effects of technology on human interaction essay

Impact of Technology on Communication Essay

• To find inspiration for your paper and overcome writer’s block
• As a source of information (ensure proper referencing)
• As a template for you assignment

Introduction

Advancement of technology in communication, media technology and online communication, the impacts of mobile phone on communication, reference list.

The realm of technology is ever-changing. New advances in applied science have forever transformed the way people interact. Exploring the impact of technology on communication and debating whether people connect with others differently seems to be the topic of the day.

Technology has allowed people to keep in touch no matter the distance. One is able to communicate 24 hours around the clock, seven days a week, 365 days on an interpersonal level.

What are the real impacts of technology on communication? How do electronics mediate and change the ways in which humans interact? How has the emergence of the Internet, mobile phones, and social networks affected society and businesses?

In order to reveal the importance of technology in communication, the essay tries to find answers to these questions. It explores how everything has changed over the years and discusses the connection between technology and communication.

To begin this examination and find answers to these questions, we begin by defining media and communication and outlining the stages of technological advancement from old age to the present day in the field of communication. The paper will highlight the use of the Internet, newspapers, radio, and other media, but it mostly dwells on the use of mobile telephony.

Communication is “the imparting or exchange of information by speaking, writing or using some other medium” (Daniel & Rod, 2011). On the other hand, media is defined as “the main means of mass communication (television, radio, and newspapers) regarded collectively.”

Technology has changed everything in the modern society. The way we communicate has been revolutionized by the advancement of new innovations in the telecommunication sector. Connecting with other people with ease is more feasible in today’s world, and this is due to speed.

Several centuries ago, books and newspapers reigned as the only choice of communication. Then later, innovators brought the radio and television before innovation was taken a notch higher with the coming of the personal computer (Johnson, 1997, p.3).

With every new innovation, the reliance on books and newspapers as the mass medium of communication continued to reduce. With time, human culture has come to understand the power and the mechanisms involved in technology and invention. In today’s world, information has permeated the cycles of change and development.

The world today, past and present, can be studied at ease with the growing information technology. Technology has advanced with sheer velocity allowing different media to shape our thinking and habits. The people who were born during the television era thought that it was the climax of innovation, but they suddenly found themselves acclimating to a new medium, the World Wide Web.

Every time a new medium rolls out, the perceptions towards the previous media you were used to change (Johnson, 1997 p5). Technology proved to be powerful in the sense that no human being can predict what will change and what won’t with certainty.

The irony of it all is the fact that the influence of technology extends beyond generations to come. It is with no doubt that technology has changed the lives of human beings; information and entertainment are being received in a more convenient way.

The innovation of having a conversation using a device called the telephone changed everything in communication. This became magical, and one couldn’t believe such innovation would exist (Tofts, 1997, p.40).

With the emergence of new media technologies, consumers have been empowered to ‘filter’ the information they want to receive. This allows them to have a choice of which news to watch or what information to listen to (Palmer, 2003, p.161).

Media consumption has been made an engaging experience with marketers studying the preferences of the consumers in order to reflect broader social changes in society. In today’s world, the computer is seen as a multi-purpose machine with work and leisure functions, therefore, creating more value.

The rise of the Internet has also made it possible to have virtual offices where the user can work from home or any convenient location. The flow of information from different media has greatly changed the social structures of society at different levels (Barry, 1999).

Digital media has enabled news and event to be channeled in real-time. The combination of the Internet and commerce has given birth to e-commerce sites providing huge potential for marketers to reach out to virtual communities.

In the world today, there are numerous media screens within our surroundings. This ranges from the television sets in our houses, computer monitors at the office, mobile phones and MP3 players in our pockets and handbag.

Even when shopping or waiting to board a plane, you’re most probably staring at screens with entertainment media (Soukup, 2008, p.5). Heavy marketing has been adopted by producers of mobile technologies targeting consumers who possess mobile phones with picture and video capacity (Goggin, 2006, p.170).

Media texts producers have termed mobile media as a “third screen,” a device that consumers carry around with much ease. Unlike television screens, broader communication networks have been integrated into personal computers and mobile phones (Goggin, 2006, p.9).

Train, buses, and airplanes have been dominated by mobile screens providing passengers with entertainment as well as other media content, especially advertisements (Caron & Carona, 2007, p.17). With a lot of commercial media content, the preferences of people change in their everyday lives.

The world of popular media has become chaotic, with hundreds of television channels to choose from, thousands of songs ready for download, and not forgetting millions of web pages to surf.

The emergence of social media like Facebook and Twitter has enabled people to manage interactions and relationships with many friends. Technologies have impacted interpersonal communication enabling people to interact more often than before.

In addition to reducing the distance between people, online communication with tools like Facebook and Twitter enables people to keep track of their contacts with friends and are more aware of the last time they interacted with them. Online communication now incorporates more than one mode of contact, including text, voice, and body language.

