friends have been shown to correlate with decreased anxiety, loneliness, and depressive symptoms (Juvonen et al., 2021). Findings from longitudinal studies and those utilizing ecological momentary assessment (EMA) follow a similar mixed pattern of results. Several studies demonstrate a temporal link between in-person communication and increased positive affect and decreased negative affect that is not matched by digitally mediated communication. For instance, both Kafetsios and 10 colleagues (2017) and Verduyn and colleagues (2021) found that in the context of in-person social encounters, individuals experienced more day-to-day positive emotion and satisfaction and less negative emotion than in digitally mediated encounters. This finding was mirrored by Hamilton and colleagues (2021), who, in comparing affect following negative and positive interactions, found that in-person interactions were generally followed by greater positive affect and less negative affect with the opposite effect seen in digital interactions. Similarly, another study found that after either text-based or in-person interactions, participants felt significantly sadder and lonelier following the former, and happier and more affectionate following the latter (Petrova & Schulz, 2022). While the aforementioned research paints an unflattering picture of the effectiveness of digital support, there is still substantial evidence that digital support is able to facilitate positive outcomes in and of itself. Indeed, one study utilizing momentary sampling found that supportive interaction through digital means led to increased positive affect, perceived social support, satisfaction with life, and sense of security (Oh et al., 2014), and another study saw that individuals who participated in online chatting or used computers with friends enjoyed increased friendship quality (Desjarlais & Willoughby, 2010). Experimental studies comparing the effectiveness of in-person and digitally mediated communication for social support and related outcomes are relatively few and far between. However, existing experimental studies largely place the utility of different communication mediums on the same spectrum as the number of cues that they afford: with in-person communication first, followed by video communication, audio communication, and text-based communication. Holtzman and colleagues (2017; under review) have evaluated this cue-based pattern in randomized controlled trials measuring social and emotional outcomes following induced stress and a supportive conversation with a friend or a confederate. Their overall pattern 11 of results highlights lower positive affect following text-based support and often, but not always, reduced satisfaction with support. In a similar trial wherein participants conversed with a confederate regarding a stressor, in-person conversations led to less worry and uncertainty compared to instant messaging conversations (Rains et al., 2017). The aforementioned cue-based pattern was also found in studies assessing outcomes of perceived affiliation and bonding between young adult female friends (Seltzer et al., 2012; Sherman et al., 2013). However, Sherman and colleagues also reported that preferred mode of communication made an impact—that is, greater bonding was derived from the use of video and audio chat in the lab-based setting when participants reported using these communication modes more often in their daily lives. Furthermore, when text-based interactions capitalized upon available cues (e.g., emoticons, capitalization, or typed laughter), perceived bonding increased (Sherman et al., 2013). Seltzer and colleagues (2012), moreover, observed that telephone call interactions led to greater releases of oxytocin than instant messaging interactions. Further experiments investigating satisfaction of basic needs, positive mood, and self-confidence have demonstrated superior effectiveness of in-person communication and support (High & Solomon, 2014; Sacco & Ismail, 2014). It is important to note, however, that the extant experimental literature is not without support of digitally mediated communication. Indeed, several experiments have demonstrated the utility of text-based communication for the purposes of increasing mood and well-being, facilitating resilience and bonding, and lowering stress (Wang et al., 2021; Yau et al., 2021). Furthermore, several studies comparing the utility of digital and in-person support have found that they have equal capacity to facilitate outcomes such as permitting accurate interpersonal perceptions (Boucher et al., 2008), and regulating negative emotions (Colasante et al., 2022). 12 Taken together, a substantial body of empirical evidence suggests that in-person communication is superior for facilitating outcomes related to social support. However, a number of studies do argue in favour of digitally mediated communication’s ability to provide effective social support, signifying that there may be complexities to these relationships not sufficiently accounted for in prior research. 1.7 The Impact of Individual Differences Much of the above literature acknowledges that a comparison between in-person and digitally mediated communication on any given outcome is not as simple as a relationship between two variables. This is reflective of aforementioned theories suggesting that contextual and individual difference factors are a key consideration in the effectiveness of digitally mediated communication (e.g., experience, preference, or comfort; Carlson & Zmud, 1999). More recent calls have been made within the areas of social media and well-being (Luchtefeld & Jordan, 2022; Vogel et al., 2015) and digitally-mediated social support (Rains & Wright, 2016) to consider individual differences in endeavouring to understand the apparent variability demonstrated in the literature. Importantly, researchers have acknowledged that technological skills and exposure, preferences, and attitudes play an important role in technology’s adaptation and one’s ability to effectively utilize it (Conrad & Munro, 2008; Rhoads, 2010). Attitudes towards technology, though broad in scope, fundamentally concern evaluative judgments ranging from favourable to unfavourable that are made regarding the use of technology (Eagly & Chaiken, 1993; Huedo-Martínez et al., 2018). Previous research has operationalized these attitudes to include preferences for engaging in technological communication (e.g., the General Online Social Interaction Propensity Scale; Blazevic et al., 2014), as well as beliefs regarding the positive or negative impact of technology (Cologna et al., 2024; Conrad & Monro, 2008) in 13 numerous realms, including but not limited to the environment (Cologna et al., 2024), the workplace (Madden, 2008), and relationships (Rosen et al., 2013). Beyond attitudes, gender, age, mental health, and personality characteristics are examples of other potentially important modifiers of comfort with and utilization of text-based communication (Forgays & Schreiber, 2014; Reid & Reid, 2010). One such characteristic that may have a substantial impact on an individual’s gravitation toward and experience with digitally mediated communication is their level of social anxiety. 1.8 Social Anxiety Broadly speaking, social anxiety refers to fear, anxiety, or avoidance of social interactions and situations involving the potential to be scrutinized or evaluated socially (McNeil & Randall, 2014). These fears and anxieties can be helpful and adaptive in certain situations, but can also become clinically significant and impairing (McNeil & Randall, 2014). Relatedly, social anxiety is commonly thought of as a continuum ranging from being “free” of social worries, to experiencing sub-clinical levels of social anxiety, to warranting a diagnosis of Social Anxiety Disorder (SAD; McNeil & Randall 2014; Rapee & Heimberg, 1997). While SAD is not extremely common, with a 12-month prevalence of approximately 7% (American Psychiatric Association, 2022), sub-clinical social anxiety is more prevalent with up to 20% of the general population reporting some level of social anxiety symptoms (Crişan et al., 2016). Furthermore, research indicates that younger individuals—specifically younger women—are more likely to be impacted by social anxiety, with 90% of occurrences having an onset before the age of 23 (Jefferies & Ungar, 2020). Regardless of severity, the experience of social anxiety is characterized by several cognitive and affective processes that impact how an individual operates within the context of social interaction. Each of these biases may reflect the fact that, on a more 14 fundamental level, social anxiety is characterized by a heightened fear of negative evaluation (or evaluation in general) by others. Individuals with social anxiety symptoms often experience fear in relation to social situations based upon a perceived risk of being evaluated negatively on their social performance (Morrison & Heimberg, 2013). There also appears to be a relationship between these fears and stress reactivity, suggesting that stressors may exacerbate an individual’s fears (Reichenberger et al., 2018). To synthesize several important cognitive biases inherent in social anxiety, Gilboa-Schechtman and Shachar-Lavie (2013) put forward a theory of nonverbal cue processing, the main premise of which being that socially anxious individuals interact more negatively with nonverbal socioemotional cues (NVCs), particularly those neutral or ambiguous in nature, both through attention and interpretation. These biases are not only present in terms of the other individual involved in the interaction, but also in how socially anxious individuals perceive themselves during an interaction (Hirsch & Clark, 2004). Extant research suggests that socially anxious individuals devote a disproportionate amount of their attention to what they perceive as negative features of social interactions, and that they tend to exaggerate these features to a greater degree than non-anxious individuals (Rapee & Heimberg, 1997). This is demonstrated in experimental research wherein socially anxious individuals are more attentive to potential threat in social interactions (Bar-Haim et al., 2007) and display higher physiological reactivity to NVCs such as direct gazes (Wieser et al., 2009). Similarly, socially anxious individuals have been theorized and empirically found to demonstrate a pattern of attentional avoidance, wherein they avoid perceived negative cues by directing their attention at themselves (Clark & Wells, 1995; Chen et al., 2021; Wieser et al., 2010). Beyond impacting the inclination to focus (or not focus) on particular socioemotional cues within social interactions, higher levels of social anxiety are 15 also associated with biases in how these cues are interpreted. Specifically, cues (or lack thereof) suggesting ambiguity or neutrality in social interactions are interpreted negatively by socially anxious individuals. This bias is observed in research utilizing facial expressions, posture, tone of voice, eye contact, word pairings, and hypothetical or role-played interactions, wherein the socially anxious misinterpret the presence of ambiguity as threatening (Amir et al., 2005; Hirsch & Clark, 2004; Pitterman & Nowicki, 2004; Quadflieg et al., 2007; Schneier et al., 2011; Stopa & Clark, 2000; Yoon & Zinbarg, 2007). Intriguingly, this interpretive bias is also seen in digital communication, with research suggesting that social anxiety symptoms can manifest uniquely in text-based scenarios. For instance, although text-based ambiguity is disliked overall (Kelly et al., 2012), socially anxious individuals are more likely to interpret neutrally valenced messages as negative and threatening (Kingsbury & Coplan, 2016), and when presented with open-ended portions of sentences on a computer, are more inclined to complete these sentences using negative information (Calvo et al., 1994). Taken together, it is evident that multiple biases impact socially anxious individuals’ affective and cognitive processes during interpersonal encounters with others. Indeed, social anxiety has been shown to influence the way that one receives social support (Piccirillo et al., 2021). However, it is important to note that despite these biases, those higher in social anxiety still desire social connection (Rubin & Coplan; 2010) and stand to benefit greatly from the receipt of social support. For individuals diagnosed with SAD, experiencing positive emotions surpassed anxiety levels as an important predictor of connectedness and desire for future affiliation in one experimental study (Taylor et al., 2017). The presence of a friend also aids socially anxious individuals in feeling more competent and attenuating negative self-focused thoughts (Pontari, 2009). Interestingly, one ecological momentary assessment (EMA) study 16 found that for those with social anxiety, the presence of only close companions (i.e., close friends, partners, or family members) facilitated enhanced mood and decreased negative affect, depression, and anxiety as compared to the presence of distant companions (i.e., acquaintances, co-workers, or strangers; Hur et al., 2020). Similarly, individuals higher in social anxiety place greater importance on receiving social support from close friends than more distant others (Coyle & Malecki, 2018). Thus, while social anxiety may impact which social support situations are more helpful and effective, the fact remains that social support is still highly desired and beneficial—perhaps more so when provided by a close friend—for socially anxious individuals. 