Abstract
Technology, relationships, and well-being represent three influential parts of our daily lives. They also represent a common area of research for scholars in a variety of disciplines. Here, we offer guidance for researchers interested in studying the connections among these issues and introduce the special issue in the Journal of Social and Personal Relationships focused on the confluence of these topics. We take each issue in turn, along with their interconnections, and describe some common pitfalls along with ideas to advance research in these areas. After analyzing the body of research on technology, relationships, and well-being, we present the special issue. We explain how our analysis of this body of research along with obstacles we encountered conducting research in this area shaped our approach to the issue. For the edification of future editors, we briefly reflect on the strengths and weaknesses of our atypical editorial process before presenting the articles that compose this issue.
Relationships have always been an integral part of people's well-being. In the modern era, technology often mediates our relational experiences: we seek information about relationship issues online; don our fitness wearable to compete with others; participate in a virtual reality intervention for social anxiety; or receive texts offering social support from a friend, a healthcare provider, or a chatbot. The closeness, quality of communication, and satisfaction within relationships influence people’s well-being (Diener, 2009; Huston et al., 1986; Rusbult et al., 1998). Emphasizing the role of technology in the associations among technology, relationships, and well-being, the Geneva Charter for Well-Being issued by the World Health Organization (2022) declared that addressing the effects of technology was one of the top five priorities for global well-being. The report contended that “Digital transformation and technological change can create new opportunities for connection, health literacy and knowledge-sharing and more effective, efficient service provision. Some features of digital systems and digital exclusion can, however, create isolation and exacerbate inequity” (p. 4). Considerations and connections among technology, relationships, and well-being are baked into that thinking.
The rapid diffusion of technologies in modern society has made it challenging for social scientific researchers to keep up. As events such as the release of the Facebook Files have demonstrated, technology companies and those who profit from them have taken active steps to conceal knowledge and prohibit researchers from addressing critical questions about technology, relationships, and well-being (see Chappell, 2021; Wall Street Journal, n.d). Restricted server data and opaque algorithms compound this issue. These events coincided with research that reports negative effects of social media experiences on some users (Marengo et al., 2021; Wells et al., 2021). Creating a debate about the effects of technology use, and social media in particular, on well-being, other studies document no association between using technology and well-being (Dickinson & Gregor, 2006), thereby making the magnitude, direction, or even existence of any effects unclear.
Indeed, scholars seeking to conduct research on technology, relationships, and well-being face many obstacles. Because academic research on these three areas of study proliferated within several different disciplines, a research team must have or develop broad expertise. This challenge is exacerbated when two or three of these large domains of research intersect. As readers of Journal of Social and Personal Relationships (JSPR) are certainly aware, relationships are messy, conceptually, operationally, theoretically, and sometimes literally. Similarly, well-being is complicated: physical and psychological well-being can be at odds, subjective data can be too motile, and objective data can be interpreted in different ways. Studying technology is forever aiming at a moving target in the functional, social, and academic sense. Daring to implement technology can mean spending many a workday in the throes of frustration. Research becomes an agonizing battle with bugs, bricks, and breakdowns, forever haunted by the possibility of waking up one day to find your study’s technology obliterated by the whims of its executives. In other words, good luck!
Despite these challenges, many researchers have forayed into the connections among technology, relationships, and well-being. Because we anticipate that these connections will continue to receive heavy interest, we begin this introduction by critically evaluating the current state of the larger literature(s) on these topics and offering some guidance for researchers in each of these areas. We then describe our unconventional approach to this special issue, given these observations as well as obstacles we experienced conducting research in this area. For the edification of future editors, we briefly reflect on the strengths and weaknesses of our atypical editorial process before presenting the articles that comprise this issue. In other words, we offer some commentary and insight into the overall body of research on technology, relationships, and well-being that inspired this special issue. We provide guidance and suggest directions for future research throughout our commentary, and then we describe the papers in our special issue that attempted to tackle some of the issues we raise.