A mobile phone is a device that has always been seen as connecting people who are far apart, thus overcoming the geographical distance between them. The number of mobile phone users has continued to increase substantially. The mobile phone has been integrated as part of people’s lives in the sense that it’s available and easy to use, keeping us connected to our families, friends, and business people (Ling, 2004, p.21-24).

The how and when the way we use our mobile phones impacts our communication not only with those we’re communicating with but also with the people within our proximity. At this point, it is paramount to note the changes that have taken place and that have allowed the adoption of mobile phones. The tremendous proliferation of this device has drastically changed the traditional communication model.

Who are the users of mobile phones, and for what purposes do they use them? Has there been any change in the way mobile phone facilitates communication? How has the face to face interaction been affected by mobile calls? Has mobile communication enhanced relationships?

These are some of the questions that arise when we try to fathom the way communication has affected our personal and professional lives. There are sentiments that mobile phones have reduced humans to emotionless beings.

There is no doubt that the revolution brought about the use of mobile phones in the way we communicate. There have been different perceptions among individuals and social levels in society in regard to mobile usage.

When we had fixed telephone lines that were put in a booth, telephones were seen as business tools only and were placed in a fixed, quiet environment. There was restriction when it came to teenagers using these phones (Agar, 2003). The ‘birth’ of mobile phones brought changes, and phone calls became a habit to many irrespective of age or location.

Today, people can use mobile phones wherever they are in private or in public. People have been addicted to their mobile phones more than any other gadget known to man, with the device remaining on throughout. Its portability enables people to carry it wherever they go (Castells, 1996).

A personal virtual network has been created whereby users can be available at all times to communicate with friends, family, and colleagues. The geographical barrier has been destroyed, making people feel close to one another, and the face to face communication has been rendered rather less important with this mediated communication (Richard, 2004, p.22).

Meetings and briefings have become obsolete, with communication being mediated by a computer or a phone. Mobile SMS (short messaging service) service and the Internet has become the preferable communication channels for most teenagers and young people all over the world (Plant, 2000, p.23).

There are places where mobile phones have become taboo devices, places like churches and crucial corporate meetings. At such places, the mobile ring is seen as a nuisance. In other scenarios, it is seen as a destructive device by acting as a third party and especially for dating couples who want to have a private conversation.

Any phone ring is seen as an ‘intruder,’ and this harms the relationship between the partners (Plant, 2000, p.29). In his research, Plant observes that there are those people who use mobile as ’a means of managing privacy where calls are carefully selected’. He categorizes this group of people as ‘hedgehogs.’

The other category is those people who use mobile phones as the key central part of their life. They become so attached to the device and cannot do without it. Plant referred to this group as ‘fox.’ They are regular users who need to feel connected with their families and friend. Their life will be dreadful if they lack the device (2000, p.32).

Telephones have promoted the use of text messaging and modernization since it’s allowing people to communicate more both verbally and by texting in a more convenient and efficient way. SMS has made communication to be more immediate, and users can customize the message at ease with the various applications installed on their mobiles (Richard, 2004, p. 100).

The advanced phones have email support as well as multimedia messages making chatting become a lifestyle for many who conduct business and those initiating intimate communication. It has emerged that SMS has made people become more united.

Users have developed abbreviated messages, which are now universally accepted as an appropriate language. The initial purpose of the phone to make calls has even lost taste with many people, especially the young generation.

According to Reid &Reid, more than 85% of teenagers prefer texting to talking on their mobile usage (Reid & Reid, 2004, p.1). There is ease of communication when it comes to texting in the sense that some formalities are eliminated, making communication more personal.

Texting has helped introverts who may lack the skills to have phone conversations allowing them to express their true self to other people leading to greater understanding and stronger relationships (Reid & Reid, 2004, p.8).

The use of mobile technology has affected the personalities of people to a great extent. Today, more people are hiding their feelings and whereabouts behind mobile phones, and this has raised suspicions among families, friends, and couples.

People go through text messages of others just to find out more about the individual who might even have no clue about what is happening. Contrary to this, most people believe that mobile is so crucial in enhancing the relationship between people no matter the distance and that it bonds us together more than it separates us (Plant, 2000, p.58).

The usage of mobile phones by children and teenagers has changed the way parents bring up their kids. Parenting has really changed as parents try to increase their surveillance and monitor their children’s mobile usage.

Their concern is to know who communicates with their kind and the kind of conversations they normally have. They are worried about the kind of social network the children create in their contact lists.

With the emergence of virtual communities, the influence of mobile phones has spilled over and affects parenting in general. Nonetheless, the primary purpose of mobile phones to facilitate communication has not changed.

There is no doubt that technology has changed the way humans communicate. Great impacts can be seen in the way communication has changed the social structures of our society at all levels. Even in years to come, technology remains the driving force of the way people interact.