1.9 Social Anxiety and Modes of Communication Given that social anxiety influences one’s attention to and interpretation of NVCs, and that modes of communication differ in their affordance of NVCs, it would follow that social anxiety may impact one’s comfort with differing communication modes (i.e., in-person vs. text-based) for both general communication and communication for social support. Indeed, empirical findings to date largely support this hypothesis. Many studies have found social anxiety and its correlates (e.g., lower self-esteem, introversion, social fears; Dunaetz et al., 2015; Fullwood et al., 2020; Zsido et al., 2020) to be associated with a higher overall preference for utilizing text-based communication including text messaging, social media, and general “online communication” (Dunaetz et al., 2015; Erliksson et al., 2020; Fullwood et al., 2020; Hutchins et al., 2021; Lee et al., 2013; Lepp et al., 2014; Reid & Reid, 2007; Skierkowski & Wood, 2012; Zsido et al., 2020). Additionally, a meta-analysis found that higher social anxiety was associated with increased comfort online (Prizant-Passal et al., 2016). As a reflection of this increased comfort, Holtgraves (2011) observed that anxious individuals gravitated specifically towards text messaging, and were also more likely to form closer-knit groups of friends over text. Similarly, 17 Weidman and colleagues (2012) found that those higher in social anxiety felt greater comfort and reported greater self-disclosure when socializing online as compared to in-person. Finally, Chan (2011) provided further support for this phenomenon with the finding that even within digitally mediated communication, asynchronous methods were preferred over synchronous methods for individuals higher in social anxiety. The well-evidenced preference that socially anxious individuals have for digitally mediated, text-based communication may reflect the utilization of these forms of communication as a safety behaviour to reduce social threats in the form of exposure to unwanted opportunities for evaluation and NVCs (Clark & Wells, 1995). Multiple scholars have suggested that for the socially anxious, the digital sphere may serve as a safe haven (Colasante et al., 2022; Hansen et al., 2024; Hunter et al., 2018), and a “social compensation hypothesis” has been put forward to suggest that socially anxious individuals utilize digitally mediated communication to compensate for their anxiety manifesting in physical social interactions (Desjarlais & Willoughby, 2010; Valkenburg & Peter, 2007). Empirical findings demonstrate, accordingly, that anxiety levels are related to a decreased willingness to participate in in-person interactions (Allred & Atkin, 2020), and that trait social anxiety is correlated with seeking online safety (Kamalou et al., 2019). This increased reliance on digital communication as a safe space for socially anxious individuals can have negative implications in the form of mobile phone addiction (Allred & Atkin, 2020; Kong et al., 2022; Zhou et al., 2021) and problematic internet (Luchtefeld & Jordan, 2022; Zsido et al., 2020) and smartphone use (Coyne et al., 2019; Mitchell & Hussain, 2018), reflecting the extent to which individuals with social anxiety depend on and form attachments to digitally mediated, relatively cue-sparse communication. 18 Despite the strong evidence that social anxiety is associated with a greater preference for text-based communication, the question remains of whether this preference translates to more beneficial outcomes following social support through this medium. In other words, it remains unclear whether the medium that socially anxious individuals prefer and believe is more beneficial to them actually is more beneficial when it comes to social and emotional outcomes. In the face of an emerging body of literature comparing in-person and text-based communication for the purpose of social support, studies investigating social anxiety’s role in this relationship are extremely sparse with mixed findings. In one experience sampling study comparing the relative utility of social support delivered digitally and in-person, the perceived efficacy of the support did not vary with regard to trait social avoidance—both media were equally effective (Colasante et al., 2022). Conversely, another study utilizing EMA and day reconstruction methods found that social anxiety was associated with decreased positive affect and increased negative affect regardless of medium used for general communication throughout the day, with both in-person and text-based interactions being less effective for socially anxious individuals (Doorley et al., 2020). Reid and Reid (2010), on the other hand, found interaction anxiousness to positively correlate with expressive control and relationship outcomes through digitally mediated communication. Finally, in a rare experimental study focusing on the potential moderating impact of social anxiety on the development of interpersonal connectedness (i.e., relationship building) with a stranger, there was no significant difference found between the ability of instant messaging and in-person communication to facilitate connectedness for those high in social anxiety, and increased levels of connectedness were found across conditions (Lundy & Drouin, 2016). However, within the instant messaging condition, individuals with higher levels of social anxiety experienced more of an increase in perceived similarity following 19 conversations with strangers than individuals lower in social anxiety. In sum, although extant evidence is variable, the possibility does exist that social anxiety levels could disrupt the previously observed patterns in the benefits of in-person over text-based communication. 1.10 The Current Study The state of the theoretical and empirical literature regarding the benefits of social support provided via in-person versus digitally mediated communication is unclear. From a theoretical standpoint, there exist numerous well-established yet competing theories that support the superiority of both in-person communication (e.g., Daft & Lengel, 1986; Short et al., 1976; Walther, 1992) and general digitally mediated communication (Dennis & Valacich, 1998; Walther 1996). On an empirical level, a number of studies have suggested that, all else being equal, in-person communication may trump text-based communication in terms of overall satisfaction with support (Juvonen et al., 2021), positive affect (Holtzman et al., 2017), and (less consistently) negative affect (Kafetsios et al., 2017; Verduyn et al., 2021). In reality, however, “all else” is unlikely to be equal. Indeed, individual differences such as social anxiety are marked by biases that may influence the extent to which one benefits from any given mode of communication (Coyle & Malecki, 2018; Zsido et al., 2020; Skierkowski & Wood, 2012). These biases occur in the realms of attention (Bar-Haim et al., 2007) as well as misinterpretation of ambiguity (Schneier et al. 2011; Yoon and Zinbarg, 2008) and NVCs (Gilboa-Schechtman & Shachar-Lavie, 2013) and influence individuals’ preference for and tendency towards communication with less NVCs (i.e., text-based communication; Allred & Atkin, 2020; Reid & Reid, 2007; Skierkowski & Wood, 2012). Thus, the muddled nature of past empirical findings could, in part, reflect the moderating impact of individual differences such as social anxiety. 20 Using data from two randomized controlled studies, the current investigation addressed the dearth of experimental research on this topic by investigating how an individual’s levels of social anxiety symptoms might moderate the relationships between communication medium (i.e., in-person vs. text-based) and outcomes of support—namely, positive and negative affect, and satisfaction with support. In both studies, participants underwent a lab-based psychological stress test and were randomly assigned to receive social support from a confederate (Experiment 1) or close friend (Experiment 2) via different modes of communication. Conducting a secondary analysis of data from these two studies presented the opportunity to test our hypotheses with independent samples and using different sources of support, and allowed us to explore whether results differ based on support provider (i.e., research confederate or close friend). To this question, prior research suggests enhanced effects of support from a close friend for those with social anxiety—as was received by participants in Experiment 2 (Coyle & Malecki, 2018; Hur et al., 2020). However, there is no definitive answer to whether this relationship will hold when text-based support is offered to those higher in social anxiety symptoms. Notably, both of these prior experiments observed an inferior ability of text-based support to elicit positive affect and satisfaction with support (compared to in-person support; Holtzman et al., 2017; Holtzman et al., under review). However, participants’ social anxiety symptoms have not been previously examined as a potential moderator of these effects in either experiment. 1.10.1 Research Aims and Hypotheses 1.10.1.1 Aim 1. The first aim of the current research was to investigate the extent to which levels of social anxiety relate to an individual’s attitudes and practices regarding mode of communication for receiving social support. It should be noted that within this research, attitudes towards technology were operationalized as the perceived impact of text-based communication 21 on relationship quality, in addition to preferences regarding digital communication in acknowledgement of aforementioned discussion surrounding the complexity of attitudes as a construct. Hypothesis 1 predicted that higher levels of social anxiety would be associated with a greater preference for, tendency toward, and perceived positive impact of text-based communication. This hypothesis stemmed from the large amount of theory and empirical findings regarding socially anxious individuals’ preferences for digitally mediated communication for general and friendly conversation (Reid & Reid, 2007; Skierkowski & Wood, 2012; Zsido et al., 2020)—whether as a safety behaviour (Holtgraves, 2011) or as a space within which bonds can be more readily, comfortably formed (Reid & Reid, 2007; Walther, 1996). 1.10.1.2 Aim 2. The second aim of the current research was to address whether levels of social anxiety would influence the effectiveness of in-person or text-based social support in facilitating satisfaction with support, increased positive affect, and decreased negative affect. Due to the aforementioned lack of clarity within proposed theories and empirical findings, three competing hypotheses are proposed. Hypothesis 2A predicted that, among those higher in social anxiety, text-based social support would lead to greater satisfaction with support and positive affect and lower negative affect, compared to those lower in social anxiety. This prediction was made in line with the hyperpersonal, channel expansion, and social information processing perspectives (Carlson & Zmud, 1999; Walther, 1996), as well as aforementioned theory suggesting that social anxiety negatively impacts NVC processing and leads to a greater comfort with (and advantage from) communication with reduced NVCs such as text messaging (Gilboa-Schechtman & Shachar-Lavie, 2013). This prediction also followed empirical findings supporting the overall utility of text-based interaction for socially anxious individuals (Colasante et al., 2022) and suggesting that these text-based benefits may be unique to more socially 22 anxious individuals (Lundy & Drouin, 2016). Conversely, Hypothesis 2B predicted that, among people higher in social anxiety, text-based support would be even less effective at boosting positive affect, attenuating negative affect, and eliciting satisfaction with support compared to in-person support. This was in line with the media richness, common ground, and social presence perspectives that have previously posited that text-based communication is less effective at facilitating connection and support in the general population (Clark & Brennan, 1991; Daft & Lengel, 1986; Short et al., 1976), theory suggesting that socially anxious individuals may interact negatively with not only the presence of NVCs, but also with ambiguity or a lack of NVCs (Calvo et al., 1994; Kingsbury & Coplan, 2016), and research on overall differences in the utility of communication methods suggesting that in-person communication is optimal (e.g., Holtzman et al., 2017). Therefore, this hypothesis posited that previously observed patterns would be intensified, rather than reversed, in the context of social anxiety. Finally, Hypothesis 2C predicted that higher social anxiety symptoms would be associated with worse social and affective outcomes, regardless of the mode of communication. This prediction followed from broader theory (Bar-Haim et al., 2005; Gilboa-Schechtman & Shachar-Lavie, 2013) and research (Doorley et al., 2020) suggesting that attentional and interpretive biases that characterize social anxiety negatively impact the effectiveness of social support, and that this would be expected across a diverse range of communication modes. 