A more nuanced approach to technology, relationships, and well-being
We begin by synthesizing and analyzing the bodies of research on well-being, technology, and relationships that are the foundations for this special issue. The introduction and diffusion of technologies can have far-reaching impacts on relationships and society more generally (McLuhan, 1964; Rogers, 1962). As technologies begin to permeate our social conscience and routines, our curiosities, hopes, and hysterias arise. The telegraph, telephone, and television were greeted with both enthusiasm and apprehension, each raising concerns about their impact on our relationships and well-being (e.g., Marvin, 1988; Standage, 1998). These competing and persistent streams of hopeful adoption and anxiety about effects lead to the ongoing technophilia and technophobia that have characterized the digital age.
The scientific literature has inevitably been shaped by, and perhaps at times motivated by, a debate between positive or negative effects. Name a technology, and it is likely that you will find some studies claiming positive effects and other studies arguing its negative effects or even null effects on relationships or well-being, as evidenced in many syntheses and reviews (Meier & Reinecke, 2021; Rains & Young, 2009; Zhang & Fu, 2021). A closer inspection of these studies reveals heterogeneous methods, measures, samples, data, and analyses that vary in quality, specificity, size, richness, and validity, yielding divergent results (High et al., 2023). What’s a researcher to conclude? As with many areas of social science, the simple answer is: it’s complicated.
Our first suggestion to researchers is to check their technophilia and technophobia at the door. Rather than expecting the best or the worst of technology, examine both, and be mindful of interpreting findings through a particular lens based on your preference or positionality. Here we urge scholars to consider diverse literature to prevent confirmation bias and third-person effects in theorizing and study design. Moreover, “hype and hysteria” over the latest technology are not sufficient rationale for a study (Thurlow et al., 2004). Studying technology simply because it is novel represents phenomenon-driven research with little to contribute to our understanding of theory or process.
Researchers must also consider users’ diverse experiences with technologies. The refrain of media effects scholars is a reminder that at best, findings apply to some of the people, some of the time. As scholars, it is our responsibility not to lose some of the people, particularly when examining well-being. Many issues related to well-being affect a relatively small proportion of the population, so we must be cautious not to overgeneralize. For example, technology use that shows no evidence of a statistically significant effect in a broad sample could have a particularly beneficial or pernicious effect on a subgroup, especially when considering when or why that group is using the technology (see High et al., 2023). Researchers should strive for research that avoids Simpson’s paradox, where the pattern observed in a sample disappears or even reverses when data are analyzed within groups and/or clusters that exist within the sample, by carefully considering the nature of their samples and any subgroups within them. Drawing conclusions based solely on the majority of users (or, more commonly, the majority of convenience samples of college undergraduates or individuals employed as online panelists) could inadvertently dismiss socially important effects and marginalize other groups.
To fully explicate the role of technology in relationships and well-being, researchers must consider the scope of the moving parts within those interactions: the technology or channel; the content, messages, or interactions within or through or about the technology; the parties participating in the interactions, including their individual differences and outcomes; the relationship between these parties; and the context in which the technology is being used (cf. Burleson, 2009; Taylor et al., 2022). Of course, these parts do not function in isolation. As Burleson (2009) noted in the context of supportive interactions, “many (and perhaps most) of these factors operate in concert with each other – combining, qualifying, and moderating each other’s influence – thereby making the task of explaining their collective effects seem gargantuan” (p. 27). Given this gargantuan task, we provide a brief overview and guidance on some of the critical aspects of the research on well-being, technology, and relationships.
Well-being
Thousands of studies have examined the relationship between technology and well-being. With such broad interest from a diverse array of authors and disciplines, it is unsurprising that many conceptualizations of well-being have emerged. Perhaps the most common is Diener’s (2009) subjective well-being, a global assessment of an individual’s general feelings of positivity, happiness, and general life satisfaction. Of particular relevance to JSPR, relational well-being focuses on people’s experiences with a particular person, such as a romantic partner, family member, or friend (Huston et al., 1986; Rusbult et al., 1998). Depending on the type of well-being under study, researchers may operationalize well-being using measures such as biometric standards (e.g., stress hormones, blood pressure), affect, quality of life, physical capabilities, anxiety, depression, or relationship satisfaction (see High et al., 2023 for a review).
We encourage scholars to be precise in communicating and implementing both conceptualizations and operationalizations of well-being in their research. In addition, rigorous theorizing regarding how a study’s variables of interest might affect well-being is required. Different types of well-being comprise different factors and are likely predicted by distinct antecedents. There are many variables in a person’s life, including aspects of their relationships that are intertwined with technology, that are likely to influence their overall sense of well-being.