The advancement of technology ensures that communication is quicker and that more people remain connected. There has been an evolution in interpersonal skills with the advancement of technology, and users should always be keen on adapting to new ways of communication.

Technology has continually brought new methods of communication leading to the expansion of mediated communication. The reality of having one message shared across a huge audience (mass communication) is now with us. A situation where neither time nor geography can limit the accessibility of information.

We have seen the merging together of newspapers and books with computer technology so that the frequency and ease of reporting information and advertisements can be increased. The exposure of both individuals and society to mediated communication has therefore affected our daily lives, particularly in our culture and the way we communicate.

Agar, J., 2003. Constant Touch: A Global History of the Mobile Phone . Cambridge: Icon Books.

Barry, W., 1999. Networks in the Global Village . Boulder Colo: Westview Press.

Caron, A, & Caronia, L., 2007. Moving cultures: mobile communication in everyday life. Montreal: McGill-Queen’s University Press.

Castells, M., 1996. The Information Age: Economy, Society and Culture, Volume 1. The Rise of the Network Society . Oxford: Blackwell.

Daniel, C., & Rod, M., 2011.The Dictionary of Media and Communications . Oxford: Oxford University Press.

Goggin, G., 2006. Cell phone culture mobile technology in everyday life. New York: Routledge.

Palmer, D., 2003. The Paradox of User Control’. 5 th Annual Digital Arts and Culture Conference (Proceedings), pp.160-164.

Plant, S., 2000. On the Mobile: the effects of mobile telephones on social and individual life . Web.

Postman, N., 1992. Technopoly: The surrender of culture to technology . New York: Vintage Books.

Reid, D. J. & Reid F. J. M., 2004. Insights into the Social and Psychological Effects of SMS Text Messaging . Web.

Richard, L., 2004. The Mobile Connection: The Cell Phone’s Impact on Society . San Francisco Morgan: Kaufmann.

Soukup, C., 2008. ‘Magic Screens: Everyday Life in an Era of Ubiquitous and Mobile Media Screens’, presented at 94 th annual Convention . San Diego .

Stephen, J., 1997. Interface Culture: How New Technology Transforms the Way We Create and Communicate . San Francisco: Basic Books.

Tofts, D., 1997. ‘ The technology within’ in memory trade: A Prehistory of Cyberculture, North Ryde: 21C Books.

• Visualization for Thinking, Planning, and Problem Solving
• History of Communication Technology
• LG Mobile Teen Texting Campaign
• Texting While Driving Should Be Illegal
• Tougher Punishment for Texting While Driving
• Electronic Communication
• The Role of Communication in Society
• Public Relations and Ethical Decisions
• Social Network Communication
• Modern Day Communication
• Chicago (A-D)
• Chicago (N-B)

IvyPanda. (2019, March 28). Impact of Technology on Communication Essay. https://ivypanda.com/essays/impact-of-technology-on-communication/

"Impact of Technology on Communication Essay." IvyPanda , 28 Mar. 2019, ivypanda.com/essays/impact-of-technology-on-communication/.

IvyPanda . (2019) 'Impact of Technology on Communication Essay'. 28 March.

IvyPanda . 2019. "Impact of Technology on Communication Essay." March 28, 2019. https://ivypanda.com/essays/impact-of-technology-on-communication/.

1. IvyPanda . "Impact of Technology on Communication Essay." March 28, 2019. https://ivypanda.com/essays/impact-of-technology-on-communication/.

Bibliography

IvyPanda . "Impact of Technology on Communication Essay." March 28, 2019. https://ivypanda.com/essays/impact-of-technology-on-communication/.

IvyPanda uses cookies and similar technologies to enhance your experience, enabling functionalities such as:

• Basic site functions
• Ensuring secure, safe transactions
• Secure account login
• Remembering account, browser, and regional preferences
• Remembering privacy and security settings
• Analyzing site traffic and usage
• Personalized search, content, and recommendations
• Displaying relevant, targeted ads on and off IvyPanda

Please refer to IvyPanda's Cookies Policy and Privacy Policy for detailed information.

Certain technologies we use are essential for critical functions such as security and site integrity, account authentication, security and privacy preferences, internal site usage and maintenance data, and ensuring the site operates correctly for browsing and transactions.

Cookies and similar technologies are used to enhance your experience by:

• Remembering general and regional preferences
• Personalizing content, search, recommendations, and offers

Some functions, such as personalized recommendations, account preferences, or localization, may not work correctly without these technologies. For more details, please refer to IvyPanda's Cookies Policy .

To enable personalized advertising (such as interest-based ads), we may share your data with our marketing and advertising partners using cookies and other technologies. These partners may have their own information collected about you. Turning off the personalized advertising setting won't stop you from seeing IvyPanda ads, but it may make the ads you see less relevant or more repetitive.