23 Chapter 2: Experiment 1 2.1 Method 2.1.1 Participants This study recruited undergraduate students at UBC Okanagan as well as emerging adults in the Okanagan community at large. The study was advertised as “How does stress affect your wellbeing?” and was described as a 90-minute laboratory investigation into coping with stress among young adults via posters in the community, advertisements on community websites and on campus, and through the university’s psychology research recruitment system. Eligibility requirements included being between 18 and 25 years of age, having fluency in English, and having the ability to provide informed consent. Participants were deemed ineligible if they had a mental health disorder diagnosis or were presently taking medication for a mental health disorder. In terms of remuneration, participants were compensated with their choice of $25 or course credit for their participation. 2.1.2 Procedure Participation in this study was carried out at UBC Okanagan’s Health Psychology research laboratory. Participants first reviewed the consent form with a research assistant—one of three upper-year undergraduate psychology students who received careful training including a detailed protocol, scripts, and close supervision and feedback throughout data collection. After providing their informed consent and giving their belongings (including any phones or computers) to the research assistant, participants underwent a 15-minute period of rest spent reading magazines with neutral content to mitigate the risk of any prior stress entering the experiment. Following this period, participants indicated their baseline levels of positive affect, negative affect, and perceived level of stress. After completing these baseline measures, 24 participants were introduced to the Trier Social Stress Test (TSST; Kirschbaum et al., 1993), wherein they were given a sheet of paper by the research assistant informing them that they would have five minutes to prepare a five-minute speech followed by a five-minute task requiring verbal arithmetic to be completed before an audience. This information sheet stated that their speech concerned why they should be hired for a hypothetical campus job, but did not disclose that the arithmetic task would involve counting backwards from 2023 in intervals of 17. After these five minutes of preparation, participants were escorted by the research assistant to a room containing a committee of three confederates (one male and two female) who had received training to maintain neutrality in their facial expressions, to make relevant notes about the participant’s responses, and to follow a standardized comment script so that participants experienced a consistently demanding experience. Participants delivered their speech, performed the arithmetic task, and were then escorted back into their original room. Once alone, they completed paper-form, post-TSST versions of the aforementioned measures. Next, a social support manipulation was performed. Prior to their participation, all participants were randomly assigned to one of three study conditions: an in-person support condition, a text messaging support condition, and a control group receiving no support. Participants in the in-person condition were physically joined by the original research assistant whereupon the two engaged in a conversation regarding the participant’s TSST experience. In both support conditions, the research assistant was trained extensively on a standardized protocol in order to maintain consistency across participants and conditions, and to provide effective emotional support utilizing evidence-based strategies such as validation, esteem-building, and understanding (Thorsteinsson & James, 1999; Uchino et al., 2011). To ensure the communication and support from the research assistant was relevant and appropriate to each participant, this 25 protocol was purposefully flexible, providing script modifications depending on the participant’s mood and TSST performance, as well as instructions for displaying supportive cues as enabled by the communication medium (e.g., smiling and nodding for the in-person condition, or using emojis and exclamation points for the text-based condition). The protocol also included question prompts to ensure that the conversation lasted for the allotted time. Moreover, the research assistant was instructed to use comments written down by the TSST committee about the participant’s task performance to customize the supportive conversation to each participant’s unique experience. Each conversation began with a short bout of small talk to increase rapport. In-person supportive conversations had a total duration of 7 to 8 minutes while text-messaging conversations with the research assistant using a laboratory phone were increased to a duration of 11 to 12 minutes to account for time spent formulating text messages (Walther, 2011). In the control condition, participants spent the 10-minute period waiting alone in their original room, with access to the aforementioned neutral reading material. Following the support manipulation, each participant then completed final follow-up measures that assessed positive and negative affect, and perceptions of the support from the research assistant. At this time, participants also provided general demographic information and information about the presence of technology in their lives, and other standardized psychosocial questionnaires including a measure of social anxiety symptoms. 2.1.3 Measures 2.1.3.1 Demographics and Technology Use. Participants responded to demographic questions and indicated the perceived impact that texting had on their relationships with others (i.e., close friends, parents, siblings, partners, and acquaintances) on a 5-point Likert-type scale ranging from 1 to 5 (where 1 represented “very negative” and 5 represented “very positive”). To 26 create a more global measure of this perceived impact, responses across all relationships were collapsed into a mean score, ranging from a possible 1 to 5. Cronbach’s alpha for this mean variable was 0.67. 2.1.3.2 Social Anxiety. Participants’ symptoms of social anxiety were assessed using the Social Interaction Anxiety Scale-6 (SIAS-6; Peters et al., 2012), based on a full-length measure of the same name (Mattick & Clarke, 1998). This is a 6-item measure wherein participants respond to statements such as “I feel tense if I am alone with just one person,” indicating the extent to which each statement applies to them on a 5-point Likert-type scale ranging from 0 to 4 (where 0 represents “not at all” and 4 represents “extremely”; Mattick & Clarke, 1998). This scale is widely used in non-clinical and clinical settings and has demonstrated satisfactory internal consistency as well as strong validity comparable to its full-length version and to other similar scales (Fergus et al., 2012). A mean score was created for the SIAS-6, with higher scores reflecting higher symptoms of social anxiety. Cronbach’s alpha was 0.79 in the current study. 2.1.3.3 Positive and Negative Affect. To assess positive and negative affect, the Derogatis Affects Balance Scale (DABS; Derogatis, 1996) was administered at three intervals (at baseline, after the TSST, and after the support manipulation). Specifically, positive affect was measured using the Joy and Contentment subscales of the DABS, and for negative affect, the Anxiety and Hostility subscales were utilized. Each item asked participants to indicate the intensity at which they were experiencing a particular feeling in that moment on a 5-point Likert-type scale ranging from 0 to 4 (where 0 represented “very slightly or not at all” and 4 represented “extremely”; Derogatis, 1996). To allow for direct comparisons with Experiment 2, responses to items “happy,” “calm,” and “anxious” were selected for inclusion in the analyses (i.e., these were the only items that were included in both Experiment 1 and 2). These items have the advantage 27 of being highly relevant to the recovery from a psychological stress test and reflect a range of affect spaces, with “calm” representing low activation and positive valence, “happy” representing moderate activation and positive valence, and “anxious” representing moderate-to-high activation and negative valence. 2.1.3.4 Satisfaction with Support. One Likert-type item was administered at the final follow-up interval to assess support condition participants’ perceptions of the support that they had received from the confederate. This item asked participants to indicate how supported they felt by the research assistant during their post-TSST conversation on a scale from 1 to 7 (where 1 represented “not at all supported” and 7 represented “extremely supported”). 2.1.4 Analytic Approach 2.1.4.1. Data Characteristics & Preliminary Analyses. These analyses were conducted utilizing a pre-existing dataset resulting from the aforementioned laboratory-based experiment. The complete dataset for this experiment included five conditions: text-based support, in-person support, text-based criticism, in-person criticism, and a control condition. However, for the current analyses, only the text-based and in-person support conditions were investigated in order to align with the current research aims surrounding the receipt of text-based and in-person supportive communication. All statistical analyses were carried out using SPSS, and hypotheses as well as preliminary and main analytic methods were pre-registered with the Open Science Framework under the DOI 10.17605/OSF.IO/ J3WST. With regard to preliminary analyses, descriptive analyses were conducted on the study variables and demographic data. Next, using independent samples t-tests, randomization checks were completed to confirm that baseline affect (i.e., “happy,” “calm,” and “anxious”) was not significantly different between the two support conditions. Additional independent samples t- 28 tests were conducted to ensure that social anxiety symptoms and perceived impacts of texting on relationships did not significantly differ between the two experimental groups. Furthermore, a Chi-Square test was conducted to confirm that sex frequencies did not significantly differ across the two experimental conditions. Previous analyses utilizing this dataset revealed no significant main or moderating effects of participant sex on satisfaction with support or affect outcome variables (Holtzman et al., 2017). A manipulation check using a paired samples t-test was completed to ensure that the TSST indeed induced a psychological stress response (i.e., increases in feeling “anxious” and decreases in feeling “happy” and “calm” from baseline to post-stress time points). An analysis of covariance (ANCOVA) was conducted to ensure that the TSST resulted in comparable post-stress levels of positive affect (i.e., “happy;” “calm”) and negative affect (i.e., “anxious”) across the two study conditions, after controlling for baseline levels. 2.1.4.2 Main Analyses. To investigate whether attitudes toward text-based communication vary as a function of social anxiety (H1), bivariate correlations tested the association between symptoms of social anxiety and the perceived impact of text messaging on participants’ relationships. To test our hypotheses regarding the moderating effect of social anxiety on the association between mode of communication (in-person vs. text-based) and study outcomes (H2A through H2C) four moderated regression models were conducted (i.e., post-support levels of “happy,” “calm,” “anxious,” and satisfaction with support as outcomes). Each moderation model included: a dummy coded variable to represent the two support conditions (text-based and in-person support), social anxiety symptoms, and an interaction term (condition x social anxiety symptoms; Aiken & West, 1991). Post-TSST levels of the outcome variables were also entered into the moderation models for “happy,” “calm,” and “anxious.” After confirming that the 29 variables of interest were homoskedastic, these analyses were carried out using the PROCESS macro for SPSS (Version 4.2; Hayes, 2022), wherein the variable for social anxiety symptoms was mean centered prior to analysis in order to make intercepts more interpretable. Furthermore, bootstrapping was used (with 5000 resamples) to combat non-normality. Effect sizes were also calculated for interaction terms, in addition to any predictors with p < .10, and compared to standardized cut-offs for Cohen’s f2, an effect size appropriate for variables within a multiple regression analysis (Selya et al., 2012). 