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Digitally connected, socially disconnected : can smartphones compromise the benefits of interacting with… Kushlev, Kostadin 2015

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DIGITALLY CONNECTED, SOCIALLY DISCONNECTED: CAN SMARTPHONES COMPROMISE THE BENEFITS OF INTERACTING WITH OTHERS? by  Kostadin Kushlev  B.A., Reed College, 2008 M.A., The University of British Columbia, 2011  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES  (Psychology)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)   August 2015 © Kostadin Kushlev, 2015 ii  Abstract Since the first computers began entering people’s homes more than 30 years ago, human-computer interactions (HCI) have become central to people’s everyday activities. A decade later, the Internet powered another technological revolution by connecting computers and transforming how people connect with one another. And less than 10 years ago, the advent of ultraportable computing devices such as smartphones has marked the beginning of yet another technological age—one in which people can connect to unlimited digital worlds everywhere they go. In this digital age of pervasive computing, people can foster a sense of connectedness with others virtually anywhere. But could this ubiquitous connectivity have hidden costs for the fabric of people’s nondigital social lives? To provide an initial insight into this question, I examine how smartphones may be affecting both the quality and the quantity of people’s in-person social interactions. I show that smartphones can fracture attention and compromise the social connectedness parents reap when spending time with their children at a summer festival (Study 1) and at a science museum (Study 2). Beyond the realm of close relationships, I find preliminary evidence that smartphones may affect the social and emotional benefits people realize when they have the opportunity to forge new relationships—both while having food together (Study 3) and while waiting together (Study 4). And, using nationally representative data from the World Values Survey (Study 5) and data from a controlled experiment (Study 6), I show that smartphones may be affecting the broader social fabric of society by compromising opportunities to cultivate a sense of trust in others. Finally, I theorize more broadly about how and when ubiquitous connectivity may undermine or support social and emotional well-being. Specifically, I propose factors that may both moderate and mediate (e.g., asynchronisity, capitalization, social signaling) the effects of ubiquitous connectivity. iii  Preface I am the primary author of the work presented in this PhD dissertation. I identified the research question guiding the presented empirical work. I designed the experiments in collaboration with Dr. Elizabeth Dunn. I coordinated the data collection and performed all data analyses. I wrote each chapter and prepared all tables and figures presented in the chapters. Additional contributions for each chapter are described below.  Chapter 1: Introduction I am the primary author of this chapter, with intellectual contributions from E. Dunn.   Chapter 2: Can smartphones diminish the social and emotional benefits parents reap from spending time with their children? A version of this chapter will be submitted for publication: Kushlev, K. & Dunn, E. W. (in prep). Can smartphones diminish the social and emotional benefits parents reap from spending time with their children? I designed the experiments, supervised data collection, conducted the analyses, and prepared the manuscript. E. Dunn provided intellectual contributions and suggested edits to the manuscript.   Chapter 3: How do smartphones affect the social and emotional benefits of social interactions with strangers? I designed the experiments, supervised data collection, conducted the analyses, and prepared the manuscript. E. Dunn provided intellectual contributions and suggested edits to the manuscript.   iv   Chapter 4: Do smartphones compromise opportunities to cultivate a sense of trust by obviating the need for people to rely on one another? A version of this chapter will be submitted for publication: Kushlev, K. & Dunn, E. W. (in prep). Do smartphones compromise trust by obviating the need for people to rely on one another? I designed the experiments, supervised data collection, conducted the analyses, and prepared the manuscript. E. Dunn provided intellectual contributions and suggested edits to the manuscript.   Chapter 5: General Discussion I am the primary author of this chapter, with intellectual contributions from E. Dunn.   The research presented in this dissertation was approved by the UBC Behavioural Research Ethics Board under certificates H11-00585, H08-02739, and H14-02287 v  Table of Contents  Abstract .......................................................................................................................................... ii!Preface ........................................................................................................................................... iii!Table of Contents ...........................................................................................................................v!List of Tables .............................................................................................................................. viii!List of Figures ............................................................................................................................... ix!Acknowledgements ........................................................................................................................ x!Dedication .................................................................................................................................... xii!Chapter 1: Introduction ................................................................................................................1!1.1! The Paradox of Ubiquitous Connectivity .......................................................................... 1!1.2! The Need to Feel Connected .............................................................................................. 4!1.3! Quantity of In-Person Interactions ..................................................................................... 6!1.3.1! Substitution ................................................................................................................. 6!1.3.2! Convenience ................................................................................................................ 8!1.4! Quality of Social Interactions ............................................................................................ 8!1.4.1! Subjective Quality of Attention .................................................................................. 9!1.4.2! Perceived Opportunity Costs .................................................................................... 10!1.4.3! Type of Use ............................................................................................................... 12!1.5! Overview of Empirical Studies ........................................................................................ 13!Chapter 2: Can Smartphones Diminish The Social and Emotional Benefits Parents Reap From Spending Time With Their Children? ............................................................................16!2.1! Synopsis ........................................................................................................................... 16!vi  2.2! Introduction ...................................................................................................................... 16!2.3! Study 1: The Festival ....................................................................................................... 19!2.3.1! Method ...................................................................................................................... 19!2.3.2! Results ....................................................................................................................... 21!2.4! Study 2: The Museum ...................................................................................................... 22!2.4.1! Method ...................................................................................................................... 22!2.4.2! Results ....................................................................................................................... 24!2.5! Discussion ........................................................................................................................ 30!Chapter 3: How Do Smartphones Affect the Social and Emotional Benefits of Interactions With Strangers? ...........................................................................................................................33!3.1! Synopsis ........................................................................................................................... 33!3.2! Introduction ...................................................................................................................... 33!3.3! Study 3: Lunch With Peers .............................................................................................. 40!3.3.1! Method ...................................................................................................................... 40!3.3.2! Results ....................................................................................................................... 44!3.3.3! Discussion ................................................................................................................. 51!3.4! Study 4: The Waiting Room ............................................................................................ 52!3.4.1! Method ...................................................................................................................... 52!3.4.2! Results ....................................................................................................................... 55!3.4.3! Discussion ................................................................................................................. 63!3.5! Discussion ........................................................................................................................ 64!Chapter 4: Do Smartphones Compromise Opportunities to Cultivate a Sense of Trust By Obviating the Need for People to Rely on One Another? ........................................................70!vii  4.1! Synopsis ........................................................................................................................... 70!4.2! Introduction ...................................................................................................................... 70!4.3! Study 5: World Values Survey ........................................................................................ 71!4.3.1! Method ...................................................................................................................... 72!4.3.2! Results ....................................................................................................................... 73!4.4! Study 6: Asking for Directions ........................................................................................ 82!4.4.1! Method ...................................................................................................................... 82!4.4.2! Results ....................................................................................................................... 85!4.5! Discussion ........................................................................................................................ 88!Chapter 5: General Discussion ...................................................................................................93!5.1! Quality of Interactions ..................................................................................................... 94!5.2! Quantity of Interactions ................................................................................................... 99!5.3! Effects in the Absence of In-Person Interactions ........................................................... 101!5.4! A Broader Model ........................................................................................................... 105!5.5! Two Possible Futures ..................................................................................................... 111!5.6! Conclusion ..................................................................................................................... 115!References ...................................................................................................................................117! viii  List of Tables   Table 2.1. Study 2. Effects of condition on different types of phone use. .................................... 28!Table 3.1. Study 3. Effects of condition: Participants with access to phones reaped consistently lower benefits of the social interaction than participants with no access. .................................... 46!Table 3.2. Study 3. Effects of self-reported phone use. ................................................................ 50!Table 4.1. Study 5. Table of correlations and descriptive statistics. ............................................. 74!Table 4.2. Study 5. Regression analyses: Using mobile phones, but not other media to obtain information predicts lower trust. ................................................................................................... 76!Table 4.3. Study 5. Multilevel models: Relying on phones for information as a predictor of trust clustered within state of residence. ............................................................................................... 79! ix  List of Figures Figure 2.1. Study 2. High phone use predicts lower attention, social connectedness, and meaning in life. ............................................................................................................................................ 25!Figure 2.2. Study 2. Attention quality and social connectedness mediate the effect of phone use on meaning. ................................................................................................................................... 26!Figure 2.3. Study 2. Indirect effect of condition on social connectedness through relevant phone use. ................................................................................................................................................ 29!Figure 3.1. Study 3. To the extent that participants with access to their phones perceived greater opportunity costs of the interaction, they felt less related to their partners than participants with no access to phones. ...................................................................................................................... 48!Figure 3.2. Study 3. To the extent that participants with access to their phones perceived greater opportunity costs of the interaction, they reported lower mood than participants with no access to phones. .......................................................................................................................................... 48!Figure 3.3. Study 4. Effects of phone access and partner presence on social connectedness. ...... 59!Figure 3.4. Study 4. Effects of phone access and partner presence on mood. .............................. 60!Figure 3.5. Study 4. Effects of phone access and partner presence on anger. .............................. 61!Figure 3.6. Study 4. Social connectedness significantly mediates the negative effect of phone access on mood. ............................................................................................................................ 63!Figure 4.2. Study 6. Indirect effects of relying on phones on emotional well-being through social connectedness and difficulty of finding the building. ................................................................... 88!Figure 5.1. Theoretical model of mediators. ............................................................................... 104!Figure 5.2. Theoretical model of moderators. ............................................................................. 105! x  Acknowledgements In my academic journey, I have been lucky to rely on the support, guidance, and ideas of my mentors, friends, and family.  I would like to thank Elizabeth Dunn—my advisor and collaborator in developing the research presented in this dissertation. Liz has provided unwavering mentorship and guidance throughout grad school and spent endless hours teaching me how to improve my writing, research, and presentation skills. I thank her for always providing detailed and thoughtful feedback (even when having to read imperfectly edited manuscripts written in the middle of the night).  I extend my deep gratitude to Toni Schmader, who has been an incredibly supportive mentor and teacher. In addition to teaching me how to use theory in order to understand my data, Toni has served as a role model for the kind of mentor I strive to be.  I thank Jiaying Zhao, in addition to Toni and Liz, who as members of my departmental dissertation committee have helped me define, refine, and improve the research presented here.  I extend special thanks to my friends and colleagues who have helped me develop the ideas presented and tested in this work. My friend and collaborator, Nick Fitz, and my friend, Mark Werner, have been especially instrumental in shaping the ideas driving my research. I would also like to thank my colleagues at the department, including my incredibly understanding lab mate, Ashley Whillans, and my other close colleagues, Lara Aknin, Alyssa Croft, Will Hall, Gillian Sandstrom, Aaron Weidman, and many others. I would also like to thank all research assistants who have made the implementation of this research possible with their tireless dedication and hard work. Special thanks are due to Jason Proulx, who was instrumental in coordinating the data collection and preparing the data for analyses. xi  In addition to the people who have directly helped me develop the work presented in this dissertation, I would be remiss if I did not acknowledge the friends who have been by my side throughout this journey and have kept me sane and motivated. For their unwavering support, friendship, and love, I thank Tiffany, Heather, Mark, and everybody else who has been by my side on this journey. I would also like to thank my friends back home in Bulgaria—Lyubo, Pavel, and Svetlana—whose friendship has been incredibly important in my journey despite the physical distance between us. I feel grateful to all my friends I have had the privilege to have throughout the years—friends who have believed in me, loved me, and been by my side in fun and difficult times alike. Special thanks to my undergraduate mentors and advisors, Kathy Oleson and Daniel Reisberg, for sparking in me the passion to become a psychological scientist and for helping me improve my research abilities and academic writing. And I also extend my enduring gratitude to all my teachers, and especially to Stanka Blazheva, my English teacher who made it all possible for me to pursue my dreams in the West.  Last (but not least), I would like to thank my parents—Sophia and Petar—and my sister, Irina, for their unconditional love, for the lessons they have taught me, and for their emotional and financial support. Thank you, Mom, Dad, and Sis. I love you dearly!  xii  Dedication   To my loving parents   1 Chapter 1: Introduction In the 1970s, computers were the size of a room. Today, a typical smartphone fits discretely in a pocket and boasts more computing power than that of the Apollo 11 when it landed on the moon (Kaku, 2011). Because of this exponential increase in computing power, mobile devices that serve as portals to virtually unlimited information, activities, and social interactions—from smartphones to smartwatches—have become ubiquitous. Indeed, devices connected to the Internet now outnumber people in the world today (Cisco, 2014). And these ultraportable connected devices have become omnipresent in our lives: Internationally, a sizable proportion of mobile technology consumers report using their mobile devices while watching TV (64%), eating at a restaurant (55%), and even interacting with their children (43%; Qualcomm, 2013).  Powered by the incredible gadgets in our pockets, we have gained the unprecedented ability to connect with family, friends, and strangers—whether they are at the other end of town or at the other end of the world. But how does this ubiquitous connectivity affect the psychological benefits we reap from interacting with family, friends, and strangers in the nondigital world? 1.1 The Paradox of Ubiquitous Connectivity Ultraportable devices are allowing people to easily connect with remote family and friends like never before in history. Smartphones, for example, are predominantly used for social purposes, such as to text or call others and to access social media sites, such as Facebook (Duggan, 2013). As a result, these ultraportable connected devices hold an immense potential to boost people’s sense of emotional connection to their family and friends (Hampton, Goulet, Raine, & Purcell, 2011). A recent study showed, for example, that people who received a greater  2 number of text messages and/or social media updates on their smartphones during the day felt more socially connected to others (Pielot, Church, & de Oliveira, 2014). In another study, people who shared positive events publicly on Facebook felt better at the end of the day, even when controlling for the effect of the positive event itself (Choi & Toma, 2014). Although the above findings highlight the potential of ultraportable devices to boost sense of connection and well-being, a growing body of research suggests that being constantly connected to remote social partners may sometimes have social and emotional costs. In one experience sampling study that tracked how people felt several times a day for two weeks, people felt lonelier at points of the day when they used Facebook more (Kross et al., 2013). Furthermore, the more people used Facebook earlier in the day, the less happy they felt later in the day, even when controlling for earlier affect. These findings suggest that people may sometimes be incurring social and emotional costs as a result of their easy access to digital social activities.  People may be incurring social and emotional costs of being more digitally connected in part because digital social interactions could sometimes be replacing nondigital, in-person interactions. But even when people’s digital activities are not reducing the quantity of their nondigital interactions, people may be diminishing the quality of their in-person interactions by distracting themselves with activities, such as texting, using social media, or sharing photos. As a result, people may be trading the rewards of interacting with others in person for the rewards of interacting with others virtually. Indeed, a large body of literature shows that in-person interactions are amongst the strongest predictors of social connectedness and subjective well-being (Lyubomirsky & Boehm, 2010; Reis, Sheldon, Gable, Roscoe, & Ryan, 2000). Importantly, people feel most socially connected and happy when they are engaging in  3 meaningful conversations with one another and are feeling understood by their social partners (Reis et al., 2000). To the extent that exchanging text messages or posting on social media may be a less effective way to stimulate meaningful conversations than talking with others in person, replacing nondigital with digital interactions may hurt subjective well-being. Consistent with this possibility, the above-mentioned experience sampling study of Facebook showed that the more people used Facebook, the less they interacted with others in person (Kross et al., 2013). Importantly, controlling for the frequency of in-person interactions—which predicted higher well-being—attenuated the negative relationship between Facebook use and emotional well-being. By replacing or diminishing in-person social interactions, therefore, our constant access to digital social activities might undermine rather than boost feelings of connectedness and emotional well-being.  As we can see from the studies mentioned above, a growing body of research has been exploring the well-being effects of our digital social activities, which are always only a tap away on our smartphones (e.g., Choi & Toma, 2014; Kross et al., 2013; Pielot et al., 2014; for a review of research on Facebook, see Wilson, Gosling, & Graham, 2012). In contrast, researchers have yet to systematically explore how the omnipresence of ultraportable connected devices in our lives—with all the functions they provide—is impacting the benefits of our nondigital social activities. This gap in the literature is puzzling. Most of us can easily remember checking our work email when vacationing with family or having an engaging conversation that was interrupted by the ping of a new text message. Indeed, about half of Americans admit that their phones have made it harder to give others undivided attention, and one-third admit their phones have made it harder to forget about work outside of work hours (Pew Research Center, 2012). In view of these widespread perceptions of how the omnipresence of connected devices is affecting  4 our lives, it becomes imperative for researchers to explore the effects of these devices on the fabric of our nondigital social lives.  Understanding how the omnipresence of connected devices is affecting our social interactions is also important on a more theoretical level. Indeed, it is the omnipresence of connected devices—rather than any one specific function that they provide—that distinguishes these devices from their stationary predecessors, such as desktop computers. Today like never before, we are always only a finger swipe away from connecting to a multitude of social, work, and leisure activities. This connectivity to a wealth of digital worlds is what distinguishes the computing devices of our time from other omnipresent devices, such as mechanical watches. Thus, it is both their connectivity and their omnipresence that distinguish the ultraportable computing devices of our time from any of their predecessors. Accordingly, I use the term ubiquitous connectivity to capture both the omnipresence and the digital connectedness that define ultraportable computing devices—from smartphones to smartwatches. It is this ubiquitous connectivity and its effects on the benefits people reap from nondigital social activities that I set out to explore in the present work.   1.2 The Need to Feel Connected As we get ever more used to having our smartphones always by our side, we seem to be developing an ever stronger need to be digitally connected. Internationally, 60% of phone owners report feeling nervous or anxious when they forget their phones at home, and an additional 20% even report feeling disoriented (Qualcomm, 2013). And about half of people in the US say they couldn't live without their phones (Pew Research Center, 2015) and that they sleep with their phones to avoid missing any messages or updates during the night (Pew Research Center, 2012).   5 This newly developed need for ubiquitous connectivity stands in contrast to our much older and more fundamental need for social connectedness. Indeed, social needs—belonging, connectedness, relatedness, and affiliation—feature prominently in virtually all influential models of human motivation (Baumeister & Leary, 1995; Fiske, 2014; Kenrick, Griskevicius, Neuberg, & Schaller, 2010; Maslow, 1943; Ryan & Deci, 2000; Ryff, 1989). In his classic pyramid of needs, for example, Abraham Maslow (1943) theorized that social needs for connection and belonging are fundamental for human flourishing—secondary only to basic survival needs for water, food, and safety. In a recent reformulation of Maslow’s classic pyramid, evolutionary theorists have kept social needs at this central place within the hierarchy of human needs (Kenrick et al., 2010). Furthermore, according to self-determination theory (Ryan & Deci, 2000), a sense of relatedness to others is one of only three universal psychological needs that are essential for human flourishing. Similarly, Baumeister and Leary (1995) integrated decades of psychological research to place the need to belong amongst the most fundamental human motivations. Most recently, Susan Fiske (2014) has also proposed that people have a fundamental social motive to belong. Beyond theory, the essential role of social needs for subjective well-being has been supported by a large body of empirical research (e.g., Cacioppo et al., 2006; Lyubomirsky & Boehm, 2010; Myers & Diener, 1995; Reis et al., 2000). Because people frequently satisfy their social needs by interacting with others in person (Kross et al., 2013; Reis et al., 2000), I focus on investigating how ubiquitous connectivity affects the satisfaction of social needs during in-person interactions. In addition, I explore whether these effects on the satisfaction of people’s social needs may have downstream consequences for people’s subjective well-being.   