When examining technology and well-being, researchers should consider the validity of their measures alongside their hypotheses or research questions. Part of studying the nuance in well-being is recognizing that different measures of well-being operate at different levels of abstraction. One common error with subjective measures of well-being is a mismatch in abstraction or temporality. For example, researchers might avoid using a global or trait measure of well-being as the dependent variable in certain lab experiments. Is it reasonable for a single manipulated message or even an interaction to budge someone’s global well-being? Along these lines, people’s subjective well-being tends to be relatively stable (Eid & Diener, 2004); therefore, it might be unlikely to change based on the amount of time they use social media. Thus, the facets of well-being researchers want to assess should logically correspond with the time frame under study and the globality of theorized causes.
It is also important to remember that all assessments of well-being are based on structural determinants to some degree (Kozma et al., 2000). That is, it is difficult to expect technology to have a measurable influence on a person’s well-being when they lack adequate access to food or housing. Other influences on well-being, such as experiencing a global pandemic, likely have a large impact on well-being (Zacher & Rudolph, 2021), to the point of subsuming any potential technology-related effects. Of course, beyond well-being, the global pandemic also shaped people’s relationships and use of technology (Bevan et al., 2023). The realities of chronic, structural and acute stressors have implications for sampling and the generalizations that can be drawn from any conclusions about technology on relationships or well-being.
Technology: Channels, messages, and affordances
There are a host of studies that examine how the introduction or use of a new technology may influence people’s sense of well-being (see High et al., 2023, for a review; Kraut et al., 1998). Here, we note specific aspects of this body of research that indicate ways researchers can strengthen the contributions of their scholarship.
To better understand how to approach an emerging technology and situate it within existing theorizing, researchers should consider three ways that channels may facilitate outcomes related to well-being. Channels can accommodate a phenomenon without affecting the outcome (Fox & McEwan, 2020). Essentially, the outcome manifests similarly regardless of what channel is used. For example, if someone congratulates you for finishing a task, you may feel the same regardless of whether the message is delivered face-to-face, over the phone, or by text. In this case, technology is not the cause of the outcome, and existing theories of communication, relationships, or well-being should be sufficient to explain any effects. Alternatively, technologies may amplify or attenuate a phenomenon, strengthening or weakening effects, respectively (Fox & McEwan, 2020). Technology does not fundamentally change how the phenomenon occurs, but the outcome may be different in magnitude in one technology or another. For example, being insulted may hurt no matter what channel a person uses, but it may hurt more or less over text than face-to-face. In these situations, some characteristic of the channel may moderate an outcome, or existing theory might need to be extended to accommodate a channel-related variable. Finally, a technology may alter a phenomenon (Fox & McEwan, 2020). This is the rare circumstance when characteristics of the channel fundamentally change the phenomenon and new theorizing is required. Because accommodation and amplification/attenuation are far more frequent than alteration, researchers must attend to theorizing and findings about the observed phenomenon independent of the technology itself.
An indispensable self-check for researchers is to avoid the assumption that the technology is necessarily to blame for an observed phenomenon. It is possible that the phenomenon manifests similarly across other channels or that it is independent of any channel. To address this possibility, one critical step is that researchers studying technology must parse the medium and the message. Too often, a channel is blamed without any evidence that it is the channel per se that is causing any effects rather than the messages being transmitted through that channel. The content available through many technologies is infinitely diverse, and users are often afforded considerable choice in the content they consume. Without accounting for this content, many claims about channel effects are likely conflated with message effects. For example, prior research has claimed that active social media use (posting, liking, and commenting) is beneficial to well-being, whereas passive social media use (merely viewing others’ content) is detrimental to well-being (e.g., Verduyn et al., 2015). Following this content-agnostic theorizing, cyberbullying others through negative comments and posting about self-harm would be hypothesized to benefit well-being, whereas using social media only to watch kitty videos would be expected to have a negative impact. In other words, the content within channels matters. Such statements should be self-evident, but what and how people communicate via technology is consequential. Although investigating interaction behavior is an improvement over an earlier generation of studies measuring “general use” or “screen time,” we advocate for greater nuance in addressing both channels and messages. After thousands of studies on technology and well-being, scholars need to address both the means and content of communication more rigorously to advance our understanding.