2.2 Results 2.2.1 Sample Characteristics Data were obtained from 345 individuals who were randomly assigned to one of five conditions. However, since the control and two criticism conditions were not included in the present analyses, data from 195 participants were excluded. An additional 22 participants were excluded from the analyses due to issues arising within the original experimental manipulation (e.g., being familiar with a research confederate). Additionally, one case was excluded from the analysis due to missing data for a predictor (social anxiety symptoms). While there were rare cases of missing outcome data (one case missing for “anxious” and two cases missing for satisfaction), these data were missing at random. Thus, to avoid inappropriate imputation (Jakobsen et al., 2017), these particular analyses were run with adjusted sample sizes without compromising the overall sample size. As such, the final sample included 127 individuals (60.3% female, 39.7% male) with a mean age of 19.93 years (SD = 1.83, range = 18-25 years). Given that the current study involved secondary data analysis, a post-hoc power analysis using G*Power (Faul et al., 2007) was conducted. Focusing on the most complex analysis—that of the 30 potential moderation of social anxiety—it was determined that the final sample size had sufficient (0.95) power to detect at least medium moderating effect sizes at an alpha level of .05. Almost two-thirds (63.5%) of participants were born in Canada (7.9% in China; 4.0% in India; 2.4% in Brazil; 1.6% each in Colombia, Korea, Malaysia, Singapore, Taiwan, UAE, and USA; 3.2% unspecified; 8% Other). The most common ethnic identity was European (55.9%), followed by East or Southeast Asian (24.4%), South Asian (7.9%), Latin, Central, or South American (3.9%), African (3.2%), Caribbean (3.1%), Indigenous (3.1%), and Arab (0.8%). Although information was not available regarding recruitment source for each participant, 96.1% of participants were full-time students and 0.8% were part-time students at the time of data collection. Participants received their first cell phone at a mean age of 14.1 years (SD = 2.33, range = 7-20 years). 2.2.2 Preliminary Analyses Means and standard deviations for all outcome variables (across all three time points and across the two conditions), as well as social anxiety symptoms and perceived impact of texting on relationship quality, can be found in Table 1. Participants in the two experimental conditions did not significantly vary in terms of baseline levels of feeling “happy” (t(124) = 0.93, p = .35, d = 0.17, 95% CI [-0.184, 0.516]), “calm” (t(125) = -0.43, p = .67, d = -0.08, 95% CI [-0.424, 0.272]), or “anxious” (t(125) = -0.81, p = .42, d = -0.14, 95% CI [-0.491, 0.206]). Furthermore, neither social anxiety symptoms (t(125) = -1.20, p = .23, d = -0.21, 95% CI [-0.561, 0.137]) nor the perceived impact of texting on relationship quality (t(124) = 0.27, p = .79, d = 0.05, 95% CI [-0.302, 0.397]) significantly differed between experimental conditions. Chi-Square test results demonstrated that biological sex frequencies did not significantly differ between conditions (χ²(1, N = 122) = 2.29, p = .14). The TSST successfully elicited a stress response as evidenced 31 by significant changes from baseline to post-stress for “happy” (t(125) = 12.30, p < .001, d = -1.10, 95% CI [0.874, 1.316]), “calm” (t(126) = 15.48, p < .001, d = -1.37, 95% CI [1.129, 1.615]), and “anxious,” t(125) = -10.16, p < .001, d = -0.91, 95% CI [-1.111, -0.696]. Relatedly, post-stress scores were comparable across study conditions for “happy” (F(1, 126) = 0.02, p = .898, η² = 0.000), “calm” (F(1, 127) = 1.13, p = .291, η² = 0.009), and “anxious” (F(1, 126) = 0.19, p = .663, η² = 0.002), controlling for baseline scores. 2.2.3 Hypothesis Testing 2.2.3.1 Hypothesis 1. There was no statistically significant association between social anxiety symptoms and the perceived impact of text-based communication on relationships, r = -.12, p = .17. 2.2.3.2 Hypothesis 2. The full moderated regression output for all outcome variables, including overall model statistics, can be seen in Table 2. The moderated regression analysis for post-support levels of “happy” revealed a non-significant interaction term between experimental condition and social anxiety, b = 0.07, p = .39, 95% CI [−0.0832, 0.2410]). The ΔR2 for the overall model following the addition of the interaction term was 0.003, indicating a negligible effect size (Cohen’s f2 = 0.01). Experimental condition was not significantly related to post-support levels of "happy," b = 0.09, p = .17, 95% CI [−0.0356, 0.2294]. There was an association between social anxiety symptoms and higher post-support levels of “happy,” but this was not statistically significant (b = 0.16, p = .07, 95% CI [-0.0060, 0.3347]) with a negligible effect size (Cohen’s f2 = 0.01). With regard to post-support levels of “calm,” the interaction term was not a significant predictor (b = 0.08, p = .40, 95% CI [-0.1375, 0.2941]). The ΔR2 for the overall model after adding the interaction term was 0.004 (a negligible effect size; Cohen’s f2 = 0.01). The effects of 32 experimental condition (b = -0.03, p = .69, 95% CI [-0.1654, 0.1183]) and social anxiety symptoms (b = -0.06, p = .54, 95% CI [-0.2919, 0.1431] were also non-significant. For post-support levels of “anxious,” the interaction term approached statistical significance, b = -0.17, p = .07, 95% CI [-0.3418, -0.0045]. The change in R2 for the overall model following the addition of the interaction term was 0.02, indicating a small effect size (Cohen’s f2 = 0.03). As such, the conditional effects of experimental condition on feeling anxious were examined at different values of the moderator (social anxiety symptoms; see Figure 1). At 1 SD below the mean of social anxiety symptoms (-0.79), the effect of experimental condition was not significant, b = -0.05, SE = 0.10, t = -0.47, p = .64, 95% CI [-0.2423, 0.1488]. However, at the mean (0.00), the effect of condition was significant (b = -0.18, SE = 0.07, t = -2.57, p < .05, 95% CI [-0.3145, -0.0406]), and at 1 SD above the mean (0.79), the effect of condition was significant, b = -0.31, SE = 0.10, t = -3.10, p < .01, 95% CI [-0.5051, -0.1115]. In other words, for participants who reported mean and higher levels of social anxiety, anxiety remained significantly higher after text message support (compared to in-person support). The effect of condition was determined to be statistically significant at values below -0.2153 (51.59% of cases) and not statistically significant above that value (48.41% of cases). For levels of satisfaction with support, a non-significant interaction between experimental condition and social anxiety symptoms was observed, b = 0.15, p = .19, 95% CI [-0.0329, 0.3697]. The ΔR2 for the overall model after the interaction term was 0.01, signifying a negligible effect (Cohen’s f2 = 0.01). Support condition was a statistically significant predictor (b = 0.18, p < .05, 95% CI [0.0082, 0.3551]), such that levels of satisfaction were higher for participants in the in-person condition. The effect size for this relationship, however, was 33 negligible (Cohen’s f2 = 0.01). However, social anxiety symptoms were not related to satisfaction with support, b = -0.08, p = .50, 95% CI [-2939, 0.1151]. 34 Chapter 3: Experiment 2 3.1 Method The second experiment was a replication and extension of the first experiment conducted by the same research group between 2017 and 2019. This randomized controlled trial (RCT) included several protocol modifications to the first study. Namely, this experiment investigated four modes of communication (in-person, video chat, voice call, text message) for support provision instead of only in-person and text-based support. Furthermore, only females were recruited, and support was provided by a close female friend instead of a confederate. 3.1.1 Participants This study recruited female undergraduate students at UBC Okanagan, and was advertised via the university’s psychology research subject pool as “Lean on Me – Stress and Relationships” and described in the same manner as Experiment 1. Inclusion criteria were similar to those of Experiment 1 (i.e., age between 18 and 25, fluency in English, ability to provide informed consent), as were exclusion criteria (i.e., diagnosis of a mental health disorder). Eligible participants were also required to recruit a close female friend aged 18 to 25 and fluent in English to participate with them, with the stipulation that the recruited close friend must not have previously participated in the study in any role. The participants were required to choose a friend with whom they had sent or received at least one text message within the previous two weeks, and upon whom they typically relied for support. Unfortunately, this study is limited by its failure to assess participant gender identity (participants were only required to endorse female sex). Notwithstanding this important limitation, the decision to recruit only female participants is supported by past research showing both sex and gender differences in emotional and physiological response to stress, such as a lower physiological response in females (Labuschagne 35 et al., 2019; Liu et al., 2017) but a higher subjective stress response in women (Gilmore et al., 2019; Kelly et al., 2008), as well as differing tendencies in the use and satisfaction with text-based communication for social support (Kimbrough et al., 2013; Ling et al., 2014; Savicki & Kelley, 2004; Seale et al., 2006). Participants were compensated with either $25 or course credit, with close friends receiving the option of course credit or $10 for their participation. 3.1.2. Procedure Participation in this experiment closely resembled that of Experiment 1 and took place at UBC Okanagan’s Health Psychology research laboratory. Random assignment to one of five conditions (in-person, video call, voice call, text message, or no-support control) occurred prior to the participant’s arrival. Participants in the in-person condition were asked to bring their friend along with them to the laboratory, whereas participants in the other support conditions (i.e., video call, voice call, and text message) were merely asked to have their friend on standby and available via smartphone for the duration of the individual’s participation. Those in the control condition were informed that their friend would not need to partake in the study. After providing informed consent to a research assistant, participants gave their belongings (including any phones or computers) to the research assistant, and spent 10 minutes reading magazines with neutral content before indicating their baseline levels of positive and negative affect. The Trier Stress Test (TSST) was then introduced and executed in the same manner, and with the same context, as Experiment 1. Modifications to Experiment 1’s TSST conduction included an audience of only two females, and video recording of the participant during the task. Importantly, video recordings were not analyzed, were permanently deleted after each participant, and were only implemented for the purposes of increasing perceived stress and 36 scrutiny. As in Experiment 1, participants were then led back into the original room and completed post-TSST measures of positive and negative affect. For this experiment, the support manipulation was constrained to 10 minutes for each condition. As such, participants underwent 10 minutes of either 1) talking with their friend in-person in the research room, 2) video chatting with their friend on a laboratory phone, 3) calling their friend on a laboratory phone, 4) texting with their friend on a laboratory phone, or 5) waiting alone in the room. For all four support conditions, close friends received instruction similar to that given to the confederate research assistant in Experiment 1. Namely, they were to converse in a manner natural to their relationship but also to ask the participant about their experience, listen to them, and try to understand their experience (Thorsteinsson & James, 1999; Uchino et al., 2011). Following this, participants completed final measures of positive and negative affect before and after the support manipulation. For the four support conditions, participants completed measures assessing their perceptions of, and satisfaction with, the support that they had received from their friend and their friendship in general. Participants in the control condition were asked to indicate their affect levels before and after their waiting period. Finally, participants completed paper questionnaires, including measures of social anxiety, and provided demographic information as well as information about their technology usage and preference patterns. 