communications technology research activity

Articles on Communications technology

Displaying 1 - 20 of 38 articles.

5G: UK risks losing its lead, but some simple steps could prevent that

Mark Kleinman , King's College London

Five myths about email at work and how to cope with communications overload

Emma Russell , Kingston University

Leaked emails: Ramaphosa’s hypocrisy on spying by the South African state

Jane Duncan , University of Johannesburg

End-to -end encryption isn’t enough security for ‘real people’

Megan Squire , Elon University

On the savanna, mobile phones haven’t transformed Maasai lives – yet

Timothy D. Baird , Virginia Tech

How to keep your mobile phone connected when the network is down

Paul Gardner-Stephen , Flinders University

Deep underground, smartphones can save miners’ lives

Sudeep Pasricha , Colorado State University

The next war will be an information war, and we’re not ready for it

David Stupples , City, University of London

How new technologies are shaking up health care

Tim Usherwood , University of Sydney

Talking to Mars: new antenna design could aid interplanetary communication

Jean Paul Santos , University of California, Los Angeles ; Joshua M Kovitz , University of California, Los Angeles , and Yahya Rahmat-Samii , University of California, Los Angeles

A promised ‘right’ to fast internet rings hollow for millions stuck with 20th-century  speeds

Bianca Reisdorf , University of Leicester and Anne-Marie Oostveen , University of Oxford

Three wireless technologies that could make 5G even faster

Mischa Dohler , King's College London

Australia’s got ICT talent – so how do we make the most of it?

Brian Anderson , Australian National University

Emergency services benefit from a high-speed world without wires

Ian Oppermann , CSIRO

We’ve got the iPhone habit, so what’s it doing to our brains?

Ian H Robertson , Trinity College Dublin

All this talk about lights hides bigger energy challenges

Allison Hui , Lancaster University and Elizabeth Shove , Lancaster University

Quantum system teleports an atom

The University of Queensland

Mobile phones are a window to the soul in modern research

John Palmer , Princeton University ; Frederic Bartumeus , Consejo Superior de Investigaciones Científicas (CSIC) , and Thomas Espenshade , Princeton University

US ‘ choke-points ’ for Australian telecoms data are no surprise

Philip Branch , Swinburne University of Technology

We could be superheroes: the era of positive computing

Rafael A Calvo , University of Sydney

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The Past, Present, and Future of Human Communication and Technology Research: An Introduction

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Scott C. D’Urso, The Past, Present, and Future of Human Communication and Technology Research: An Introduction, Journal of Computer-Mediated Communication , Volume 14, Issue 3, 1 April 2009, Pages 708–713, https://doi.org/10.1111/j.1083-6101.2009.01459.x

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The study of computer-mediated communication (CMC) and new communication technologies (NCTs) is an established and growing field not only with respect to the new technologies becoming available, but also in the many ways we are adopting them for use. Historically, I have contended that this area of communication research deserves recognition as a primary area of communication studies alongside that of interpersonal, organizational, health, and rhetorical studies among others. While the CMC area is still in its infancy, its impact on a variety of areas of human existence cannot be ignored. That said, when I began to work on this special section of the Journal of Computer-Mediated Communication ( JCMC ), it led me to more systematically consider the question of its place within the larger discipline of communication. This line of research has been gathering strength for more than 25 years and is now a strong and healthy subdiscipline in communication. This special section of JCMC seeks to tie together its rich past, diverse present, and an exciting future of possibilities and challenges. This takes place through a series of essays by some of the key contributors in the field today.

Most of the established areas of research in communication are centered on a solid base of theories. The CMC field is no different. From the work on social presence ( Short, Williams, & Christie, 1976 ), information (media) richness ( Daft & Lengel, 1984 , 1986 ), critical mass ( Markus, 1987 ), social influence ( Fulk, Schmitz, & Steinfeld, 1990 ), social information processing (SIP) ( Walther, 1992 ), social identity and deindividuation (SIDE) ( Spears & Lea, 1992 ), adaptive structuration ( DeSanctis & Poole, 1994 ), hyperpersonal interaction ( Walther, 1996 ), and channel expansion ( Carlson & Zmud, 1999 ) to the mindfulness/mindlessness work of Timmerman (2002) , theory development is central to CMC research. While it can be argued that some CMC theories are not exclusive to the study of CMC, the same can be said of some of the core theories of other primary areas such as interpersonal and organizational communication. What is more important is that scholars in this field of research are using these theories as the basis for research today.

CMC research continues to find its way into many top journals today (see, for example, Gong & Nass, 2007 ; Katz, 2007 ; Ramirez & Wang, 2008 ; Stephens, 2007 ) within our discipline, as well as in sociology, social psychology, and business management (see, for example, D’Urso & Rains, 2008 ; Katz, Rice, & Aspen, 2001 ; Walther, Loh, & Granka, 2005 ). Key contributions to this field date back over 25 years (see, for example, Barnes & Greller, 1992; Baym, 1995; Chesebro, 1985 ; Hunter & Allen, 1992 ; Jones, 1995 ; Korzenny, 1978 ; Parks & Floyd, 1996 ; Reese, 1988 ; Rice, 1980 ; Rice, 1984 ; Sproull & Kiesler, 1986 ; Steinfield, 1992 ). This diversity of publication outlets and the longevity of this research line are but a few of the examples of the breath and depth of CMC research. One key trait of most established fields is the existence of a flagship journal that is the home for that genre of research. In the case of CMC research, JCMC is considered by many to fulfill that role. Published in an online format since 1995, JCMC is now an official publication of the International Communication Association (ICA). Beyond journal publications, it is rather difficult these days to peruse the bookshelves in communication research and not notice the plethora of volumes dedicated to the study of CMC. The importance of the Internet in today's society has undoubtedly played a role in this publication trend; however, many of the books are scholarly and present some of today's best research in this area.

As has been seen with the number of articles and books published on this topic, the numbers of scholars who study CMC are also increasing. Though a number of the key scholars in this field are housed in other areas such as organizational and interpersonal communication, their work routinely looks at how CMC impacts communication (see Contractor & Eisenberg, 1990 ; Fulk, Flanagin, Kalman, Monge, & Ryan, 1996 ; Rice, 1993 ). One key factor in determining if CMC research should be a distinct subset of communication research can be seen at annual conferences such as the National Communication Association (NCA) and ICA. Here, graduate students who are preparing to enter the job market are seeing more and more openings for faculty positions with CMC as a potential area of specialization. This trend does not appear to be going away anytime soon.

Both NCA and ICA have prominent divisions in their respective organizations concerned with understanding CMC. In ICA, the Communication and Technology Division is now the largest in the entire association. In NCA, the Human Communication and Technology Division has a sustained membership of over 500. Looking back at the past several NCA conference programs, one cannot help but notice the presence of this division through the sponsorship of numerous panels and papers. As the recent Cochair for this division, I felt it was time that we made our presence more prominent within NCA. In 2007, we invited a number of prominent scholars to participate in a unique double-length panel discussion. Each of the 10 panelists, featured in the special section, presented and discussed their thoughts on the past, present and future of research in CMC with the audience. The success of the panel, and the interest generated by the panel, led to this special section.

Having reconsidered my original thoughts on identifying CMC research as a primary area of communication research, I have come to the conclusion that it may have become a moot point. CMC scholars are uniquely positioned to study the vast impact that communication technologies have had and are having on our society. Looking back at the past volumes of JCMC , the diversity of topics covered includes: interpersonal, medical, psychological, organizational, political, behavioral, and management studies. This diversity of research across disciplines places the CMC field in a unique position to be at the heart of many disciplinary endeavors in communication. However, is it a distinct and separate field of communication research? Yes, but without its cross-disciplinary approach, its overall impact on communication research may be seen as implausible.

To highlight the varied aspects of CMC research, this special section presents the thoughts of some of the prominent scholars in today's field of CMC. Rice (this issue) begins with what is most likely unique common experience for many as we struggle with our day-to-day interactions with technology. The particular story that Rice relates to us focuses on the embeddedness of CMC in our lives today and the challenges we face in understanding them in a larger context. These experiences and our understanding of their importance to our research are of particular interest to Baym (this issue) who notes that our interactions with technology are seen as a welcome trend. However, we must remain cautious as to what and how we research CMC, both now and in the future. Parks (this issue) offers that a microlevel approach to studying CMC may be problematic as compared to a broader approach to the technologies and their usage over time. To illustrate this point, Jackson's (this issue) discussion of the blending of technologies and concepts through “mashups” drives home the need for a broader approach to how we not only use, but research CMC.

One of the fastest growing areas of CMC research, social networking, represents what Barnes (this issue) considers another aspect of the convergence of CMC and human interaction. This falls in line with Contractor's (this issue) call for understanding the motivations behind why we seek these networked connections through mediated means. The development of future theory and research in this area will have the potential for far reaching implications across the CMC discipline.

From a theory standpoint, Walther (this issue) wonders whether our fields' development suffers from efforts at theoretical consolidation, rather than diversification of explanations and their boundary conditions that are critical in CMC research. Scott (this issue) provides potential directions for research and theory development, but does so with caution, because as he explains, “we can't keep up” with the technological innovations, and it may not be in our best interest to do so. Poole (this issue) sees consolidation of our efforts as a potential route through a combined process of data collection and sharing similar to how other disciplines operate. However we choose to proceed, it is clear, as Fulk and Gould (this issue) note, that we face many challenges ahead, but that the potential to really enhance the field of CMC research lies in our ability to meet these challenges.

I hope you enjoy what we have assembled here in this special section. There are many areas of research, theory development, and new communication technologies for us to ponder now and in the future. We find ourselves in an exciting period in CMC research history and the future looks very promising.

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Markus , M. L. ( 1987 ). Toward a “critical mass” theory of interactive media: Universal access, interdependence and diffusion . Communication Research , 14 , 491 – 511 .

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Ramirez , A. & Wang , Z. ( 2008 ). When online meets offline: An expectancy violations theory perspective on modality switching . Journal of Communication , 58 , 20 – 39 .

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Rice , R. E. ( 1993 ). Media appropriateness: Using social presence theory to compare traditional and new organizational media . Human Communication Research , 19 , 451 – 484 .