6 Because of the multitude of terms used by theorists to refer to social needs, I use the terms social connectedness and relatedness interchangeably throughout this work to refer to the needs of people to belong, affiliate, and feel a sense of connection with others. Theorists, however, have identified social aspects of other basic human needs that are also relevant to the present investigation. Because humans are a social species, the survival need for safety (Maslow, 1943; Kentrick et al., 2010), for example, encapsulates not only the need to feel safe from nature’s elements, but also the need to trust others. Indeed, Fiske (2014) identifies trust as a fundamental social motive distinct from the need to belong. Thus, I also explore the effects of ubiquitous connectivity on trust in addition to its effects on relatedness. I use the broader term social needs to refer collectively to people’s needs for relatedness and trust. 1.3 Quantity of In-Person Interactions  The effects of ubiquitous connectivity on people’s social needs may depend on how ubiquitous connectivity affects both the quality and the quantity of in-person interactions. Smartphones may reduce the quantity of in-person interactions by providing an alternative source of information or digital activities. These devices may, for example, obviate the need to talk to others in order to pass the time when bored, such as when spending time at home on a quiet afternoon or when waiting at the bus stop or at the doctor’s office. Smartphones may also obviate the need to rely on the kindness of strangers when people need information on the go, such as when finding a restaurant or finding a friend’s house.  1.3.1 Substitution  Our smartphones provide access to a wealth of information and entertaining activities wherever we might be. About half of American cell phone users, for example, use their phones to obtain directions or other information based on their current location (Duggan, 2013).  7 Similarly, about half of cell phone owners use their phones to entertain themselves by listening to music (Duggan, 2013). When seeking information or looking for ways to relieve boredom, then, people increasingly seem to rely on their electronic gadgets rather than on fellow human beings. Given that people often use their portable devices for these purposes in public locations (Qualcomm, 2013), the usefulness of these devices in providing information and entertainment may decrease the likelihood of casual social interactions with other community members. After all, why try to spark a conversation with a stranger on public transit and risk potential rejection, when we can safely entertain ourselves on our phones? But by obviating the need to rely on others to obtain information or to pass the time, ultraportable connected devices may often prevent people from cultivating a sense of connectedness and trust with other members of society—from neighbours to strangers.  But can brief social interactions with strangers really satisfy people’s social needs? Recent research suggests that the answer is yes. In one study, coffee shop customers were randomly assigned to either have a friendly interaction with the barista while getting their coffee or to have an efficient interaction by minimizing conversation (e.g., Sandstrom & Dunn, 2014). The friendly customers subsequently felt a greater sense of belonging than the efficient customers. In other words, having a friendly interaction with a stranger helped people to satisfy their basic need for relatedness. And by satisfying this need, the friendly customers also felt happier.  The emotional benefits of casual social interactions have also been documented in other situations. In one study, for example, commuters in a large American city were randomly assigned either to talk with a stranger on their bus or to behave as they normally would (Epley & Schroeder, 2014). Participants who were asked to interact with strangers found their commute  8 more pleasant and felt happier than those who commuted as usual. Thus, to the extent that our ultraportable connected devices often obviate the need for casual social interactions—even with complete strangers—these devices may compromise opportunities to satisfy our social needs and boost subjective well-being.  1.3.2 Convenience  While sometimes replacing casual in-person interactions, our connected gadgets can also provide convenient new ways for us to achieve our goals. Indeed, when people were asked to describe what they like most about their phones, the most commonly used word in their responses was “convenience” (Pew Research Center, 2012). This usefulness may provide benefits for our well-being, thus offsetting the well-being costs of missed social opportunities. Imagine, for example, that Will is seeking directions to a park in an unfamiliar neighborhood. Will could talk to a few friendly strangers to help him get to the park, thus fostering his sense of social connectedness. Alternatively, he can use his smartphone to easily obtain a visual map with precise directions to the park. While missing out on opportunities to interact with friendly strangers, Will may also be less likely to get lost, and thus get to the park sooner, spending more time enjoying the park. Will’s smartphone may thus help boost his emotional well-being. Indeed, empirical evidence shows that factors that support people’s current goals predict higher emotional well-being (e.g., Brunstein, Schultheiss, & Grässmann, 1998; Sheldon & Elliot, 1999). In short, to the extent that ubiquitous connectivity affords easy access to information that can help people achieve their goals, these devices may enhance emotional well-being and offset the costs of missed social opportunities. 1.4 Quality of Social Interactions In addition to reducing the quantity of casual in-person interactions, ubiquitous  9 connectivity may affect the quality of in-person interactions. Thus, for example, our portable connected devices may undermine the quality of social interactions by making us less attentive to our ongoing social interactions. A mother who is looking at pictures on Pinterest while spending time with her son in the park, for example, may share less in her son’s joy as he frolics in the water fountain. Smartphones could also diminish the quality of in-person interactions by reminding their users of other things that they may want to be doing instead. The dinner conversation with friends may, for example, become less pleasant when a user’s phone reminds her of a looming deadline on a project she could be working on instead of “hanging out”. Portable devices may, however, also enhance the quality of social interactions by providing information and activities that are relevant to ongoing social interactions. While spending time with his daughter at a science museum exhibit, for example, a father could use his smartphone to answer his daughter’s questions about T-Rex, thus enriching rather than impoverishing their shared experience at the museum.  1.4.1 Subjective Quality of Attention  Our portable connected devices provide easy and immediate access to a wealth of entertaining activities and social interactions (e.g., searching Google, watching YouTube, or messaging friends on Facebook). Consequently, these devices can be an unlimited source of distracting activities that are not otherwise available in the nondigital environment. By adding new sources of distraction, ubiquitous connectivity may diminish people’s subjective quality of attention during in-person social interactions.  Experimental research has provided preliminary evidence that the use of phones can impair people’s ability to pay attention to the nondigital world around them. In one study, having a conversation even on a hands-free cell phone during simulated driving resulted in a two-fold  10 increase in the failure to detect traffic signals (Strayer & Johnson, 2001). Another study showed that our phones could cause inattentional blindness—an impaired ability to notice distinctive stimuli in the physical environment. That is, people who were talking on a cell phone while walking were less likely to see a street entertainer—a unicycling clown—than people not using a cell phone (Hyman, Boss, Wise, McKenzie, & Caggiano, 2009).  To the extent that phones can impair people’s ability to pay attention to their physical environment, these devices should also make people less attentive to their nondigital social interactions. Being less attentive during social interactions may in turn have negative consequences for people’s sense of relatedness and emotional well-being. In support of this possibility, past research has shown that people who are less mindfully attentive experience a lower sense of relatedness and less positive emotions (Brown & Ryan, 2003; Feldman, Hayes, Kumar, Greeson, & Laurenceau, 2006). In short, by compromising people’s subjective quality of attention, ubiquitous connectivity may compromise the social and emotional benefits people reap from interacting with others in person. 1.4.2 Perceived Opportunity Costs Ultraportable connected devices can be a source of unlimited other activities from virtually any domain of life (e.g., work, friends, family). A work email during dinner with family or a message from a sexual partner during lunch with a friend may be all it takes to make people feel they want to be doing something else instead of interacting with the people around them. In other words, ubiquitous connectivity may make people more aware of the opportunity costs associated with an ongoing social interaction.  People do not spontaneously perceive the opportunity costs of ongoing activities (Frederick, Novemsky, Wang, Dhar, & Nowlis, 2009). Thus, for example, an accountant who is  11 staying late at the office to finish an urgent audit may not think of what other activities she is missing out on, unless she is explicitly reminded of these alternative activities. This unawareness may quickly change, however, after she receives a text message from her friends inviting her to join them for drinks. In other words, our portable connected devices may prompt us to perceive the opportunity costs of our activities more frequently than ever before. Such increased opportunity costs should, of course, depend on how easily alerted people are of new notifications. Friends who keep their phones on “ring” mode and out on the dinner table, for example, may perceive greater opportunity costs of their interaction than friends who keep their phones on silent and out of sight.  By reminding people of alternative activities, smartphones may compromise the social connectedness people reap from interacting with others in person. In one study, for example, students who were interacting next to a mobile phone felt less socially connected to each other at the end of the interaction (Przybylski & Weinstein, 2013). Specifically, pairs of students were asked to engage in one-on-one conversations about an important topic (i.e., the most meaningful events that occurred in the past year). Some students had this conversation in the presence of a mobile phone placed unobtrusively on a table in the same room, whereas others interacted in the presence of a paper notebook. Students who interacted next to the mobile phone felt less related, trusting, and empathetic towards each other than students who interacted next to the paper notebook. The authors argued that the phone might have implicitly reminded participants of their existing social networks, thus interfering with people’s desire to form new relationships (Przybylski & Weinstein, 2013). No research, however, has ever directly explored whether by making alternative social interactions and relationships more salient, phones can diminish how connected people feel when interacting with others in person.  12 1.4.3 Type of Use By providing constant access to distracting activities and reminding us of alternative activities, portable connected devices could interfere with the social and emotional benefits we reap from in-person social interactions. But these effects may be reduced—or even reversed—depending on exactly how people use their portable gadgets. When people use their devices to obtain information that is relevant to ongoing social interactions, they may feel more engaged with the people around them. In other words, the effects of portable devices on quality of attention and perceived opportunity costs may be moderated by whether people use their devices to access content that is relevant or irrelevant to their ongoing social interactions. Thus, by providing ubiquitous connectivity to relevant information and activities, portable devices may boost social connectedness and subjective well-being. Imagine a person who is chatting with a group of friends about her recent trip to Bali and brings her friends closer to her experiences by showing photos of the trip on her smartphone. Such sharing of positive events with others has been associated with experiencing more positive emotions (Gable, Reis, Impett, & Asher, 2004; Hicks & Diamond, 2008). Thus, to the extent that people use their portable devices to create shared experiences with the people around them, ubiquitous connectivity may help to promote well-being.  People can enrich their in-person social interactions by using their portable connected devices to access relevant information and activities. But even when people use their portable devices to access digital activities that are not relevant to their ongoing in-person interactions, they may feel a greater sense of connectedness. Indeed, being able to connect with our family or friends who are not physically present may support our need for relatedness even when we have the opportunity to interact with others in person. In other words, the sense of being connected to  13 remote others may offset the costs of missed opportunities to connect with the social partners around us. Texting with a good friend we have not seen for a while may, for example, provide a greater sense of social connectedness than making small talk with a fellow patient in the doctor’s office waiting room. In short, leveraging our ubiquitous connectivity to achieve social goals by connecting with valued others may offset the costs of missed social opportunities in the nondigital world, especially when such social opportunities may be relatively poor sources of social connectedness.  1.5 Overview of Empirical Studies Ubiquitous connectivity may compromise the emotional benefits we reap from social environments by diminishing the sense of social connectedness we can otherwise derive from in-person social interactions. By distracting us, reminding us of alternative activities, and sometimes obviating our need to rely on others, portable connected devices may compromise opportunities to cultivate a sense of relatedness with those around us. These social costs of being digitally connected may in turn have negative downstream consequences for our subjective well-being. But the net effects of ubiquitous connectivity on well-being when we are interacting with others may depend on how exactly we are using our smart gadgets and on how useful these devices are in supporting our goals. Thus, I set out both to explore the psychological mechanisms underlying the effects of ubiquitous connectivity on social connectedness and well-being and to uncover some of the factors that may moderate these effects.  Smartphones are currently the most prototypical example of an ultraportable connected device: They are omnipresent and provide connectivity to virtually unlimited information, activities, and remote social partners. Accordingly, in the present series of studies, I use the smartphone as a proxy for investigating the effects of ubiquitous connectivity during in-person  14 social interactions. In particular, I first examine whether smartphones can affect the social and emotional benefits people reap from interactions with close others (Chapter 2). I then explore whether smartphones affect the benefits people reap when they have the opportunity to forge new relationships with their peers (Chapter 3). Finally, I look at how smartphones are affecting people’s general sense of trust towards complete strangers (Chapter 4). In Chapter 2, I explore how smartphones affect the social and emotional benefits people reap from interactions with close social partners. In particular, I manipulate how much parents are using their smartphones while they are spending time with their children at a summer festival (Study 1) and at a science museum (Study 2). This strong manipulation allows me to explore whether using smartphones can compromise opportunities to cultivate a sense of social and emotional well-being by diminishing attention quality. By asking parents to report how they used their smartphones at the science museum, I am also able to examine whether using smartphones to obtain relevant information (e.g., information about the exhibits) mitigates the detrimental effects of heavy phone use during social interactions. Finally, I also explore whether parents can offset the costs of missed social opportunities in their physical environment by using their phones to support their need for relatedness through connecting with remote social partners (e.g., texting, using Facebook).  In Chapter 3, I explore the effects of smartphones when people have the opportunity to form new social relationships with peers. In order to examine the effects of people’s natural smartphone use, I use a minimal manipulation by randomly assigning participants simply to have access or no access to their phones. Unlike in Chapter 2, where I ask parents to use their phones as much as possible, in Chapter 3, participants who have access to their phones can use them as much or as little as they wish. Thus, in Study 3, I examine the social and emotional benefits  15 students derive from interactions with peers while they are having lunch together. In Study 4, I explore how access to smartphones affects relatedness and well-being while people are simply waiting together in a room. In one condition of this study, participants wait in pairs, thus having an opportunity to engage in social interaction. In another condition, participants wait by themselves. This design allows me to directly explore whether the effects of smartphones depends on the availability of social partners in the nondigital environment. Thus, I am able to explicitly test my central assumption that phones affect connectedness mainly to the extent that they prevent people from reaping the benefits of in-person social interactions. In Chapter 4, I explore whether smartphones may have broader effects on the social fabric of society by obviating the need to rely on the kindness of strangers, thus compromising opportunities to cultivate a sense of trust in other members of society. In Study 5, I analyze a nationally representative data from the World Values Survey to examine whether people who use their phones more frequently to obtain information trust others less. In Study 6, I randomly assign students to look for an unfamiliar building on campus either by using their phones or without using their phones. In addition to measuring trust, in this study I also measure social connectedness and the usefulness of phones in locating the building. This experiment thus allows me to pit the social and emotional costs of missing out on social interactions against the possible emotional benefits of the convenience afforded by smartphones.   Together, the six studies presented in this work provide an initial glimpse into some of the social and emotional costs and benefits of ubiquitous connectivity.   16 Chapter 2: Can Smartphones Diminish The Social and Emotional Benefits Parents Reap From Spending Time With Their Children?1 2.1 Synopsis  In Chapter 2, we explored how smartphones affected the social and emotional benefits people reaped from interactions with close social partners. In particular, we manipulated how much parents were using their smartphones while they were spending time with their children at a summer festival (Study 1) and at a science museum (Study 2). This strong manipulation allowed us to explore whether using smartphones could compromise opportunities to cultivate a sense of social connectedness and well-being by diminishing attention quality.  2.2 Introduction We live in the era of ultraportable computers. Smartphones are the fastest-selling product in history: In the 20 seconds that it took to write this sentence, about 1000 smartphones were shipped to their future users (The Economist, 2015). At this rate, 80% of the world’s adult population will own smartphones by 2020 (Ericsson, 2014). This trend towards ever more powerful and portable computing devices has all the trappings of a new technological era, revolutionizing when, where, and how people work, socialize, and play (Slade, 2012; Thompson, 2013; Turkle, 2011).  The critical factor that sets apart the age of smartphones from the past age of desktops and laptops is the omnipresence of devices that serve to connect us to virtually unlimited online activities. We can easily message a friend just to check in, catch up with our friends’ recent activities on Facebook, or check how many people have liked our most recent sunset photo on                                                 1 A version of this chapter will be submitted for publication: Kushlev, K. & Dunn, E. W. (in prep). Can smartphones diminish the social and emotional benefits parents reap from spending time with their children?  17 Instagram. But talking to a friend on the other side of the globe could divert attention away from the people in our physical world. For some, even one of the most intimate shared experiences with another person—having sex—is no match for the draw of their portable gadgets. Indeed, about 1 in 5 smartphone users aged 18-34 admit having used their phones during sex (Jumio, 2013).  There is no shortage of speculation about how the ubiquitous connectivity afforded by smartphones is reshaping our lives. Sociologists (Tomlinson, 2007; Turkle, 2011), media theorists (Rushkoff, 2013), and technology experts (Lindley, 2015) have all argued that the ubiquity of connected digital technology is fracturing our attention when we work, socialize, and play. Beyond the walls of the ivory tower of academia, society in general seems to share the concerns over our digitally scattered attention: Digital detox, for example, has been recognized as an expression by The Oxford English Dictionary.  In contrast to this abundance of speculation, research on the psychological impact of portable connected technology is scant. Most research has focused on how mobile phones impair driving by compromising drivers’ attention (Drews, Yazdani, Godfrey, Cooper, & Strayer, 2010; Strayer & Drews, 2007; Strayer & Johnson, 2001), and rightly so: 55% of US adults admit to using their smartphones while driving. But many people also use their phones while spending time with valued others—1 in 3 Americans report, for example, checking their smartphones while sharing a dinner with a date or during a child’s function, such as a school play (Jumio, 2013). Yet, almost no research has explored how smartphones may be affecting our in-person social interactions by diverting attention away from our social partners. Ample evidence suggests that attention is an essential factor in reaping emotional benefits from daily activities (Bryant, 2003; Erisman & Roemer, 2010; Quoidbach, Berry, Hansenne, &  18 Mikolajczak, 2014). Within the context of social interactions more specifically, attention may be critical for cultivating a sense of connection and relatedness with others (Brown & Ryan, 2003), as well as for realizing the emotional benefits of social interactions. Indeed, sense of relatedness has been postulated to be a fundamental human need that is essential for subjective well-being (Fiske, 2014; Lyubomirsky & Boehm, 2010; Ryan & Deci, 2000; Ryff, 1989). To extent that smartphones demand their users’ limited attentional resources (e.g., Drews et al., 2010), therefore, these devices may interfere with opportunities to cultivate a sense of relatedness and undermine opportunities to cultivate a sense of emotional well-being when people are interacting with others in person.  To examine the effects of smartphones on relatedness and well-being, we focus on the benefits people derive from one of the most fundamental social relationships—between parents and their children. Parents have been shown to reap a sense of relatedness, meaning, and positive emotions from spending time with their children, especially when they are doing fun activities together (Nelson, Kushlev, English, Dunn, & Lyubomirsky, 2013; for a review see, Nelson, Kushlev, & Lyubomirsky, 2014). Could the demands levied on parents’ attention by their smartphones hinder parents’ ability to experience the full benefits of spending time with their children?  To explore parental experience in a naturalistic environment where parents may be particularly likely to reap the benefits of being with their children, we recruited parents at a summer festival (Study 1) and at a science museum (Study 2). To explore the effects of phones on subjective attention quality, social connectedness, and well-being, we asked parents to either use their phones as much as possible or to use their phones as little as possible. We hypothesized  19 that high phone use would fracture parents’ attention and lower their sense of connectedness, thus diminishing their subjective well-being.  2.3 Study 1: The Festival 2.3.1 Method Participants. Parents were recruited while they were spending a day with their children at the Vancouver International Children’s Festival—an outdoor event that features a plethora of fun and educational activities for children. For this initial experiment, we recruited as many participants as we could during the five days of the festival. In total, 90 parents participated in the study (Median age = 37; 73% women). Parents were incentivized with an entry in a draw to win $100. Procedure. Parents were approached by trained research assistants and invited to take part in a brief study. Because parents may think they should feel happy when spending time with their children, the research assistants simply explained that we were interested in their experiences at the festival, rather than mentioning that we were studying parents’ feelings about spending time with their children.  Prior to condition assignment, we informed parents that they might be asked to modify their phone use by either using their phones a lot or limiting their phone use, thereby ensuring that everyone was willing to participate regardless of their condition assignment; thus, random assignment was not compromised by people selectively opting out of the study after receiving the experimental instructions. After agreeing to participate, parents were randomly assigned either to use their phones as much as they safely could during their time at the festival (high-use condition) or to refrain from using their phones as much as possible (low-use condition).   