3.1.3 Measures 3.1.3.1 Demographics and Technology Use. Participants responded to a demographic questionnaire as well as questions regarding their technology use and preferences. To assess preferences and tendencies for use of communication modes, participants ranked several modes of communication (in-person, talking on the phone, video chatting, text messaging, Snapchat, 37 Facebook, or Instagram) in terms of how they preferred to receive support and how they actually tended to receive support (i.e., #1, #2, #3). Communication modes were then categorized into asynchronous (text messaging, Snapchat, Facebook, or Instagram) or synchronous (in-person, talking on the phone, video chatting) forms of communication. The decision to categorize based on synchronicity aligns with methodology used in prior research (Doorley et al.; 2020), and is also informed by theory placing digital communication on a spectrum of cue richness, with asynchronous text-based communication at the “leanest” end and synchronous voice and video communication closer to in-person in terms of cue affordances (Daft & Lengel, 1986; Grondin et al., 2019). While the specified social media platforms can include video- and image-based elements, asynchronous private and public messaging (in the absence of voice or face-to-face communication) is highly prevalent and social media platforms support asynchronous interaction more generally (Faelens et al., 2019; Frison & Eggermont, 2020). In the current study, if an asynchronous digital communication mode was ranked within either a participant’s #1 or #2 placement for either question, that participant was coded as a “1.” If asynchronous digital communication was not within these top two placements, the participant was assigned a “0.” This created two variables (one for preference, and one for tendencies) assigned values of “1” (preference or tendency toward asynchronous digital support) and “0” (other). 3.1.3.2 Social Anxiety. As in Experiment 1, participants’ social anxiety symptoms were assessed using the Social Interaction Anxiety Scale-6 (SIAS-6; Peters et al., 2012). SIAS-6 scores were collapsed into one mean variable. Cronbach’s alpha for this mean variable was 0.86. 3.1.3.3 Positive and Negative Affect. Based on previous research (Côté et al., 2011), a measure of positive and negative affect was completed at three time points: at baseline, after the TSST, and following the conversation with their close friend. Each time, participants responded 38 to eight items representing positive affect (e.g., happy, relieved, calm) and eight items representing negative affect (e.g., anxious, angry, annoyed) on a 7-point Likert-type scale ranging from 1 to 7 (where 1 represented “did not feel at all” and 7 represented “felt very strongly”). As was the case with Experiment 1, responses to items “happy,” “calm,” and “anxious” were selected for inclusion in the analyses to allow for direct comparison with the elements of affect measured in Experiment 1. 3.1.3.4 Satisfaction with Support. For participants in the four support conditions, participants indicated their satisfaction with the support that they had received on a scale of 0 to 7, where 0 represented “not at all” and 7 represented “extremely.” 3.1.4 Analytic Approach 3.1.4.1 Data Characteristics & Preliminary Analyses. As was the case for Experiment 1, the analyses for Experiment 2 also utilized archival data from recent laboratory-based research. In line with our approach to Experiment 1, this experiment’s control condition was not included in the current analyses. After obtaining demographic and descriptive data, randomization checks (one-way ANOVAs, Chi Square Tests) were completed to confirm that baseline affect (i.e., baseline levels of “happy,” “calm,” and “anxious”), preferred and most common modes of communication for the purpose of support, and social anxiety levels did not systematically vary across experimental conditions. A manipulation check (repeated-measures ANOVA) was conducted to confirm that the TSST also produced an increase in reports of feeling “anxious” and a reduction in feeling “happy” and “calm” from baseline to post-stress time points. An analysis of covariance (ANCOVA) was performed to confirm that the TSST yielded similar post-stress levels of "happy," "calm," and "anxious" across the experimental conditions after controlling for baseline levels. 39 3.1.4.2 Main Analyses. To evaluate whether preferences and tendencies regarding receipt of support across communication mediums differ systematically based on social anxiety (H1), two independent t-tests were conducted to compare levels of social anxiety symptoms between those who favoured asynchronous digital communication and those who did not. In parallel to the analyses in Experiment 1, to examine the moderating influence of social anxiety symptoms on the relationships between communication medium (i.e., text-based or in-person) and study outcomes (“happy,” “calm,” “anxious,” and satisfaction with support; H2A through H2C), four moderated regression models were run using PROCESS (Hayes, 2022) with bootstrapping and associated confidence intervals, and with relationships of interest contextualized with Cohen’s f2 effect sizes and associated cut-offs (Selya et al., 2012). It should be noted that, although this experiment originally collected data for participants in four support conditions, only text-based and in-person conditions were analyzed in order to maintain comparability with Experiment 1, and reduce the likelihood of Type II error due to multiple comparisons. 3.2 Results 3.2.1 Sample Characteristics Data were originally obtained from 354 participants. Data from the 76 participants assigned to the control group were not included. Furthermore, six participants were excluded from the analyses due to data collection issues (e.g., a participant discontinuing during the experiment, being familiar with a research confederate, or their close friend not responding to contact attempts). As with Experiment 1, a small number of participants were missing outcome data (< 7 cases) at random. To avoid inappropriate imputation (Jakobsen et al., 2017), analyses with missingness were run with adjusted sample sizes. The final analyzed sample included 272 40 participants (Mage = 19.31, SD = 1.42, range = 18-24 years). This full sample was utilized to address H1 with optimized power and H2 was addressed using a smaller subset of the sample (n = 136; i.e., those assigned to either the in-person or text-based support group). Statistical power to detect effects for the most complex analysis (that of the moderating effect of social anxiety on support outcomes across communication mediums) was assessed using G*Power (Faul et al., 2007). It was determined that our sample for this analysis achieved sufficient power (0.95) to detect at least medium effects at an alpha level of .05. Displaying similar patterns as observed in Experiment 1, a large proportion of participants were born in Canada (70.6%), followed by China (4.4%), unspecified non-Canadian countries (3.3%), USA (2.9%), the United Kingdom (2.6%), and Mexico (1.5%). Also akin to Experiment 1, the most common ethnic identity was European (68.9%), with other identities including East or Southeast Asian (18.8%), South Asian (8.6%), African (4.8%), Indigenous (3.0%), Latin, Central, or South American (3.0%), Arab (1.1%), and Other (1.9%). 99.6% of participants were full-time students and 0.4% were part-time students at the time of data collection. Eighteen percent of participants indicated that asynchronous digital forms of communication (i.e., text messaging, Snapchat, Facebook, or Instagram) were either their first or second preferred means of receiving support, while 82% ranked synchronous communication (i.e., in-person, talking on the phone, or video chatting) as their first or second preference for such communication. Conversely, in terms of actual communication tendencies, nearly half of participants (46.3%) reported that asynchronous digital communication was their first or second most common means for receiving support, while 53.7% of participants most commonly received support through synchronous forms of communication. 3.2.2 Preliminary Analyses 41 Descriptive statistics for all outcome variables (across all three time points and across the four conditions), as well as social anxiety symptom scores, can be found in Table 3. Participants across experimental conditions did not significantly differ in baseline levels of “happy” (F(3, 267) = 0.54, p = .65, η² = 0.01, “calm” (F(3, 266) = 0.24, p = .87, η² = 0.003), or “anxious” (F(3, 268) = 1.41, p = .24, η² = 0.02). Social anxiety symptoms did not significantly differ across conditions, F(3, 268) = 0.85, p = .47, η² = 0.001). Additionally, preference for asynchronous digital communication (χ²(3, N = 272) = 2.25, p = .52) and more common usage of such communication (χ²(3, N = 272) = 4.19, p = .24) did not significantly differ across conditions. The TSST successfully elicited a stress response as evidenced by significant changes from baseline to post-stress for “happy” (F(1, 267) = 497.66, p < .001, ηp² = 0.65), “calm” (F(1, 267) = 597.18, p < .001, ηp² = 0.69), and “anxious,” F(1,267) = 230.22, p < .001, ηp² = 0.46. Post-TSST affect scores were also comparable across conditions for “happy” (F(4, 265) = 1.04, p = .38, η² = 0.01), “calm” (F(4, 264) = 1.11, p = .35, η² = 0.01), and “anxious” (F(4, 267) = 0.69, p = .56, η² = 0.01), controlling for baseline scores. 3.2.3 Hypothesis Testing 3.2.3.1 Hypothesis 1. Social anxiety symptoms were significantly higher among participants who ranked asynchronous digital communication as their preferred way in which to receive support, t(270) = -2.01, p < .05, d = -0.32. Social anxiety symptoms were also significantly higher among participants who ranked asynchronous digital communication as the means through which they actually tended to receive support, t(270) = -2.49, p < .05, d = -0.30. 3.2.3.2 Hypothesis 2. Moderated regression output for all outcome variables, including overall model statistics, can be seen in Table 4. The analysis for post-support levels of “happy” returned a non-significant interaction term (experimental condition x social anxiety symptoms), b 42 = 0.04, p = .79, 95% CI [-0.2731, 0.3841]. The ΔR2 for the overall model following the addition of the interaction term was 0.001, indicating a negligible effect size (Cohen’s f2 = 0.001). Experimental condition was significantly related to post-support “happy” (b = 0.45, p < .001, 95% CI [0.1922, 0.7074], small effect size with Cohen’s f2 = 0.09), but social anxiety symptoms were not, b = 0.03, p = .86, 95% CI [-0.3025, 0.3576]. For post-support levels of “calm,” the interaction term was not significant, b = -0.14, p = .40, 95% CI [-0.4829, 0.1883]. The change in R2 for the overall model after adding the interaction term was 0.005 (a negligible effect; Cohen’s f2 = 0.005). However, support condition was a statistically significant predictor, b = 0.27, p < .05, 95% CI [0.0090, 0.5397], indicating that levels of calm were higher among those who received in-person support. Social anxiety symptoms were not a significant predictor, b = -0.18, p = .28, 95% CI [-0.5249, 0.1676]. With regard to post-support levels of “anxious,” there was no significant interaction between condition and social anxiety symptoms, b = -0.01, p = .94, 95% CI [-0.2258, 0.2167]. The ΔR2 for the overall model following the addition of the interaction term was 0.000, signifying a negligible effect size (Cohen’s f2 = 0.0001). Support condition was also not a significant predictor (b = -0.18, p = .09, 95% CI [-0.3919, 0.0210]), nor were social anxiety symptoms, b = 0.24, p = .07, 95% [0.0166, 0.4871]. However, both of these represented small effects (Cohen’s f2 = 0.02 and 0.03, respectively). For participants’ satisfaction with support, the moderated regression revealed no significant effect of the interaction term, b = -0.11, p = .22, 95% CI [-0.2875, 0.0759]. The ΔR2 for the overall model following the addition of the interaction term was 0.01, signifying a negligible effect size (Cohen’s f2 = 0.01). Support condition was a statistically significant predictor (b = 0.24, p < .001, 95% CI [0.1061, 0.3925]) with a small effect size (Cohen’s f2 = 43 0.09), but social anxiety symptoms were not a significant predictor, b = -0.14, p = .10, 95% CI [-0.3288, 0.0346]. 