Short , J. , Williams , E. , & Christie , B. ( 1976 ). The social psychology of telecommunication . London : John Wiley .

Spears , R , & Lea , M. ( 1992 ). Social influence and the influence of the “social” in computer-mediated communication . In M. Lea (Ed.), Contexts of computer-mediated communication (pp. 30 – 65 ). London : Harvester-Wheatsheaf .

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Timmerman , C. E. ( 2002 ). The moderating effect of mindlessness/mindfulness upon media richness and social influence explanations of organizational media use . Communication Monographs , 69 , 111 – 131 .

Walther , J. B. ( 1992 ). Interpersonal effects in computer-mediated interaction: A relational perspective . Communication Research , 19 , 52 – 90 .

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Scott C. D’Urso (Ph.D., 2004, University of Texas at Austin) is an Assistant Professor of Communication Studies at Marquette University, where he teaches courses focused on organizational and corporate communication and new communication technology. Scott's primary research interests include organizational use of communication technologies such as e-mail, instant messaging and chat. He has published manuscripts on privacy and surveillance in the workplace, communication channel selection, crisis communication and stakeholder issues. He is currently working on several projects including digital divides in organizations, virtual team decision-making, and the role of online identity creation and privacy concerns with social networking websites. Prior to a career in academia, Scott worked for several years as a multimedia specialist/manager of a multimedia production department for a government defense contractor in the Southwest.

The author wishes to thank Yun Xia, and all of the officers of the Human Communication and Technology Division of NCA (past and present) as well as all of the authors who contributed to this special section, and finally, Aimee R. Hardinger, who served as editorial assistant for this special section.

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Probing the Role of Information and Communication Technology (ICT) in Enhancing Research: An Epilogue of Accessible Research Tools

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Information and Communication Technology (ICT) has revolutionized the way researchers conduct their work. It has enabled them to access a wealth of information through online databases, collaborate with colleagues across the globe, and analyze vast amounts of data quickly and accurately. This paper explores the role of ICT in enhancing research tools, highlighting the benefits it provides to researchers in terms of increased efficiency, improved accuracy, and greater access to resources. It also discusses some of the challenges associated with using ICT in research, such as data security and privacy concerns, and offers potential solutions. Overall, the paper concludes that ICT is an essential tool for researchers and will continue to play an increasingly important role in advancing scientific knowledge and innovation.

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Vinay, M., Jayapriya, J. (2023). Probing the Role of Information and Communication Technology (ICT) in Enhancing Research: An Epilogue of Accessible Research Tools. In: Tuba, M., Akashe, S., Joshi, A. (eds) ICT Infrastructure and Computing. ICT4SD 2023. Lecture Notes in Networks and Systems, vol 754. Springer, Singapore. https://doi.org/10.1007/978-981-99-4932-8_47

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Technology and Communication Research Paper Topics

• Archiving of Internet Content
• Code as Law
• Communication Infrastructure
• Communication Technology and Democracy
• Communication Technology Standards
• Crime and Communication Technology
• Development of Information and Communication Technology
• Digital Divide
• Digitization and Media Convergence
• Domain Names
• Domestication of Technology
• Economics of Technology
• E-Government
• Human–Computer Interaction
• Information and Communication
• Information Literacy
• Information Overload
• Information Society
• Internet Ratings Systems
• Internet Research Ethics
• Language and the Internet
• Link Analysis
• Log-File Analysis
• Network Organizations through Communication Technology
• Online Media
• Open Access Journals
• Open Source
• P2P Networking
• Personal Communication by CMC
• Personal Publishing
• Search Engines
• Sex and Pornography Online
• Social Construction of Technology
• Technology and Globalization
• Technology as Fashion
• Technology Assessment
• Technology for Mobility
• Technology of Internet
• Terrorism and Communication Technologies
• Ubiquitous Computing
• Virtual Communities

Endogenous and Exogenous Perspective

ICT innovation can be treated conceptually as either exogenous or endogenous to a social system. The exogenous perspective treats ICTs as if they are objects isolated from the social, political, and economic environment in which they are produced and consumed. If it is technology that is the determining factor in social organization, then what is left for the researcher is an observer role. The exogenous perspective emphasizes the efficiency and rationality of an autonomous technological system where there is little room for human agency.

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In contrast, from an endogenous perspective, research focuses on the way ICTs become woven into the fabric of life – in terms of morality, the economy, culture, or the political world and on the specific, material conditions under which technology is produced and consumed. Technology is regarded as part of the social fabric where actors sanction certain forms of change and not others. Power is understood to be located in the interwoven alignment of state (administrative and military), private capital, and civil society interests. In this view, the emphasis is on the way technology mediates human relationships and on the constraints that distort benefits that might otherwise accrue to those who are not at the center of economic and political power (Curran et al. 2012; Mansell 2012; Silverstone 2007).

Among the many strands of research in this context are studies of the political implications of the information society for democracy and participation in public debate and in electoral processes and whether a right to communicate should be enshrined in international law (Jørgensen 2013). Within sociology, research on the domestication of technologies (Hartmann 2013) has helped to reveal that ICT artifacts are not prefigured by technology designers for their users, and that older and newer media and ICTs are appropriated in unpredictable ways depending on the cultural specificities of their use. Economic analysis tends to focus on the diffusion of ICTs and the implications for productivity in the economy since these technologies are classed as general-purpose technologies and associated with major transformations when they become widely dispersed across all ICT-using sectors of the economy (Freeman 2007).

Research Topics

The disruptive characteristics of innovations in ICTs have given rise to many debates about their positive or negative implications for the global order, with research emphasizing links between local and distant places and the sometimes unifying, and at other times fragmenting, consequences. There is no stable definition but the term virtual community generally applies to online interactions that give rise to new forms of relationships and new organizational forms. Research focuses on the network relations among activists, bloggers, scientists and many other communities of users of social media. The digital platforms that support these communicative activities are increasingly being used by researchers to map the architecture of networks and social relations with a focus on the directionality of communication, synchronicity, content modularity, interactivity, personalization, and meaning construction. Research on issues of information control, privacy, and security raised by user-generated content, the co-creation of content, and interactive Web 2.0 applications is beginning to tackle the implications of ‘big data’ analytics which uses web-harvesting, ratings systems, and identity profiling to support corporate and state information collection and processing activities (Mayer- Schönberger & Cukier 2013). Digital means of interacting online support the networking activities of individuals and of networked organizations which enable virtual teamworking and outsourcing, raising questions about the ownership of creative capabilities, privacy, and trust, whether the public can have confidence in the digital services provided by governments, and whether new forms of interaction are consistent with democratic practice.

When the diffusion of ICTs is uneven, or where the distribution of the gains as a result of investing in them is uneven, this is referred to as a digital divide. For some it is an article of faith that ICTs hold the solutions to economic, political, and cultural problems, while others argue that digital divides mean that it is unlikely that these technologies will alleviate deeply rooted social and economic problems. This concept has been criticized for its oversimplification of the factors that give rise to inequality and research focusing on digital literacies (including information literacy or media literacy) and cultural differences have yielded insight into the many forms and consequences of digital exclusion (Livingstone and Helsper 2010). Differences in views about the relationship between technology and communication and the persistence of digital divides are reflected in research on whether a global media and communication policy environment is feasible and the roles of the nation state and multistakeholder groups in governing digital media.

Different framings of the relationship between globalization and communication are echoed in research on the governance regimes that enable the production and consumption of ICTs and media content, locally and globally. The governance of Internet has become a hotly contested area of research drawing on legal expertise and examining the values embedded in the architecture of the Internet and other digital applications. Brown and Marsden (2013) provide comprehensive examinations of the proliferation of policies, regulations, and legislation in response to the global spread of digital networks and their applications, especially the Internet. In addition, there is research on specific online behaviors and whether there should be sanctions for ‘bad’ behavior in the case of hacktivism or crime and terrorism.

Finally, the relationship between technology and communication raises many issues with respect to ethical conduct within the humanities and the social sciences. Guidelines with respect to Internet-related research have been developed nationally and by organizations such as the Association of Internet Researchers (AoIR). Different methods raise concerns about the risks involved to researchers and to those they study.

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• Castells, M. (2009). Communication Power. Oxford: Oxford University Press.
• Curran, J., Fenton, N., & Freedman, D. (2012). Misunderstanding the internet. London: Routledge.
• Freeman, C. (2007). The ICT paradigm. In R. Mansell, C. Avgerou, D. Quah, & R. Silverstone (eds.), The Oxford handbook of information and communication technologies. Oxford: Oxford University Press, pp. 34–54.
• Hartmann, M. (2013). From domestication to mediated mobilism. Mobile Media and Communication, 1(1), 42–49.
• Jørgensen, R. F. (2013). Framing the net: The Internet and human rights. Cheltenham: Edward Elgar.
• Livingstone, S. & Helsper, E. (2010). Balancing opportunities and risks in teenagers’ use of the Internet: The role of online skills and Internet self-efficacy. New Media & Society, 12(2), 309–329.
• Mansell, R. (2012). Imagining the Internet: Communication, innovation and governance. Oxford: Oxford University Press.
• Mayer-Schönberger, V. & Cukier, K. (2013). Big data: a revolution that will transform how we live, work and think. London: John Murray.
• Silverstone, R. (2007). Media and morality: On the rise of the mediapolis. Cambridge: Polity.

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Karunaratne, Indika & Atukorale, Ajantha & Perera, Hemamali. (2011). Surveillance of human- computer interactions: A way forward to detection of users' Psychological Distress. 2011 IEEE Colloquium on Humanities, Science and Engineering, CHUSER 2011. 10.1109/CHUSER.2011.6163779.

June 9, 2023 / 1 comment / Reading Time: ~ 12 minutes

The Digital Revolution: How Technology is Changing the Way We Communicate and Interact

This article examines the impact of technology on human interaction and explores the ever-evolving landscape of communication. With the rapid advancement of technology, the methods and modes of communication have undergone a significant transformation. This article investigates both the positive and negative implications of this digitalization. Technological innovations, such as smartphones, social media, and instant messaging apps, have provided unprecedented accessibility and convenience, allowing people to connect effortlessly across distances. However, concerns have arisen regarding the quality and authenticity of these interactions. The article explores the benefits of technology, including improved connectivity, enhanced information sharing, and expanded opportunities for collaboration. It also discusses potential negative effects including a decline in in-person interactions, a loss of empathy, and an increase in online anxiety. This article tries to expand our comprehension of the changing nature of communication in the digital age by exposing the many ways that technology has an impact on interpersonal interactions. It emphasizes the necessity of intentional and thoughtful communication techniques to preserve meaningful connections in a society that is becoming more and more reliant on technology.