20 After receiving the phone use instructions, parents were handed the survey in a pre-stamped and pre-addressed envelope. Participants could mail the survey after completing it or hand it directly to a research assistant at the festival. We asked parents to wait at least 30 minutes before completing the questionnaire (thus ensuring that the manipulation could have an effect). Because we were interested in capturing parents’ current feelings (rather than their recollections of how they felt), we also instructed participants to complete the questionnaire before they left the festival. Parents indicated where and when (i.e., the time and date) they completed the survey. Approximately 38% of participants completed the survey outside our suggested time window. Although including these participants diluted the effects, we included all participants to preserve random assignment, providing a conservative test of our hypotheses.  Measures. At the top of the survey sheet, participants were instructed to answer all questions with reference to their experience at the festival after receiving the experimental instructions. To minimize participant burden in this field experiment and ensure the recruitment of a broad sample of participants, we selected brief measures of each outcome. To assess participants’ subjective quality of attention, we adapted two items (α = .64) from the Attention Subscale of the Cognitive and Affective Mindfulness Scale-Revised (CAMS-R; Feldman, Hayes, Kumar, Greeson, & Laurenceau, 2006). Specifically, participants were asked to indicate how easily distracted they felt, and how easy it was for them to concentrate on what they were doing. To assess social connectedness, we adapted two items (α = .70) from the Social Connectedness Scale (Lee, Draper, & Lee, 2001). In particular, we selected the item with the highest loading to the main factor of the scale—I  felt distant from people (reverse-scored)—and one other face-valid item: I felt close to people. All items were anchored on a scale from 0–not at all to 6–very much.  21  To measure subjective well-being, we first asked participants to report how they were feeling at the festival, from 0–very bad to 6–very good (Killingsworth & Gilbert, 2010). Because meaning is one of the most commonly documented benefits of parenting (for a review, see Nelson et al., 2014), we also measured how much meaning and purpose in life parents felt during the event (0–not at all; 6–very much). This single-item meaning measure was previously validated (Ashton-James, Kushlev, & Dunn, 2013) against the Meaning in Life Questionnaire–Presence Subscale (Steger, Frazier, Oishi, & Kaler, 2006). Finally, as a manipulation check, participants were asked how much they had used their phones, from 0–not at all to 6–constantly.  2.3.2 Results The manipulation was successful: Parents reported using their phones more in the high-use condition (M = 3.66, SD = 1.54) than in the low-use condition (M = 1.26, SD = 1.33), t(87)= 7.88, p < .001. As hypothesized, parents assigned to use their phones more reported worse quality of attention (M = 3.80, SD = 1.48) than parents who limited their phone use (M = 4.37, SD = 1.26), t(87)= −1.96, p = .05, d = −.40. Although we found no significant main effects on social connectedness, mood, and meaning (p’s > .46), parents in the high-use condition scored consistently lower on our measures of social connectedness (d = −.15), mood (d = −.13), and meaning (d = −.16) than parents in the low-use condition.   As we expected, attention quality was positively correlated with social connectedness (r = .46, p < .001), mood (r = .28, p < .01), and meaning (r = .35, p < .001). Accordingly, we explored whether by fracturing parents’ attention, high phone use might have downstream consequences for social connectedness, mood, and meaning. Specifically, we ran three separate meditational analyses using bootstrapping with 50,000 replications (Hayes, 2013). To the extent that parents were less attentive in the high-use condition than in the low-use condition, our  22 manipulation had significant indirect effects on social connectedness, indirect effect = −.26, 95% CI [−.60; −.02], mood, indirect effect  = −.14, 95% CI [−.37; −.01], and meaning, indirect effect = −.19, 95% CI [−.46; −.02].  Overall, the findings in Study 1 provide initial evidence that smartphones can make people feel distracted, which in turn hinders them from reaping the social and emotional benefits of spending time with their children. We were only able to detect indirect effects of our manipulation on social connectedness, mood, and meaning, perhaps in part because the hectic outdoor environment made it difficult for some participants to complete our survey during the suggested time window (i.e., 30 minutes after receiving our instructions but before leaving the festival). Thus, we set out to replicate Study 1 in a less chaotic, indoor location—a science museum. 2.4 Study 2: The Museum 2.4.1 Method Participants. Parents were recruited while they were spending time with their children at Science World—a science museum featuring a plethora of fun and educational activities for children. We calculated a target sample size (n =198) based on a priori power analyses with 80% probability of detecting effects of d = .40, α = .05, two-tailed. We postulated this a priori effect size because we found an effect of d = .40 on attention quality in Study 1, and because we expected to detect larger effects on social connectedness, mood, and meaning due to greater participant compliance. We overshot our sample size goal by 2 participants and, therefore, the final sample size consisted of 200 parents (Median age = 38; 56% women). They were incentivized with an entry in a draw to win a one-year membership for one adult and one child to Science World (worth $110).  23 Procedure. The procedure was identical to the procedure of Study 1. Before being assigned to condition, parents were informed that they might be asked to increase or decrease how much they used their phones.2 Participants were assigned either to use their phones as much as they safely could or to limit their phone use as much as possible. They were then given the survey and asked to explicitly agree to complete it at least half an hour after receiving the instructions, but before leaving the museum. Compared to Study 1, where 38% of participants did not complete the study within this suggested time window, only 10% of participants in the present study completed the study outside this time window. As in Study 1, we present the findings including all participants.  The survey contained the same measures of attention, social connectedness, mood, and meaning as Study 1. To explore whether the effects of higher phone use depend on how people use their phones (e.g., to check social media), we additionally measured how much (0–not at all; 6–constantly) participants used their phones for a range of different purposes. We measured, for example, whether people used their phones for social purposes (e.g., social media, text messaging) and to enhance their own or their children’s experience at the museum by, for example, obtaining relevant information about the exhibits. As in Study 1, participants could mail their surveys in pre-stamped and pre-addressed envelopes. To increase the completion rate further, we also gave participants the opportunity to leave their surveys in boxes placed in the museum.                                                  2 In Study 2, we recorded the reasons people gave for deciding not to participate in the study. Of the total number of people we approached (n = 481), less than 5% did not want to use their phones more than usual, 1% did not want to limit their phone use, and approximately 50% did not want to participate for other reasons (e.g., leaving the museum soon).  24 2.4.2 Results  Main effects. The manipulation was successful: Parents reported using their phones more in the high-use condition (M = 3.88, SD = 1.59) than in the low-use condition (M = .69, SD = 1.15), t(198) = 16.38, p < .001.  Replicating the effects of Study 1, we found that parents asked to use their phones a lot reported lower attention quality (M = 3.32, SD = 1.52) than parents asked to limit their phone use (M = 4.20, SD = 1.27), t(197) = −4.43, p < .001, d = −.63 (see Figure 2.1). Extending the effects of Study 1, we found that parents in the high-use condition felt less socially connected (M = 3.39, SD = 1.66) than parents in the low-use condition (M = 4.43, SD = 1.19), t(197) = −5.11, p < .001, d = −.73. Although we found no difference in mood between parents in the high-use (M = 4.64, SD = 1.01) and low-use conditions (M = 4.73, SD = .92), t(190)= −.64, p = .42, d = −.09, parents in the high-use condition experienced lower meaning (M = 3.40, SD = 1.69 vs. M = 3.90, SD = 1.62), t(195)= −2.12, p = .04, d = −.30. Overall, we found medium-to-large effects of high phone use on quality of attention and social connectedness, and small effects on our measures of subjective well-being.     25   Figure 2.1. Study 2. High phone use predicts lower attention, social connectedness, and meaning in life.  Note. Error bars represent standard errors of the mean. Mediated effects. To explore whether high phone use compromised opportunities to cultivate social connectedness and subjective well-being by scattering attention, we ran a series of mediational analyses using bootstrapping with 50,000 replications (Hayes, 2013). Quality of attention partially mediated the effect of condition on social connectedness, indirect effect = −.45, 95% CI [−.73; −.24], leaving a significant direct effect of condition on connectedness, b = −.59, 95% CI [−.95; −.23]. In other words, above and beyond compromising attention, high phone use lowered feelings of social connectedness. Accordingly, we next explore whether quality of attention and social connectedness together could explain the significant negative effect of phone use on meaning.  As shown in Figure 2.2, the effect of condition on meaning was completely mediated by attention quality and social connectedness. In particular, high phone use predicted lower meaning 0!1!2!3!4!5!6!A*en-on! Social!Connectedness!Mood! Meaning!High!Phone!Use!Low!Phone!Use! 26 by fracturing attention, thus undermining social connectedness, which in turn undermined meaning3, indirect effect = −.11, 95% CI [−.24; −.04]. In addition, sense of connectedness mediated the condition effects on meaning independently from its association with attention quality, indirect effect = −.15, 95% CI [−.35; −.04]. Attention quality, however, did not mediate the effect of condition on meaning above and beyond its association with social connectedness, indirect effect = −.05, 95% CI [−.24; .12]. In other words, attention quality accounts for the effect of condition on lower meaning only to the extent that fractured attention is associated with lower sense of connectedness. Overall, then, we find that high phone use can compromise parents’ ability to boost their well-being both by scattering parents’ attention and by undermining their sense of connectedness.  Figure 2.2. Study 2. Attention quality and social connectedness mediate the effect of phone use on meaning. Notes. All values represent unstandardized regression coefficients obtained through bootstrapping (Hayes, 2013). The range in brackets represents the 95% confidence interval of the indirect effect. *p < .05; **p < .01; ***p < .001                                                 3 Importantly, switching the positions of the mediators, so that social connectedness predicts quality of attention produced a nonsignificant indirect effect  = -.03, 95% CI [-.16; .08], providing support for the hypothesized directionality of the meditational path.  27 Moderation by type of smartphone use. People can use their smartphones for many different purposes—from answering work emails and playing video games to obtaining information relevant to their ongoing social activities and socializing with remote social partners. Some types of phone use may boost rather than compromise social connectedness. If a father, for example, used his phone to find information about the next science demo at the museum in order to take his daughter to see it, he may have boosted his sense of connectedness. And even if a mother is missing an opportunity to cultivate a sense of connectedness with her son as he is watching a science demo with delight, she may be able to offset those costs by cultivating a sense of connectedness with her friends on Facebook. Thus, we next explore whether the effects of phone use on social connectedness depend on how people used their phones.  Although we wanted to examine whether the type of phone use moderates the effect of condition on social connectedness, we could not rely on moderation analyses because moderation models assume that the predictor is uncorrelated with the moderators. Because our manipulation was successful in increasing all types of phone use (see Table 2.1), we employed mediational models to explore whether the effect of phones on social connectedness depends on how people use their phones. In other words, we examined whether higher phone use may have positive rather than negative effects on social connectedness when people use their phones in certain ways (e.g., to socialize with others remotely).  As shown in Table 2.1, correlational analyses indicated that the only type of phone use that predicted feeling more socially connected was the use of phones to obtain relevant information about people’s activities at the museum (r = .13, p = .06). Accordingly, we employed mediational analyses (Hayes, 2013) to explore whether parents in the high-use condition who used their phones to access relevant content felt more socially connected than  28 parents in the low-use condition. Indeed, as shown in Figure 2.3, parents in the high-use condition felt more socially connected to the extent that they used their phones more to obtain  Table 2.1. Study 2. Effects of condition on different types of phone use.  Condi-tion Social Connec-tion Relevant Use Take photos Sharing photos Social media Texting/calling Entertain self Work Condition 1 -.34*** .15* .41*** .24*** .40*** .39*** .30*** .33*** Social Connection  1 .13† -.05 -.05 -.13† -.15* -.12† -.18* Relevant Use   1 .27*** .36*** .38*** .24*** .44*** .35*** Taking photos    1 .48*** .34*** .47*** .22** .26** Sharing photos     1 .60*** .36** .31*** .38*** Social media      1 .47*** .44*** .37*** Texting/ calling       1 .46*** .40*** Entertain self         1 .45*** Work          1 Notes. All numbers represent correlation coefficients (r). Condition is coded as 0–Low Use, 1–High Use.  †p < .10; *p < .05; **p < .01; ***p < .001   29 Figure 2.3. Study 2. Indirect effect of condition on social connectedness through relevant phone use. Notes. All b’s represent unstandardized regression coefficients obtained through bootstrapping using 50,000 resamples (Hayes, 2013). The range in brackets represents the 95% confidence interval of the indirect effect.   *p < .05; **p < .01; ***p < .001 All other types of phone use, including using phones more to connect with remote social partners by texting/calling or using social media, were either not associated or negatively associated with social connectedness (Table 2.1). A series of mediational analyses (Hayes, 2013), however, showed no evidence of mediation of the negative condition effects on social connectedness by any of the different types of use, including  texting/calling, using social media, taking photos, sharing photos, entertaining oneself in nonsocial ways (e.g., playing games), and accessing work-related content (e.g., work email).  Overall, these analyses provide little evidence that the way people use their phones during social activities moderates the effect of phone use on social connectedness. That said, our findings suggest that phone users might be able to boost their sense of connection if they use their phones to obtain content relevant to their nondigital social activities. More broadly, the lack of mediation by most types of phone use suggests that phones may compromise connectedness  30 not because of the digital activities that they provide, but because of the nondigital activities that they interfere with. 2.5 Discussion   With a swipe of a finger, people can gain instant access to social, work, and leisure activities, even when sharing experiences with valued others such as their children. We showed that by gaining such access to unlimited digital worlds, people might be losing some of the benefits they could reap from nondigital social activities. A field experiment at a summer festival showed that smartphones scattered the attention of parents who were assigned to use their phones a lot. Replicating and extending this finding in another field experiment at a science museum, we showed that phone use could also undermine opportunities to cultivate the sense of connectedness of parents who are spending time with their children. Fractured attention and diminished social connectedness in turn compromised parents’ sense of meaning—a central benefit of raising children (Nelson et al., 2014). In short, parents who frequently take advantage of the digital activities provided by the powerful gadgets in their pockets may often be foregoing opportunities to harvest the fruits of joyful times with their children. Interestingly, parents who used their phones to enhance the shared activities with their children avoided the perils of frequent phone use. Importantly, however, using their phones to socialize with others—by texting or immersing themselves into the world of their online social network—did not sufficiently offset the social costs associated with transporting their minds away from their nondigital social activities. These results dovetail with recent research showing that people do not reap the same well-being benefits when they engage with their digital social networks as they do when they interact with others in person (Kross et al., 2013); specifically, people who socialized more with others in person earlier in the day felt better later in the day,  31 while people who used Facebook more earlier felt worse later. Yet, other research has shown that people can reap subjective benefits from socializing online (Hampton, Goulet, Raine, & Purcell, 2011; Wilson et al., 2012). The present findings suggest that engaging with our online social world may be detrimental to well-being to the extent that the constant access to digital worlds afforded by our smartphones can disengage us from the people right by our side.  While providing a first glimpse into the perils of ubiquitous connectivity, our findings open tantalizing new questions. A scattered mind may hinder parents from fully relating to their children, who in turn may feel less socially connected to their parents, potentially compromising the formation of a secure attachment in children. Indeed, less responsive and attentive parenting has been associated with lower secure attachment in children (Benoit, 2009; Green & Goldwyn, 2002; van den Boom, 1994). Because securely attached children are more likely to grow up into emotionally healthy adults (Ainsworth, Blehar, Waters, & Wall, 1978; Bowlby, 1960; Howe, 2011; Brennan & Shaver, 1995), the present research highlights the potential risks of growing up surrounded by adults immersed in omnipresent digital worlds. Our findings may thus have implications for therapy and interventions to promote both more mindful parents and more joyful children.  Coda. We are increasingly besieged by powerful portable devices than can teleport us to any digital world and connect us with anyone. Psychological science has a lot to contribute to understanding how and why our ubiquitous connectivity to these digital worlds could enrich or impoverish our experience in the real world (c.f., Pennebaker, 2014; Rentfrow & Gosling, 2003; Rozin, 2001). While a growing area of research explores how specific online activities—from Facebook to email (Kushlev & Dunn, 2015; Wilson et al., 2012)—affect subjective experience, researchers have yet to explore how ubiquitous access to digital worlds is affecting the benefits  32 people reap from nondigital activities. Of course, major technological innovations—from the vending machine to the automobile—have also had considerable impact on our relationships with others (Slade, 2012). At the dawn of the current technological revolution, however, psychological scientists are in a unique position to apply rigorous experimental methods and develop sound psychological theory. Thus, psychological science can shape a future in which humanity can harness the full potential of portable devices to connect us with one another. Making first strides into this exciting area of research, we have shown that by scattering attention, smartphones can obstruct people from satisfying the basic human need to feel socially connected.  33 Chapter 3: How Do Smartphones Affect the Social and Emotional Benefits of Interactions With Strangers? 3.1 Synopsis  In Chapter 2, we examined the effects of smartphone use on the benefits parents derived from spending time with their children. Moving beyond the realm of close relationships, in Chapter 3, we investigated the effects of smartphones when people had the opportunity to form new social relationships. Specifically, participants were given the opportunity to talk to peers while having lunch (Study 3) or while waiting for an appointment (Study 4). In contrast to Studies 1 and 2, in which participants were asked to use their phones as much or as little as they could, in Studies 3 and 4, we examined the effects of people’s natural smartphone use. In particular, we randomly assigned participants either to have access to their phones and use them as they pleased or to have no access to their phones. Additionally, in Study 4, some participants were randomly assigned to wait by themselves rather than in the company of a peer. This additional manipulation allowed us to explore the social and emotional effects of smartphones when people waited alone.  3.2 Introduction As smart mobile devices become ever more ubiquitous, we are gaining constant access to digital activities anytime and anywhere. And we seem to be taking full advantage of this access. One-fourth of adults, for example, report checking their portable Internet-connected devices every 30 minutes (Qualcomm, 2013). Objective measures of phone use suggest that the actual number might be much higher: On average, people were found to reach for their phones more than 150 times a day (Meeker & Wu, 2013). How does this constant interaction with our phones affect how connected we feel to one another?   34 The easy access to smartphones and other mobile gadgets provides a convenient way to connect with remote others, which may furnish users with opportunities to feel more socially connected. Indeed, 65% percent of US cell phone users say that their phones have made it a lot easier to keep in touch with the people they care about (Pew Research Center, 2012). But the ubiquitous connectivity provided by our phones may be a mixed blessing. In fact, 1 in 4 Americans (24%) say that the worst thing about their phones is being constantly reachable by others, making constant reachability the most disliked aspect of owning a phone—more so even than common annoyances, such as unreliable service (12%) and low battery life (8%; Pew Research Center, 2012). Constant reachability may be annoying to users in part because phones now permeate people’s lives regardless of what they are doing. About half of adults internationally report using their phones while partying with others (52%) and while enjoying a meal at a restaurant (55%); almost two-thirds report using their phones on public transit (62%; Qualcomm, 2013). But, could ubiquitous connectivity be more than just a pet peeve and affect people’s well-being by interfering with how connected people feel to those around them?  In Chapter 2, I showed that people who used their smartphones more were forgoing some of the benefits they could reap from interacting with their children. In the present chapter, I want to explore whether smartphones may hinder peers from relating to each other during casual social interactions—from meeting new people over lunch to having a friendly chat with a stranger on the bus.  Although people satisfy their fundamental need to feel related mostly through interactions with friends and family (Reis et al., 2000; Ryan & Deci, 2000), people can foster a sense of relatedness even through brief interactions with strangers (Sandstrom & Dunn, 2014). In one experiment, customers who were asked to have a friendly interaction with the barista while  35 getting their coffee (e.g., smile, make eye contact, and have a brief conversation) subsequently felt more socially connected than customers who had an efficient interaction with the barista (Sandstrom & Dunn, 2014). The friendly customers also felt happier than the efficient costumers. Similarly, people asked to interact with strangers on public transit during their commute—compared to those asked to commute as normal—experienced a boost to their emotional well-being (Epley & Schroeder, 2014). Importantly, commuters did not expect these benefits of interacting with strangers. This pattern of findings suggests that people may be frequently forgoing opportunities to boost their well-being through casual conversations without being aware of these missed opportunities. Thus, while people may be compelled to resist using their phones when interacting with their family or friends, people may be all too willing to distract themselves with their phones when they have opportunities to interact with mere strangers.  Portable devices like smartphones may affect the benefits people reap from casual social interactions in more than one way. To the extent that phones serve as sources of entertainment, these portable computers may make users less likely to seek casual in-person interactions when bored. In addition to reducing the quantity of casual interactions, phones may reduce the quality of ongoing interactions. Because people check their phones anytime and anywhere (Pew Research Center, 2012; 2015), phones may disrupt the flow of ongoing interactions, preventing users from realizing the potential of casual social interactions to boost relatedness and well-being.  But do people really use their phones frequently enough to interfere with occasional opportunities to spark friendly conversations with strangers? In a recent experience sampling study, more than a thousand smartphone owners across the United States were asked twice a day over the period of a week to report where they had used their phones in the preceding hour (Pew  36 Research Center, 2015). Even within this 14-hour snapshot of an entire week, about half of the respondents reported using their phones while waiting in line (53%), spending time at a community place (51%), and walking from place to place (50%). These findings suggest that people use their phones in public frequently enough that phone use may at least sometimes replace or interfere with casual social interactions.  To illustrate how phones may interfere with casual social interactions, imagine two strangers waiting at the doctor’s office. Perhaps because one patient got bored with browsing through yet another travel magazine, she initiates a conversation with the other patient. The conversation starts with small talk, but progresses to more meaningful topics. Just as the patients discover that they are both doing research, ping(!): One of them receives a message and decides to respond—it will only take a moment after all. As the patients are resuming the conversation, another ping announces the arrival of another message. By the time the patients are giving yet another try at having an engaging conversation, the doctor calls one of them in. Had it not been for their connected gadgets, our protagonists could have discovered common research interests or even exchanged emails to follow up.  The above example illustrates a type of casual interaction during which people are talking to each other simply to pass the time—whether it is while waiting for an appointment or while waiting for the bus. In another type of interaction with novel social partners, people are actively seeking to establish new relationships with others. Think professional networking events, block parties, and dorm ice-breakers. In such situations, people may be motivated to limit their phone use, potentially minimizing any negative effects that phones could have. At least for some, however, the compulsion to check may often override other motivations. Internationally, 17% of  37 adults check their mobile devices during every single meal, regardless of whom they are dining with—a number that almost doubles (29%) in young adults, ages 18–25 (Qualcomm, 2013).  