44 Chapter 4: Discussion The current research aimed to investigate the relationship between social anxiety symptoms, preferences and attitudes towards text-based communication, and the moderating role of social anxiety symptoms on the effectiveness of text-based versus in-person social support. Results showed that individuals higher in social anxiety symptoms were more likely to prefer asynchronous digital support (e.g., text messaging), and that they more commonly received support through this means. However, the perceived impact of text messaging on participants’ social relationships was generally positive across the sample, and did not differ based on levels of social anxiety. Our investigation also revealed that, for interactions between participants and research confederates, social anxiety played a moderating role in post-support anxiety levels, with individuals higher (but not lower) in social anxiety symptoms experiencing significantly greater anxiety following support provided over text than support delivered in-person. Otherwise, across the two studies, no significant moderating effects of social anxiety were found in the association between mode of communication and support outcomes. Thus, while social anxiety symptoms may relate to stronger preferences for text-based, asynchronous forms of communication, these preferences do not seem to translate into better outcomes for mood or satisfaction with support when support is provided via text messaging. 4.1 Social Anxiety and Attitudes Surrounding Communication In our analysis of Experiment 2 data, we found that those with higher levels of social anxiety symptoms were significantly more likely to prefer receiving support through asynchronous digital forms of communication, such as texting and use of social media platforms. Furthermore, individuals with more social anxiety symptoms reported actually receiving support through asynchronous digital means more commonly than those less socially anxious. These 45 findings provide support for our first hypothesis (H1), and are consistent with a substantial body of literature that associates social anxiety symptoms with a higher reported preference for digitally mediated communication (Dunaetz et al., 2015; Erliksson et al., 2020; Fullwood et al., 2020; Hutchins et al., 2021; Lee et al., 2013; Lepp et al., 2014; Reid & Reid, 2007; Skierkowski & Wood, 2012; Zsido et al., 2020), as well as a greater tendency to engage with such communication mediums (Allred & Atkin, 2020; Holtgraves, 2011; Lee et al., 2013; Weidman et al., 2012). From a theoretical standpoint, our findings appear to align with established conceptualizations of social anxiety—notably, the pervasive fear of negative evaluation in social situations (Morrison & Heimberg, 2013) manifesting as an increased focus on presumed negative cues and a tendency to negatively misinterpret ambiguous cues (e.g., Bar-Haim et al., 2007; Gilboa-Schechtman & Shachar-Lavie, 2013). This theoretical basis suggests that such biases are particularly evident for NVCs, which are more abundant in in-person communication. This can be argued to exacerbate a fundamental aspect of social anxiety: the avoidance of situations that pose an increased potential for social performance evaluation (Bar-Haim et al., 2007; Morrison & Heimberg, 2013; McNeil & Randall, 2014; Weiser et al., 2009), a pattern reflected in our findings. While social evaluation is indeed possible through text-based communication, the key difference for those with higher social anxiety levels may lie in the potential for evaluation that is uncontrollable by the recipient—as often occurs in in-person interactions. Conversely, the asynchronicity of text-based communication gives individuals the ability and time to meticulously craft and edit messages, allowing them to present themselves in a way that minimizes vulnerability to negative scrutiny and better controls how they are evaluated (e.g., hyperpersonal theory; Walther, 1996). This is an advantageous aspect of text-based 46 communication for more socially anxious individuals, which may explain the preferences and tendencies observed in our study and in similar research. Moreover, this finding provides support for the social compensation hypothesis, which argues that the socially anxious utilize digitally mediated communication in an attempt to compensate for anxieties surrounding in-person communication and to mitigate its evaluative effects (Valkenburg & Peter, 2007). These results also support the notion of asynchronous, specifically text-based, communication as a "safe haven" for more socially anxious individuals due to its relative lack of NVCs and reduced opportunities for evaluation, as discussed above and observed in prior research (Hansen et al., 2024; Colasante et al., 2022; Hunter et al., 2018). Within this safe space, individuals higher in social anxiety may find more comfort engaging in text-based communication for support or otherwise (Prizant-Passal et al., 2016). Taken together, our findings from Experiment 2 corroborate existing theories and research on social anxiety, highlighting the significant role of asynchronous digital communication in providing a comfortable and preferred medium for support among socially anxious individuals. On the other hand, for Experiment 1, we found that participants higher in social anxiety symptoms did not differ from those lower in social anxiety symptoms in terms of their perceptions of the impact that text messaging has on their relationships with close friends, parents, siblings, partners, or acquaintances. Rather, participants in our sample reported an overall high (positive) mean level of perceived impact of text messaging on relationships with others. These findings do not support our hypothesis (H1) that those with greater social anxiety would perceive a more positive impact of text-based communication. It should be noted that our participants were emerging adults and “digital natives,” many of whom received their first cell phone in early adolescence and were likely exposed to internet-based communication (e.g., 47 email, instant messaging) relatively early in life (Colasante et al., 2022; Sherman et al., 2013). Per channel expansion theory (Carlson & Zmud, 1999), which links familiarity with digital communication to increased effectiveness, the digitally proficient may view text messaging as more advantageous generally due to its widespread use, status as a social norm, and ease of use, regardless of social anxiety levels. Early and extensive exposure to digital technology among the digital natives in our research may have balanced out the perceived positive impact of text-based communication on interpersonal relationships across levels of social anxiety. The overall high positive regard for texting observed in this study suggests that the modern ubiquity and popularity of digital communication might obscure any additional or unique benefits of texting that are perceived among individuals with increased social anxiety symptoms (Bush, 2023; Petrosyan, 2023; Statistics Canada, 2021). Another possible interpretation could be that text-based communication may have a positive impact on relationships for people high and low in social anxiety symptoms, but for different reasons. While individuals with higher social anxiety levels might use digital communication to compensate for perceived shortcomings and sources of anxiety in their social interactions (in line with the social compensation hypothesis; Valkenburg & Peter, 2007), more extraverted, less socially anxious individuals might be leveraging the same digital platforms to further expand their already deep and extensive social networks (in line with the “social enhancement hypothesis”; Valkenburg & Peter, 2007; Waytz & Gray, 2018). Essentially, the net effects observed could be due to both compensatory and enhancement dynamics in our sample, leading to no significant overall correlation. 4.2 Social Anxiety and Effectiveness of Communication Modes 48 Our finding from Experiment 1 that social anxiety had a moderating impact on the relationship between communication medium and feelings of post-support anxiety is worthy of close attention. Although this interaction effect did not reach the traditional p value cut-off for determining statistical significance, we chose to further probe this interaction for several reasons. First, there exists a debate regarding what some researchers see as the rigid nature of significance testing using p values of less than .05 (Andrade, 2019). Second, an emerging recommendation from statisticians is to replace, or at least contextualize, null hypothesis significance testing and associated thresholds with a focus on effect sizes and confidence intervals (Benjamin et al., 2018). Third, our investigation is in response to a notable dearth of research addressing the relationship between social anxiety levels and digitally mediated communication for support, and is an effort to inform future studies by assessing the potential of this research avenue. Thus, we interpreted this non-significant (p = .07) interaction term in the context of confidence intervals that did not cross zero [-0.3418, -0.0045] and a small—not negligible—effect size (Cohen’s f2 = 0.03) indicating that a more in-depth analysis of this relationship was warranted. Probing this interaction revealed that for individuals lower in social anxiety symptoms, there were no significant differences in post-support anxious feelings whether they received support in-person or over text. However, for individuals with levels of social anxiety symptoms at the observed mean, receiving support from a research confederate through text-based communication was significantly less effective in reducing feelings of anxiety when compared to receiving in-person support. The same significant relationship was also observed for participants higher in social anxiety symptoms, with the size of the latter coefficient nearly doubling that of the former. These findings are in support of our hypothesis H2B, predicting that at higher levels of social anxiety symptoms, text-based support would be significantly less effective. This 49 relationship, observed at average and above-average social anxiety levels, can be interpreted in the context of theories about social anxiety—specifically, the misinterpretation and attribution of negativity to ambiguous cues in interactions (e.g., Gilboa-Schechtman & Shachar-Lavie, 2013; Rapee & Heimberg, 1997; Wieser et al., 2009). Indeed, extant research suggests that this negative interpretation does occur in text-based spheres (Calvo et al., 1994; Kingsbury & Coplan, 2016). In our research, the observed effects were particularly pronounced in text-based supportive communications with a confederate. In interpreting this, it can be argued that the absence of an established relationship between the unfamiliar support provider and participant inhibited an understanding of the supporter’s communication style, their intentions, or the evaluative nature of the interaction—especially with the participants in the midst of a research experiment. As such, this particular situation may have introduced greater levels of ambiguity, leading to increased misinterpretation and, consequently, heightened anxiety. Conversely, in in-person interactions with a confederate, the presence of NVCs, despite their potential ambiguity, might have been more reassuring for individuals with higher social anxiety. This finding also adds to the aforementioned body of literature arguing for the superiority of in-person communication both in general (Hamilton et al., 2021; Kafetsios et al., 2017; Petrova & Schulz, 2022; Verduyn et al., 2021) and for the purposes of support (e.g., Holtzman et al., 2017; Lewandowski et al., 2011; Rains et al., 2017). However, our findings extend this area of research by highlighting the importance of considering individual differences (such as social anxiety levels) when evaluating the effectiveness of in-person and text-based support, as there was no observed superiority of in-person communication for those lower in social anxiety symptoms when receiving support from a confederate. This finding consequently introduces caveats to the cues-filtered-out family of theories arguing for a general inferiority of digital 50 communication in providing outcomes comparable to in-person communication facilitated by cue-rich interactions (e.g., social presence theory [Short et al., 1976]; media richness theory [Daft & Lengel, 1986]; common ground theory [Clark & Brennan, 1991]), suggesting that this generalized hypothesis may not hold across all contexts. Otherwise, we found no significant interaction effects for levels of "happy," "calm," or satisfaction with support in Experiment 1, indicating that social anxiety levels did not notably influence the outcomes of in-person versus text support from a confederate. This provides partial support for our hypothesis H2C, and aligns with previous research observing no significant moderating impact of social anxiety on the effectiveness of in-person vs. digitally mediated support (Colasante et al., 2022) or text-based support specifically (Lundy & Drouin, 2016). However, being that H2C also predicted a significant negative effect of social anxiety symptoms on support outcomes, and that none of these main effects reached statistical significance, H2C was not fully