Introduction:

Technology has significantly transformed our modes of communication and interaction, revolutionizing the way we connect with one another over the past few decades. However, the COVID-19 pandemic has acted as a catalyst, expediting this transformative process, and necessitating our exclusive reliance on digital tools for socializing, working, and learning. Platforms like social media and video conferencing have emerged in recent years, expanding our options for virtual communication. The impact of these changes on our lives cannot be ignored. In this article, we will delve into the ways in which technology has altered our communication and interaction patterns and explore the consequences of these changes for our relationships, mental well-being, and society.

To gain a deeper understanding of this topic, I have conducted interviews and surveys, allowing us to gather firsthand insights from individuals of various backgrounds. Additionally, we will compare this firsthand information with the perspectives shared by experts in the field. By drawing on both personal experiences and expert opinions, we seek to provide a comprehensive analysis of how technology influences our interpersonal connections. Through this research, we hope to get a deeper comprehension of the complex interactions between technology and people, enabling us to move mindfully and purposefully through the rapidly changing digital environment.

The Evolution of Communication: From Face-to-Face to Digital Connections:

In the realm of communication, we have various mediums at our disposal, such as face-to-face interactions, telephone conversations, and internet-based communication. According to Nancy Baym, an expert in the field of technology and human connections, face-to-face communication is often regarded as the most personal and intimate, while the phone provides a more personal touch than the internet. She explains this in her book Personal Connections in the Digital Age by stating, “Face-to-face is much more personal; phone is personal as well, but not as intimate as face-to-face… Internet would definitely be the least personal, followed by the phone (which at least has the vocal satisfaction) and the most personal would be face-to-face” (Baym 2015).  These distinctions suggest that different communication mediums are perceived to have varying levels of effectiveness in conveying emotion and building relationships. This distinction raises thought-provoking questions about the impact of technology on our ability to forge meaningful connections. While the internet offers unparalleled convenience and connectivity, it is essential to recognize its limitations in reproducing the depth of personal interaction found in face-to-face encounters. These limitations may be attributed to the absence of nonverbal cues, such as facial expressions, body language, and tone of voice, which are vital elements in understanding and interpreting emotions accurately.

Traditionally, face-to-face interactions held a prominent role as the primary means of communication, facilitating personal and intimate connections. However, the rise of technology has brought about significant changes, making communication more convenient but potentially less personal. The rise of phones, instant messaging, and social media platforms has revolutionized how we connect with others. While these digital tools offer instant connectivity and enable us to bridge geographical distances, they introduce a layer of blockage that may impact the depth and quality of our interactions. It is worth noting that different communication mediums have their strengths and limitations. Phone conversations, for instance, retain a certain level of personal connection through vocal interactions, allowing for the conveyance of emotions and tones that text-based communication may lack. However, even with this advantage, phone conversations still fall short of the depth and richness found in face-to-face interactions, as they lack visual cues and physical presence.

Internet-based communication, on the other hand, is considered the least personal medium. Online interactions often rely on text-based exchanges, which may not fully capture the nuances of expression, tone, and body language. While the internet offers the ability to connect with a vast network of individuals and share information on a global scale, it may not facilitate the same depth and authenticity that in-person or phone conversations can provide. As a result, establishing meaningful connections and building genuine relationships in an online setting can be challenging. Research and observations support these ideas. Figure 1. titled “Social Interaction after Electronic Media Use,” shows the potential impact of electronic media on social interaction (source: ResearchGate). This research highlights the need to carefully consider the effects of technology on our interpersonal connections. While technology offers convenience and connectivity, it is essential to strike a balance, ensuring that we do not sacrifice the benefits of face-to-face interactions for the sake of digital convenience.

Figure 1:  Increased reliance on electronic media has led to a noticeable decrease in social interaction.

The Limitations and Effects of Digital Communication

In today’s digital age, the limitations and effects of digital communication are becoming increasingly evident. While the phone and internet offer undeniable benefits such as convenience and the ability to connect with people regardless of geographical distance, they fall short in capturing the depth and richness of a face-to-face conversation. The ability to be in the same physical space as the person we’re communicating with, observing their facial expressions, body language, and truly feeling their presence, is something unique and irreplaceable.

Ulrike Schultze, in her thought-provoking TED Talk titled “How Social Media Shapes Identity,” delves further into the impact of digital communication on our lives by stating, “we construct the technology, but the technology also constructs us. We become what technology allows us to become” (Schultze 2015). This concept highlights how our reliance on digital media for interaction has led to a transformation in how we express ourselves and relate to others.

The influence of social media has been profound in shaping our communication patterns and interpersonal dynamics. Research conducted by Kalpathy Subramanian (2017) examined the influence of social media on interpersonal communication, highlighting the changes it brings to the way we interact and express ourselves (Subramanian 2017). The study found that online communication often involves the use of abbreviations, emoticons, and hashtags, which have become embedded in our online discourse. These digital communication shortcuts prioritize speed and efficiency, but they also contribute to a shift away from the physical action of face-to-face conversation, where nonverbal cues and deeper emotional connections can be fostered.

Additionally, the study emphasizes the impact of social media on self-presentation and identity construction. With the rise of platforms like Facebook, Instagram, and Twitter, individuals have a platform to curate and present themselves to the world. This online self-presentation can influence how we perceive ourselves and how others perceive us, potentially shaping our identities in the process. The study further suggests that the emphasis on self-presentation and the pressure to maintain a certain image on social media can lead to increased stress and anxiety among users.

Interviews:

I conducted interviews with individuals from different age groups to gain diverse perspectives on how technology and social media have transformed the way we connect with others. By exploring the experiences of a 21-year-old student and an individual in their 40s, we can better understand the evolving dynamics of interpersonal communication in the digital age. These interviews shed light on the prevalence of digital communication among younger generations, their preference for convenience, and the concerns raised by individuals from older age groups regarding the potential loss of deeper emotional connections.

When I asked the 21-year-old classmate about how technology has changed the way they interact with people in person, they expressed, “To be honest, I spend more time texting, messaging, or posting on social media than actually talking face-to-face with others. It’s just so much more convenient.” This response highlights the prevalence of digital communication among younger generations and their preference for convenience over traditional face-to-face interactions. It suggests that technology has significantly transformed the way young people engage with others, with a greater reliance on virtual interactions rather than in-person conversations. Additionally, the mention of convenience as a driving factor raises questions about the potential trade-offs in terms of depth and quality of interpersonal connections.

To gain insight from an individual in their 40s, I conducted another interview. When asked about their experiences with technology and social media, they shared valuable perspectives. They mentioned that while they appreciate the convenience and accessibility offered by technology, they also expressed concerns about its impact on interpersonal connections. They emphasized the importance of face-to-face interactions in building genuine relationships and expressed reservations about the potential loss of deeper emotional connections in digital communication. Additionally, they discussed the challenges of adapting to rapid technological advancements and the potential generational divide in communication preferences.

Comparing the responses from both interviews, it is evident that there are generational differences in the perception and use of technology for communication. While the 21-year-old classmate emphasized convenience as a primary factor in favor of digital communication, the individual in their 40s highlighted the importance of face-to-face interactions and expressed concerns about the potential loss of meaningful connections in the digital realm. This comparison raises questions about the potential impact of technology on the depth and quality of interpersonal relationships across different age groups. It also invites further exploration into how societal norms and technological advancements shape individuals’ preferences and experiences.

Overall, the interviews revealed a shift towards digital communication among both younger and older individuals, with varying perspectives. While convenience and connectivity are valued, concerns were raised regarding the potential drawbacks, including the pressure to maintain an idealized online presence and the potential loss of genuine connections. It is evident that technology and social media have transformed the way we communicate and interact with others, but the interviews also highlighted the importance of maintaining a balance and recognizing the value of face-to-face interactions in fostering meaningful relationships.

I have recently conducted a survey with my classmates to gather insights on how technology and social media have influenced communication and interaction among students in their daily lives. Although the number of responses is relatively small, the collected data allows us to gain a glimpse into individual experiences and perspectives on this matter.

One of the questions asked in the survey was how often students rely on digital communication methods, such as texting, messaging, or social media, in comparison to engaging in face-to-face conversations. The responses indicated a clear trend towards increased reliance on digital communication, with 85% of participants stating that they frequently use digital platforms as their primary means of communication. This suggests a significant shift away from traditional face-to-face interactions, highlighting the pervasive influence of technology in shaping our communication habits.

Furthermore, the survey explored changes in the quality of interactions and relationships due to the increased use of technology and social media. Interestingly, 63% of respondents reported that they had noticed a decrease in the depth and intimacy of their connections since incorporating more digital communication into their lives. Many participants expressed concerns about the difficulty of conveying emotions effectively through digital channels and the lack of non-verbal cues that are present in face-to-face interactions. It is important to note that while the survey results provide valuable insights into individual experiences, they are not representative of the entire student population. The small sample size limits the generalizability of the findings. However, the data collected does shed light on the potential impact of technology and social media on communication and interaction patterns among students.

Expanding on the topic, I found an insightful figure from Business Insider that sheds light on how people utilize their smartphones (Business Insider). Figure 2. illustrates the average smartphone owner’s daily time spent on various activities. Notably, communication activities such as texting, talking, and social networking account for a significant portion, comprising 59% of phone usage. This data reinforces the impact of digital communication on our daily lives, indicating the substantial role it plays in shaping our interactions with others.  Upon comparing this research with the data, I have gathered, a clear trend emerges, highlighting that an increasing number of individuals primarily utilize their smartphones for communication and interaction purposes.

Figure 2: The breakdown of daily smartphone usage among average users clearly demonstrates that the phone is primarily used for interactions.