But can checking a portable device once or twice during lunch do that much harm to a social interaction? Preliminary research suggests the answer is yes. Minimal use—and even the mere presence of phones during social interactions—has been shown to compromise how connected people feel to each other during social interactions (Misra, Cheng, Genevie, & Yuan, 2014). In a naturalistic observational study, researchers went to coffee shops and observed pairs of customers for 10 minutes while they were interacting with each other. The researchers recorded whether any of the customers in each pair placed a mobile device (e.g., smartphone) on the table or held one in their hands during the 10-minute interaction. Participants were then asked to indicate how socially connected they felt to each other. People felt less socially connected when they interacted with each other while having their mobile devices placed on the table than while having their mobile devices put away in their purses or pockets.  Of course, the above findings are essentially correlational because participants were not randomly assigned to have their phones or not. Experimental research has, however, also provided preliminary evidence that phones may hinder people from cultivating a sense of connectedness during in-person interactions. In one experiment, pairs of strangers asked to discuss a meaningful topic were randomly assigned to talk in the presence of either a mobile phone or a paper notebook placed unobtrusively on a table next to them (Przybylski & Weinstein, 2013). As compared to pairs who talked in the presence of the notebook, those who talked next to the phone felt less socially connected to each other at the end of their interaction. These findings suggest that the simple presence of a mobile phone—even one that does not belong to any of the people interacting—can hamper opportunities to cultivate a sense of  38 connectedness. Experimental research has yet to examine, however, whether access to one’s own phone during a social interaction boosts or diminishes social connectedness.  Why should minimal use of phones and even the simple presence of phones prevent people from harvesting the benefits of in-person interactions? One possibility is that even the simple presence of phones may be enough to distract people from their in-person social interactions. Another, related possibility is that these devices may remind their users of all the other people they could be spending time with, and all the other things they could be doing instead. To illustrate how even minimal phone use may compromise opportunities to cultivate new social connections, consider Jim, who just moved into a new dorm and is attending an icebreaker lunch with his floor mates. Right as Jim and his peers are settling at their lunch table, Jim receives a message from his best buddies who are asking him if he would like to join them for lunch. Jim says no, of course, but he cannot help but wonder what fun he might be missing out on. The presence of the phone on the lunch table is not helping either, serving as a reminder of the fun time Jim’s friends are currently having without him. By perceiving such opportunity costs of social interactions, users may enjoy interactions less and walk away feeling less related to their peers than they might otherwise have. The present research. We set out to examine how phones would affect interactions with peers both when people have the explicit goal to establish new social relationships and when people are simply passing the time in the presence of others. In Study 3, we asked groups of students to get to know each other over lunch, thus modeling a real-world icebreaker social event. In Study 4, we asked pairs of students to wait for 10 minutes for their study appointment, thus modeling a real-world waiting room situation in which people may not be pursuing the goal to meet new people. In both studies, we manipulated whether participants had access or no  39 access to their phones. By not instructing participants to use their phones any differently than they normally would, we strengthened the external validity of the studies. Specifically, our minimal manipulation allowed us to explore how people would naturally use their phones in each situation and to assess whether such normal phone use would have any impact on their sense of relatedness and emotional well-being.  In both studies, we also explored whether phones may compromise the benefits people reap from social interactions by stimulating a desire for alternative activities. As in Chapter 2, we assessed sense of relatedness as our primary dependent variable. Because relatedness is a basic human need essential for subjective well-being (Reis et al., 2000; Ryan & Deci, 2000), we also explored whether phones would have downstream consequences for participants’ emotional well-being. Because phones can serve as conduits for social interactions with remote social partners, we also wanted to explore whether phones might have potential benefits for their users’ sense of relatedness and emotional well-being. When people are waiting alone and have no opportunities to interact with others in person, phones may boost how socially connected people feel by supplying otherwise unavailable ways to contact others. To explore this possibility, in Study 4, we also manipulated whether people were waiting alone or with another participant. This 2 (phone vs. no phone) X 2 (waiting alone vs. waiting together) design allowed us to examine whether phones might be a source of relatedness and emotional well-being when people were waiting alone, but undermine opportunities to cultivate relatedness and emotional well-being when people were waiting in the company of a peer.   40 3.3 Study 3: Lunch With Peers 3.3.1 Method Participants. We recruited students from the University of British Columbia, who participated for class credit and received lunch during the study. Because of the lack of previous research exploring the effects of access to phones on relatedness and well-being during social interactions, we initially assumed a medium effect size (Cohen’s d = .5). To detect effects of this size, we calculated that we needed a sample of 128 participants based on a priori power analyses (80% power; α = .05, two-tailed). After recruiting this initial sample, we found that the actual effect size on our main outcome—sense of relatedness—was half the effect size we postulated initially (d = .25). Thus, we decided to double our sample size to 256 participants. To allow for possible exclusions, we recruited about 10% more participants. Thus, the final sample consisted of 271 participants (Median age = 19; 77% women). The initial power analyses and the extension of data collection are registered on the Open Science Framework.4  Procedure. Students were invited to the lab in groups of three to six students per session. Participants were informed that their goal was to simply interact and get to know each other. To make the interaction feel as natural as possible, we served food (sushi). All sessions took place between 11 am and 3 pm, thus providing participants with the opportunity to interact with each other during their lunch. Participants were given 40 minutes to get to know each other because pilot testing suggested that 40 minutes provided participants with enough time to comfortably eat their lunch and engage in conversation with each other. The sessions were video recorded with                                                 4 http://tinyurl.com/Kushlev-Lunch-Study     41 the awareness of participants. Due to technical problems with the camera (e.g., low battery), five of the 55 sessions were not recorded (although the camera was present in all sessions). To examine whether smartphones affect the benefits people reap when trying to get to know others, we randomly assigned participants to either have access or have no access to their phones. We assigned all participants from a single session to the same condition, so that participants having lunch together either all had access to their phones or all had no access. In both conditions, the experimenter emphasized that although this is a lab study, they should behave as they normally would while meeting others over food. In the no phone condition, participants were asked to switch their phones off and put them away. In the phone condition, participants were asked to keep their phones on normal settings and keep them on the table. If participants decided not to follow these instructions, the experimenter did not prompt them further so as to avoid raising suspicions about the purpose of the study. Furthermore, participants in the phone condition were not specifically instructed to use their phones during the interactions. By devising such a minimal manipulation, we wanted to see whether even simply having easy access to phones might impact the benefits people reaped from social interactions. To reinforce the sense of participants that they could behave as they normally would while sharing lunch with peers, we allowed participants in both conditions to have access to any other personal belongings they brought with them to the study (e.g., backpack, purse). After participants had interacted for 40 minutes, they completed a questionnaire assessing how they felt during lunch.  Measures. To measure our main dependent variable—sense of relatedness—we asked participants to complete the eight-item Relatedness subscale of the Intrinsic Motivation Inventory (α = .83; Deci et al., 2001; Gagne, 2003). This scale is designed to measure relatedness  42 specifically during interactions with others (e.g., I felt close to my interaction partners). As a broad assessment of how much participants enjoyed the interaction, participants completed the Interest/Enjoyment subscale of the Intrinsic Motivation Inventory (α = .90; Ryan, 1982). The scales for relatedness and enjoyment were anchored at 1–not at all true; 4–somewhat true; 7–very true. In addition to these measures of overall relatedness and enjoyment, participants also indicated how much they liked and trusted each of their interaction partners separately, from 1–not at all to 7–a great deal (Human, Biesanz, Parisotto, & Dunn, 2012). We also assessed how close participants felt to each of their peers, using a self-other overlap measure (Aron, Aron, & Smollan, 1992).  To measure emotional experience, students completed an 18-item scale empirically developed to measure three separate factors of affective experience (Schimmack & Reisenzein, 2002). These factors were valence (pleasant vs. unpleasant), energetic arousal (awake vs. sleepy), and tense arousal (tense vs. calm). Each of these scales included three positive items (e.g., pleasant, wakeful, relaxed) and three negative items (e.g., unpleasant, sleepy, restless). Although we were primarily interested in the effects of phones on affect valence—i.e., mood—this broad assessment of affect allowed us to explore whether the presence of phones would influence other aspects of emotional experience. All items were anchored on a scale from 1–not at all to 7–very much. Composite scores were formed by obtaining the difference between the positive and the negative items of each subscale; as a result, the composite scores vary from –6 to +6. To explore the mechanisms by which the presence of smartphones might impact the benefits people reaped from social interactions, we measured perceived opportunity costs. Specifically, we asked participants to report to what extent they felt they wanted or needed to be  43 doing other activities instead (Kushlev, 2011). As in Chapter 2, we also assessed the quality of participants’ attention with two items from the Attention subscale of the Cognitive and Affective Mindfulness Scale-Revised (CAMS-R; Feldman et al., 2006). The measures of both opportunity costs and attention quality were anchored at 1–not at all and 7–very much.  After indicating how they had felt during the lunch, participants reported how frequently they had used their phones during the social interactions on a scale from 1–not at all to 7–constantly. To objectively assess the frequency of phone use, we also had coders watch the videos and record each time participants touched, typed, or looked at their phones. Coders also recorded how long each participant touched, typed, or looked at their phones, providing a measure of the overall duration of participants’ interactions with their phones. Each video was coded by two independent coders, who were blind to hypotheses. To estimate the reliability of ratings, we used a one-way random model—the most conservative model—and found high intraclass correlations between coders for both the frequency (ICC = .94) and duration of use (ICC = .97). Thus, we averaged the ratings of each coder to form a single frequency composite and a single duration composite for each participant. These objective measures were highly correlated (r = .69, p < .001). Both coded frequency and coded duration were also highly correlated with participants’ own self-reports of their phone use (r = .67 and r = .79, respectively; p’s < .001).  Finally, students were asked to provide demographic information, including age and gender. For exploratory purposes, we also measured personality by asking participants to complete the Ten Item Personality Inventory—TIPI (Gosling, Rentfrow, & Swann, 2003). We were thus able to assess each of the Big Five personality traits: extraversion, openness, agreeableness, conscientiousness, and neuroticism.   44 3.3.2 Results  Analytic strategy. To account for nonindependence between participants within the same session, we employed multilevel modeling (MLM) with participants clustered within lunch group. For all subsequent MLM models we report, we used maximum likelihood estimation with unstructured covariance matrix, treated predictors as fixed effects, and allowed only the intercept to vary as a random effect. All analyses were conducted on SPSS21.  Manipulation checks. Overall, our minimal manipulation—affording participants easy access versus no access to their phones—had a correspondingly small effect on self-reported phone use. On our 7-point scale, participants who had access to their phones reported only one-point higher phone use (M = 2.10, SD = 1.39) than participants who were asked not to use their phones (M = 1.11, SD = .56). This difference, however, was significant (fixed effect estimate = .10, p < .001). Notably, seven participants assigned to keep their phones away reported using their phones during the interactions, with some reporting higher use than the average of participants assigned to have access to their phones (e.g., two no phone participants selected 5 on the 7-point scale). These seven participants shared sessions with participants who followed the instructions not to use their phones; the noncompliance of the seven participants thus effectively created a third condition—one in which people who did not use their phones shared lunch with peers who did use their phones. Unlike in Chapter 2, therefore, (where we kept all participants in the analyses because each noncompliant participant did not affect the experience of other participants), we excluded the seven noncompliant participants and all participants who shared sessions with them—making 10% of the total sample. This left a final sample of 245 participants. Even after excluding participants, condition was not associated with age, gender, and personality traits, suggesting that the exclusion did not substantively compromise random  45 assignment. And importantly, the pattern of presented results was substantively unchanged with or without excluding people who did not follow the experimental instructions. Additionally, 56 participants who had access to their phones reported not using their phones at all during the interaction. Because we did not specifically instruct participants to use their phones, however, we kept these participants in the analyses.  Benefits of the social interaction. Using a series of multilevel modeling analyses (Table 3.1), we first examined whether access to phones influenced the benefits people reaped during the social interaction. Although we found no significant effects on measures of relatedness, liking, trust, enjoyment, and affect, we found consistent trends for all outcomes. As shown in Table 3.1, people with access to phones scored lower on virtually all dependent variables than people with no access to phones, with effects varying from d = −.01 to d = −.20. The average effect size was d = −.08. In short, although we observed consistent negative effects of phones on a wide range of measures, our sample size was not enough to detect significant effects of our minimal manipulation. For the condition effect on relatedness (d = −.11), for example, posthoc power analyses indicated that we had only 14% chance to detect a significant effect given our sample size.       46 Table 3.1. Study 3. Effects of condition: Participants with access to phones reaped consistently lower benefits of the social interaction than participants with no access.   Cohen’s d Fixed effect estimate Random effect  estimate Relatedness −.11 −.11 (.13) .00 (.00) Enjoyment −.06 −.05 (.15) .10 (.06)† Liking −.06 −.05 (.10) .00 (.00) Trust −.07 −.06 (.16) .10 (.07) Self-other overlap  −.04 −.09 (.17) .12 (.08) Mood −.01 −.01 (.16) .00 (.00) Energetic Arousal −.20 −.48 (.35) .79 (.31)* Tense Arousal  −.05 −.09 (.23) .06 (.15) Attention −.10 −.10 (.14) .01 (.05) Opportunity Costs .36 .61 (.22)** .01 (.13) Notes. Fixed and random effects estimates are based on analyses using multilevel modeling. Numbers in parentheses are standard errors. By their nature, Cohen’s d-scores were calculated without accounting for clustering within lunch group. Consequently, d-scores may be smaller for outcomes with random effect estimates > 0, which suggest that clustering matters for estimating the effects of condition.  †p < .10; *p < .05, **p < .01 Attention and opportunity costs. Our minimal manipulation did not produce a significant effect on attention, fixed effect estimate = −.10, p = .47, d = −.10. The presence of phones during the interaction, however, generated greater perceived opportunity costs—a sense that participants wanted or needed to be doing other things instead, fixed effect estimate = .61, p  47 < .01, d = .36 (Table 3.1). Thus, even minimal use of phones seems to result in significantly greater awareness of the opportunity costs associated with making new social connections.  We predicted that phones would reduce how related people feel to others to the extent that phones remind people of other activities they may want to be doing instead. Accordingly, we explored whether by stimulating desire for other activities, the presence of phones may have indirect effects on relatedness. Bootstrapping mediational analyses with 50,000 resamples (Hayes, 2013) indicated that to the extent that people who had access to phones perceived greater opportunity costs than those with no access to phones, people with phones felt less related to others, indirect effect = −.17, 95% CI [−.31; −.05]5 (see Figure 3.1). Similarly, by increasing perceived opportunity costs, the presence of phones indirectly predicted worse mood, indirect effect  = −.20, 95% CI [−.36; −.07]6 (see Figure 3.2). These indirect effects remained significant and substantively unchanged when we controlled for age, gender, and the Big Five personality traits (relatedness: indirect effect = −.16, 95% CI [−.31; −.06]; mood: indirect effect = −.18, 95% CI [−.34; −.07]). In short, to the extent that phones reminded people of alternative activities they might want to be doing instead, phones seemed to obstruct their users from reaping the full social and emotional benefits of interacting with others.                                                   5 These mediational analyses do not account for nonindependence within session. However, the intraclass correlation (ICC) was < .01, suggesting that not accounting for clustering within session would have negligible effects on estimation of significance. 6 ICC = 0, suggesting no clustering within session.  48  Figure 3.1. Study 3. To the extent that participants with access to their phones perceived greater opportunity costs of the interaction, they felt less related to their partners than participants with no access to phones.  Notes. All b’s represent unstandardized regression coefficients obtained through bootstrapping using 50,000 resamples (Hayes, 2013). The range in brackets represents the 95% confidence interval of the indirect effect.   *p < .05; **p < .01; ***p < .001   Figure 3.2. Study 3. To the extent that participants with access to their phones perceived greater opportunity costs of the interaction, they reported lower mood than participants with no access to phones.  Notes. All b’s represent unstandardized regression coefficients obtained through bootstrapping using 50,000 resamples (Hayes, 2013). The range in brackets represents the 95% confidence interval of the indirect effect.   *p < .05; **p < .01; ***p < .001  49 Self-reported phone use. Because of our minimal manipulation and the large number of participants in the phone condition who did not use their phones at all, we explored whether self-reported phone use would predict the benefits people reaped from their interactions (see Table 3.2). We found that people who used their phones more during lunch perceived greater opportunity costs. Turning to measures of relatedness, we found that the more people used their phones during the interaction, the less related they felt to others, the less they liked and trusted the other participants, and the less they enjoyed the interaction overall (Table 3.2)7 While consistent with our hypotheses, these correlational findings may also indicate the opposite causal path: Students who felt less related to their peers tended to reach more frequently for their phones. If this is the case, people who used their phones to offset their lack of enjoyment and sense of relatedness for others may reap some benefits for their well-being. People who reported using their phones more, however, also reported lower emotional well-being: worse mood, lower energetic arousal, and more tense arousal (Table 3.2)8. In short, while interacting with their peers, people who used their phones more reaped fewer benefits associated with social interactions—from relatedness to emotional well-being.                                                     7 Our objective measures of frequency and duration of use showed a similar pattern of relationships to measures of social connection. Importantly, both greater frequency and greater duration of looking, touching, or typing predicted lower sense of relatedness (r’s = −.12 and −.17; p’s = .06 and .01, respectively). 8 Our objective measures of frequency and duration of use showed a pattern of relationships to affect that was similar to the pattern of relationships between self-reported use and affect. Importantly, both greater frequency and greater duration of looking, touching, or typing predicted worse mood (r’s = −.13 and −.22, respectively; p’s < .05).  50 Table 3.2. Study 3. Effects of self-reported phone use.  Correlation Coefficient (r) Fixed Effect Estimate Random Effect  Estimate Relatedness −.17** −.15 (.05)** .01 (.04) Enjoyment −.17** −.16 (.06)** .13 (.06)* Liking −.17* −.11 (.04)* .00 (.00) Trust −.13* −.12 (.06)* .09 (.07) Self-other overlap  .01 .01 (.06) .17 (.09)† Mood −.22*** −.27 (.07)***  .01 (.07) Energetic Arousal −.30*** −.52 (.12)*** .64 (.29)* Tense Arousal  .14* .20 (.10)* .09 (.15) Attention −.27*** −.26 (.06)*** .03 (.05) Opportunity Costs .27*** .40 (.09)*** .00 (.00) Notes. Fixed and random effects estimates are based on analyses using multilevel modeling. Numbers in parentheses are standard errors. By their nature, correlation coefficients (r) were calculated without accounting for clustering within lunch group. Consequently, r-values may be smaller for outcomes with random effect estimates > 0, which suggest that clustering matters for estimating the effects. †p < .10; *p < .05, **p < .01; **p < .001 Although the above pattern of findings suggests that more frequent phone use could compromise the benefits of social interactions, these correlations could be due to individual differences between participants. Neuroticism, for example, predicted higher self-reported phone use (r = .13, p = .05) and conscientiousness predicted lower cell phone use (r = −.16, p < .05). And while neuroticism predicted marginally lower relatedness (r = −.12, p = .06) and lower mood (r = −.17, p < .01), conscientiousness predicted marginally higher mood (r = .12, p = .07). Even after controlling for personality, as well as age and gender, multilevel modeling analyses  51 showed that people who used their phones more still felt a lower sense of relatedness (fixed effect estimate = −.14, p < .01) and reported worse mood (fixed effect estimate = −.21, p < .001). Overall, this pattern of results suggests that above and beyond individual differences, having access to one’s phone may hamper opportunities to cultivate a sense of relatedness and emotional well-being.  3.3.3 Discussion  During lunch with peers, people who were randomly assigned to have access to their phones felt that they had more alternative activities they could be doing than people who had their phones tucked away. To the extent that the presence of phones fostered such desire for alternative activities, people felt less related to each other and felt worse during the interaction. Although we did not detect significant main effects of our minimal manipulation on the social and emotional benefits people reaped during the social interaction, all effects of phone presence were notably negative. Furthermore, correlational analyses indicated that to the extent that people did use their phones more during the interaction, they felt less related and reaped fewer emotional benefits from interacting with their peers.   Because participants were explicitly asked to interact with each other but not explicitly asked to use their phones, it is unsurprising that we found weak effects of our manipulation. Furthermore, many participants reported that they felt it would have been rude to use their phones while they were meeting people for the first time. Indeed, 56 participants (41%) of those who were allowed to use their phones chose to refrain from taking advantage of this opportunity. Thus, when people are motivated not to use their phones because of social norms, many people may successfully control their phone use, minimizing any negative effects of phones on social connectedness and emotional well-being. Still, most participants (59%) did use their phones  52 despite the social norms of phone use associated with meeting new people or participating in a lab study. Our findings thus provide preliminary evidence that simply having phones within easy reach may have negative effects on at least some people’s ability to cultivate a sense of relatedness and emotional well-being when meeting new people.  