Another necessary strategy for more rigorously investigating technology is to pick apart its facets and mechanisms. Attempting to theorize about a single technology or platform is ill-advised given technologies often evolve or go extinct. To develop robust and persistent theoretical explanations of the effects of technologies, researchers should focus on the affordances or characteristics of these channels rather than channels as static entities (Fox & McEwan, 2017). Affordances are the perceived possibilities for use that emerge in the relationship between a user and a technology (Gibson, 1979). For example, text-messaging affords more accessibility and persistence compared to face-to-face interaction. Presuming both channels are available, it is easier to get a message to a receiver via texting than having to meet face-to-face, and texting maintains a record of the interaction whereas speaking face-to-face does not. Notably, technologies typically offer a constellation of affordances (McEwan, 2021), so researchers should explore interactions among affordances as well as variations in these constellations across channels.
Scholars must also begin to address the broader sociotechnical ecosystem, including assessments of technology repertoires. Communicators generally incorporate multiple channels that they weave throughout their daily lives (Caughlin & Sharabi, 2013; Madianou & Miller, 2012; McEwan, 2021). The fact that people use multiple channels throughout their day can make it difficult to trace the effects of a singular channel to a global assessment of well-being. Once researchers have a better grasp on how constellations of affordances are working within a channel as well as how people are using these channels in conjunction, richer questions can be addressed: How do people compare and weigh affordances across technologies? How do people regulate affordances across multiple channels within relationships (see Sweeney et al., 2024)? Do variations in affordances across technologies have differential impacts on well-being?
Despite the complexities of studying technologies, this guidance provides several relatively simple ways for scholars to improve research and theorizing regarding technology. These tactics include designing studies to determine whether technology is the cause (e.g., by comparing a phenomenon across different channels), carefully conceptualizing the role technology is playing to determine what theoretical modifications may be needed, and embracing the complexity of the effects of technology on daily life by examining multiple channels or exploring multiple affordances.
Relationships
Understanding with whom we are using technology to connect, who we might be using technology to avoid, and what relationships we have (and lack) is important for disentangling the potential effects of technology on well-being. More generally, interpersonal associations have a profound impact on people’s well-being (Lansford et al., 2005; Rook, 1984); therefore, they are likely to play a critical role when assessing the impact of technologies that entail social implications. When technology alerts us to where we might feel left out, where we might engage in upward social comparison, where others are able to transmit hurtful messages, or where our perception of a lack of support is exacerbated (e.g., Black, this volume; Huang & Yao, this volume; Taylor & Choi, this volume), it may detract from our well-being. Conversely, when technology facilitates connection, brings us into our social circle, or helps us maintain our relationships (see Chen & Lu, this volume; Lee et al., this volume; Liu et al., this volume; Yue et al., this volume), it may improve our well-being.
Some research on technology, relationships, and well-being has adopted theorizing from the literature on relationships. For example, both attachment styles and family communication patterns help to determine people’s well-being (Andrews & Hicks, 2017; Curran & Allen, 2017; Merz & Consedine, 2009; Schrodt, 2020). Applying existing theories about relationships to research on technology and well-being is reasonable, given that the early communication patterns people experience within their family influence how they relate and engage in a variety of interactions into adolescence and adulthood (High & Scharp, 2015; Koerner & Fitzpatrick, 2006; Ritchie & Fitzpatrick, 1990). These traditional means of studying relationships can receive renewed interest when studying them alongside technology.
Many studies about technology and relationships only apply relational theories at a surface level, and others overlook established findings and theories in the relational literature entirely. Although studying a particular relational process online might seem novel, it is worthwhile to remember that it might not be novel to the people engaging in the process. In other words, for most intact relationships, there is little reason to study relationships anew online. Rather, partners are incorporating technology into their existing relational history and patterns. Scholars should not assume, or they should at least test, the boundary conditions of theories of technology when applying them to existing relationships. Several traditional theories of computer-mediated communication, including social information processing theory (Walther, 1992) and the hyperpersonal model (Walther, 1996), were designed with strangers in mind. This has not stopped researchers from applying them to intact relationships without pausing to question whether they are appropriate or the most relevant perspective for a set of predictions. Ideally, testing a theory involves interrogating its explanatory mechanisms, and when studying relationships, there are both relational and technological variables to consider. Given many phenomena under study are at their core relational phenomena, researchers interested in technology must develop a deep knowledge of related relational theories to develop sound research.