The Digital Make Over:

In today’s digital age, the impact of technology on communication and interaction is evident, particularly in educational settings. As a college student, I have witnessed the transformation firsthand, especially with the onset of the COVID-19 pandemic. The convenience of online submissions for assignments has led to a growing trend of students opting to skip physical classes, relying on the ability to submit their work remotely. Unfortunately, this shift has resulted in a decline in face-to-face interactions and communication among classmates and instructors.

The decrease in physical attendance raises concerns about the potential consequences for both learning and social connections within the academic community. Classroom discussions, collaborative projects, and networking opportunities are often fostered through in-person interactions. By limiting these experiences, students may miss out on valuable learning moments, diverse perspectives, and the chance to establish meaningful connections with their peers and instructors.

Simon Lindgren, in his thought-provoking Ted Talk , “Media Are Not Social, but People Are,” delves deeper into the effects of technology and social media on our interactions. Lindgren highlights a significant point by suggesting that while technology may have the potential to make us better individuals, we must also recognize its potential pitfalls. Social media, for instance, can create filter bubbles that limit our exposure to diverse viewpoints, making us less in touch with reality and more narrow-minded. This cautionary reminder emphasizes the need to approach social media thoughtfully, seeking out diverse perspectives and avoiding the pitfalls of echo chambers. Furthermore, it is crucial to strike a balance between utilizing technology for educational purposes and embracing the benefits of in-person interactions. While technology undoubtedly facilitates certain aspects of education, such as online learning platforms and digital resources, we must not overlook the importance of face-to-face communication. In-person interactions allow for nuanced non-verbal cues, deeper emotional connections, and real-time engagement that contribute to a more comprehensive learning experience.

A study conducted by Times Higher Education delved into this topic, providing valuable insights. Figure 3. from the study illustrates a significant drop in attendance levels after the pandemic’s onset. Undeniably, technology played a crucial role in facilitating the transition to online learning. However, it is important to acknowledge that this shift has also led to a decline in face-to-face interactions, which have long been regarded as essential for effective communication and relationship-building. While technology continues to evolve and reshape the educational landscape, it is imperative that we remain mindful of its impact on communication and interaction. Striking a balance between digital tools and in-person engagement can help ensure that we leverage the benefits of technology while preserving the richness of face-to-face interactions. By doing so, we can foster a holistic educational experience that encompasses the best of both worlds and cultivates meaningful connections among students, instructors, and the academic community.

Figure 3:  This graph offers convincing proof that the COVID-19 pandemic and the extensive use of online submission techniques are to blame for the sharp reduction in in-person student attendance.

When asked about the impact of online submissions for assignments on physical attendance in classes, the survey revealed mixed responses. While 73% of participants admitted that the convenience of online submissions has led them to skip classes occasionally, 27% emphasized the importance of in-person attendance for better learning outcomes and social interactions. This finding suggests that while technology offers convenience, it also poses challenges in maintaining regular face-to-face interactions, potentially hindering educational and social development, and especially damaging the way we communicate and interact with one another. Students are doing this from a young age, and it comes into huge effect once they are trying to enter the work force and interact with others. When examining the survey data alongside the findings from Times Higher Education, striking similarities become apparent regarding how students approach attending classes in person with the overall conclusion being a massive decrease in students attending class which hinders the chance for real life interaction and communication. the convenience and instant gratification provided by technology can create a sense of detachment and impatience in interpersonal interactions. Online platforms allow for quick and immediate responses, and individuals can easily disconnect or switch between conversations. This can result in a lack of attentiveness and reduced focus on the person with whom one is communicating, leading to a superficial engagement that may hinder the establishment of genuine connections.

Conclusion:

Ultimately, the digital revolution has profoundly transformed the way we communicate and interact with one another. The COVID-19 pandemic has accelerated this transformation, leading to increased reliance on digital tools for socializing, working, and learning. While technology offers convenience and connectivity, it also introduces limitations and potential drawbacks. The shift towards digital communication raises concerns about the depth and quality of our connections, as well as the potential loss of face-to-face interactions. However, it is essential to strike a balance between digital and in-person engagement, recognizing the unique value of physical presence, non-verbal cues, and deeper emotional connections that face-to-face interactions provide. By navigating the digital landscape with mindfulness and intentionality, we can harness the transformative power of technology while preserving and nurturing the essential elements of human connection.

Moving forward, it is crucial to consider the impact of technology on our relationships, mental well-being, and society. As technology continues to evolve, we must be cautious of its potential pitfalls, such as the emphasis on self-presentation, the potential for increased stress and anxiety, and the risk of forgetting how to interact in person. Striking a balance between digital and face-to-face interactions can help ensure that technology enhances, rather than replaces, genuine human connections. By prioritizing meaningful engagement, valuing personal interactions, and leveraging the benefits of technology without compromising the depth and quality of our relationships, we can navigate the digital revolution in a way that enriches our lives and fosters authentic connections.

References:

Ballve, M. (2013, June 5). How much time do we really spend on our smartphones every day? Business Insider. Retrieved April 27, 2023. https://www.businessinsider.com/how-much-time-do-we-spend-on-smartphones-2013-6

Baym, N. (2015). Personal Connections in the Digital Age (2nd ed.). Polity.

Karunaratne, Indika & Atukorale, Ajantha & Perera, Hemamali. (2011). Surveillance of human-       computer interactions: A way forward to detection of users’ Psychological Distress. 2011 IEEE Colloquium on Humanities, Science and Engineering, CHUSER 2011.             10.1109/CHUSER.2011.6163779.  https://www.researchgate.net/figure/Social-interaction-vs-electronic-media-use-Hours-per-day-of-face-to-face-social_fig1_254056654

Lindgren, S. (2015, May 20). Media are not social, but people are | Simon Lindgren | TEDxUmeå . YouTube. Retrieved April 27, 2023, from https://www.youtube.com/watch?v=nQ5S7VIWE6k

Ross, J., McKie, A., Havergal, C., Lem, P., & Basken, P. (2022, October 24). Class attendance plummets post-Covid . Times Higher Education (THE). Retrieved April 27, 2023, from https://www.timeshighereducation.com/news/class-attendance-plummets-post-covid

Schultze, U. (2015, April 23). How social media shapes identity | Ulrike Schultze | TEDxSMU . YouTube. Retrieved April 27, 2023, from https://www.youtube.com/watch?v=CSpyZor-Byk

Subramanian, Dr. K .R. “Influence of Social Media in Interpersonal Communication – Researchgate.” ResearchGate.Net , www.researchgate.net/profile/Kalpathy-Subramanian/publication/319422885_Influence_of_Social_Media_in_Interpersonal_Communication/links/59a96d950f7e9b2790120fea/Influence-of-Social-Media-in-Interpersonal-Communication.pdf. Accessed 12 May 2023 .

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Is Technology Enhancing or Hindering Interpersonal Communication? A Framework and Preliminary Results to Examine the Relationship Between Technology Use and Nonverbal Decoding Skill

Mollie a. ruben.

1 Department of Psychology, University of Maine, Orono, ME, United States

2 Center for Healthcare Organization and Implementation Research, VA Boston Healthcare System, Boston, MA, United States

Morgan D. Stosic

Jessica correale, danielle blanch-hartigan.

3 Department of Natural and Applied Sciences, Bentley University, Waltham, MA, United States

Associated Data

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Digital technology has facilitated additional means for human communication, allowing social connections across communities, cultures, and continents. However, little is known about the effect these communication technologies have on the ability to accurately recognize and utilize nonverbal behavior cues. We present two competing theories, which suggest (1) the potential for technology use to enhance nonverbal decoding skill or, (2) the potential for technology use to hinder nonverbal decoding skill. We present preliminary results from two studies to test these hypotheses. Study 1 ( N = 410) found that global screen time was unrelated to nonverbal decoding skill. However, how participants spent their time using technology mattered. Participants who reported more active technology use (i.e., posting content) self-reported that their nonverbal decoding skill (as measured by the Emotional Sensitivity subscale of the Social Skills Inventory) was superior but performed worse on objective measures of decoding skill (using standardized tests including the Diagnostic Analysis of Nonverbal Accuracy-Adult Faces and the Workplace Interpersonal Perception Skill). By contrast, passive users performed significantly better on objective measures of nonverbal decoding skill; although they did not self-report any difference in their skill compared to less passive users. Study 2 ( N = 190), and a mini-meta analysis of both studies, replicated this pattern. These effects suggest a roadmap for understanding the theoretical relationship between technology use and nonverbal communication skills. We also provide recommendations for future research, including the use of experimental designs to determine causal pathways and to advance our conceptual understanding of the relationship between technology use and nonverbal decoding skill.

Introduction

A young-professional is woken up to the sound of a buzzing alarm, and grudgingly rolls over to grab their phone. Perhaps this individual begins their morning by passively scrolling through their Facebook feed in order to determine their colleague’s reaction to the heated presidential debate the night before. Or maybe they snap a quick picture of their #OOTD (i.e., Outfit of the Day) to send to their close friend. After returning home from a long day of work-based videoconference calls, this individual may spend the next few hours sucked into the whereabouts of their favorite social media influencer, or casually swiping through some dating profiles. Before retiring to bed, however, they make sure to post a quick inspiring quote to their Twitter profile.

This scenario, while fictitious, illustrates the increasing relationship many individuals have with technology from the instant they wake up, to the instant they go to bed. Technology serves various functions, from increasing office productivity, facilitating big data collection, enhancing record keeping, and above all else, providing a distinctly digital way for humans to communicate with one another. Indeed, the rate of communicative instances via technology per day in 2020 is astounding: 350 million photos uploaded to Facebook, 500 million tweets, 3 billion snapchats, and over 26 billion texts by Americans alone ( Aslam, 2020a , b ; Sayce, 2020 ; Tocci, 2020 ).

While the digital revolution has certainly changed the way individuals can communicate, little empirical results exists regarding the effect of technology on an individual’s communication skills. Specifically, because technology markedly changes the available information individual’s use to decode the communicative intents of others (e.g., determining a friend’s emotional state via short text message instead of their facial expression), are those who spend large quantities of time communicating online better or worse decoders of nonverbal information? Not only is nonverbal decoding a crucial component of general social and communication skills, but it has been tied to better interpersonal outcomes (e.g., Hall et al., 2009 ), can be easily assessed with validated, reliable, and standardized objective measures, and can be improved with practice and feedback trainings (e.g., Schlegel et al., 2017b ). Therefore, the question of whether technology may affect nonverbal decoding, or how accurately a perceiver can recognize and interpret the nonverbal behaviors of another person, is important to empirically address.