3.4 Study 4: The Waiting Room In Study 4, we further explored whether simply having access to phones could compromise the benefits of casual social interactions. Unlike in Study 3, we created a situation in which participants would not feel obliged to interact with their social partners. Specifically, we devised a real-world waiting room situation and placed no external incentives on participants either to use their phones or to interact with each other. Thus, we wanted to test whether phones might have stronger effects on the social and emotional benefits people reaped from casual social interactions when there were no explicit social norms dictating that phone use was inappropriate. In addition to manipulating whether participants had access to their phones as in Study 3, we manipulated whether they waited alone or together with another participant. This design allowed us to explore whether phones may boost sense of relatedness when people cannot otherwise interact with anybody. We also explored whether phones may provide benefits for people’s emotional experience by providing entertaining activities to pass the time. In addition to measuring overall mood, we also assessed how angry and irritable participants felt. We reasoned that by supplying activities to pass the time, phones might make their users less angry about having to wait.  3.4.1 Method  Participants. We recruited students from the University of British Columbia, who participated in exchange for class credit. As in Study 3, we initially assumed medium effect sizes  53 of the phone manipulation (d = .50). To detect effects of this size, power analyses indicated that we needed 128 participants (power = 80%; α = .05, two-tailed). After collecting this initial sample size, we found small rather than medium effects of the manipulation. Thus, as in Study 3, we decided to double our sample size. We ran participants in pairs for a total of 152 sessions and 265 participants. Thirty-seven participants showed up alone to sessions where they had been randomly assigned to wait together with their partner. Thus, our final sample consisted of 228 participants who were successfully assigned to condition (Median age = 20; 69% women). The initial power analyses and the extension of data collection are available on the Open Science Framework.9  Procedure. All participants were scheduled to arrive in pairs for any one timeslot. After arriving in the lab for the study, participants were asked to leave their belongings in a locked cabinet. Participants were randomly assigned to either leave their phones along with their other belongings (no phone condition) or to keep their phones on them during the study (phone condition). All participants were then asked to take a seat in a room and wait for the study to begin. Because this was the first study using a waiting paradigm to explore the social effects of phone use, we wanted to isolate the strongest effects of phones we could detect; we thus provided participants with no access to other materials they could use to pass the time (e.g., magazines). To manipulate the availability of social partners while waiting, participants either waited alone or together with another participant in the same room. Conditions were assigned on a per-session basis, so that participants either both had access to their phones or both did not have access.  To create a realistic experience of waiting for an appointment, participants were led to                                                 9 http://tinyurl.com/Kushlev-Waiting-Study   54 believe that due to scheduling issues, the experimenter was running a few minutes late and would start the study as soon as possible. To avoid provoking unnecessary stress while waiting, the experimenter informed participants that the study would be completed on time despite this delay. All participants were left in the room to wait for the study to begin with the door closed. After 10 minutes, the experimenter re-entered the room, apologized for the delay, and administered the questionnaires.  Measures. To assess how related participants felt to others, we used the Social Connectedness Scale (Lee, Draper, & Lee, 2001). Unlike the relatedness scale in Study 3, which assessed how socially connected people felt specifically to their lunch companions, the scale in the present study assessed participants’ general sense of social connectedness. For example, participants rated how close they felt to others in general and how connected and in tune they felt with the world around them. This scale, thus, allowed us to assess and compare how socially connected participants felt both when they waited alone and when they waited in pairs. Because interactions with close others were beyond the scope of the present investigation, we did not include items from the original scale that specifically assessed connection with friends (e.g., I felt understood by the people I know). Because we wanted to assess state sense of relatedness, we also excluded some items that measured trait sense of relatedness (e.g., I fit in well in new situations). The eight remaining items demonstrated good reliability (α = .91), which was commensurate with the reliability of the full original scale (α = .94). Participants indicated their agreement with each statement on a scale from 1–strongly disagree to 7–strongly agree.  To assess emotional well-being, we used the same affect measure as in Study 3 (Schimmack & Reisenzein, 2002), which includes subscales assessing mood (e.g., pleasant vs. unpleasant), energetic arousal (e.g., awake vs. sleepy), and tense arousal (e.g., tense vs. calm).  55 All items were anchored on scales from 0–not at all to 6–very much, but because composite scores for each subscale represent the difference between positive and negative items of the subscale, the scores vary from −6 to +6. Additionally, to explore whether the presence of phones may have positive effects on emotional experience by reducing anger, especially when people are waiting alone, we used two items from the hostility subscale of the PANAS-X: irritable and angry (0–not at all; 6–very much; α = .70).  Finally, to explore the effect of the manipulation on behavior, we asked participants to rate how much they used their phones while waiting and how much they talked to the other student: 0–none of the time; 1–a little bit of the time; 2–some of the time; 3–most of the time; 4–the whole time.  3.4.2 Results  Analytic strategy. We employed multilevel modeling (MLM) to estimate whether the responses of participants who waited together were nonindependent. To avoid underestimating nonindependence due to the independence of responses by participants who waited alone, participants who waited together were clustered together, whereas participants who waited alone were all clustered within one single group. Thus, the models estimate nonindependence by taking into consideration the nonindependence of responses separately for each pair of participants waiting together, while treating the independence of participants who waited alone as a single cluster. Because condition—waiting alone versus together—was used to determine how participants were clustered, this approach may underestimate the effects of partner presence, as well as the interaction effects between partner presence and phone access. In statistical terms, using a variable X to cluster participants may underestimate the fixed effects of X (i.e., the effect of interest) by splitting the variance explained by X between the fixed and random effects in the  56 model. Thus, whenever we find evidence for nonindependence, we present results from both the MLM model and regular ANOVA. Whenever we find no evidence of nonindependence, we present results from the ANOVA only. We considered any intraclass correlation (ICC) > 0 as evidence for nonindependence of responses.  In all MLM models, we used maximum likelihood estimation with unstructured covariance matrix, treated predictors as fixed effects, and allowed only the intercept to vary as a random effect. We used SPSS21 for all analyses. In each case, we first present the results of a model estimating the effect of each condition controlling for the other condition. We then estimate interaction and simple effects by predicting each dependent variable from each of the two conditions and their product.   Manipulation checks. Because we manipulated whether people have any access to their phones, we should expect no phone use in the no access condition. It is still possible, however, that participants who had access to their phones did not use them at all. As a result, we proceed to examine whether our phone access manipulation resulted in any greater phone use. We found no evidence for nonindependence when predicting how much people used their phones (ICC = 0). A two-way ANOVA with the phone access and partner presence manipulations as predictors showed a significant main effect of access to phones, F(1, 221) = 79.94, p < .001. As in Study 3, however, people with access to their phones reported using their phones only slightly more (M = 1.40, SD = 1.44; Median = 1–a little bit) than people with no access (M = .15, SD = .63; Median = 0–not at all)10. In addition, people who waited alone used their phones more (M = 1.02, SD =                                                 10 Seven participants reported using their phones during the waiting period despite having left their phone in a locked cabinet prior to entering the waiting room. These participants may have either misunderstood the question or reported their phone use before the beginning of study. Specifically, because some of these participants arrived before their scheduled time, they may  57 1.41) than people who waited together (M = .45, SD = .98), F(1, 221) = 22.36, p < .001. Finally, we found a significant interaction effect between phone access and partner presence, F(1, 221) = 15.50, p < .001. Specifically, there was a larger difference in phone use between people with access and those with no access to their phones when waiting alone (M = 1.97, SD = 1.45 vs. M = .19, SD = .66) than when waiting together (M = .77, SD = 1.14 vs. M = .09, SD = .58). Importantly, however, simple effects analyses indicated that these differences in phone use between the phone and no phone conditions were highly significant both when people were waiting alone and when they were waiting together (p’s < .001).  Turning to how much participants reported interacting with each other, we explored whether people who waited together interacted with each other at all. First, we tested whether the participants who waited together reported similar duration of their interactions. Indeed, we found strong evidence for nonindependence (ICC = .88). Put simply, to the extent that people who waited together interacted with each other, their reports of the duration of the interaction were highly correlated. MLM analyses indicated that that the presence of another participant resulted in spending more time engaging in social interactions, but because the fixed effect of this condition is partially subsumed within the significant random effect of the model, the fixed effect was only marginal, fixed effect estimate = 2.42, p = .08. Yet, participants who waited together reported talking to each other substantially more (M = 2.54, SD = 1.43; Median = 3–most of the time), than those who waited alone (M = .12, SD = .50; Median = 0–not at all).11 Indeed, a two-                                                                                                                                                       have had to wait for the study before the 10-minute waiting period within the study procedure. We kept those participants in subsequent analyses because it would have been highly unlikely that they actually used their phones during the study waiting period. 11 Even though they waited alone in the room, nine participants reported that they had talked to the other participant who was scheduled for the same study session. These participants may have  58 way ANOVA indicated a highly significant effect of waiting with a partner on the duration of interaction, F(1, 221) = 306.13, p < .001. Regardless of the type of analyses, the presence of phones had no effect on the duration of the social interaction: fixed effect estimate  = .00 and F(1, 221) = .01,  p’s > .93. There was also no interaction effect between partner presence and phone access: fixed effect estimate = .02 and F(1, 221) = .01, p’s > .92.  Social Connectedness. We found no evidence for nonindependence when predicting relatedness from condition (ICC = 0). As hypothesized, a two-way ANOVA indicated a main effect of phone access on how socially connected people felt: People who had their phones while waiting felt less socially connected than people who waited with no access to their phones, F(1, 221) = 4.67, p = .03 (see Figure 3.3). Interestingly, we found no main effect of the presence of others, F(1, 221) = .85, p = .36. This lack of effect may be due to our measure, which was constructed to capture participants’ general sense of social connectedness and therefore may not have been influenced by the brief interactions they had with each other. We found no interaction between phone access and partner presence, F(1, 221) = .23, p = .63. Specifically, regardless of whether participants waited alone or together, the presence of phones was associated with lower sense of relatedness (see Figure 3.3). Notably, however, we observed a somewhat larger negative effect of phone access when people were waiting together (d = −.39) than when people were waiting alone (d = −.22).  The above pattern of findings (Figure 3.3) suggests that the nonsignificance of the interaction effect may be due to insufficient power. In particular, we calculated sample size with the assumption that we would observe a cross-over interaction between conditions. Instead, we                                                                                                                                                        misunderstood the question and included in their answers the time they talked to the other participant before the beginning of the study. We kept these participants in the analyses.   59 observed a reduction of the effect of phones when people were waiting alone as compared to when they were waiting together, but in both scenarios, the effects were in the same direction. To detect an interaction based on effect reduction, one may need 800% more subjects per cell than to detect an interaction based on effect reversal (Simonsohn, 2014). Thus, we decided to explore further the effect of phones with a series of planned comparisons. Social Connectedness  Figure 3.3. Study 4. Effects of phone access and partner presence on social connectedness. Note. Error bars represent standard error of the mean.  Planned comparisons indicated that people who waited together without access to phones felt more socially connected than people in all other conditions although this effect was marginal F(1, 221) = 3.74, p = .06. Furthermore, there was no difference in social connectedness between people who waited together and had access to their phones and people who waited alone with or without their phones, F(1, 221) = .11, p = .74. In other words, phones decreased how socially connected people felt when they had the opportunity to interact with others to the level of social connectedness they experienced when they were alone. This overall pattern of results is 4!4.2!4.4!4.6!4.8!5!5.2!5.4!5.6!5.8!6!Wai-ng!Alone!! Wai-ng!With!Partner!No!Phone!Phone! 60 consistent with our hypothesis that people who have access to their phones may be missing out on casual opportunities to enhance their feelings of connection with others.  Affect. We found no evidence for nonindependence of responses within dyads when predicting mood from condition (ICC = 0). As with social connectedness, a two-way ANOVA indicated that the presence of phones predicted lower mood: Participants waiting with their phones felt worse than those who waited without their phones, F(1, 220) = 3.27, p = .04. Consistent with past research documenting the emotional benefits of interacting with others in person, we also found a main effect of waiting in the company of another participant: People waiting together felt better than those waiting alone F(1, 220) = 5.09, p = .03. We found no interaction between the partner presence and phone access, F(1, 220) = 1.21, p = .27. As shown in Figure 3.4, however, the negative effect of phones was larger for people who waited in the company of a peer (d = −.47) than for people who waited alone (d = −.13).  Mood  Figure 3.4. Study 4. Effects of phone access and partner presence on mood. Note. Error bars represent standard error of the mean.  2!2.2!2.4!2.6!2.8!3!3.2!3.4!3.6!3.8!4!Wai-ng!Alone!! Wai-ng!With!Partner!No!Phone!Phone! 61  In contrast to mood, we found no effects of either condition on energetic arousal (ICC = .27) and tense arousal (ICC = 0) during the waiting period. Specifically, phone access did not predict differences in energetic arousal, fixed effect estimate = −.02, p = .94, and neither did partner presence, fixed effect estimate = .50, p = .75; there was also no interaction between phone access and partner presence, fixed effect estimate = −.15, p = .76. Phone access also did not predict differences in tense arousal, F(1, 220) = .08, p = .78, and neither did partner presence, F(1, 220) = .04, p = .83; there was also no interaction between phone access and partner presence, F(1, 220) = .09, p = .76. We did, however, find a main effect of phones on anger (ICC = .00), but contrary to our predictions, people who had their phones on them felt angrier than people without their phones, F(1, 220) = 4.31, p = .04 (see Figure 3.5). There was no main effect of waiting alone or together, F(1, 220) = .01, p = .93, and no interaction effect between the two manipulations, F(1, 220) = .59, p = .44. Overall, then, phones seem to hinder users from reaping the emotional benefits of social interactions and even compromise well-being by inducing anger. Anger  Figure 3.5. Study 4. Effects of phone access and partner presence on anger.  Note. Error bars represent standard error of the mean.  0!0.2!0.4!0.6!0.8!1!1.2!1.4!1.6!1.8!2!Wai-ng!Alone!! Wai-ng!With!Partner!No!Phone!Phone! 62 Perceived opportunity costs. As in Study 3, we explored whether phones may prompt a desire for other things people could be doing instead (ICC = .25), but MLM analyses showed no effect of phones on such perceived opportunity costs, fixed effect estimate = -.27; p = .30. We also found no main effect of waiting alone or together, fixed effect estimate = -.85, p = .41, and no interaction effect, fixed effect estimate = -.13, p = .81.  Mediation analyses. The presence of phones undermined opportunities to cultivate both feelings of social connectedness and positive affect while waiting. Consequently, we wanted to explore whether the negative effect of phones on mood could be explained by the negative effect of phones on social connectedness. Using bootstrapping with 50,000 resamples (Hayes, 2013), we ran a mediational model predicting mood from the phone manipulation while using social connectedness as a mediator12 (Figure 3.6). Controlling for whether people were waiting alone or together, we found that sense of connectedness completely mediated the effect of phone presence on mood, indirect effect = −.25, 95% CI [−.51; −.02], leaving a nonsignificant direct effect of phone access on mood, b = −.25, 95% CI [−.68; .19]. Thus, by reducing how connected people felt to others, phones compromised opportunities to cultivate feelings of emotional well-being.                                                    12 The estimates of these mediational analyses should be unaffected by nonindependence because the intraclass correlation for the model was 0.  63   Figure 3.6. Study 4. Social connectedness significantly mediates the negative effect of phone access on mood.  Notes. All b’s represent unstandardized regression coefficients obtained through bootstrapping using 50,000 resamples (Hayes, 2013). The range in brackets represents the 95% confidence interval of the indirect effect.   *p < .05; **p < .01; ***p < .001 3.4.3 Discussion  Extending the findings of Study 3, Study 4 demonstrated that phones were detrimental to cultivating a sense of relatedness and positive mood when people were waiting together. Contrary to our predictions, however, phones did not help individuals feel more connected when they were waiting alone. Specifically, both when waiting alone and when waiting together, people with phones felt less related and reported lower mood than people without phones. Thus, in addition to undermining the benefits people could otherwise reap from casual interactions, phones seemed to directly lower relatedness and mood. Providing further evidence that phones can directly compromise well-being, phones also made participants angrier regardless of whether they were waiting alone or together.  64 Although the direction of the phone effects were the same regardless of whether people were waiting alone or together, the effects differed in magnitude. Specifically, planned comparisons indicated that the negative effects of phones were more pronounced when people had the opportunity to interact with others in person than when they were waiting alone. In short, we found evidence that phones undercut people’s chances to cultivate a sense of connectedness and boost emotional well-being when in the company of a stranger. Additionally, we found suggestive evidence that phones may directly compromise connectedness and emotional well-being at least in the confined space and time frame of waiting for an appointment.  3.5 Discussion In two experiments, we gave university students the opportunity to interact with each other while they could either use their phones as they pleased or had no access to their phones. As in a real-world icebreaker event, participants in Study 3 were instructed to try to get to know one another. As in a real-world waiting room situation, participants in Study 4 were simply left to pass the time while waiting for their study appointment. Although we did not explicitly ask participants in either study to use their phones, participants who had access to their phones used their phones slightly but significantly more than people who did not have access to their phones. While using their phones minimally, participants in Study 3 actively interacted with one another for 40 minutes. Perhaps unsurprisingly then, phones produced correspondingly small effects on relatedness and mood, which our study was not adequately powered to detect. Notably, however, these statistically insignificant effects of phone access were consistently negative. Extending this consistent pattern of findings in Study 3, Study 4 detected significant negative effects of phones on both relatedness and mood. Together, the consistent findings of Studies 3 and 4 point to the  65 conclusion that phones could compromise the benefits people reap from interactions even with mere strangers.  Despite the consistent negative effects of phone access across the two studies, it is notable that people reported minimal phone use across the studies. And in Study 3, a large proportion of participants did not use their phones at all. These findings suggest that when people are motivated by social norms to refrain from using their phones, they can successfully do so. Thus, while our results suggest that phones can prevent people from reaping the benefits of casual social interactions, many people seem to be able to minimize these negative effects. Still, correlational analyses showed that people who used their phones more felt less related and in worse mood at the end of the interaction. These relationships could not be explained by third factors, such as personality and demographic differences between participants. Of course, the causality in these relationship could be bi-directional: People who used their phones more might have felt less related, and people who felt less related might have been more prone to using their phones more. Even if this is the case, however, it is notable that phones did not help users boost their relatedness or mood despite phones’ theoretical capability to connect people with remote social partners. Thus, the results of Study 3 provide initial evidence that phones may sometimes diminish how socially connected people feel when interacting with new social partners, or at the very least fail to boost social connectedness and emotional well-being. Extending the results of Study 3, Study 4 provided further evidence that portable devices can disrupt social interactions. Specifically, people who had their phones available while waiting felt less related and reported worse emotional well-being. Importantly, these effects were mostly driven by the negative effects of phones on the relatedness and mood of people who waited together. This pattern of findings suggests that even brief interruptions by the mini-computers in  66 people’s hands may disrupt the flow of interactions, compromising the benefits they could otherwise reap from those interactions. Replying to a text message takes only a few seconds away from an interaction, and the beep of a reminder for an upcoming event may only take a split second to ignore. But briefly transporting our minds away from the person in front of us may be all that is needed to stem the flow of our conversation. Just as dropping a stone in the path of a newly formed stream of water can stem its formation, so may the popping of a new notification stem the flow of a newly formed social interaction.   Contrary to our predictions, we found little evidence that phones had the power to provide a source of relatedness and positive emotions when people were waiting alone. Of course, this failure of phones to generate a sense of relatedness may be specific to the brief waiting situation we created. Over the period of an entire day, for example, phones may have a net positive effect on how related people feel. Indeed, previous research has shown that people who receive a greater number of phone messages throughout the day feel more socially connected (Pielot et al., 2014). Still, our findings do suggest that phones are less reliable sources of relatedness than in-person interactions. While a smile and a friendly conversation with the barista at the local coffee shop may be all that is needed to boost how connected people feel right away (Sandstrom & Dunn, 2014), a friendly text and a smiley face sent to a friend may boost how socially connected the sender feels only if and when the receiver responds.  Although smartphones may be unreliable in boosting relatedness in a short time frame, these devices provide reliable access to other activities that may satisfy other psychological needs. Research has shown, for example, that playing video games—a common activity on smartphones (Shaul, 2013)—can effectively satisfy basic psychological needs, such as a sense of competence (Przybylski, Weinstein, Murayama, Lynch, & Ryan, 2012; Ryan, Rigby, &  67 Przybylski, 2006). Yet, we found that people who waited alone did not experience any boost to their well-being when they had access to their phones. This lack of positive effects may, of course, be specific to situations like the one participants experienced in Study 4. In particular, participants were not told how long they would be waiting, and therefore, they might not have engaged in any activities for long enough to boost their sense of competence. When people know how long they would have to wait to see the doctor or get home on public transit, they may be much more likely to use their mini-computers to immerse themselves into a single engaging activity—whether it is reading a feature article in the New York Times or playing Angry Birds. More broadly, these possibilities highlight the importance of examining how situational factors moderate phone use, thus determining when phones could promote versus undermine subjective well-being.  Although phones generally produced stronger effects in Study 4 than in Study 3, phones significantly stimulated desire for other activities only in Study 3. And, to the extent that phones generated greater perceived opportunity costs, phone access had a significant indirect effect on both relatedness and mood. The stronger effect of phones on perceived opportunity costs in Study 3 highlights the fact that the situation in which phones are used may often determine how and why phones compromise the benefits people reap from their interactions. It is possible, for example, that phones may build impatience with ongoing activities as more time passes by. To be sure, participants in Study 3 spent four times as much time at lunch as participants in Study 4 spent waiting. It is notable, however, that even in the relatively shorter time of waiting in Study 4, people assigned to have access to their phones felt angrier than people assigned to wait without their phones. And although the effect of phones in Study 4 on perceived opportunity costs was not significant, the direction of the effect was the same as in Study 3. Thus, phones  68 may have made participants more impatient in both studies, but the shorter time frame of Study 4 might not have been enough for phones to produce a strong enough desire for other activities. Although we found consistent negative effects of phones across the two studies, the effects in both studies were small, with most effects in Study 3 not statistically significant. Still, if replicated, these small effects may have practical and theoretical significance. Because people use their phones frequently and everywhere, even the small impact of phones on relatedness over many occasions may produce considerable effects in the long term. Furthermore, because people might find it difficult to notice such small effects, phones may have long-term consequences without the awareness of their users. Similarly, because people underestimate the power of casual social interactions to promote well-being (Epley & Schroeder, 2014), people may not make any effort to limit phone use in order to foster such social interactions. In contrast, when people want to spend quality time with their family and friends, they may often make conscious efforts to reduce the use of their portable devices. Paradoxically, then, the small effects of smartphones on casual social interactions may have disproportionately large effects on the net benefits people reap from interacting with others.  