Similarly, studies examining well-being need to distinguish between effects of relationship processes and the effects of technology on well-being. For example, a study may examine couples who play video games together over time and observe an improvement in relational well-being. Without a point of comparison, however, it is unclear whether the technology drove the effect or whether the same effect would have occurred if the couples played board games or took a walk instead. Rather than a technological effect, this could be an effect of any relational maintenance behavior.
Technology allows people to initiate, expand, and maintain connections with a variety of types of relationships (Hall, 2020; Kerbo et al., 1978; Parks & Floyd, 1996; Stafford et al., 1999). Theorizing by researchers interested in technology can overlook or homogenize variations in relationships that are fundamental to relationship research. One example is in the generic use of “friends” to refer to the individuals a user is connected to on a social networking site, promulgated by the name of Facebook’s feature that links two parties’ profiles. As relationship researchers are aware, a friend is a type of relationship that can be distinguished from other relationships, such as strangers, acquaintances, romantic partners, family members, co-workers, and enemies. Indeed, characteristics of relationships are critical in interpreting the role of technology on well-being. Relational behaviors facilitated by technology may be received and interpreted differently across different types of ties that can be differentiated by a host of relational variables, including length, type, closeness, satisfaction, turbulence, and voluntariness (Taylor et al., 2022). Similarly, choices regarding channels and the integration of technology may be interpreted within the context of a relationship (Caughlin & Sharabi, 2013). Some types of relational connections may feel challenged by technology use (Sharabi & Dorrance Hall, 2021), whereas other relational connections may persist or thrive because of technology (Madianou & Miller, 2012). It is not profitable to assume that findings generalize to all types of relationships, and the best understanding of technology in that context is likely to be achieved when researchers identify and combine variables related to technology and relationships.
To this point, we have attempted to provide points of consideration for researchers interested in studying the intersection of technology, relationships, and well-being. In terms of well-being, we noted that different types of well-being are likely predicted by different antecedent factors. As a result, researchers should be realistic about what aspects of a relationship or technology can influence well-being, especially because well-being is often rooted in a host of stable structural determinants. When focusing on technology, researchers can work to better parse media from the messages they transmit, while recognizing that technology might not always be the cause of any effects observed in a relationship. Scholars can advance theory and research by studying the mechanisms for any effects and considering how relational partners incorporate multiple technologies into their lives. In terms of relationships, scholars can think more critically about which theories of relationships and technology can be most profitably applied to studies seeking to explain variations in well-being. Documenting the variables that differentiate certain relationships can be one means to selecting appropriate theory and extending theory in this domain.
Now that we have provided an analysis of the research on technology, relationships, and well-being, we next turn to a description of the process we used for this special issue and the studies that grappled with the points we raised throughout this introduction.
The special issue process
When we first crafted the call for a special issue of JSPR examining the combination of technology, well-being, and relationships, we were hopeful. We sought methodologically rigorous theoretical work, but we were also driven by two issues that have always concerned us about the process of publishing research. For one, would it not be better if studies were submitted for a preliminary review, wherein they receive preliminary feedback before data are collected? Many among us have cringed upon learning from a reviewer that we missed a critical portion of the literature or overlooked a fatal flaw in the method—something that would have been easy to address before completing the research, but at this point has consigned our effort to the study graveyard. Given the complexity of this research area as outlined above, it seemed like a special issue on technology, relationships, and well-being could benefit from opening our call to not just completed but also proposed studies.
A second issue is that no one loves a p > .05. Null means having no value or useless. No matter how groundbreaking the theorizing or rigorous the method, when our results fail to be statistically significant, somehow our research becomes valueless. We wanted to acknowledge that there is a great deal to learn from a well-theorized and well-designed study, regardless of its eventual significance level. We also hoped to encourage intellectual creativity in submissions. The pressure to develop “safe” studies that will have predictable, significant findings is a particular hindrance to researchers who take on the daunting task of studying emerging technologies. Although thoughtful theorizing and programmatic approaches can ameliorate some concerns, both the development and use of technology can be unpredictable, making research inherently risky. We also consider the bias against nonsignificant findings to be an equity issue given that the publication pressure is greater among early career and otherwise tenuously-employed researchers, who may be further disincentivized from pursuing risky and potentially groundbreaking ideas. As guest editors, we wanted to open the door for work that might be considered risky to undertake, so we were explicit to authors (and eventual reviewers) that acceptance for publication would not be contingent on statistical significance. Nor was our call limited to statistics because we welcomed both qualitative and quantitative approaches. We hoped that these editorial decisions would help make this process more accommodating for authors, enable greater learning opportunities earlier in the research process, prompt more creative proposals, and ultimately generate more rigorous science.