Supplementing or even fully replacing face-to-face communication with technology-mediated communication affects both the number of nonverbal cues, as well as the types of nonverbal cues that individuals use to decode communicative meaning ( Vinciarelli, 2017 ). For example, text messages may not allow access to important vocal cues (e.g., pitch, tone, inflections), but may have distinct timing and spacing cues to draw from Döring and Pöschl (2008) . By contrast, video conferencing technologies may allow access to vocal cues, but may limit the ability to engage in mutual eye gaze or perceive body movements and gestures ( Ferrán-Urdaneta and Storck, 1997 ; Neureiter et al., 2013 ). If individuals rely more heavily on technology-mediated, as opposed to face-to-face, interactions as a primary means of communication, it seems likely that the nonverbal decoding skill individuals ordinarily employ in face-to-face communication would be impacted (e.g., worsened, or perhaps enhanced).

This paper applies communication skills theories and conceptual accounts of technology use to examine the role of technology use on an individual’s ability to accurately perceive the nonverbal behavior displayed by others (i.e., nonverbal decoding skill). For the purposes of this paper, we define technology use as any technology or application on a smart phone that contributes to communication online (e.g., use of social media sites, texting, emailing). Cell phone use is the predominant method of technology use by young adults in the United States today with 96% of 18–26 years-old young adults reporting ownership of a smart phone ( Pew Research Center., 2019 ). Therefore, for the remainder of the paper, when discussing technology use, we are referring specifically to smart phone use.

We start by reviewing two competing hypotheses, that technology use either enhances or hinders communication skills. We then present results from two cross-sectional studies and a mini meta-analysis of these studies on the relationship between technology use and nonverbal decoding skill to inform our understanding of which of the competing hypotheses is more likely supported. Finally, we make recommendations for future research aimed at disentangling the causal relationship between technology use and nonverbal decoding skill.

Technology Use May Enhance Communication Skills

The most effective way to improve nonverbal decoding skill is by practicing decoding nonverbal cues and receiving feedback on the accuracy of one’s perceptions ( Blanch-Hartigan et al., 2012 ; Schlegel et al., 2017a ). Regarding the relationship between technology use and nonverbal decoding skill, some theorists have argued that technology-mediated communication may enhance communication skills by providing a safe environment to practice sending and receiving nonverbal cues, and allowing for feedback regarding the accuracy of one’s perceptions (e.g., Stritzke et al., 2004 ; Ellison et al., 2007 ; Valkenburg and Peter, 2009 ). Because it is unusual in face-to-face interactions to receive feedback about one’s decoding ability, it may be that spending more time using technology to interact with others may facilitate face-to-face interactions by providing this type of practice and feedback to users on a regular basis.

Liberated Relationship Perspective

One hypothesis which falls into this “enhancement” framework is the Liberated Relationships Perspective ( Hu et al., 2004 ). This theory argues that increased internet usage has allowed individuals who may not typically engage in conversation the opportunity to engage with one another through technology-mediated communication. Some of the constraints may be psychological, such as in cases of shyness and social anxiety ( Stritzke et al., 2004 ), or physical, such as in cases of distant geographical locations ( Ellison et al., 2007 ). According to this framework, internet usage may afford an increase in the number of interactions an individual is able to engage in. If the internet supplements, instead of detracts from, face-to-face interactions, individuals may have increased opportunities to practice nonverbal decoding with a greater number and variety of communication partners.

Internet Enhanced Self-Disclosure Hypothesis

While not directly related to communication skill, the Internet Enhanced Self-Disclosure Hypothesis also provides support for improved nonverbal decoding skill with increased technology use ( Valkenburg and Peter, 2009 ). This theory posits that greater technology use may enhance social connectedness and wellbeing by enhancing online self-disclosure . The authors define online self-disclosure as “online communication about personal topics that are typically not easily disclosed, such as one’s feelings, worries, and vulnerabilities” (p. 2). Because online platforms allow for the sharing of intimate information to a significantly greater degree than do face-to-face interactions, it is likely that individuals are afforded more opportunities to practice decoding and receive feedback regarding affective information. Individuals who engage in technology-mediated communication more frequently may become more skilled decoders of nonverbal information, perhaps for affective information in particular.

Technology Use May Hinder Communication Skills

While these two “enhancement” theories describe the ways in which increased technology usage may allow individuals more opportunities to practice decoding nonverbal communication, others have argued a competing perspective. Specifically, researchers have argued that technology may hinder specific communication skills. Spending time communicating via technology may result in less face-to-face interactions and therefore less practice decoding nonverbal information in whole, as well as from specific cue channels (e.g., vocal tone) which are reduced or absent in many technology platforms ( Kraut et al., 1998 ; Nie, 2001 ; Patterson, 2019 ). In this way, the type of communication skills learned or practiced in technology-mediated communication are not equivalent to, and may even hinder, the skills required to decode nonverbal behavior in face-to-face interactions.

Reduction Hypothesis

In the early 1990s, several researchers theorized that the internet had detrimental effects on adolescent wellbeing and social connectedness ( Kraut et al., 1998 ; Nie, 2001 ). It was assumed that because the internet motivates adolescents to form superficial online relationships with strangers that are less beneficial than their real-world relationships, time spent online occurs at the expense of time spent with existing relationships. The Reduction Hypothesis posits that it is the lack of or decrease in face-to-face interacting that leads to detrimental communicative consequences rather than technology itself ( Valkenburg and Peter, 2009 ).

Valkenburg and Peter (2009) propose two important updates to this theory based on changes in how individuals use the internet to communicate since the Reduction Hypothesis was first introduced. First, in the second half of the 1990s, it was hard to maintain a pre-existing social network on the internet because not a lot of people had access to it, often resulting in online friends separate from offline friends. Today, with more widespread access and utilization of the internet and social media, individuals spend more time online connecting with people they also spend time with in face-to-face interactions as opposed to forming online-only relationships with strangers ( Valkenburg and Peter, 2009 ). However, the communication skills, such as nonverbal decoding, that individuals develop through online interactions may not translate to actual face-to-face interactions. As such, time spent online may stunt the development of nonverbal decoding necessary for face-to-face interactions. Therefore, although our internet habits have changed, the Reduction Hypothesis is still relevant to theorizing regarding the effects of technology use on nonverbal decoding ability.

Cues-Filtered–Out Theory

In addition to reducing the amount of time individuals spend interacting face-to-face, theorists have also noted that many technology-mediated communication platforms greatly reduce both the number as well as the kinds of nonverbal cues technology users are exposed to. Cues absent from some technology-mediated communication (e.g., social media, texting, emailing) can include physical appearance, tone of voice, facial expression, gaze, posture, touch, space, and gestures ( Kiesler et al., 1984 ; Siegel et al., 1986 ). These nonverbal cues are important in expressing relative status, affect, relationship roles, and many other interpersonal dimensions. This Cues-Filtered-Out Theory ( Culnan and Markus, 1987 ; Sproull and Kiesler, 1986 ) suggests that without these cues available, especially for low bandwidth technology (i.e., communication systems with access to only one or two channels such as vocal, kinesics, or proxemics), certain communicative functions are lost. Although higher bandwidth systems may allow for certain nonverbal cues, these cues are often more obvious and lack complexity, which may cause individuals to lose the ability to decode more subtle nonverbal cues (e.g., facial expressions are more complex than emoji’s, vocal intensity is more complex than CAPITALIZING words). Therefore, this theory suggests that the filtering out of important nonverbal cues (e.g., especially for individuals who use low bandwidth technology systems) impacts an individual’s ability to receive practice and feedback on the accuracy of their nonverbal decoding attempts, thereby hindering nonverbal decoding skill ( Walther and Parks, 2002 ).

Current Research and Hypotheses

The primary objective of the current research is to empirically examine the relationship between technology use and nonverbal decoding skill via two studies and a mini meta-analysis combining results from these two studies. Because individuals may use technology the same amount but differ in how they spend their time online, we measured users’ online communication activity via objective global screen time use taken from iPhone users, as well as the degree of self-reported active technology use (posting selfies and photographs, responding to others’ posts) and the degree of self-reported passive technology use (scrolling through photographs and others’ posts but not responding or posting themselves). In addition, we also sought to be thorough in our assessment of nonverbal decoding skill, as researchers have demonstrated that there are different kinds of decoding skills subsumed by a higher-order global decoding skill ( Schlegel et al., 2017a ). Therefore, we employed three distinct measures of nonverbal decoding, two objective assessments of skill using a standardized, validated, and reliable test of emotion recognition [i.e., Diagnostic Analysis of Nonverbal Accuracy-Adult Faces (DANVA-2AF; Nowicki and Duke, 1994 )] and a newly developed test that assesses relevant decoding ability in the workplace such as inferring behavioral intentions, personality traits, status, interpersonal attitudes (dominance/cooperativeness and motivations), behavioral outcomes, and thoughts and feelings [i.e., the Workplace Interpersonal Perception Skill (WIPS; Dael et al., in preparation )], and one self-report measure [the Emotional Sensitivity subscale of the Social Skills Inventory (SSI; Riggio, 2005 )]. Together, we utilized these various measures of technology and nonverbal decoding skill in order to test the preceding competing hypotheses: (1) more technology use is related to better nonverbal decoding skill vs. (2) more technology use is related to poorer nonverbal decoding skill.

Materials and Methods

Participants.

Data were collected from 410 participants in the University of Maine introductory participant pool for a study on perceiving nonverbal signals in others. Of these, 51% were male and 48% were female. A total of 377 (92%) participants identified as white, 15 (4%) as Asian, 14 (3%) as American Indian or Alaska Native, 12 (3%) as Black, 2 (0.5%) as Native Hawaiian or Pacific Islander, and 33 (8%) as Other. Their ages ranged from 18 to 29 ( M = 19.09, SD = 1.56). A power analysis conducted using G ∗ Power ( Faul et al., 2007 ) assuming a small to medium effect ( r = 0.15) of technology use on nonverbal decoding skill indicated that 343 participants would be needed to achieve 80% power using an alpha level of 0.05 (two-tailed). The final sample of participants exceeds this threshold, indicating that the present study is sufficiently powered to detect small to medium effects.