Users may not be the only ones to ignore the small effects of portable computing devices on the benefits people reap from casual social interactions. Researchers may also be drawn to focus more on how phones are affecting the benefits people reap from interactions with their family and friends than from interactions with strangers. Indeed, research on the benefits of strong social ties vastly exceeds research on the benefits of interacting with weak social ties and strangers (c.f., Sandstrom & Dunn, 2014). Our findings suggest, however, that to accurately assess the net effects of phones on relatedness and well-being, researchers need to study how  69 phones affect interactions with strangers in addition to how they affect interactions with friends and family.  A notable limitation of the present study is the limited age range of our participants: All of our participants were undergraduate students. This prevents us from making any claims about older individuals. Across a wide range of phone functions, young adults use their phones more than their older counterparts (Pew Research Center, 2012; 2015). It is possible, therefore, that phones may have all but negligible effects on the sense of relatedness and well-being of older users. Still, it is worth noting that today’s young adults would be tomorrow’s adults, and therefore, our findings may have greater implications for the well-being of future generations.  In conclusion, in Chapter 3, I extended the findings of Chapters 2, where I found that phones can obstruct users from reaping the benefits of spending time with children. Specifically, the current findings suggest that phones could stem the flow of social interactions both when people are conversing with each other while waiting for an appointment and when people are socializing with their peers over lunch. As a result, phones can sometimes prevent their users from harnessing casual opportunities to cultivate a sense of relatedness and well-being.     70 Chapter 4: Do Smartphones Compromise Opportunities to Cultivate a Sense of Trust By Obviating the Need for People to Rely on One Another?13 4.1 Synopsis In Chapters 2 and 3, we explored whether smartphones might affect the social and emotional benefits people reaped when spending time with close others and when forging new social connections. In Chapter 4, we explored whether smartphones might have broader effects on the social fabric of society by obviating the need to rely on the kindness of strangers, thus compromising opportunities to cultivate a sense of trust in others. In Study 5, we analyzed a nationally representative dataset from the World Values Survey to examine whether people who used their phones more frequently to obtain information trusted others less. In Study 6, we randomly assigned students to look for an unfamiliar building on campus either by using their phones or without using their phones, and then we asked them to report how much they trusted strangers.  4.2 Introduction In 2013, approximately 6 billion people worldwide had access to mobile phones, far surpassing the 4.5 billion with access to basic sanitation (ITU, 2013; WHO, 2014). And in the time it took to type this sentence, about 1000 smartphones were shipped to new owners (The Economist, 2015). Smartphones provide unprecedented access to information, enabling individuals to harness the full resources of the Internet virtually anytime and anywhere. But could this ubiquitous access to information carry unforeseen consequences for the fabric of social life?                                                   13 A version of this chapter has been prepared for publication: Kushlev, K. & Dunn, E. W. (in prep). Do smartphones compromise trust by obviating the need for people to rely on one another?  71 We propose that by enabling people to rely on technology for information, smartphones may obviate the need for people to rely on each other, thereby undermining opportunities to cultivate a sense of trust. Across disciplines, from sociology and psychology to economics and medicine, trust is viewed as critical for individual well-being, economic prosperity, and even physical health (DeNeve & Cooper, 1998; Erikson, 1959; Fukuyama, 1996; Fiske, 2014; Helliwell & Wang, 2011; Kawachi, Subramanian, & Kim, 2007; Knack & Keefer, 1997; Sztompka, 1999). Receiving help from other people, such as getting directions from a friendly stranger, may provide a mechanism for building a sense that others can be trusted. Although transformative technologies—from the mechanical watch to the automobile—have generated concerns about unintended consequences throughout history (Marvin, 1998; Putnam, 1995), we are uniquely positioned to study the social effects of the current technological revolution as it unfolds. Specifically, drawing both on data from a nationally representative survey (Study 5) and on data from a controlled experiment (Study 6), we investigate whether relying on smartphones for information predicts lower trust in others.   4.3 Study 5: World Values Survey In Study 5, we analyzed data from a large nationally representative sample of Americans in Wave 6 of the World Values Survey. Respondents were asked to indicate how frequently they relied on their mobile phones and on other sources for obtaining information, as well as how much they trusted people of different groups—from family to neighbors and strangers. We were thus able to examine our hypothesis that using phones as a source of information would predict lower trust towards other members of society (e.g., neighbors, strangers).   72 4.3.1 Method Participants. We used data from Wave 6 of the World Values Survey (WVS) of 2232 respondents (Median age = 46; 52% female); respondents were polled in 2011. Out of these respondents, 45 did not respond to the question about how frequently they used their mobile phones for information, leaving 2187 participants for analyses on this critical variable (Median age = 46; 52% female). We applied the weights provided by WVS, thus adjusting the sample to represent the US population.   Measures. Respondents were asked to indicate how frequently they relied on various sources to obtain information (1–daily, 2–weekly, 3–monthly, 4–less than monthly, 5–never). We recoded the scales so that higher scores indicated greater frequency of obtaining information through each source. In addition to mobile phones, the sources included daily newspapers, printed magazines, TV news, radio news, email, the Internet, and friends/colleagues. In order to form a composite score indicating people’s overall tendency to seek information using methods other than mobile phones, we calculated the average of all methods of obtaining information except mobile phones (α = .72).   Participants were also asked to indicate how much they trusted people of various groups:   1–completely, 2–somewhat, 3–not very much, 4–not at all. We recoded the scales so that higher scores indicated higher trust in people of each group. Specifically, people were asked how much they trusted their family, neighborhood, people they knew personally, people they were meeting for the first time, people of other religions, and people of other nationalities. For convenience in describing the findings, we use strangers in lieu of ‘people you met for the first time’, and neighbors in lieu of ‘neighborhood’.   73  Respondents were also asked to report their age, sex (male, female), race/ethnicity (e.g., White, Black, East Asian, Hispanic), and employment status (e.g., full-time, part-time, retired, student). They also reported their highest level of education on a scale from 1–no formal education to 9–university degree. In addition, respondents estimated what income group their family belonged to within their country on a scale from 1–lowest group to 10–highest group. Full information on all variables can be obtained from the WVS website.14  4.3.2 Results  Descriptives. As shown in Table 4.1, respondents most frequently relied for information on TV news, family/colleagues, and the Internet. As compared to most other methods of obtaining information, phones represented a relatively less frequent method of accessing information. Notably, however, the relatively low use of phones for accessing information may be due to respondents who were owners of simple mobile phones with no access to the Internet. As shown in Table 4.1, the different methods of obtaining information were generally positively associated with each other.   Next, we explored how much participants trusted people of various groups.  Unsurprisingly, people most trusted members of their own family and people they knew personally (see Table 4.1). People least trusted strangers. Trust scores for all groups (e.g., strangers, family) were positively related (see Table 4.1).                                                14 http://www.worldvaluessurvey.org/WVSDocumentationWV6.jsp  74 Table 4.1. Study 5. Table of correlations and descriptive statistics.   Information Source Trust  News-papers Maga-zines TV news Radio news Mobile phones Email Inter-net Friends/ colleagues Strangers Neigh-bors People of another religion People of another nationality Family Familiar others Mean                      (Standard Dev.) 3.27 (1.53) 2.51 (1.10) 4.17 (1.22) 3.53 (1.48) 2.58 (1.75) 3.32 (1.61) 3.89 (1.43) 4.02     (1.14) 2.20  (.71) 2.76  (.68) 2.71  (.68) 2.66  (.68) 3.65  (.61) 3.21  (.61) Correlations (r)               Newspapers 1 .478*** .385*** .227*** .022 .153** .068** .164*** .123*** .172*** .153*** .110*** .094*** .130*** Magazines  1 .249*** .274*** .202*** .272*** .251*** .274*** .117*** .158*** .124*** .121*** .083*** .107*** TV news   1 .291*** .044* .114*** .064** .227*** .056** .159*** .129*** .081*** .130*** .097*** Radio news    1 .152*** .203*** .225*** .298*** .073*** .082*** .095*** .069** .082*** .081*** Mobile phones     1 .500*** .386*** .340*** −.073*** −.050* −.079*** −.113*** .025 −.024 Email      1 .678*** .447*** .071*** .079*** .098*** .058** .062** .096*** Internet       1 .455*** .051* .045* .065** .051* .080*** .091*** Friends/ colleagues        1 .126*** .147*** .169*** .141*** .130*** .201*** Trust in strangers         1 .467*** .509*** .502*** .195*** .409*** Trust in neighbors          1 .446*** .394*** .331*** .468*** Trust in people of another religion           1 .747** .238** .424** Trust in people of another nationality            1 .229** .418** Trust in family             1 .380** Trust in familiar others              1   *p < .05; **p < .01; ***p < .001 75 Main analyses. As hypothesized, correlational (Table 4.1) and linear regression analyses (Table 4.2) indicated that the more people relied on their phones for information, the less they trusted neighbors and strangers, as well as outgroup members such as people from other religions and people from other nationalities. Importantly, relying on mobile phones for information had no bearing on how much people trusted members of their own family and people whom they knew personally (Tables 4.1 and 4.2). The finding that mobile information had little bearing on how much people trusted their family and familiar others is consistent with our theorizing that the access to mobile information anytime and anywhere erodes trust by obviating the need for people to rely on fellow members of the community, but not on close others. To the extent that news media covers primarily negative stories (e.g., war, terrorism, crime; Robinson, 2007), reading the news may breed distrust regardless of how and where people access the information. In contrast to this possibility, we found that people who obtained information through media other than mobile phones—including radio, TV, newspapers, and even the Internet—trusted others more (Table 4.2). Furthermore, even after controlling for how frequently people obtained information through sources other than their phones, people who more frequently relied on their phones for information still trusted others less (Table 4.2). Demographic factors measured in the survey—age, sex, income, education, employment status, and race and ethnicity—could also not explain why people who frequently sought information on their mobile phones trusted others less (Table 4.2).      76 Table 4.2. Study 5. Regression analyses: Using mobile phones, but not other media to obtain information predicts lower trust.    Standardized Regression Coefficients (Betas)  Trust in… Strangers Neighbors People from another religion People from another nationality Familiar others Family Freq. of using phones for information With no controls1 −.07**  −.05* −.08*** −.11***  −.02 .03  Controlling for using other media2  −.15***  −.15***  −.18***  −.20***  −.11***  −.04   Controlling for using other media & for demographics3  −.08***  −.05*  −.10***  −.14***  −.07**  −.02  Freq. of using other media for information With no controls4 .14***  .19*** .19*** .14***  .18*** .15*** Notes. All numbers are standardized regression coefficients corresponding to β1 from the equations provided below. Demographic factors include age, sex, income, education, employment status, race and ethnicity. Other media include daily newspapers, printed magazines, TV news, radio news, email, the Internet, and friends/colleagues; ratings for those sources of information were averaged to form an overall composite of obtaining information through methods other than phones.  *p < .05; **p < .01; ***p < .001 Regression equations:  1 Trust = β0 +  β1Info-on-phone + ε ;  2 Trust = β0 +  β1Info-on-phone + β2Info-on-other-media + ε ;  3 Trust = β0 +  β1Info-on-phone + β2Info-on-other-media + β3Age + β4Sex + β5Income + β6Education  + β7Full-time-employed + β8Part-time-employed + β9Self-employed + β10Retired + β11Housewife + β12Student + β13Unemployed + β14White + β15Black + β16South-Asian + β17East-Asian + β19Arabic + β20Hispanic + ε ; 4 Trust = β0 + β1Info-on-other-media + ε ;    77  In addition to the demographic factors examined above, the type of region people live in—rural or urban—may be another factor that could explain why mobile phone information is associated with lower trust. Thus, for example, people in rural areas may be less likely to use their phones for information while also being more likely to trust their neighbors. The World Values Survey, however, provides no data on whether respondents reside in an urban or rural area, precluding us from controlling for this factor. The most specific geographical information provided in the survey is the state in which respondents lived at the time of the interview. Combining this geographical information with data from US Census Bureau15, we calculated the percent of people in each US state who live in urban areas and the percent of people who live in rural areas. Thus, for example, most people reside in urban areas in states such as New York (83%) and California (90%), but not in states such as West Virginia (33%) and Montana (26%). This between-state variation in the proportion of residents who live in urban versus rural areas allowed us to examine whether the type of residential area could explain the relationship between trust and using phones to obtain information.  To control for urban and rural population at the state level, we employed multilevel modeling (MLM), treating person as Level 1 and state as Level 2 in the analyses. Specifically, in a series of MLM analyses, we predicted person-level trust in each group (e.g., strangers) from person-level frequency of using phones for information, while controlling for state-level proportion of the population living in urban areas and proportion living in rural areas. For each analysis, we estimated the fixed effects of both person-level and state-level predictors. Just as in a regular regression, if the urban/rural proportion of the population of the state people live in accounts for the relationship between trust and the frequency of using phones for information,                                                 15 https://www.census.gov/geo/reference/ua/urban-rural-2010.html   78 we should find nonsignificant fixed effects of using phones for information on trust. For each model, we also estimated the random intercept, which accounts for clustering within state. A significant random intercept would indicate that trust varies significantly between US states, even after controlling for the urban versus rural proportion of the population within each state.  As shown in Table 4.3 below, the results of the MLM analyses mirrored the findings of the regular regressions presented in Table 4.2. Specifically, even after controlling for variation in the proportion of urban and rural residents between states, people who used phones for information more frequently were less likely to trust strangers, neighbors, people of other religions, and people of other nationalities. Using phones for information was not associated with trust in familiar others or in family (Table 4.3). Overall, this pattern of findings suggests that the relationships between mobile phone information and trust in various groups are unlikely to be due to the type of area people live in.   In sum, Study 5 suggests that people who rely on their phones as sources of information trust strangers, neighbors, and outgroup members less. This pattern of findings provides initial evidence that ubiquitous information may be compromising trust specifically by obviating the need to rely on those around us, such as our neighbors and friendly strangers.     79 Table 4.3. Study 5. Multilevel models: Relying on phones for information as a predictor of trust clustered within state of residence. Model 1: Trust in strangers      Fixed Effects  Estimate Std. Error Df t  p    Intercept (γ00) 2.056 .480 63.57 4.29 < .001    Information on phones  (β1i) −.028 .008 2254.52 −3.26  .001    % urban popul. in state (γ1j) −.009 .005 61.05 −1.78 .080    % rural popul. in state (γ2j) −.010 .007 58.29 −1.58 .119 Random Effects  Intercept (u0j) Estimate .012 Std. Error .006  Wald Z 1.83 p .067  Residual (εij) .493 .015  33.13  < .001 Model 2: Trust in neighbors      Fixed Effects  Estimate Std. Error Df t p    Intercept (γ00) 1.497 .446 70.68 3.36 .001    Information on phones  (β1i) −.017 .008 2248.49 −2.11 .035    % urban popul. in state (γ1j) −.009 .005 67.86 −1.94 .057    % rural popul. in state (γ2j) −.008 .006 65.57 −1.32 .192 Random Effects  Intercept (u0j) Estimate .009 Std. Error .005  Wald Z 1.75 p .081  Residual (εij) .451 .014  33.11 < .001        80 Table 4.2. continued  Model 3: Trust in people from other religions    Fixed Effects  Estimate Std. Error df t  p    Intercept (γ00) 2.281 .437 90.08 5.22 < .001    Information on phones  (β1i) −.030 .008 2247.99 −3.65 < .001    % urban popul. in state (γ1j) −.001 .004 86.55 −.33 .744    % rural popul. in state (γ2j) .000 .006 83.07 .00 .998 Random Effects  Intercept (u0j) Estimate .007 Std. Error .004  Wald Z 1.76 p .079  Residual (εij) .459 .014  33.20 < .001 Model 4: Trust in people from other nationalities    Fixed Effects  Estimate Std. Error df t  p    Intercept (γ00) 2.241 .453 76.28 4.94 < .001    Information on phones  (β1i) −.045 .008 2241.21 −5.52 < .001    % urban popul. in state (γ1j) −.002 .005 73.27 −.36 .721    % rural popul. in state (γ2j) −.006 .007 71.00 −.98 .330 Random Effects  Intercept (u0j) Estimate .010 Std. Error .005  Wald Z 1.95 p .051  Residual (εij) .446 .013  33.10 < .001            81 Table 4.2. continued  Model 5: Trust in friends and acquaintances    Fixed Effects  Estimate Std. Error df t  p    Intercept (γ00) 1.415 .387 63.20 3.66  < .001    Information on phones  (β1i) −.006 .007 2247.23 −.77 .444    % urban popul. in state (γ1j) −.004 .004 60.72 −.97 .337    % rural popul. in state (γ2j) −.006 .006 58.06 −1.02 .312 Random Effects  Intercept (u0j) Estimate .005 Std. Error .004  Wald Z 1.35 p .176  Residual (εij) .373 .011  33.05 < .001 Model 6: Trust in family members    Fixed Effects  Estimate Std. Error df t p    Intercept (γ00) .859 .382 68.75 2.25 .028    Information on phones  (β1i) .008 .007 2257.70 1.05 .295    % urban popul. in state (γ1j) −.004 .004 66.04 −1.11 .269    % rural popul. in state (γ2j) −.008 .005 63.21 −1.51 .136 Random Effects  Intercept (u0j) Estimate .005 Std. Error .004  Wald Z 1.47 p .142  Residual (εij) .358 .011  33.16 < .001 Notes. For all models, we used maximum likelihood estimation with unstructured covariance matrix on SPSS 21.   Generalized model equation: Trustij= γ00 +  β1iInfo-on-phonei + γ1jUrbanj + γ2jRuralj +  u0j  + εij i = person level; j = state level       82 4.4 Study 6: Asking for Directions  Although the results of Study 5 were robust to alternative explanations, causality cannot be inferred from correlational data. In Study 6, therefore, we designed an experiment to test whether relying on phones for information interferes with opportunities to cultivate a sense of trust by obviating the need to rely on others for help. Specifically, we tested whether relying on smartphones rather than on other people to obtain on-the-go information would lead phone owners to forgo opportunities to boost their sense of trust in others. To explore this hypothesis, we randomly assigned participants either to rely on their phones or not to rely on their phones when looking for the location of an unfamiliar building. After the search, participants were asked to report how much they trusted strangers. 4.4.1 Method Participants. Ninety-eight undergraduate students from the University of British Columbia completed the study in exchange for course credit. The total number of participants was determined based on a priori power analyses assuming a large effect size of d = .8. This assumption was based on previous research examining the effects of casual social interactions on sense of belonging (Sandstrom & Dunn, 2014). The power analyses were registered on Open Science Framework.16 Six participants were excluded from the analyses for failing to follow instructions. All six participants were instructed to use their phones, but failed to do so because of various issues (e.g., no battery, no Internet access). This exclusion left a final sample of 92 participants (Median age = 19.50; 84% women). Procedure. We asked each participant to locate an unfamiliar campus building and then complete a short survey. To identify suitable buildings, we gave participants a list of eight                                                 16 http://tinyurl.com/Kushlev-Directions-Study   83 buildings and asked them to indicate whether they were familiar with each of them. We were careful not to reveal the purpose of this building questionnaire, so that participants would not be motivated to provide false information in order to find their assigned building more easily. Thus, only after completing the building questionnaire, participants were told that their goal would be to find one of the buildings on the list. Specifically, each participant was assigned to locate the first unfamiliar building from the list.  We randomly assigned participants to one of two conditions in a between subjects design. Specifically, participants were asked to locate their assigned buildings either by relying on their smartphones or without relying on their smartphones. In the no phone condition, participants left their phone with a research assistant for the duration of the study. In both conditions, participants left their belongings (e.g., backpack) in the lab, thus ensuring that they could not use other electronic devices to obtain information.  The buildings that participants had to find were located .75 to 1.1 km away from the lab, or 10 to 13 minutes by walking. All participants had 30 minutes to find their assigned building. To make sure all participants could keep track of the time even when they did not have their phones on them, we gave participants basic wristwatches. To minimize discomfort during inclement weather, we gave participants umbrellas if it was raining. Participants who did not find their assigned buildings within the allotted time were instructed to return to the lab. Seven participants did not find their building within the allotted time; two were in the phone condition and five were in the no phone condition. These participants were included in the analyses. At the end of their search, participants answered a battery of questions. This questionnaire was administered by a different experimenter from the one who assigned participants to condition.  84 Thus, the experimenter who administered the questionnaire was both blind to condition and blind to hypotheses, which should minimize experimenter effects.  Measures. To assess whether people who relied on smartphones were less likely to rely on other people, we asked participants to indicate how many people they talked to in person in order to find the building. Participants provided their responses on a scale from 0–None to 4–4 or more. To assess whether phones were useful in locating the building, we asked participants to report how difficult it was for them to locate the building, from 0–not at all to 6–very much.  Trust. Because we hypothesized that participants with no access to their phones would rely on strangers to obtain directions, we were mainly interested in assessing trust in strangers.  Specifically, participants were asked to indicate how much they trusted strangers from 1–cannot be trusted at all to 5–can be trusted a lot. This face-valid item from the Canadian General Social Survey (GSS) was our main dependent variable. We also included two additional items from the GSS. As a broad measure of trust, participants indicated how much they trusted people in general (1–most people can be trusted; 2–you cannot be too careful in dealing with people). As a situational measure of trust, participants imagined that they had lost their wallet or purse that contained $200. Participants then rated how likely it was that their wallet or purse would be returned to them with the money inside (1–very likely; 2–somewhat likely; 3–not at all likely). To examine whether our manipulation had an effect across all three measures of trust, we first reverse-scored the latter two items so that higher numbers indicated greater trust. We then standardized each of the three items and computed the mean of the resulting z-scores (α = .60). Social connectedness. To assess social connectedness, we asked participants to report how connected to others and to the world more generally they felt while looking for the building on eight items from the Social Connectedness Scale-Revised (Lee, Draper, & Lee, 2001).  85 Because interactions with close others were beyond the scope of the present study, we did not measure how socially connected participants felt to their friends (e.g., I felt understood by the people I know). The eight selected items demonstrated a strong reliability (α = .86), which was similar to the reliability of the original full scale (α = .94). Participants indicated their agreement with each statement on a scale from 1–strongly disagree to 7–strongly agree.  