supported. The role of social anxiety in post-support levels of “anxiety,” but lack of association in predicting post-support levels of “happy” and “calm” may potentially reflect that anxiety is a more salient emotion for those higher in social anxiety in the context of a social stressor. Our observed pattern of results also raises the question of whether similar findings would be observed for these particular outcomes when investigating social support for different types of stressors (e.g., daily stressors), or even in the context of positive events (e.g., celebrations, milestones). In Experiment 2, we found no significant interaction between social anxiety and mode of supportive communication for any of the outcome variables tested (i.e., “happy,” “calm,” “anxious,” or satisfaction with support). This was again partially in line with our hypothesis H2C as there was no significant interaction, and again, social anxiety symptoms did not significantly 51 predict support outcomes—similar to the patterns observed by Colasante and colleagues (2022) and Lundy and Drouin (2016). While these results fall mostly in line with our non-significant findings from Experiment 1, it should be noted that while the previous experiment investigated support delivered by a research confederate, Experiment 2’s participants were receiving support from one of their closest friends. As such, our finding that social anxiety symptoms did not moderate the impact of communication medium on support outcomes when being supported by a close friend (on all outcomes, including levels of “anxious”) could be contextualized by other findings that those higher in social anxiety see greater benefit overall (e.g., mood, affect, reductions in anxiety and depression) from the presence of close companions than other more distant sources (Coyle & Malecki, 2018; Hur et al., 2020). It is important to reiterate that, in Experiment 1, individuals with higher levels of social anxiety experienced heightened anxious feelings following interactions when supported by a confederate via text. As mentioned, this was potentially due to the unfamiliarity of the other individual compounded with the use of a more ambiguous form of communication, creating a more difficult interpretive landscape and introducing more opportunities for negative misinterpretation (Gilboa-Schechtman & Shachar-Lavie, 2013). Thus, with the familiarity of a close friend providing a more comfortable and less ambiguous context for support in this experiment, our findings suggest that individuals' social anxiety levels therefore played a lesser part in the effectiveness of support, with the mode of communication emerging as the primary indicator instead. Interestingly, we found that text-based communication was not significantly more effective in any analysis for people higher in social anxiety levels. This appears to contrast Gilboa-Schechtman and Schachar-Lavie’s (2013) nonverbal cue processing theory that posits the tendency of those higher in social anxiety to negatively misinterpret and respond to ambiguous 52 NVCs inherent in in-person communication as a reason for decreased effectiveness of in-person communication for such individuals. Relatedly, other research findings showing that the socially anxious make negative interpretations even in the absence of NVCs (Calvo et al., 1994; Kingsbury & Coplan, 2016) and the greater chance of misunderstandings via text messaging more broadly (Johnson et al., 2016) warrant further investigation. Taken together, our research findings revealed a notable disconnect between preference for text-based support and its perceived impact among those higher in social anxiety in daily life, vis a vis the social and emotional outcomes of text-based support demonstrated in a controlled laboratory setting. We observed that social anxiety levels either did not have a moderating impact on the relationship between communication mode and support outcomes when interacting with a close friend, or resulted in less effective text-based communication (in reducing feelings of anxiety when being supported by a confederate). In two randomized controlled studies, we found no evidence that texting was more effective for providing support compared to in-person support for people higher in social anxiety. This pattern challenges research predictions formed upon the preference for digital communication repeatedly observed among individuals with higher levels of social anxiety, both within our study and others (Dunaetz et al., 2015; Erliksson et al., 2020; Fullwood et al., 2020; Hutchins et al., 2021; Lee et al., 2013; Lepp et al., 2014; Reid & Reid, 2007; Skierkowski & Wood, 2012; Zsido et al., 2020). Our findings also suggest a range of potentially important and relevant concepts to consider when studying socially anxious individuals’ relationship with communication modalities for support and connection. This includes general attitudes and preferences for communication, the perceived impact of communication modalities on relationship and support outcomes, and outcomes of support measured in more objective or real-time contexts. 53 4.3 Practical and Clinical Implications The results of our investigation contribute to the ongoing dialogue, both within the general public and in clinical settings, regarding the communication channels through which individuals with varying levels of social anxiety might turn for effective support. From a practical standpoint, our research suggests that individuals with higher levels of social anxiety might benefit from exploring different methods of communication for support that could be more aligned with their needs. With research pointing to a rise in social anxiety among young adults (Jefferies & Ungar, 2020; Kindred & Bates, 2023; Lai et al., 2023) that coincides with increased social media use and digital communication (Allred & Atkin, 2020; Holtgraves, 2011; Lee et al., 2013; Weidman et al., 2012), this reframing is particularly important for individuals with higher social anxiety symptoms in the digital age. Individuals with higher levels of social anxiety often favour text-based communication for support, as it is believed to provide a safer and more controlled environment for social interactions (Colasante et al., 2022; Hansen et al., 2024; Hunter et al., 2018). However, our findings indicate that such communication may not be as beneficial as anticipated, and that this preference may not be directly linked to favourable outcomes. In fact, for those higher in social anxiety, text-based support was found to be less (not more) effective in mitigating anxiety, a core aspect of social anxiety, compared to in-person interactions with an unfamiliar other. Moreover, satisfaction with support seemed more dependent on the mode of communication, and not levels of social anxiety. This may provide an argument against the reliance of the socially anxious on digitally mediated communication, especially given its pervasiveness in contemporary society more broadly. Our findings suggest that when key support outcomes (i.e., decreases in anxiety) are at stake, communicating with less familiar others in-person (when possible) may be particularly important and additionally present 54 an opportunity for those with higher levels of social anxiety to challenge themselves by opting for cue-rich interactions. Beyond our research context, there may be other situations wherein those higher in social anxiety might normally exhibit avoidance of in-person interaction with less familiar others, but could benefit even more from engaging in such communication rather than text-based interaction (e.g., in online dating spheres, remote work, crisis text lines). Overall, it appears that for individuals higher in social anxiety, their symptoms influence the attitudes they hold about text-based versus in-person communication, as well as the decisions they make (e.g., avoiding in-person communication). However, social anxiety levels were not directly implicated in supportive outcomes. When social anxiety did have an impact, it did so in concert with communication mode, and was associated with worse outcomes of support delivered via text-based communication. In most cases, however, it was the mode of communication (i.e., whether support was delivered over text or in-person), not social anxiety, that was significantly related to the support outcomes measured herein. While there were rare discrepancies in the main effects of condition found in our research (c.f. Holtzman et al., under review), this could be attributed to our use of single items instead of combined affect subscale variables. Nevertheless, the finding that levels of social anxiety are not predicting how individuals feel following support suggests that the extent of their benefit from support relates more to how they are receiving said support, rather than their social anxiety levels. However, with individuals higher in social anxiety symptoms more inclined to choose less effective modes of communication, the effect of social anxiety can potentially be seen not in the efficacy of communication mediums, but in the prevention of access to these more effective communication mediums. This presents an intriguing lens through which to understand social anxiety symptoms 55 and how they relate to communication in both helpful and unhelpful ways, both in the general population and potentially in clinical populations. While the current experiments did not recruit participants with a diagnosis of SAD, our findings may still provide valuable insights for treating clients with social anxiety in clinical settings. Insights gleaned from this sub-clinical research could inform psychoeducation given to individuals with social anxiety by clinicians about the discrepancy between their preferred communication methods and the actual effectiveness of these methods in reducing their levels of anxious feelings. Furthermore, being that exposure therapy is regarded as the gold-standard treatment for SAD (Chowdhury & Khandoker, 2022), our findings that in-person supportive interactions more effectively reduce feelings of anxiety—particularly when interacting with less familiar others—strengthen the rationale for employing such therapy techniques. 4.4 Strengths, Limitations, and Future Directions The current research drew upon a number of inherent strengths. In particular, the pre-registration of our hypotheses and analytic methods on the Open Science Framework is a strength of our investigation insofar as it supports transparency in research and the reduction of reporting bias (van t’Veer & Giner-Sorolla, 2016). Furthermore, given that individual differences (i.e., social anxiety) and outcomes following supportive communication represent a novel area of investigation, our use of two randomized controlled experiments contributes high-quality data to this relatively sparse discourse. This approach is particularly advantageous, as many existing studies addressing this topic are cross-sectional or non-experimental in design. Through our experimental analysis, we tested fundamental questions surrounding social anxiety as a context in which to assess the efficacy of text-based support from two important and realistic sources: familiar and unfamiliar others. Additionally, our consistency in methodological and statistical 56 approach across two studies adds to the robustness and replicability of these investigations—namely in the ability to directly match variables utilized between studies (e.g., social anxiety symptoms, “happy,” “calm,” “anxious,” and satisfaction with support) and to use similar analytic methods. Relatedly, notwithstanding differences in sex-related recruitment, participants were relatively similar across experiments in terms of various demographic characteristics including age, student status, ethnicity, and levels of social anxiety symptoms. This similarity across several demographic factors strengthens the consistency of the findings across these variables and minimizes the potential confounding effects that might arise from demographic discrepancies. It should be acknowledged that our research has several limitations. Importantly, while the demographic similarity between our experimental samples is a strength for comparability, it also limits our findings’ generalizability. As such, it remains to be seen if the same patterns observed herein would be found in other demographic groups (e.g., older adults, non-students, non-digital natives, etc.). It can be argued that this represents the often-necessary tradeoff in research between replicability and generalizability, where achieving both simultaneously is rarely realistic (National Academies of Sciences, Engineering, and Medicine, 2019). Relatedly, the controlled, lab-based nature of our experiments has consequences for the ecological validity of our research. For instance, participants in our samples were limited to one mode of communication, one source of support, and one standardized source of stress. These parameters therefore limit our ability to understand how those higher in social anxiety seek and receive support in their daily lives. This has been addressed in recent studies utilizing EMA, experience sampling, and day reconstruction methods spanning one to two weeks (Colasante et al., 2022; Doorley et al., 2020) and could be further expanded upon with intensive longitudinal studies in the future. 