Technology Use

Three separate measures of technology use were collected from participants. For iPhone users, participants were instructed to navigate to their phone settings and extract their average daily screen time over the last 7 days in minutes ( N = 263). This screen time metric is a real-time report of how much time a participant spends with their phone screen turned on in an average week (i.e., listening to music with one’s screen off is not included). To ensure participants did not alter their responses in order to appear more socially desirable, we also required that they upload a screenshot of this information. In addition to this objective measure of technology use, participants were asked to self-report on a scale of 0–10 from “does not describe me at all” to “describes me very well” how well the following statements described their technology use, “I tend to be an active user, posting frequently” and “I tend to be a passive user, scrolling through posts and photos.” These two questions comprised our self-report measures of technology use: the degree to which a participant endorsed themselves as an active user separately from the degree to which a participant endorsed themselves as a passive user. Because active user endorsement and passive user endorsement were single item questions rather than a single bipolar item, participants could report any combination of active and passive technology use. That is, a participant could endorse a high degree of active use and a high degree of passive use, they could report a low degree of both, or a high degree of one and not the other. For all analyses, we entered both continuous variables to examine how the independent contribution of active and passive use predicted our outcomes of interest.

Nonverbal Decoding Measures

The newly developed WIPS test (Workplace Interpersonal Perception Skill; Dael et al., in preparation ; a = 0.67) assesses multiple aspects of decoding skill using 41 brief video segments with and without sound from three types of role-played workplace interactions: a recruiter-applicant negotiation, a helpdesk trouble-shooting scenario, and a company team meeting. Each segment is paired with a multiple-choice question for which the correct answer was based on actual behavior (what happened in the interaction during or after the video segment), instructions that the actors received (e.g., to be competitive), actors’ self-reported personality, or post-interaction evaluations (e.g. perceptions of the other as competitive) and response options varied from 2 options to 6 options depending on the item. In this way, participants must decode multiple simultaneous nonverbal cues (e.g., tone of voice, facial expression) in order to accurately assess the interpersonal characteristics of any given situation. For some items, the video consisted of multiple short segments (e.g., You will see the same person in two different negotiations signing a contract. In which negotiation did the person negotiate the better deal for herself?) while other videos were based off of just one video (e.g., In the following video, you will see 6 people enter the room for a team meeting. Who is the team leader?). Accuracy is calculated as the proportion correct responses compared against a criterion or correct response for each segment.

Participants also completed the Diagnostic Analysis of Nonverbal Accuracy-Adult Faces (DANVA-2AF; Nowicki and Duke, 1994 ; a = 0.60), a test of emotion recognition ability using static and posed photographs. This measure presents 24 photographs of adult faces with high and low intensity portrayals of the four basic emotions of happiness, anger, sadness, and fear. Accuracy was calculated as the proportion correct.

Finally, participants completed the Emotional Sensitivity (ES; a = 0.80) subscale of the Social Skills Inventory (SSI; Riggio, 2005 ). The ES subscale consists of 15 self-report items, with a 5-point response scale ranging from “Not at all like me” to “Exactly like me.” The ES subscale specifically assesses self-reported skill for decoding emotional and other nonverbal messages (e.g., I always seem to know what people’s true feelings are no matter how hard they try to conceal them) . For analysis purposes, a sum was calculated across items.

Our second study was an exact replication of Study 1 launched approximately 3 months after Study 1 with data from 190 participants from the University of Maine introductory participant pool. Because we had not hypothesized a priori the effect of active and passive technology use on nonverbal decoding skill, we wished to collect a second sample of participants in order to investigate whether the pattern of results we describe in Study 1 would replicate. The demographics of this second sample were comparable to those from our first study, with 91 male participants (48%) and 99 females (52%). Of these, 179 (94%) identified as white, 9 (5%) as Asian, 5 (3%) as Black, 2 (1%) as American Indian or Alaska Native, 1 (0.5%) as Native Hawaiian or Pacific Islander, and 6 (3%) as Other. Participant’s ages ranged from 18 to 31 ( M = 19.43, SD = 1.57). A power analysis conducted using G ∗ Power ( Faul et al., 2007 ) assuming a small to medium effect derived from Study 1 ( r = 0.20) indicated that 191 participants would be needed to achieve 80% power using an alpha level of 0.05 (two-tailed).

To test our competing hypotheses about the relationship between technology use and nonverbal decoding skill, we first examined bivariate correlations between our study variables. Next, we ran a series of linear regressions on the whole sample in Study 1 and Study 2 controlling for participant gender to examine the independent contribution of active and passive technology use on each of our nonverbal decoding skill measures (accuracy scores on the WIPS test, accuracy scores on the DANVA, and self-reported emotional sensitivity).

To combine results from Study 1 and Study 2, a mini meta-analysis ( Goh et al., 2016 ) was performed for each technology use variable and each nonverbal decoding variable. We used fixed effects in which the mean effect size (i.e., mean correlation) was weighted by sample size. All correlations were Fisher’s z transformed for analyses and converted back to Pearson correlations for presentation.

Means, standard deviations, and bivariate correlations are presented in Table 1 . Contrary to what would be predicted by either theoretical framework, screen time use was unrelated to every measure of nonverbal decoding skill we employed. However, when examining the ways in which participants self-reported spending their time online, a more complex pattern emerged. Specifically, more active technology use was related to higher self-reported nonverbal decoding skill ( r = 0.20, p < 0.001) but lower accuracy score on the WIPS ( r = −0.17, p < 0.001). That is, participants who identified as more active users (i.e., posting frequently) believed that they were better judges of others’ nonverbal communication, but performed significantly worse on an objective test of nonverbal decoding skill (i.e., the WIPS test). On the other hand, participants who reported being more passive users (i.e., reading through posts and looking at other people’s photographs) were significantly more accurate in decoding nonverbal behavior, as measured by the WIPS ( r = 0.14, p = 0.005), although they did not self-report any differences in their nonverbal decoding skills from less passive users as highlighted by the correlation between passive user endorsement and self-reported skill on the ES subscale of the SSI ( r = 0.04, p = 0.484). Neither self-reported passive nor active technology use was significantly related to an individual’s ability to decode facial expressions of emotions, measured via the DANVA-2AF ( p ’s > 0.07).

Study 1 and study 2 means, standard deviations, and bivariate correlations between technology use, nonverbal decoding skill, and gender.

Gender, Technology Use, and Nonverbal Decoding Skill

Because active and passive technology use were not mutually exclusive (i.e., an individual could report being high on active and passive use), and because gender is related to both technology use ( Jackson et al., 2008 ) as well as nonverbal decoding skill ( Hall and Gunnery, 2013 ), we wished to determine the independent effects of active and passive technology use on nonverbal decoding skill while controlling for gender. Therefore, we first entered active use, passive use, and gender into a regression predicting accuracy scores on the WIPS. Active use remained a significant negative predictor (β std = −0.21, p < 0.001; Table 2 ), suggesting that those who are more active users were worse at decoding nonverbal behavior. Passive use also remained a significant positive predictor (β std = 0.11, p = 0.02), where those who reported spending their time looking at others’ posts and pictures were more accurate in decoding nonverbal behavior. Further, these two effects were significant even after controlling for gender, which also significantly predicted higher scores on the WIPS test (β std = 0.21, p < 0.001; female coded as 1, male coded as 0). Approximately 8% of the variance in WIPS test scores was accounted for when active use, passive use, and gender were entered as predictors.

Regression results from study 1 and study 2 examining the independent contribution of technology use variables on nonverbal decoding skill.

We next entered active use, passive use, and gender into a regression predicting accuracy scores on the DANVA-2AF. None of these variables, apart from gender (β std = 0.17, p = 0.001), significantly predicted scores on the DANVA-2AF ( Table 2 ). Approximately 4% of the variance in DANVA-2AF scores was accounted for by these predictor variables.

When active use, passive use, and gender were entered into a regression predicting self-reported nonverbal decoding skill, active use remained a significant positive predictor (β std = 0.18, p < 0.001), such that those who were more active users self-reported that they were better at decoding nonverbal information from others ( Table 2 ). While more passive use was unrelated to self-reported nonverbal decoding skill, gender remained a marginally significant positive predictor (β std = 0.10, p = 0.052) indicating that females reported being more skilled nonverbal decoders than males. Approximately 5% of the variance in self-reported nonverbal decoding skill was accounted for when active use, passive use, and gender were entered as predictors.

While results from Study 1 were neither supportive of an enhancing or suppressing effect of global technology usage on nonverbal decoding skill, we did find that the ways individuals used technology mattered (i.e., actively versus passively). Because this active/passive relationship was not hypothesized a priori , we examined these effects in a separate sample of participants. Therefore, akin to Study 1, we first examined the bivariate correlations between our measures of technology use and nonverbal decoding skill. We once again found that screen time use was unrelated to objective measures of nonverbal decoding skill—i.e., the DANVA and WIPS ( p’s > 0.20). However, in Study 2 objective screen time use was significantly and positively related to self-reported nonverbal decoding skill ( r = 0.17, p = 0.050) ( Table 1 ).

Replicating Study 1’s findings, active technology use was also related to higher self-reported nonverbal decoding skill ( r = 0.25, p = 0.001), but lower objective nonverbal decoding skill as measured by the WIPS ( r = −0.16, p = 0.028). Individuals who identified as more passive users were once again significantly more accurate in decoding nonverbal behavior, as measured by the WIPS ( r = 0.27, p < 0.001), although they did not self-report any differences in their nonverbal decoding skills from less passive users ( r = −0.03, p = 0.653). Neither self-reported passive nor active technology use was significantly related to an individual’s ability to decode facial expressions of emotions, measured via the DANVA-2AF ( p’s > 0.167).