Mood. To measure participants’ emotional well-being, we used a six-item scale that captured participants’ mood while looking for the building (Schimmack & Rainer, 2002). The scale included three positive items (i.e., pleasant, good, positive) and three negative items (i.e., unpleasant, bad, negative). Participants indicated how they felt on a scale from 0–not at all to 6–very much. Mood scores were calculated by subtracting the average of the three negative items from the average of the three positive items (Schimmack & Rainer, 2002). Mood scores thus varied from –6 to +6, with higher scores indicating better mood.  4.4.2 Results Unsurprisingly, the easy access to information proved useful in finding the buildings. As compared to participants not depending on their phones, participants who relied on their phones found locating the buildings less difficult (M = 3.18, SD = 1.86 vs. M = 1.54, SD = 1.63), t(89) = -4.46, p < .001. With their usefulness, smartphones also obviated the need to rely on other people in the university community. Participants who used their phones talked to fewer people to obtain directions (M = .29, SD = .68, Mode = 0) than participants who could not depend on their phones (M = 2.36, SD = 1.30, Mode = 2), t(66) = −9.32, p < .001.17                                                  17 Degrees of freedom and the t-test value were adjusted because the Levene test for equality of variance indicated unequal variances between experimental groups, F = 25.83, p < .001.  86 Did people pay a social price for relying on their phones rather than on the kindness of strangers? Participants using phones reported lower trust in strangers (M = 2.98, SD = .83) than people without phones (M = 3.31, SD = .70), t(89) = −2.06, p = .04, d = −.44. Combining across measures of trust in strangers, trust in others more generally, and trust in somebody returning a lost wallet/purse, we also found that participants who used their phones reported lower trust, t(89) = −2.25, p = .03, d = −.48 (see Figure 4.1).  Trust  Figure 4.1. Study 6. Effect of phone reliance on trust.  Notes. Bars represent standardized means (z-scores). Error bars represent standard errors.  Mirroring the findings on trust, we also found that people who relied on their phones felt less socially connected (M = 4.55, SD = 1.18) than those who relied on the kindness of strangers (M = 5.03, SD = 1.00), t(89) = −2.10, p = .04, d = −.44. Finally, we explored whether phones affected participants’ mood, but we found no significant difference in mood between participants who relied on their phones (M = 3.03, SD = 2.26) and those who did not (M = 2.80, SD = 2.42), !1#!0.8#!0.6#!0.4#!0.2#0#0.2#0.4#0.6#0.8#1#Phone#No#Phone# 87 t(90) = .47, p = .64, d = .10. Given the extensive past research and theorizing suggesting that social connectedness should predict emotional well-being (e.g., Reis et al., 2000; Ryan & Deci, 2000), this lack of effect on mood suggests that any negative effects of relying on phones through social connectedness may have been offset by the beneficial effects of the convenience provided by phones. In other words, while phones may have compromised emotional well-being through their negative effect on social connectedness, phones may have simultaneously boosted emotional well-being by making it easier for people to find the building.  Mediational analyses using bootstrapping supported the above possibility (Hayes, 2013). Specifically, by making it easier for people to find the building, phones had an indirect positive effect on emotional well-being, indirect effect = 1.07, 95% CI [.55; 1.91]. Accounting for this usefulness of phones left a marginally significant negative direct effect of using phones on emotional well-being, b = −.82, 95% CI [−1.78; .13], p = .09. Could this negative effect on mood be explained by the negative effect of phones on social connectedness? As shown in Figure 4.2, social connectedness significantly mediated the negative direct effect of phones on mood, indirect effect = −.54, 95% CI [−1.15; −.12]. Accounting for the effects of condition on social connectedness and difficulty of finding the building left a nonsignificant direct effect of condition on mood, b = −.28, 95% CI [−1.16; .61], p = .53 (see Figure 4.2). Together, these findings suggest that the emotional benefits that phones afford with their convenience may sometimes be compromised by the emotional costs of missing out on social interactions.  In sum, extending the correlational findings of Study 5, Study 6 provided experimental evidence that by gaining easy access to information through mobile phones, people lost out on opportunities to cultivate trust within their communities.   88  Figure 4.2. Study 6. Indirect effects of relying on phones on emotional well-being through social connectedness and difficulty of finding the building.  Notes. All b’s represent unstandardized regression coefficients obtained through bootstrapping using 50,000 resamples (Hayes, 2013). The range in brackets represents the 95% confidence interval of the indirect effect.   *p < .05; **p < .01; ***p < .001 4.5 Discussion Across a large nationally representative sample of Americans and an experimental study of Canadian university students, we found consistent evidence that relying on phones to obtain information had a negative effect on trust. Specifically, using data from the World Values Survey (Wave 6), we found that people who relied more on their phones to obtain information trusted strangers less. In contrast, relying on phones for information had no bearing on how much people  89 trusted close others, such their family, friends, and acquaintances. Zooming into one common situation in which people rely on their phones for information—seeking directions to an unfamiliar location—we found evidence for one possible mechanism by which phones might compromise opportunities to cultivate a sense of trust. In particular, we randomly assigned students either to use or not to use their phones when seeking directions and found that people who relied on their phones relied less on strangers, subsequently feeling a lower sense of trust in strangers. As compared to participants who did not use their phones, those who used their phones felt a lower sense of social connectedness. In other words, to the extent that people substituted relying on electronic devices for relying on fellow human beings, people missed out on opportunities to cultivate a sense of trust and social connectedness. Going beyond speculation about how the ongoing technological revolution in ubiquitous connectivity is transforming society, therefore, we found empirical evidence that the convenience inherent in always-accessible information might be lowering trust within society.  While participants in Study 6 who relied on their phones to find a building missed opportunities to cultivate a sense of trust in strangers and a sense of social connectedness more generally, these participants also found the search less difficult than participants not relying their phones. In other words, although reliance on phones had social costs, these devices also provided convenience benefits. In terms of how good participants felt while looking for the building, these costs and benefits of phones cancelled each other out. Indeed, we found no main effect of phone reliance on people’s emotional well-being. Mediational models showed, however, that relying on phones had simultaneously a positive effect on well-being by affording convenience and a negative effect on well-being by compromising opportunities to cultivate a sense of social connectedness. These findings suggest that the convenience afforded by smartphones may  90 sometimes fail to boost people’s emotional well-being because of the social costs associated with relying on these devices. Although people may be fully aware of the emotional benefits of the conveniences that their smartphones afford, people may often not expect the emotional costs of feeling less socially connected. Indeed, in a separate experiment, people asked to imagine seeking directions (i.e., the scenario experienced by participants in Study 6) predicted that they would find the building more easily and feel happier when they could rely on their phones than when they could not. Interestingly, people also expected the social costs of relying on their phones, predicting that they would feel less socially connected when they could rely on their phones. Thus, while people seem to have intuitions about the social costs of replacing human interactions with phone interactions, people fail to account for the emotional consequences of such social costs.  In Study 6, we demonstrated the costs to trust and social connectedness of one particular type of phone use—relying on phones to obtain directions while on the go. Although the results of Study 5 suggest that these costs may add up to a overall negative effect on trust within society, it is possible that using phones to obtain other types of information may promote the satisfaction of social needs. In a recent survey, for example, more than half (56%) of American smartphone users reported relying on their phones to obtain information about community events or activities, with about 1 in 5 (18%) doing so frequently (Pew Research Center, 2015). Accordingly, future research should examine whether such types of phone use may help satisfy people’s fundamental social needs.  The focus of the present research has been to document some of the social and emotional costs of connected mobile devices. Accordingly, we have not focused on exploring the many situations in which the usefulness of smartphones may far outweigh any potential social costs.    91 One important direction for future research, therefore, is to explore how the access to mobile information may impact emotional well-being in situations where relying on other people for information may not be possible or desirable. In Study 6, we showed that within the largely safe environment of a Canadian university campus, people did not realize any emotional benefits of being able to rely on their phones. In contrast, a female phone user who has found herself in a dangerous neighborhood, for example, may realize emotional benefits of relying for directions on her phone by being able to avoid talking with strangers she may feel afraid to approach. Additionally, in many other situations, people may not have the option to solicit the help of strangers simply because there are no people present in the physical surroundings. Indeed, when asked what they would have trouble doing without their smartphones, people most frequently report that they would not be able to get directions when they need them (Pew Research Center, 2015). Thus, future research should examine the many situations in which relying on phones for information may be beneficial to emotional well-being. Coda. More than 100 years ago, French philosopher Guillaume Ferrero postulated the Principle of Least Effort: Organisms tend to seek the easiest way to achieve the greatest outcome (Ferrero, 1984). The principle has since been further developed by the linguist, George Zipf (1949), and used in information science, where it has been identified as one of the main principles guiding information seeking behavior (Mann, 1990). Extending this interdisciplinary work, our findings provide evidence for the psychological and social costs of the Principle of Least Effort. By easily accessing information on electronic devices, people may forgo opportunities to foster a sense of trust within their communities. People may thus be well advised to avoid always relying on their useful mobile gadgets, and at least sometimes heed the famous  92 words of Blanche Dubois in Streetcar Named Desire and “depend upon the kindness of strangers.”   93 Chapter 5: General Discussion In the era of ubiquitous connectivity, I set out to investigate whether ultraportable devices like smartphones might sometimes disconnect rather than connect us with one another. Across six studies, I found that ubiquitous digital connectedness could sometimes compromise opportunities to cultivate a sense of social connection with the people right by our side. In Chapter 2, parents randomly assigned to use their phones a lot—compared to those assigned to limit their phone use—felt less socially connected and found less meaning during social activities with their children. Moving beyond the realm of close relationships, in Chapter 3, I provided preliminary evidence that phones could have effects on the sense of social connectedness and emotional well-being people reaped from casual social interactions with strangers and peers. Finally, in Chapter 4, I moved from exploring the effects of phones on the quality of ongoing in-person interactions to exploring the effects of phones on the quantity of social interactions. Specifically, I examined whether relying on phones may be compromising trust in others by obviating the need to rely on fellow community members when needing information, such as directions to an unfamiliar address. Across experimental and correlational studies, relying on phones for information was associated with trusting others less. Armed with these findings, I propose a preliminary theoretical model that outlines the characteristics and processes through which ubiquitous connectivity can compromise or enhance relatedness and subjective well-being (Figure 5.1). I hope my theoretical model will stimulate much needed research that will shed light on the psychological and situational factors that mediate (Figure 5.1) and moderate (Figure 5.2) the effects of portable smart technology on people’s sense of relatedness and subjective well-being.   94 5.1 Quality of Interactions  A quick glance at a notification, a hastily composed email, or a scroll through an Instagram feed—these actions are but a breeze with the powerful computing devices in our hands. Such easy access to unlimited digital worlds can fracture attention, which people could otherwise be dedicating to the people standing next to them. Such attention costs, however, are far from unique to portable devices. Computing devices have been competing for our attention at least since the advent of the personal computer. Answering an email might distract a mother from a meaningful interaction with her child regardless of whether she used a desktop computer or a hand-held device. What makes ultraportable computing devices unique, however, is that they extend the realm of fractured attention from the confined spaces of one’s home or workplace to virtually any location. It is this omnipresence of portable computing devices that makes them unique in their potential to affect the benefits people reap from interacting with others in person (Figure 5.1). Indeed, participants in Chapter 2 felt less socially connected while they were spending time with their children at a summer festival and at a science museum—experiences unlikely to be affected by more stationary computing devices. Specifically, parents felt less attentive when they were assigned to use their phones as much as possible than when they were assigned to use their phones as little as possible. By scattering attention, phones compromised how socially connected parents felt during the experiences they shared with their children and made the experience less meaningful. Thus, the omnipresence of phones obstructed parents from harvesting some of the fruits of spending time with their children. More broadly, these findings suggest that the constant availability of digital worlds can hinder people from harvesting the benefits they can reap in the nondigital world around them.  95 In addition to omnipresence, another critical factor that distinguishes portable computing devices from stationary computing devices is the frequency and intermittence with which people use them. While people would seem to use their desktops computers for longer at any one time than they would their smartphones, people generally use their phones more intermittently, in brief but frequent bursts—more than 100 times a day by some estimates (Meeker & Wu, 2013). While such high frequency of use seems to be the norm with smartphones, observing a person getting up and sitting back in front of her desktop more than 100 times a day would be an odd sight indeed (unless, of course, the person happens to be going through a major writer’s block while trying to write her dissertation). Thus, greater intermittence of use—using devices in short bursts but often—seems to be an essential factor that characterizes smartphones and other portable computing devices (Figure 5.1).  In Chapter 2, I asked parents to use their phones as much as possible, thus preventing me from making any claims about the effects of normal intermittent use on sense of connection and well-being. Although most parents probably used their phones somewhat intermittently in order to keep an eye on their children, some might have dedicated their attention to their phones for longer periods, leaving somebody else (e.g., their partner) to keep an eye on the children. In Chapter 3, however, I explored how people would naturally use their phones during social interactions, thus allowing me to more directly explore whether even brief intermittent use may have an impact on the benefits people reap from social interactions. I found that people reported spending very little time on their phones both when they were getting to know each other over lunch and when they were simply waiting for an appointment. Even such minimal use, however, consistently compromised the potential of social interactions to boost people’s experience. Notably, the negative effects of phones were significant when people simply waited in the  96 presence of another participant (Study 4), but did not reach statistical significance when people were getting to know one another over lunch (Study 3). Indeed, in this latter situation, many participants chose to refrain from using their phones. These findings suggest that social norms dictating when it is inappropriate to use phones may determine whether and when smartphones prevent people from realizing the benefits of social interactions.  Still, the consistency of the negative phone effects on virtually all outcome measures across Studies 3 and 4 suggests that the small effects of even minimal phone use may be practically and theoretically significant.  Why should even intermittent use of minimal duration reduce the benefits people reap from social interactions? Part of the answer may lie with a fundamental characteristic of online digital worlds—the continuous, fast, and constant flow of information (c.f., Rushkoff, 2013). The digital worlds of social media, blogs, and news feeds are in constant flux. Just like a person who dips her foot in a stream of water would have her foot submerged in different water molecules from one moment to the next, a person who looks at her Facebook feed would be exposed to different updates one moment to the next. Accordingly, theorists have argued that digital worlds are characterized by the condition of immediacy (Tomlinson, 2007)—every moment a person is not looking at a digital world is a moment gone, an opportunity missed (Figure 5.1). Such immediacy can thus induce people to experience a fear of missing out (Przybylski, Murayama, DeHaan, & Gladwell, 2013)—a sense that there are always new things to watch, read, and comment on. FoMO—as fear of missing out is popularly known—has been shown to predict lower sense of relatedness and emotional well-being. In a study with a representative sample of 2079 British adults, for example, FoMO was negatively associated with relatedness, general mood, and overall life satisfaction (Przybylski, et al., 2013). Extending these findings, the present  97 research provides evidence that FoMO may be a critical factor in explaining why phones compromise sense of relatedness and well-being. Specifically, in Study 3, I found that participants assigned to have access to their phones—compared to those who did not—had a stronger sense that there were other things they wanted or needed to be doing instead of getting to know their peers over lunch. Perceiving such opportunity costs of their interactions with peers, in turn, predicted lower sense of relatedness and emotional well-being (Figure 5.1).  Brief intermittent use may also frustrate the formation of social connections in ways that I did not directly examine in the present research. Virtually all portable computing devices today require their users to look down at the display and away from other people who may be present in their immediate physical environment. As harmless as this behavior may appear, averted eye gaze can send strong social signals of exclusion, thus compromising sense of connection and well-being (Figure 5.1). In fact, people who experience the averted eye gaze of others feel more ostracized and socially excluded, experience a lower sense of belonging and self-esteem, feel more sad and less happy, and even find their very existence less meaningful (Wirth, Sacco, Hugenberg, & Williams, 2010). Thus, to the extent that phones require us to avert our eyes from others, sending subtle signals of rejection, these devices may diminish the well-being of those around us.  Although no research has directly shown that using phones can engender feelings of social exclusion, empirical data do suggest that people are negatively affected by others’ use of mobile computing technology. In 2013, over half (52%) of US employees reported feeling annoyed by people who check their phones during an in-person conversation (Harris Interactive, 2013). And, in a qualitative study, children annoyed with a parent’s phone use reported having hid the parent’s phone, put it in the oven, or even thrown it in the toilet (Steiner-Adair, 2013).  98 Future research is needed to directly examine how and when smart portable devices can reduce relatedness and well-being by sending signals of disinterest.  To the extent that connected portable devices can fracture attention, activate thoughts of alternative activities, and send social signals of disinterest, these devices can be detrimental to relatedness and subjective well-being. When portable devices are being used to obtain information relevant to ongoing activities people are sharing with others, however, these devices may enhance sense of connection (Figure 5.2). Indeed, I found that parents felt more, not less socially connected when they used their phones to obtain relevant information about the exhibits in the science museum they were attending with their children (Study 2). Similarly, a phone user may not feel any less connected to a friend when looking up the score of an ongoing basketball game that the two friends are currently discussing. Future research should examine whether phone use that supports ongoing conversations and activities may be beneficial to interactions beyond those between parents and their children.  Another way in which phones may enhance relatedness and well-being is by allowing people to easily share past experiences with each other while interacting in person (Figure 5.2). Imagine a mother who uses a phone to show pictures of her newborn baby while having dinner with the baby’s grandmother, or a woman playing a short video of her recent tropical vacation for her friends. By allowing easy and instant access to digital worlds in such situations, phones may promote feelings of relatedness and well-being. Although no research has specifically examined how sharing experiences through portable computing devices may impact relatedness and well-being, abundant research suggests that sharing positive events with others is associated with experiencing more positive emotions from these events (Gable, Reis, Impett, & Asher, 2004; Hicks & Diamond, 2008; Langston, 1994). Such capitalization (Figure 5.1) on experiences  99 predicts higher life satisfaction even more strongly than other behaviors shown to enhance positive experiences, such as staying “in the moment” (Quoidbach et al., 2010). In short, by providing new convenient ways to share experiences, portable computing devices may promote subjective well-being. 5.2 Quantity of Interactions  Ultraportable smart devices may stem the flow of a conversation or fracture people’s attention, thus obstructing people from reaping the social and emotional benefits of in-person social interactions. But these devices may also prevent interactions from occurring in the first place by obviating the need to rely on others, further undermining opportunities to cultivate a sense of connection with others.  Smartphones are certainly convenient and useful tools that can supply people with instant access to information on the go. Looking for a nearby café to get some work done? No problem, just ask Siri! Wondering where to find the best Italian restaurant in this neighbourhood? The answer is only a tap away on Yelp. But by obviating the need to rely on fellow humans, this convenience could have unexpected negative consequences for the cultivation of trust towards other members of our communities. Such substitution of interactions with fellow humans for interactions with electronic devices can thus compromise fundamental aspects of a healthy society, such as a sense of trust in the strangers around us (Figure 5.1).  In Chapter 4, correlational data from a large nationally representative sample showed that the more people used their phones for information, the less they trusted strangers. Extending these correlational findings, I showed that people randomly assigned to rely on their phones for information when needing directions to find a building talked to fewer strangers than people randomly assigned not to rely on their phones; at the end of the search, participants who relied on  100 their phones trusted strangers less and felt less socially connected than participants who relied on the kindness of others. In turn, this reduced social connectedness predicted lower emotional well-being—an effect that was, however, offset by the usefulness of phones in finding the building. In an additional study not reported in the present work, I found that people asked to imagine finding a building with or without their smartphones predicted the negative effects of reliance on phones on social connectedness. They failed, however, to predict the downstream consequences of compromised social connectedness for emotional well-being. Thus, they expected to feel better when relying on their phones. Together, these findings suggest that replacing social interactions with computer interactions may have negative effects on people’s subjective well-being without their awareness.  Although substitution may have a negative effect on how much people trust others, portable electronic devices also provide new ways in which people can connect with each other, thus potentially enhancing sense of relatedness and trust. By providing people with access to digital worlds and connecting them with others in those digital worlds, portable computing devices may boost serendipity—the chance to stumble upon desirable social partners (Figure 5.1). To illustrate, let us consider serendipity in the context of dating. Meeting a romantic partner is a bit like a lottery—you play and you sometimes win. Before we had digital dating worlds like Tinder or Plenty of Fish, people could play the dating lottery only in the nondigital world. Joining a couple of dating apps may be like buying more lottery tickets, each one increasing one’s chance of finding that ideal partner simply by increasing the chance of serendipitously stumbling upon their profile.  In addition to fostering serendipitous new encounters with suitable dating partners, ultraportable connected devices may act like a social lubricant, helping people more easily break  101 the ice with new social partners (Figure 5.1). Past research and theory have indeed suggested that communicating online can be a social lubricant (Reinie & Wellmann, 2012; Suler, 2004). Thus, individuals who may find it difficult to accost a desirable dating partner in the real world may have no trouble sending a message to a “hottie” on Tinder.   Another way in which portable computing devices may enhance sense of connection and well-being is the spontaneity they afford (Figure 5.1). Ubiquitous connectivity means that we can easily make impromptu plans to connect with our family and friends. John happens to be driving through the neighborhood of a friend he has not seen for a while. John sends a message, and before long, he is having dinner with his buddy. Had it not been for the phones of John and his friend, this impromptu dinner would have been much less likely to materialize. Spontaneity, however, may have a flip side: People can also easily cancel social engagements in the last moment, thus forgoing opportunities to connect. Future research should examine when and for whom the spontaneity afforded by smart gadgets enhances social connectedness. Personality traits like conscientiousness may be one important moderator to consider. People who are highly conscientious, for example, may be unlikely to use their phones to spontaneously cancel social engagements, but may be just as likely as less conscientious people to spontaneously arrange a new social engagement (Figure 5.2).  