We received 53 preliminary proposals wherein authors outlined the theoretical justification, hypotheses or research questions, methods, stimuli or measures, and contributions of their research. Every proposal was reviewed by a lead editor. After this round, 15 were not considered further, mostly due to issues of fit, and one was withdrawn by the authors.
The remaining 37 proposals received a full review by all three editors on 14 predetermined criteria including theoretical argumentation, methodological choices, validity, contributions, and fit. Studies that had not yet collected data were also evaluated on completeness and perceived executability within the established timeline. All proposals were provided substantial feedback, and 16 were invited to submit full papers. Six had collected data, one had collected some data, and nine had not initiated data collection. Unfortunately, the designated time for completing studies and papers overlapped with the COVID-19 pandemic, which presented hardships for some of these projects. We received 11 completed papers that were then subject to the usual peer review process, yielding the ten papers that appear in this special issue. These papers were led by five assistant professors, three post-doctoral scholars, and two graduate students. We are encouraged by emerging scholars leading interesting research in this area.
Given that we adopted a different editorial approach than JSPR and other social science journals, we want to offer some insights on the process for future editors and curious onlookers. Many scholars expressed appreciation and excitement regarding our invitation of proposed (rather than complete) studies, and we believe this helped us attract early career scholars. Several authors expressed gratitude about the preliminary feedback that helped strengthen their study before data were collected. We were very pleased that several authors took us up on our offers to videoconference about the proposal, answer questions over email, and help troubleshoot when problems arose during data collection. Although reviewing proposed studies required considerably more involvement and time, we felt the process was more rewarding for everyone involved.
We must also acknowledge the challenges in this approach. It cannot be accomplished without an open-minded and patient journal staff, and we are grateful to JSPR editor-in-chief Melissa Curran and manuscript coordinator Rachael Perez for enduring our quasi-experiment. Flexible and amenable reviewers are also a must; we are also thankful for our knowledgeable, gracious reviewers who accommodated our abnormal process and provided stellar feedback. We also recommend for future editors that reviewers be involved at the proposal stage of the research. The timelines for data-yet-collected are inherently unpredictable and thus risky. The pandemic exacerbated this struggle, and we felt our authors’ frustrations and disappointments when we could no longer continue to extend deadlines. We question how well our approach would work at a journal operating under the traditional requirements and parameters of print (i.e., lacking supplementary materials or having a fixed publication deadline). It is also possible that some authors use the feedback from a preliminary review as an opportunity to get an opinion on their paper, which they then submit to a different outlet. There are no obligations in the preliminary review process, which entails risks for editors.
A final consideration is that if you want to publish an issue filled with studies yet unseen, you must attract great scholars doing great work. With this, we are happy to introduce the contents of our special issue.
The Issue
The authors in this issue produced research under the most strenuous of circumstances, and we are grateful for their efforts. Six of the ten papers in this special issue collected data after the proposal was accepted, suggesting our solicitation of not-yet-run studies was successful despite interruptions from the pandemic.
The papers in this special issue adopt a range of methods to explore the intersection of technology, relationships, and well-being. Although we solicited both qualitative and quantitative work in our call, the full papers submitted only employed quantitative approaches. The methods included in the special issue included surveys (Buehler et al., Chen & Lu, Lee et al., this volume), longitudinal surveys (García-Manglano et al., this volume; Taylor & Choi, this volume, Yue et al., this volume), experience sampling methods (Anderl et al., this volume), comparison surveys at different time points (Yue et al., this volume), and experiments (Black, this volume; Huang & Yao, this volume; Liu et al., this volume).