57 One limitation that should be revisited is a reliance on measures of biological sex and failure to assess participants’ gender identity, which precluded the ability to make comparisons and interpretations based on gender. This shortcoming highlights the importance of adopting comprehensive and inclusive measurement of sex and gender to allow researchers to more directly pose important questions about the role of sex and gender as they relate to preferences and perceived impact of text-based communication, social anxiety, and the effectiveness of support received by a familiar versus unfamiliar other. A further limitation of the current research relates to the distribution of social anxiety symptoms in our two samples. While social anxiety symptoms were slightly positively skewed in both of our samples, a substantial proportion (47.2% in Experiment 1 and 37.9% in Experiment 2) exhibited SIAS-6 scores above established cut-offs for “discriminating between those with and without a diagnosis of social phobia” (Peters et al., 2012, p. 72)—which is now referred to as social anxiety disorder (McNeil & Randall, 2014)—in clinical and non-clinical samples (e.g., Hansen et al., 2024; Mathis, 2023; Peters et al., 2012). An exclusion criterion in both experiments was a formal mental health disorder diagnosis, which would have included SAD. However, research has shown that formal diagnoses of SAD are rare within the recruited age group despite struggling disproportionately with symptomology, with one such study of prevalence across seven countries finding that 18% of individuals from 16 to 29 years old viewed themselves as not having social anxiety, but reached or surpassed the threshold for SAD (Jefferies & Ungar, 2020). Finally, our social anxiety symptom scores observed within both experiments had sufficient range and variability to find significant effects that are in line with past research on preferences and tendencies for text-based communication among those higher in 58 social anxiety, and greater anxiety in socially stressful and unfamiliar situations (Holtgraves, 2011; Morrison & Heimberg, 2013; Prizant-Passal et al., 2016). Finally, as suggested by post-hoc power analyses for our secondary usage of these data, our power to detect small effects for our most complex analysis (moderated multiple regression) was limited. However, interaction terms that did not achieve a level of significance sufficient to warrant further analysis had negligible effect sizes, and our findings were generally consistent across our two experiments. This suggests that even with a larger sample, our findings would likely remain unchanged. Our research, in focusing on social anxiety symptoms, did not isolate other potential moderating variables of interest in the relationship between communication medium and effectiveness of supportive communication. Future research could therefore explore how other variables may act as moderators when utilizing more adequately powered samples. One particular avenue of interest could be that of preferences for communication. According to channel expansion theory (Carlson & Zmud, 1999), increased familiarity and comfort with—and consequent preference for—a communication channel (e.g., digitally mediated) can enhance its perceived effectiveness. Furthermore, one study found that frequency of use for digital communication (i.e., video chat) enhanced outcomes between friends when utilizing that communication mode (Sherman et al., 2013). However, it remains unclear whether explicit preferences might directly shape the overall effectiveness of the communication channel. The social information processing theory (Walther, 1992) further suggests that the effectiveness of digitally mediated communication might also depend on the traits of the individuals using these platforms, such as their personal preferences. Future research could delve into how these 59 preferences affect the outcomes of interactions across various digital platforms, for support and otherwise. Furthermore, due to the design of the original experiments on which our secondary analyses were conducted, we did not explore forms of social media as a potential mode of supportive communication. Future investigations could include—or even focus on—this important avenue through which support can be conveyed and received, particularly in this digital age. Social media provides various avenues for support that can prove beneficial when engaged with actively, such as messages, comments, and group posts (Godard & Holtzman, 2024). As previously discussed, research also suggests a positive correlation between social media use and social anxiety (Lai et al., 2023; Nan et al., 2024), and greater passive social media use (i.e., use without interacting with others) and social anxiety (Godard & Holtzman, 2024), highlighting the relevance of these platforms in the lives of individuals with higher social anxiety levels. Further research could examine how these different online platforms impact users, particularly those with more social anxiety, to reflect a growing modality through which communication and support more regularly take place. Finally, with our research focusing on emotional support, we did not account for other forms of supportive exchanges (e.g., informational or instrumental; Taylor, 2011; Wang et al., 2021). Indeed, these different types of social support could manifest differently across communication modes for individuals differing in levels of social anxiety levels. For instance, emotional support might not translate as effectively through digitally mediated communication as it does in-person. In contrast, informational support within stressful contexts (e.g., offering ideas and solutions) or instrumental support (e.g., offering to do an errand) may be perceived as more equally effective via digital channels. As such, an endeavour to understand how these 60 different forms of social support manifest across communication modes and levels of social anxiety may prove insightful. 4.5 Conclusion The current research represented a novel investigation into both preferences for, and effectiveness of, different communication modes for the receipt of support. In an advancement beyond extant literature, our research delved deeper into how individual differences (i.e., social anxiety) might impact previously observed relationships between communication mode and support outcomes. This research is timely in light of the large-scale societal shift toward digital communication, and given literature reflecting the tendency of those with increased social anxiety symptoms to gravitate toward digital communication. With the state of the literature consisting of competing theories and broader-scale findings (e.g., that in-person communication is optimal for the general population) contrasted by uncertainty regarding more specific inquiries (e.g., whether and which individual differences might modulate this relationship), our research endeavoured to tease apart the associations between these multiple intertwined variables. Our findings suggest that while more socially anxious individuals prefer to communicate for support via asynchronous digital means, and do not differ from less anxious individuals in terms of the perceived impact of text messaging on their relationships, these preferences and perceptions do not seem to translate into greater effectiveness of this form of communication. This information provides valuable guidance regarding communication and support strategies for individuals with increased symptoms of social anxiety. 61 Bibliography Aiken, L. S., & West, S. G. (1991). 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The connection and background mechanisms of social fears and problematic social networking site use: 86 A structural equation modeling analysis. Psychiatry Research, 292, 113323-113323. https://doi.org/10.1016/j.psychres.2020.113323 87 Tables Table 1: Experiment 1: Means and Standard Deviations for Main Study Variables Experiment 1: Means and Standard Deviations for Main Study Variables Variable In-Person (n = 63) Text-Based (n = 64) Happy Baseline 2.19 (0.84) 2.33 (0.88) Post-stress 1.11 (1.05) 1.23 (1.19) Post-support 1.98 (1.04) 1.84 (0.95) Calm Baseline 2.81 (0.91) 2.73 (1.06) Post-stress 1.22 (1.14) 1.00 (1.10) Post-support 2.19 (0.98) 2.16 (0.91) Anxious Baseline 1.29 (1.02) 1.14 (1.01) Post-stress 2.43 (1.23) 2.27 (1.12) Post-support 0.78 (0.87) 1.03 (0.98) Satisfaction with Support 6.48 (0.72) 6.14 (1.15) Social Anxiety 1.46 (0.84) 1.29 (0.74) Texting Impact 3.80 (0.60) 3.82 (0.46) Note. Columns are stratified by experimental condition. Affect and social anxiety scores had a possible range of 0-4, texting impact had a range of 1-5, and satisfaction with support had a possible range of 1-7. 88 Table 2: Experiment 1: Moderated Regression Results for Post-Support Outcomes Experiment 1: Moderated Regression Results for Post-Support Outcomes Variable b SE B t p Bootstrapped 95% CI Happy (R2 = 0.43, F(4, 122) = 23.46, p < .001) Post-Stress Happy 0.59 0.06 9.55 < .001 [0.4758, 0.6984] Condition 0.09 0.08 1.38 .17 [-0.0356, 0.2294] Social Anxiety 0.16 0.09 1.80 .07 [-0.0060, 0.3347] Condition x Social Anxiety 0.07 0.09 0.86 .39 [-0.0832, 0.2410] Calm (R2 = 0.30, F(4, 122) = 13.27, p < .001) Post-Stress Calm 0.46 0.07 7.02 < .001 [0.3294, 0.5709] Condition -0.03 0.07 -0.40 .69 [-0.1654, 0.1183] Social Anxiety -0.06 0.09 -0.62 .54 [-0.2919, 0.1431] Condition x Social Anxiety 0.08 0.09 0.84 .40 [-0.1375, 0.2941] Anxious (R2 = 0.33, F(4, 121) = 15.06, p < .001) Post-Stress Anxious 0.38 0.06 6.10 < .001 [0.2662, 0.4883] Condition -0.18 0.07 -2.57 .01 [-0.3215, -0.0444] Social Anxiety 0.16 0.09 1.76 .08 [-0.0066, 0.3596] Condition x Social Anxiety -0.17 0.09 -1.84 .07 [-0.3418, -0.0045] Satisfaction with Support (R2 = 0.05, F(3, 121) = 1.99, p = .120) Condition 0.18 0.09 2.04 .04 [0.0082, 0.3551] Social Anxiety -0.08 0.11 -0.68 .50 [-0.2939, 0.1151] Condition x Social Anxiety 0.15 0.11 1.33 .19 [-0.0329, 0.3697] 89 Table 3: Experiment 2: Means and Standard Deviations for Main Study Variables Experiment 2: Means and Standard Deviations for Main Study Variables Variable In-Person (n = 72) Text-Based (n = 64) Happy Baseline 4.32 (1.42) 4.00 (1.26) Post-stress 2.11 (1.45) 2.34 (1.26) Post-support 5.22 (1.59) 4.64 (1.54) Calm Baseline 4.92 (1.30) 4.72 (1.49) Post-stress 2.14 (1.22) 2.02 (1.19) Post-support 4.67 (1.56) 4.18 (1.57) Anxious Baseline 2.73 (1.21) 2.60 (1.28) Post-stress 4.41 (1.83) 4.76 (1.85) Post-support 2.13 (1.14) 2.52 (1.53) Satisfaction with Support 6.41 (0.71) 5.94 (0.98) Social Anxiety 2.22 (0.89) 2.26 (0.78) Note. Columns are stratified by experimental condition. Affect scores had a possible range of 1-7, social anxiety scores had a range of 0-4, and satisfaction with support had a possible range of 0-7. 90 Table 4: Experiment 2: Moderated Regression Results for Post-Support Outcomes Experiment 2: Moderated Regression Results for Post-Support Outcomes Variable b SE B t p Bootstrapped 95% CI Happy (R2 = 0.19, F(4, 130) = 7.45, p < .001) Post-Stress Happy 0.43 0.10 4.45 < .001 [0.2656, 0.5987] Condition 0.45 0.13 3.41 < .001 [0.1922, 0.7074] Social Anxiety 0.03 0.16 0.18 .86 [-0.3025, 0.3576] Condition x Social Anxiety 0.04 0.10 0.27 .79 [-0.2731, 0.3841] Calm (R2 = 0.07, F(4, 126) = 2.55, p < .05) Post-Stress Calm 0.18 0.10 1.71 .09 [-0.0161, 0.3649] Condition 0.27 0.14 2.01 .04 [0.0090, 0.5397] Social Anxiety -0.18 0.17 -1.10 .28 [-0.5249, 0.1676] Condition x Social Anxiety -0.14 0.17 -0.84 .40 [-0.4829, 0.1883] Anxious (R2 = 0.18, F(4, 130) = 7.31, p < .001) Post-Stress Anxious 0.23 0.06 3.98 < .001 [0.1305, 0.3287] Condition -0.18 0.11 -1.71 .09 [-0.3919, 0.0210] Social Anxiety 0.24 0.13 1.83 .07 [0.0166, 0.4871] Condition x Social Anxiety -0.01 0.13 -0.08 .94 [-0.2258, 0.2167] Satisfaction with Support (R2 = 0.12, F(3, 132) = 5.74, p < .001) Condition 0.24 0.07 3.41 < .001 [0.1061, 0.3925] Social Anxiety -0.14 0.09 1.67 .10 [-0.3288, 0.0346] Condition x Social Anxiety -0.11 0.09 -1.25 .22 [-0.2875, 0.0759] 91 Figures Figure 1: Experiment 1: Visualization of SIAS-6 x Condition Interaction for “Anxious” Experiment 1: Visualization of SIAS-6 x Condition Interaction for “Anxious” Note. The interaction term for this model approached statistical significance (p = .068). b = -0.09, p = .64 b = -0.36, p < .05 b = -0.62, p < .01
Thesis/Dissertation
eng
Psychology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Holtzman, Susan
In-person vs. text-based support : relationships between social anxiety, communication attitudes, and support effectiveness
Text