We deconstructed these effects by entering active use, passive use, and gender into three separate linear regressions predicting the WIPS, DANVA-2AF, and self-reported nonverbal decoding skill. We regressed our three predictor variables on scores from the WIPS. Replicating regression results from Study 1, active technology use was a marginally significant negative predictor of nonverbal decoding skill (β std = −0.13, p = 0.085), passive use remained a significant positive predictor of nonverbal decoding skill (β std = 0.25, p = 0.001), and gender was a significant predictor, with females scoring higher on the WIPS test compared to males (β std = 0.27, p < 0.001). This model accounted for 15% of the variance in WIPS scores.

Next, we regressed active use, passive use, and gender on scores from the DANVA-2AF. Once again, gender was the only significant positive predictor (β std = 0.32, p < 0.001), with females scoring significantly higher than males. Approximately 11% of the variance in DANVA-2AF scores was accounted for by these three predictors.

When active use, passive use, and gender were entered into a regression predicting self-reported nonverbal decoding skill, active use was a significant positive predictor, similar to Study 1, (β std = 0.21, p = 0.006), such that those who were more active technology users self-reported having more skill in decoding nonverbal information. Reporting more passive technology use was unrelated to self-reported nonverbal decoding skill. Gender remained a significant positive predictor (β std = 0.31, p < 0.001) indicating that females self-reported more nonverbal decoding skill than males. Approximately 16% of the variance in self-reported nonverbal decoding skill was accounted for when active use, passive use, and gender were entered as predictors.

Mini Meta-Analysis

Finally, we conducted a mini meta-analysis ( Goh et al., 2016 ) in order to provide a consistent account regarding the relationship between technology use and objective and self-reported measures of nonverbal decoding skill across these two studies. After combining these effects across both studies, we found that individuals who self-reported more active technology use self-reported higher nonverbal decoding skill (M r = 0.22, p < 0.001), but scored lower on one objective index of nonverbal decoding skill (i.e., the WIPS test: M r = −0.17, p < 0.001). Moreover, individuals who self-reported more passive use scored significantly higher on both objective indices of nonverbal decoding (i.e., the WIPS test: M r = 0.18, p < 0.001 and the DANVA2-AF: M r = 0.09, p = 0.023), but did not self-report higher levels of nonverbal decoding skill (M r = 0.02, p = 0.667; Table 3 ).

Mini meta-analysis results from study 1 and study 2 examining combined correlations between measures of technology use and nonverbal decoding skill.

While many have theorized about the potential positive or negative effects that technology may have on communication skills, no studies to date have empirically examined the relationship between technology use and nonverbal decoding skill. In order to begin to understand the ways in which technology use and nonverbal decoding skill are related, we measured multiple facets of each construct to more thoroughly examine their empirical relationships with one another.

While overall screen time was unrelated to any measure of nonverbal decoding skill, interesting and consistent patterns emerged when looking at the way individuals spent their time using technology. Specifically, individuals who reported actively posting and engaging with technology-mediated communication self-reported that they were more accurate at decoding the nonverbal behaviors of others. However, these more active users were more likely to score lower on objective measures of nonverbal decoding skill. Conversely, individuals who reported spending their time online passively viewing others’ posts and photos scored higher on objective nonverbal decoding skill but did not self-report that their skills were any better.

These findings lend support to the role of practice and feedback as an effective way to increase nonverbal decoding skill ( Blanch-Hartigan et al., 2012 ). Passive users of communication technology likely receive practice in decoding nonverbal cues simply by being exposed to other users’ content (e.g., pictures, posts, videos) and thus a greater frequency of nonverbal cues. Indeed, the average screen time reported across both studies was about 5 h a day, meaning that passive users may spend up to 5 h each day practicing decoding nonverbal cues. In contrast to “other-focused” passive users, active users likely lose out on a plethora of communication cues as they report spending their time online engaging in “self-focused” activities. That is, although active users likely receive a great deal of practice encoding their own thoughts, feelings, attitudes, etc., they do not receive this same practice when it comes to decoding the thoughts, feelings, attitudes, etc. of others.

Therefore, these results support both the hypothesis that technology use enhances nonverbal decoding skill, and the hypothesis that technology use worsens nonverbal decoding skill. The key lies in how one spends their time using technological platforms. Those who use technology to practice making judgments of others may benefit from time online and learn skills to enhance their face-to-face interactions. However, greater technology use may have the opposite effect for those who choose to spend their time online creating and posting their own content, instead of interacting with the content of others. In these cases, technology may have adverse effects on an individual’s nonverbal decoding skill in face-to-face interactions.

The current research is not without limitations. First, we are limited by our homogenous sample of college participants in one US state. More research is needed to see if the relationship between active and passive technology use and nonverbal decoding skill will generalize more broadly. In addition, while the WIPS test has many advantages to other tests of nonverbal decoding ability (e.g., good reliability and validity, real-world workplace context, dynamic stimuli, many domains of nonverbal sensitivity), it is not yet a published, validated test of decoding ability. Additionally, although self-reporting active and passive technology use provides valid information regarding the way participant’s view their online activity, or the way they are motivated to be, future studies should confirm these self-reports with objective measures in order to assess the accuracy of individual’s self-perceptions. We also examined one aspect of technology use on smartphone devices and the questions focused on self-reported social media use. The role of other technology-mediated communication platforms, such as teleconferencing or interactive video gaming, deserve future study. In our regression models, only 4–16% of the variance in decoding skills was explained by our predictors; therefore, there are many other factors that impact decoding skill ability which should be explored in future work. While the WIPS test is not validated yet (i.e., in prep), it is more ecologically valid than many other available standardized tests of decoding ability because it includes many workplace scenarios and dynamic video rather than focusing on one domain (e.g., emotion recognition like the DANVA-2AF) or using just static photographs where participants often show a ceiling effect on accuracy. In addition, and explained extensively below, we cannot make causal claims about the direction of the relationships given that our data was cross-sectional.

Suggestions to Further Theories of Technology Use and Nonverbal Decoding Skill

Although our data suggest that the way in which an individual communicates with technology may impact nonverbal decoding skills globally (i.e., as measured by the WIPS test), we only observed a marginally significant effect to suggest that technology use was related to an individual’s ability to decode facial expressions of emotion measured via the DANVA-2AF. While it may be that technology truly does not impact this facet of nonverbal decoding skill, it is also possible that we did not measure technology use at a detailed enough level to reveal any meaningful relationships. Although participants reported technology use generally, different social media and technology communication platforms are vastly different in their bandwidth and each emphasize distinct cue channels. For example, while some platforms emphasize visual cues (e.g., Instagram, Snapchat) others may underscore more verbal cues (e.g., Facebook, Twitter). Collapsing technology use across all platforms may dilute interesting relationships between particular social media apps, cue channels, and nonverbal decoding skill. For instance, it may be that individuals who passively use applications which highlight posting pictures or videos receive more practice in decoding facial expressions, and therefore may score higher on emotion decoding tests such as the DANVA-2AF. Therefore, we urge future researchers to be thoughtful in selecting the most relevant nonverbal decoding skill measure for their particular study Stosic and Bernieri (in prep) taking into account domain (e.g., emotion recognition or general workplace decoding skills) as decoding ability does not appear to be a single skill ( Schlegel et al., 2017a ), and to further explore the ways in which specific technology-mediated platforms, opposed to global technology use, impact vital communication skills.

In addition to delineating more precise constructs, the areas of technology and nonverbal communication research would benefit from an increase in experimental designs. While we have interpreted our data as technology use potentially influencing nonverbal decoding skills, it is highly plausible that the causal relationship is reversed. Individuals who are more accurate perceivers of others’ nonverbal behavior may be more likely to use technology in a passive way because they are more practiced, more comfortable, or more engaged with others. Those who are less accurate perceivers of others’ nonverbal behavior may use technology more actively because they are more self-focused or find perceiving others to be more challenging or less rewarding. The correlational nature of the current studies does not allow us to untangle the direction of these effects. Therefore, we urge future work to consider experimental designs to examine the causal relationship between technology use and communication ability, particularly nonverbal decoding skill.

While experimental designs on this topic are rare, we are aware of one study that employed a quasi-experimental design to manipulate technology use. Age-matched cohorts of preteens attended a summer camp in a staggered order such that one group went earlier than the other group ( Uhls et al., 2014 ). While at camp, electronics including television, computers, and mobile phones were not allowed. The first group to attend camp was the experimental group ( N = 51) and the group that stayed at school while the first group was at camp was considered the control group ( N = 54). After just 5 days of interacting face-to-face without the use of any technology, preteens’ recognition of nonverbal emotion cues from photographs and videos (using the DANVA-2 Child and Adult Faces and the Child and Adolescent Social Perception Measure) was significantly greater compared to the control group. From this, we can gather that the short-term effects of increased opportunities for face-to-face interaction, combined with time away from screen-based media and digital communication, improved preteens’ understanding of and ability to decode nonverbal emotion cues.

Completely removing technology can be difficult in a real-world context; however, there are a variety of methods we propose to untangle the relationship between technology use and nonverbal decoding skill. There are applications and settings on most smartphones that display an alert when the user has reached a screen time maximum for the day. Researchers could consider a dose-response experiment in which they randomly assign different allowed hours of screen time to users each day for a series of days. One could then understand if different doses of screen time lead to higher or lower levels of nonverbal decoding skill.

In another potential research design, researchers could randomly assign the way technology is used by participants. Researchers could assign individuals as “passive users” who are not allowed to post but must read through others’ posts and/or photographs. Some questions to consider are whether or not this would facilitate practice, contribute to learning, and improve nonverbal decoding skill. Another quasi-experimental design could follow emerging adolescents with or without phones and assess differences in their nonverbal decoding skills, accounting for covariates and confounders such as gender, socioeconomic status, parents’ educational levels, and baseline communication skills.

In addition to experimentally manipulating technology use, research could examine and potentially rule out the reverse causality claim that nonverbal decoding skill is driving technology use. To do this, researchers could train participants on nonverbal decoding skill using validated trainings, such as the Geneva Emotion Recognition Test training (GERT; Schlegel et al., 2017b ), and then assess whether technology use changes over time or if training nonverbal decoding skill makes technology-mediated communication smoother or more rewarding.