5.3 Effects in the Absence of In-Person Interactions  The focus of the present work has been on revealing whether ubiquitous connectivity sometimes obstructs people from reaping a sense of social connectedness during in-person interactions. For comparison purposes, however, I also explored whether phones would affect how socially connected people feel when they are by themselves and are thus not forgoing any opportunities to engage in social interactions in person. I reasoned that smartphones might boost  102 sense of connectedness when people are alone by allowing them to remotely connect with others. In contrast to this possibility, however, I found that people who had their phones on them while waiting alone felt no better and no more socially connected than people who waited alone without their phones.  In fact, rather than boosting relatedness and mood when people waited alone, phones had small negative effects on relatedness and mood. Although these small effects did not reach statistical significance and require replications with bigger samples, there are reasons to believe that phones could sometimes directly diminish people’s sense of social connection and emotional well-being. One such reason may lie in a common difference between online and in-person communications—the synchrony of replies of social partners. When people are interacting with a social partner right in front of them, the conversation is synchronized—when one partner asks a question, the other likely answers the question right away. When people are interacting with a social partner online, the interaction is often asynchronous—a message sent to a friend may not be answered for a while. To describe these response delays that characterize online communications, theorists have proposed the term asynchronisity (Figure 5.1)—the condition of not talking to others in real time, in which responses may take “minutes, hours, days, or even months” (Suler, 2004).  While asynchronisity may often characterize digital communications, the ubiquitous connectivity that characterizes smartphones implies that phone users should be reachable everywhere they go. Thus, people may expect to be able to reach their family and friends at anytime, but this expectation may be just an illusion. This rift between expectations and reality may sometimes result in feeling more disconnected rather than more connected. Supporting this possibility, experimental evidence has shown that not receiving an expected response during an  103 ongoing text message conversation can put people in a worse mood, reduce their sense of meaning in life, and undermine their sense of belonging (Smith & Williams, 2004). In Study 4, therefore, participants might have felt less rather than more connected when waiting alone with their phones because even when they texted friends, they might not have gotten a response within the 10 minutes of waiting. Still, over a longer period of time, phones may offset these initial costs to relatedness by surprising their users with the belated responses from their friends (Figure 5.2).  In addition to being unreliable sources of social connection due to the timing of responses, ultraportable connected devices may be unreliable sources of social connection due to the variable adequacy of the responses (Figure 5.2). While in nondigital communication people are likely to consistently receive appropriate responses from those around them, people communicating in the digital world often receive responses of variable quality. A phone user who shares photos of his recent trip to Hawaii with a group of friends over lunch is likely to receive immediate and appropriate response from his friends. A phone user who posts photos of his trip to Hawaii on Facebook may or may not receive ‘likes’ and comments from his friends online. The response may vary depending on trivial factors, such as what time of day the person posted the photos and what else is happening on his friends’ Facebook walls. The quality of the response may in turn affect how socially connected people feel to others. Indeed, even after controlling for the number of their Facebook friends, people who receive fewer responses to their posts on Facebook have been shown to feel a lower sense of belonging (Greitemeyer, Mügge, & Bollermann, 2014).  In addition to the unexpected effects of phones on the relatedness and mood of participants who waited alone, phones unexpectedly increased how angry participants felt while  104 waiting. This effect on anger materialized regardless of whether people waited alone or together. The sense of immediacy associated with ubiquitous connectivity provides one way to understand this surprising finding. According to Tomlinson (2007), a critical factor that characterizes immediacy is the expectation for instant gratification—any desire can be satisfied here and now (Figure 5.1). Thus, by fostering a sense of immediacy in participants, phones may have made waiting all the more irritating. After all, if people can check in with a friend on the other side of the globe with a swipe of a finger, why should they have to wait for study materials being prepared in the next room? Further research is needed to examine whether portable computing devices may be priming expectations for instant gratification, thus making people more prone to experiencing negative feelings when they cannot get what they want on demand.   Figure 5.1. Theoretical model of mediators.       105  Figure 5.2. Theoretical model of moderators. 5.4 A Broader Model   My goal in this work has been to explore how portable computing devices affect the benefits people realize from in-person social interactions. I have, therefore, focused exclusively on how smartphones affect people’s sense of relatedness and how these devices affect emotional well-being through their effects on relatedness. But, of course, people have other psychological needs that are also essential for well-being (e.g., Fiske, 2014; Ryan & Deci, 2000). A full model of the well-being effects of ubiquitous connectivity, therefore, needs to consider how ultraportable devices may satisfy or thwart basic psychological needs other than relatedness.  According to self-determination theory (SDT), people have at least two other basic psychological needs in addition to their need for relatedness: autonomy and competence (Ryan & Deci, 2000). Autonomy captures people’s need to feel that they are choosing their activities, whereas competence captures people’s need to feel that they are capable in completing tasks. An accomplished professor writing a book on her life’s work may be feeling a high sense of autonomy because she finds writing the book enjoyable for its own sake, as well as a high sense of competence because she is an expert on the topic.   106 By affording easy and instant access to unlimited digital activities, connected portable devices may help people satisfy their needs for autonomy and competence, thus enhancing well-being. The sheer range of possible activities smartphones provide could, for example, foster a sense of autonomy by giving people a greater number of possible activities to choose from. Imagine a student who is bored with writing a paper and feels no sense of autonomy as she is sluggishly composing sentence after sentence to fill the page. Thanks to her phone, however, a chat with her eager-to-please boyfriend is only a finger swipe away, allowing her to engage in a more freely chosen and intrinsically pleasurable activity, and thus feel a greater sense of autonomy. And perhaps, after an uplifting chat, she may even reap a sense of autonomy from writing her essay.  But the availability of activities from any life domain at anytime may be a double-edged sword. By activating goals that compete with people’s current activities, connected portable devices may decrease the subjective autonomy associated with the activities. Indeed, according to organismic integration theory—a subtheory of self-determination theory—people feel a greater sense of autonomy during activities that are in line with their current goals (Deci & Ryan, 1985). In Study 3, phones made participants want to do other things rather than interact with their peers over lunch, suggesting that phones may indeed have the power to compromise opportunities to cultivate feelings of autonomy by reminding people of other valued goals and activities.  In addition to having consequences for autonomy, portable computing devices may also affect people’s need for competence. Research has shown, for example, that playing video games can effectively satisfy people’s need for competence (Przybylski et al., 2012; Ryan et al., 2006). To the extent that smartphones can provide easy access to video games—from Candy Crush to  107 Angry Birds—these devices could supply their users with activities to boost their sense of competence while on the go. A commuter who is playing a video game on her phone may miss opportunities to boost her sense of relatedness by sparking up a casual conversation with a friendly stranger, but she may also reap a greater sense of competence by leveling up on Candy Crush. This ability of phones to provide their users with engaging activities while they are otherwise idle may have a net positive effect on subjective well-being despite sometimes resulting in missed opportunities for casual interactions.  Notably, even before the advent of the smartphone, people were not necessarily being particularly social on public transit. A popular photograph18 of commuters in the 1950’s, for example, shows all commuters on a train forgoing the opportunity to interact with each other for the chance to read their newspapers (Getty Images). Rather than replacing social interactions, then, smartphones may simply be replacing other media that people used to employ to pass the time. To the extent that phones can provide reliable access to engaging activities while replacing only occasional social interactions, phones may be resulting in net benefits for their users’ emotional well-being. Unlike newspapers or other single-function devices, however, smartphones provide a potpourri of functions, which may conflict with each other. While a newspaper is a device to keep oneself informed and a game console is a device for playing video games, a smartphone is essentially many devices in one—a person can use it to play video games and to keep abreast of current affairs, but also to text, read emails, or watch cat videos on YouTube. In other words, if a newspaper were a knife and a game console were a corkscrew, then smartphones would be more                                                 18 http://paleofuture.gizmodo.com/commuters-reading-their-newspapers-on-a-train-in-philad-1472729259  108 like a digital Swiss Army Knife whose features expand with every new app. Indeed, conceiving of these devices as phones is a function of habit rather than an accurate description of the purposes they serve. Consequently, the potential of connected portable devices to enhance well-being by providing competence-boosting activities may materialize only when these activities are not being disrupted by other available functions. Because our phones can ping or ring at any moment, however, focusing on one activity may often prove challenging. Thus, the current iterations of portable connected devices with their zillion functions may be poorer at fostering a sense of engagement and competence than the single-function devices that preceded them. Even if, on balance, smartphones do help people to feel more competent and boost their own well-being, activities that captivate users’ attention may undermine the well-being of others. A high-school student may be happily playing Candy Crush comfortably seated on the bus, while an older commuter is standing right in front of him in need of a seat. To understand the broader effects of ubiquitous connectivity on well-being, therefore, future research should explore how connected devices impact the well-being of both their users and the people around the users.  Theorists have also proposed other basic human motives that may be affected by our ubiquitous connectivity. Susan Fiske (2014), for example, has proposed that people are fundamentally motivated to feel in control. By furnishing people with instant resources they can draw upon in difficult situations (e.g., information, friends, family), ultraportable connected devices may sometimes foster a sense of control. Imagine that Mike’s car breaks down on the way to his bar exam. There are no taxis or public transit nearby, and Mike is going to be late unless he does something quickly. Rather than feeling helpless, Mike can use his phone to call a cab or his cousin who lives nearby in order to get a ride to his destination on time. Indeed, 53% of US smartphone users report that they have relied on their phones to get help in emergency  109 situations, with 50% of users reporting using their phones specifically to deal with car emergencies on the road (Pew Research Center, 2015).  While ubiquitous connectivity may be helpful in emergencies, being constantly reachable might also compromise how in control people feel in nonemergency situations. Imagine a worker trying to compile a report before a looming deadline while new emails keep pouring in every minute with more requests that demand attention. In such situations, being constantly reachable by others may undermine people’s sense of control. In support of this possibility, in our previous research, my collaborator and I showed that people felt more stressed when they were assigned to keep their email notifications on and check their email frequently than when they were assigned to keep email notifications off and check their email infrequently (Kushlev & Dunn, 2015). Importantly, we assessed stress by asking participants to report, for example, whether they felt things were out of their control, whether they could keep up with all the things they had to do, and whether they felt on top of things. In other words, we assessed stress in part by tapping into people’s sense of control. Our results suggested, therefore, that being constantly reachable by others might be detrimental to how in control people feel over their own activities.  Ubiquitous connectivity may have different effects on sense of control in emergency and nonemergency situations. If this is the case, then more broadly, ultraportable devices may boost sense of control in situations when people need to reach others but diminish sense of control in situations when people are interrupted by others. Thus, future research should examine whether connected portable devices boost sense of control by making others easily reachable to users when users need them, and whether these devices can reduce sense of control by making users easily reachable by others even when users are trying to accomplish something else.  110 Another basic social motive proposed by Fiske (2014) that could be satisfied with the help of connected portable devices is the motive for self-enhancement—the motivation to be perceived by others as a person of worth. To be sure, people commonly use their connected devices to make themselves look better in the eyes of others by sharing their positive experiences and accomplishments on social media (Duggan & Smith, 2013), including their beautiful weddings, desirable job promotions, and adventurous trips. With a few taps on her phone, a person can present a positive image of herself and her life to thousands of followers and hundreds of friends all around the globe. Seeing that more than one hundred people have liked one’s new profile picture on Facebook may distract one from an ongoing conversation, but it could also boost one’s sense of worth. Past research supports the possibility that promoting oneself online may be beneficial for well-being. On Facebook, for example, it is the people who actively share and engage with content on the network—and not those who passively view the posts of others—who feel better from using Facebook (Burke, Marlow, & Lento, 2010).  In addition to accounting for the effects of ubiquitous connectivity on basic psychological needs other than relatedness, a full model of the well-being effects of connected mobile devices needs to consider how the effects of these devices may differ across various populations. In the present work, I explored some of the costs of smartphones in populations in western countries. In countries with less educated and affluent people, the benefits of ultraportable connected devices may far outweigh the costs. Smartphones, for example, may be the only way to access the Internet in many parts of the world, holding great potential for connecting people. A mobile phone platform is allowing small-scale farmers in Kenya, for example, to easily communicate with each other, so that they can sell their produce directly to consumers for up to four times as much profit (Aljazeera, 2014). In addition, the effects of ubiquitous connectivity on subjective  111 well-being in people with disabilities may be different from the effects I observed in the populations of the present research. While children with autism, for example, may not be particularly drawn to sparking casual conversations with strangers, some of them may be enthralled by socializing with voice-activated personal assistants on smartphones, such as Siri. In a recent New York Times article, a mother recounts just this type of social relationship between her autistic son and Siri (Newman, 2014). In short, future research should examine the social and emotional effects of ubiquitous connectivity in diverse populations.   In sum, connected portable devices have a great potential both to satisfy and to undermine opportunities to satisfy basic psychological needs and motives—not only needs for relatedness, trust, and belonging, but also for autonomy, competence, control, and self-enhancement. To fully understand how smart portable gadgets affect well-being, future research needs to look beyond the effects of these devices on relatedness and explore the psychological and situational factors that determine when portable devices support versus hamper the satisfaction of psychological needs.  5.5 Two Possible Futures Omnipresence, convenience, intermittence, and immediacy—those are at least some of the factors that characterize ubiquitous connectivity and distinguish ultraportable computers from their stationary predecessors. As compared to desktop computers, smartphones are more omnipresent, provide more convenience, encourage more intermittent use, and facilitate greater immediacy. As compared to smartphones, the devices of the future promise to be even more omnipresent, convenient, intermittent, and instantly gratifying. Apple predicts, for example, that its recently released smartwatch will be easier to use and “less obtrusive” than Apple’s mighty iPhone. To check a notification on her Apple Watch, a person will not need to waste precious  112 seconds to pull the device out of her pocket, unlock it, and tap on the right app. A peak towards her arm as if checking the time, and violà, the message is revealed. How convenient!  The present research provides a first glimpse of the unexpected consequences of such convenience for user experience. If one could raise an arm and get directions from Siri—the Apple computerized personal assistant—why waste time asking friendly strangers? If we can check a notification in a split second, what harm could be done to the flow of an ongoing conversation with the person facing us? While such thinking may prompt people to reach for their smart gadgets ever more frequently, the present findings suggest that the costs of brief intermittent use are far from negligible. From sacrificing our limited attentional resources to obviating the need to rely on those around us, ultraportable devices may be compromising opportunities to satisfy our basic psychological needs to feel connected and trust one another. Imagine a woman—call her Celia—who is having a dinner date with an attractive guy. Celia likes the guy, and as the date progresses, she opens up about her recent divorce. Then, in a split second, her partner glances down at his Apple Watch. She notices but continues her story as if nothing has happened—after all, it was only a split second. But then he does it again, and again. What feelings of connection could Celia hope to cultivate with such a distracted and apparently disinterested partner? In one possible future, then, we will continue to be besieged by devices that offer us more of the same—ubiquitous connectivity that often comes at the expense of social connectedness. Such a future would be a missed opportunity—a missed opportunity to harness the full potential of our powerful portable devices to connect us with some without disconnecting us from others. And the portable devices of the future have a great potential to connect us indeed. Smartwatches, for example, provide new ways to help people feel more connected with others. A user of Apple  113 Watch, for example, is able to share her heartbeat with her lover, and she can gently tap him on the arm, just to say, “I am thinking of you.” Given emerging research on the power of touch to communicate emotions (Hertenstein, Keltner, App, Bulleit, & Jaskolka, 2006), such innovations hold an immense potential to connect people and improve well-being. But if connections with people at the other end of town come at the expense of connections with people sitting right across from us, we will be failing to minimize the costs and to maximize the benefits of such innovations. If a tap on the arm from a lover takes a smartwatch user away from catching up with old friends, then people are simply replacing connecting with those around them for connecting with those away from them. Even if this substitution of in-person with remote interactions resulted in net benefits for social connectedness and emotional well-being, this would be hardly satisfactory. We should strive for a future where we harness the potential of smart devices to connect us while minimizing the surreptitious ways in which these devices disconnect us from one another.  In another possible future, then, smart portable gadgets will maximize well-being by taking into consideration people’s psychological needs and by being aware of people’s current activities, environment, and goals. In other words, our devices will be smart not only because they are technologically advanced, but also because they are psychologically smart. Indeed, rather than simply exploring the effects of the most popular current mobile devices—smartphones—I have used smartphones as a proxy for elucidating the effects of ubiquitous connectivity more broadly. It is my hope that the theoretical model I have proposed here (Figure 5.1) provides a preliminary blueprint for understanding how to make future devices more psychologically smart, thus harnessing the full potential of smart technology to connect us with one another.   114 I am far from the first to propose a model for designing devices that better serve people’s psychological needs. Mann (1996; 1998), for example, proposed several necessary attributes of wearable devices—from smartwatches to smart glasses, broaches, and clothes—that are similar to the ones proposed in the present work. According to Mann, wearables must be “un-monopolizing” of users’ attention, while being attentive to the environment and communicative with the people in that environment. Other theorists have further proposed that wearables should allow eyes-free interaction (Todi & Luyten, 2014), thus minimizing attention costs and avoiding sending negative social signals to others. What distinguishes the model I have proposed here from this previous work is that this model is based on empirical findings and established psychological theory. It is only by situating new theory within established theory and supporting new theory with empirical findings that researchers could hope to shape the future of ultraportable technology.  Researchers are already developing the technological capabilities necessary to design psychologically smart devices. Wearable devices are capable of detecting, for example, where their users are located and what they are doing (Hardegger, Nguyen-Dinh, Calatroni, Tröster, & Roggen, 2014; Ishimaru et al., 2014; Vidal, Nguyen, & Lyons, 2014), as well as how they are feeling (Hardegger et al., 2014; Gruenerbl et al., 2014; Hernandez, Riobo, Rozga, Abowd, & Picard, 2014; for reviews on affective computing, see Picard, 1997; 2010). For example, using eye-tracking technology on Google Glass—a prototype of smart computerized glasses—researchers were able to identify whether users were typing, reading, eating, or talking, with an accuracy of up to 100% (Ishimaru et al., 2014). Similarly, a prototype algorithm on smartphones was trained to recognize the optimal time to interrupt users with notifications based on information about their activities, location, and the time of day (Pejovic & Musolesi, 2014).  115 Psychologically smart devices could harness such activity recognition capabilities in order to support people’s current activities and goals. To minimize interruptions to the flow of the conversation between Jenny and her partner, for example, Jenny’s smartwatch could release her new emails only after she has finished talking with her partner. Similarly, a smartphone that is psychologically smart may suspend the delivery of any work emails as soon as a father picks up his son from school, and release the emails only after the son’s bedtime.  Moving beyond recognizing where user attention is directed, researchers have started to develop technology that could read people’s internal states, such as their physiological arousal. Wearable electrodermal sensors, for example, were successfully used to gauge how engaged children felt during social interactions (Hernandez et al., 2014). Applied more broadly, such sensors can be used to provide people with feedback in order to increase awareness of their psychological states, thus potentially helping them make more informed choices about their behavior. A smartwatch can indicate to its user that the message she just checked decreased her engagement with the conversation she was having with the friend in front of her, thus prompting her to refrain from checking any more notifications.   In sum, as smart devices become ever more integrated into our daily lives, these devices need to become more integrated with our psychological needs. By minimizing attention costs, discouraging unnecessary substitution, and supporting our current goals, the smart devices of the future could maximize people’s subjective well-being.  5.6 Conclusion  In 1889, the British Medical Journal warned readers of “aural overpressure”—a new malady due to “almost constant strain of the auditory apparatus” in people who used telephones for long periods of time. Symptoms of the condition apparently included nervousness, buzzing in  116 the ears, giddiness, and neuralgic pains (Marvin, 1988). More than a century later, and with a lot more phones around us, we seem to be all but free of this supposed malady. This example highlights the fact that new technologies have always generated concerns about the effect they may have on people’s subjective well-being. Far from endorsing unbridled concerns about the perils of ubiquitous connectivity for well-being, I have begun to identify the processes (Figure 5.1) and levers (Figure 5.2) that determine how and when being constantly connected could boost or hurt relatedness and well-being. My exploration of situations when smartphones would compromise opportunities to cultivate a sense of well-being has produced some concrete evidence about how to minimize the negative effects of our smart gargets. It is my hope that this empirical evidence and my broader theoretical model would stir future research focusing both on minimizing the costs and maximizing the benefits of ubiquitous connectivity, ultimately shaping the psychologically smart technology of a more connected future.   117 References Ainsworth, M. D. S., Blehar, M. C., Waters, E., & Wall, S. (1978). Patterns of attachment: A psychological study of the strange situation. Hillsdale, NJ: Earlbaum. Aljazeera (2014). Life apps. 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