This collection also explores several types of relationships, including romantic relationships (Black, this volume; Chen & Lu, this volume; Huang & Yao, this volume), parent-child relationships (Buehler et al., this volume), family (García-Manglano et al. this volume), friendships (Liu et al., this volume), network members (Taylor & Choi, this volume), and ties between support providers and receivers (Yue et al., this volume). To further investigate relational aspects, these studies adopt theoretical approaches such as attachment (Black, this volume; Chen & Lu, this volume; Huang & Yao, this volume), family communication patterns (Buehler et al., this volume), relational turbulence (Huang & Yao, this volume), and relationship maintenance (Liu et al., this volume; Taylor & Choi, this volume). The studies here also explored many different facets of well-being, including social connectedness (Anderl et al., this volume), stigma (Lee et al., this volume), loneliness (Taylor & Choi, this volume), mental health (García-Manglano et al. this volume), and relationship satisfaction (Liu et al., this volume).
Unfortunately, the pandemic prevented some of our technologically creative proposals from being completed (notably, one study involving robots and another on selfies). However, the article by Liu, Kang, and Wei appears to be the first study published in JSPR to explore the implications of using artificial intelligence (AI) in personal relationships, specifically looking at how AI augmentation of a personal message affects relational uncertainty and satisfaction. Further, Taylor and Choi are among very few publications in JSPR that have examined how people perceive the role of algorithms on social media. Other studies examined technologies such as social media (Black, this volume; Yue et al., this volume), texting (Huang & Yao, this volume), and smartphones (Anderl et al., this volume; Garcia et al., this volume).
Some studies here also directly address some critical shortcomings we have noted regarding the study of technology. For example, some papers advance our knowledge by interrogating channel affordances (e.g., Chen & Lu, this volume; Lee et al., this volume). Other studies examined multiple channels. For example, Buehler et al. adopted the communication interdependence perspective, examining if multiple channels were used in a segmented fashion or more cohesively (Caughlin & Sharabi, 2013), whereas Lee et al. and Chen and Lu examined differences across channels.
We end this description with a brief synopsis of each paper included in this special issue. Interested readers are, of course, encouraged to read each paper.
Anderl et al. conducted a short-term longitudinal study with eighteen data points over six days. Although there have been many studies examining general smartphone usage and well-being outcomes, Anderl et al.‘s unique design allowed them to assess whether screen time usage within the hour prior to the data collection affected participants perceptions of psychological well-being and social connectedness. They were able to determine that greater smartphone use in the preceding hour did lead to lower well-being outcomes. In addition, their work provides evidence that there may be a cyclical relationship between social connectedness and smartphone use leading to ongoing negative effects.
Black conducted two studies in which they first identified behaviors that indicate commitment by romantic partners on social media and second examined the influence of those behaviors on relational satisfaction and security. In doing so, they tested the influence of attachment styles as a moderating variable. This set of studies builds on research on relational communication when people confront attractive alternatives online, and it does so by focusing on romantic relationships on Facebook. These studies not only establish what behaviors signal commitment to romantic partners online but also how such behaviors influence relational well-being.
Buehler et al. synthesized research on family communication patterns and the communicative interdependence perspective (Caughlin & Sharabi, 2013) to develop a model in which they posited that family communication patterns shape people’s interdependent use of technology, which in turn, influences their well-being. This study extends the communicative interdependence perspective to relationships between adult children and their parents. It examines a range of channels of communication and reports indirect effects between family communication patterns and people’s well-being based on their interdependent use of technology with their parents. Buehler et al.‘s study combines previously distinct bodies of research to understand how the use of technology and family communication patterns influence well-being in parent-child relationships.
Chen and Lu combine research on attachment styles and affordances to examine what compels people to seek support via texting or face-to-face channels of communication. They focused on the affordances of accessibility, availability of social cues, and conversation control to understand both how attachment shapes the importance of these affordances and the extent to which the affordances correspond with people’s likelihood to seek support via text message or in a face-to-face interaction. They documented several interesting indirect effects between attachment styles and people’s likelihood of seeking support in different channels based on the importance of the affordances they attributed to those channels. Overall, this study furthers our understanding of the factors that compel people to seek support in various channels.
García-Manglano et al. present longitudinal data spanning four years to examine Spanish youths’ use of social media and the corresponding effects on mental health. The study parses out the influence of different motivations of technology use on the mental health of emerging adults. They note that time spent with digital devices has a small negative effect on mental health but that interventions would do better to target specific motivations such as reducing escapism and increasing communication with family ties.