As the use of technology-mediated communication continues to expand, it is crucial for psychological research to address the positive and negative consequences of technology use on communication skills, in particular nonverbal communication. The current research suggests that it may not be the technology use itself, but rather how actively or passively users engage with technology, that facilitates or hinders nonverbal decoding skill. We ultimately found support for all hypotheses (i.e., Liberated Relationship Perspective, Internet Enhanced Self Disclosure Hypothesis, Reduction Hypothesis, and Cues Filtered Out Theory) but the ways in which the hypotheses were supported depended on how users interacted with technology. Our results showed that those who use technology in a more passive way (reading and look at others’ posts) had higher nonverbal decoding accuracy. That is, more passive users may benefit from time online and learn skills to enhance their face-to-face communication (supporting the Liberated Relationship Perspective and Internet Enhanced Self Disclosure Hypothesis). For those who reported more active use (creating and posting their own content), they had lower nonverbal decoding accuracy. For these more active users, technology may have adverse effects on their ability to read and respond to others in face-to-face communication (supporting the Reduction Hypothesis and Cues Filtered Out Theory).

We believe these results to be encouraging, as some of the fears regarding the negative impact of technology on an individual’s communication skills may not come to fruition if technology is used in a more passive, observational manner rather than an active, self-focused manner. Beyond these results, we also provide researchers with suggestions to further the field of technology use and communication skills. Due to the growing diversity in technology-mediated communication platforms, we urge researchers to account for the different functions theses platforms afford users. In addition, and perhaps most importantly, we urge researchers to explore experimental designs to determine causal pathways in the complex relationship between technology and communication skills. Researchers are beginning to understand how the technological revolution is changing the ways in which humans navigate social interactions. A deeper appreciation for this complexity can lead to the development of interventions to enhance and not hinder our communication skills with the increasing presence and benefits of technology in our lives.

Data Availability Statement

Ethics statement.

The studies involving human participants were reviewed and approved by the University of Maine IRB. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

MR, MS, and JC contributed to conception, design of the study, and wrote the first draft of the manuscript. MR organized the database and performed the statistical analysis. DB-H wrote sections of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We thank research assistant, Vasiliqi Turlla, for her help in data collection and data cleaning and Herbert Ruben for always asking what technology was doing to our communication skills.

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• Internet Seen as Positive Influence on Education but Negative on Morality in Emerging and Developing Nations
• 1. Communications Technology in Emerging and Developing Nations

Table of Contents

• 2. Online Activities in Emerging and Developing Nations
• 3. Influence of Internet in Emerging and Developing Nations
• Methods in Detail

Internet access differs substantially across the 32 emerging and developing countries polled, with the lowest rates of internet use in South Asian and sub-Saharan African nations. Within countries, computer owners, young people, the well-educated, the wealthy and those with English language ability are much more likely to access the internet than their counterparts. To access the internet, people increasingly use smartphones rather than more cumbersome fixed landline connections and computers. Around the world, both smartphones and basic-feature phones alike are used for sending messages and taking pictures.

Internet Usage in Emerging and Developing Nations

Across these 32 nations, the percentage of people who use the internet varies widely. Overall, a median of 44% access the internet, including half or more in 13 countries. Internet use is highest in the wealthiest of the emerging nations, particularly in Chile and Russia, where more than seven-in-ten have internet access. Though these rates are relatively high, they lag behind the U.S., where 87% have online access. The lowest internet rates are in some of the poorest countries surveyed. Just 8% of Pakistanis and 11% of Bangladeshis either say they access the internet at least occasionally or own a smartphone. Two-in-ten or fewer have access in Uganda (15%), Tanzania (19%) and India (20%).

Within countries, internet access differs substantially by a number of key demographics, including age and education. Younger people ages 18 to 34 are more likely to report accessing the internet than their older counterparts in every country polled, including differences of more than 15 percentage points in all but three countries available for analysis. Especially large differences occur in Asia, with age differences of 40 points or more in five countries. For example, in Thailand 83% of young people are online, compared with just 27% of older Thais.

Education is also associated with internet use rates. In all nations surveyed with a sufficient sample size to analyze, those with a secondary education or higher were more likely to access the internet than those with less than a secondary degree. These divisions are especially prominent in Latin America. In six of the nine Latin American countries surveyed, the well-educated access the internet at rates of 50 percentage points or more than less-educated people. This difference is particularly stark in Chile, where 87% of well-educated people use the internet, compared with 18% of those with less than a secondary degree.

In addition to age and education, internet use is more common among people who have some English language ability. In every nation surveyed with a sufficient sample size to analyze, those who can speak or read some English, or completed the survey in English, accessed the internet at much higher rates than those who have no facility with English.

Explaining Internet Usage

To further explore the relationship between demographics, English language ability and internet usage, we used a statistical technique called multivariate regression, which allowed us to test the individual impact of a number of factors on internet usage while holding other variables constant (see Appendix A for details). Overall, we find that computer ownership, age, English language ability and education have the biggest impact on whether or not someone uses the internet.

Those who own computers, those who can speak or read some English, and those with a secondary education or higher are considerably more likely to use the internet. In addition to these factors, having a higher income, being male and being employed have a significant, positive impact on internet use, though to a lesser degree.

Age also has a significant influence on internet use, controlling for other demographics. In emerging and developing markets, older people are significantly less likely than their younger counterparts to engage in online activity.

Internet Capable Technology

Around the world, people often log on to the internet using home computers and internet-capable smartphones. Overall, a median of 38% across the 32 nations surveyed say they have a working computer in their household. In 11 countries, half or more own computers, including 78% in Russia – comparable to the 80% of Americans who say they have a computer in their household. Computer ownership is relatively high in a number of Latin American nations. Majorities in Chile (72%), Venezuela (61%), Argentina (58%) and Brazil (55%) have computers in their homes. Computer ownership rates are lowest in sub-Saharan African nations. Roughly a quarter or fewer have computers at home in every one of these countries, with the fewest in Uganda, where just 3% say they have a computer.

Those with higher incomes are more likely than their poorer neighbors to own computers in all countries available for income analysis. Similarly, in all countries available for analysis, those with a secondary education or higher are considerably more likely to own a computer than those with less than a secondary education. For example, 81% of well-educated Chileans have computers in their home, compared with 26% of those with less than a secondary education. Young people are also more likely than those 35 and older to own computers in 20 emerging and developing nations.

A small but growing number of people use internet-capable smartphones – a median of 24% in emerging and developing countries own this type of device. Only in two of the countries polled do more than half have a smartphone – 58% in Chile and 55% in China, on par with the 58% of Americans who report owning this kind of device. A third or more of people in 10 countries say they own a smartphone, including 48% in Lebanon and 47% in Malaysia. About 10% or fewer Tanzanians, Bangladeshis, Ugandans and Pakistanis own smartphones.

In every country surveyed, there is a significant age difference on smartphone ownership. Young people (those under 35) are significantly more likely than their older counterparts to own an iPhone, BlackBerry, Android or other internet-capable mobile phone.

Large age gaps occur in a number of Asian countries in particular. For instance, in Malaysia, 72% of 18- to 34-year-olds own a smartphone, while only 27% of those 35 and older own one. Differences of 30 percentage points or more also exist in China, Thailand and Vietnam.

Smartphone ownership is also higher among the more educated. In all of the nations polled, those with a secondary degree or higher are more likely to own a smartphone than the less educated. This is especially true in Jordan, where 67% of the well-educated own a smartphone, compared with just 13% of those with less education – a difference of 54 percentage points. A similar gap exists in Chile.

Many Own Cell Phones, Few Have Landlines

Beyond smartphone ownership, cell phone ownership more broadly is very common, with a median of 84% in emerging and developing nations owning some type of cell phone. In eight emerging and developing countries, about nine-in-ten or more own mobile phones, comparable to the 90% of Americans with cell phones. Unlike other technologies, people in sub-Saharan African nations, including Nigeria, Senegal and Ghana, use mobile phones at similar rates to the rest of the emerging and developing world. Pakistan is the only country surveyed where less than half (47%) have a mobile phone.

While cell phone ownership has increased drastically over the past decade, particularly in Africa, landline connections have remained relatively low – likely due to the lack of infrastructure required for reliable connections. Across the 32 countries surveyed, a median of just 19% say they have a working landline connection in their home, including as few as 1% in Ghana, Nigeria, Uganda and Bangladesh. Instead of waiting for landline access, many in emerging and developing nations have bypassed fixed phone lines in favor of mobile technology.

Landline use is highest in Lebanon, where 79% report having a fixed telephone connection, considerably more than the 60% of Americans who do. (The share of wireless-only households in the U.S. has been growing rapidly over the past decade as landline ownership falls). About half or more in Venezuela (59%) and Argentina (51%) also have landline telephones.

As with cell phones, the well-educated and those with higher incomes are more likely to have landline connections. In 23 countries, those with a secondary education or higher are more likely to have a landline phone in their house. The wealthy are more likely to have fixed telephone lines in 17 of the countries polled.

Texting Most Popular Use of Cell Phones

Whether they are using basic feature cell phones or internet-capable smartphones, most cell phone owners use their mobile devices for more than simple phone calls. A median of 76% in emerging and developing markets say they have used their cell phones to send text messages in the past 12 months. In a number of countries, texting is nearly universal. In the Philippines, Venezuela, Indonesia and South Africa, 95% or more of cell phone owners say they text regularly. By comparison, 81% of American cell phone owners report ever sending a text message, according to a 2013 Pew Research poll. Half or more in all but two countries – Thailand and Pakistan –regularly send texts.

While fewer people report taking pictures or video with their mobile phones, a median of 55% do so. Taking pictures and video is most popular in several Latin American countries – about two-thirds or more of Venezuelans (75%), Chileans (72%), Mexicans (68%) and Argentines (66%) regularly snap photos with their phones.

Though texting and taking photos or video on their mobile phones are relatively frequent for all people, young people are much more likely to do so. Young people, those ages 18 to 34, text more regularly than those 35 and older in 30 countries. In particular, young Nicaraguans text considerably more than their older counterparts – 89% of cell phone owners ages 18 to 34 text, compared with fewer than half of older people (45%). Significant age gaps also exist in taking photos and video on mobile phones in 31 countries. In Tunisia, where video of local protests helped ignite the Jasmine Revolution, 60% of young people take pictures or video on their phones, compared with just 25% of those age 35 and older. Age differences of 35 percentage points or more occur in more than a third of the countries surveyed.

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