Huang and Yao consider how violations of expectations related to chronemics, specifically the speed of a reply, shape outcomes in conflict scenarios. A first study works to understand the typical temporal dynamics of contemporary online interactions. A second study documented people’s emotional reactions when those chronemic expectations are violated during a conflict taking place via text-messaging, along with how contextual, personal, and relational variables moderate those outcomes. These studies use a sample of romantic partners and enhance our understanding of the chronemic dynamics at play and their impact on contemporary relationships.
Lui, Kang, and Wei examined how outsourcing relationship maintenance to other people or to artificial intelligence affects recipients. In a 3 × 3 experiment, participants were presented with a hypothetical scenario in which a friend crafts a message to either provide social support, to give advice, or to wish them a happy birthday. Participants were informed the friend either wrote this message on their own, received help from a friend, or received assistance from artificial intelligence. When the friend got help, whether from a friend or an AI, participants perceived the friend expended less effort, which increased partner uncertainty and reduced relationship satisfaction. This study provides some early insights into how AI augmentation of relationship maintenance tasks may affect interpersonal relationships. This study suggests many future directions for research on the use of technology for relationship-related communication and its impact on relational well-being.
Lee et al. explored how specific affordances related to technology use might be related to well-being outcomes. Their framework suggested that college students might choose different technologically-mediated channels for social support depending upon the severity and level of stigma associated with their problems. They found that for participants who prefer using technology to seek social support, affordances such as persistence and conversational control were more likely to facilitate comfort with support seeking as the perceived stigma associated with a stressor increased. The results are unique because the researchers allowed people to select a preferred channel of mediated communication, rather than focusing on a pre-determined channel.
Taylor and Choi examined algorithms in social media, specifically Instagram, from the lens of how the user perceives the algorithm to be responsive or insensitive. Again, we see how investigating specific populations may be of use to researchers interested in technology and well-being. In study one, perceived algorithmic responsiveness (PAR) was related to less loneliness of young adults but more loneliness for older Instagram users. In study two, they employed a longitudinal design to investigate the direction of the relationship between PAR and loneliness. Although they did not find a hypothesized bidirectional relationship, they did find some evidence that loneliness may lead people to view algorithms as less responsive. In addition, perceiving algorithms to be more response led people to engage in more relational maintenance.
Yue et al. explored the relationships among active social media use, perceived social support, and well-being. They sampled residents of Wuhan, China through surveys during and right after lockdowns due to COVID-19. A sequential mediation model determined that active social media use benefits people’s perceived network responsiveness and supportiveness, which then shape their well-being. Interestingly, their findings were largely consistent during and after lockdowns, thereby demonstrating the complementary role of social media and offline interactions. This research contributes to an understanding of the impact of social media-based communication when offline contact is limited and demonstrates the robust associations among active social media use, perceived support, and well-being.
Conclusion
Guided by the belief that the best understanding of technology, relationships, and well-being comes from a thorough integration of these complex bodies of literature, we challenged authors to propose research ideas that synthesized these domains of research. We were, in turn, challenged both by the pandemic and by our own editorial process. Although no special issue could possibly provide a definitive answer to how technology, relationships, and well-being are related, the beginning of an answer to that question requires thinking more deeply about why and how those variables might be connected. Along the same lines, the studies included in this special issue represent a robust body of research that often goes beyond correlating the amount of time people spend on technology with their well-being. Instead, they utilize theory to inform their arguments and isolate variables that might shape the associations among technology, relationships, and well-being. To harken back to Burleson (2009), the task before scholars interested in this domain of research might still be gargantuan, but scholars now have example studies to guide their thinking. As we advised, these studies examine both positive and negative outcomes of using technology for relationships; specify mechanisms through which their effects might be felt; and integrate theorizing on technology, relationships, and well-being to ground their thinking. As technologies come and go, the studies in this special issue are grounded in affordances and research on relational variables and messages that remain stable and can be referenced by future research. Perhaps most encouragingly, these studies embrace the details that make technology, relationships, and well-being so complex and so interesting. There remain many nuances to understanding how and why the use of technology in and about relationships might influence well-being, but the studies included in this special issue set the stage for how such research can be conducted.
Footnotes
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