Personalized advertising may be considered a "sale" or "sharing" of the information under California and other state privacy laws, and you may have the right to opt out. Turning off personalized advertising allows you to exercise your right to opt out. Learn more in IvyPanda's Cookies Policy and Privacy Policy .

• Subject List
• Take a Tour
• For Authors
• Subscriber Services
• Publications
• African American Studies
• African Studies
• American Literature
• Anthropology
• Architecture Planning and Preservation
• Art History
• Atlantic History
• Biblical Studies
• British and Irish Literature
• Childhood Studies
• Chinese Studies
• Cinema and Media Studies
• Communication
• Criminology
• Environmental Science
• Evolutionary Biology
• International Law
• International Relations
• Islamic Studies
• Jewish Studies
• Latin American Studies
• Latino Studies
• Linguistics
• Literary and Critical Theory
• Medieval Studies
• Military History
• Political Science
• Public Health
• Renaissance and Reformation

Social Work

• Urban Studies
• Victorian Literature
• Browse All Subjects

How to Subscribe

• Free Trials

In This Article Expand or collapse the "in this article" section Technology, Human Relationships, and Human Interaction

Introduction, introductory works.

• Reference Works
• Organizations
• Technology-Mediated Communication
• Theoretical Approaches
• Social Work Practice Implications

Related Articles Expand or collapse the "related articles" section about

About related articles close popup.

Lorem Ipsum Sit Dolor Amet

Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia Curae; Aliquam ligula odio, euismod ut aliquam et, vestibulum nec risus. Nulla viverra, arcu et iaculis consequat, justo diam ornare tellus, semper ultrices tellus nunc eu tellus.

• Computational Social Welfare: Applying Data Science in Social Work
• Digital Storytelling for Social Work Interventions
• Ethical Issues in Social Work and Technology
• Evidence-based Social Work Practice
• Health Social Work
• Human Needs
• Impact of Emerging Technology in Social Work Practice
• Internet and Video Game Addiction
• Technology Adoption in Social Work Education
• Technology for Social Work Interventions
• Technology in Social Work
• Virtual Reality and Social Work

Other Subject Areas

Forthcoming articles expand or collapse the "forthcoming articles" section.

• Abolitionist Perspectives in Social Work
• Randomized Controlled Trials in Social Work
• Social Work Practice with Transgender and Gender Expansive Youth
• Find more forthcoming articles...
• Export Citations
• Share This Facebook LinkedIn Twitter

Technology, Human Relationships, and Human Interaction by Angela N. Bullock , Alex D. Colvin LAST REVIEWED: 27 April 2017 LAST MODIFIED: 27 April 2017 DOI: 10.1093/obo/9780195389678-0249

The utilization of technology to create and maintain relationships among people has become commonplace. According to the Pew Research Center, the percentage of American adults who own a tablet computer increased from 3 percent in 2010 to 45 percent in 2015, and the percentage of American adults who own a cell phone increased from 53 percent in 2000 to 92 percent in 2015. Furthermore, in 2015, 76 percent of online adults used some type of social networking site, compared to 8 percent in 2005. Technology is often introduced into a social system with the stated intention of making life easier for people. As technology becomes more pervasive in everyday life, the assessment of technology’s presence in relationships and its impact on how humans interact with one another is an emerging area of study. There are many perspectives on the relationship between technology and human interactions and relationships. It is purported that the integration of technologies in everyday life can have profound effects on human relationships, in both positive and negative ways. More notably, technologies impact on or interfere with how individuals engage in interpersonal relationships, behave within relationships, and project feelings and meanings including displays of emotions and love. Essentially, the new technological landscape now connects to what it means to be human.

This section presents a sample of early works that guided research into the fostering of relationships and interpersonal interactions through technology. Kiesler, et al. 1984 looks beyond the efficiency and technical capabilities of computer communication technologies and provides insight into the psychological, social, and cultural significance of technology. Jones 1994 provides a comprehensive examination of the varying aspects of social relationships in cyberspace. Preliminary studies that provide best-practice recommendations for the adoption of technology-based intervention in social work practice include Pardeck and Schulte 1990 ; Cwikel and Cnaan 1991 ; Schopler, et al. 1998 ; and Gonchar and Adams 2000 . Lea and Spears 1995 ; Kraut, et al. 1998 ; and Nie and Erbring 2000 offer early insight into how the Internet began to shape the way humans interact.

Cwikel, Julie, and Ram Cnaan. 1991. Ethical dilemmas in applying second-wave information technology to social work practice. Social Work 36.2: 114–120.

These authors consider ethical dilemmas brought about by the use of information technology in social work practice. They examine the effects on the client–worker relationship of the use of client databases, expert systems, therapeutic programs, and telecommunications.

Gonchar, Nancy, and Joan R. Adams. 2000. Living in cyberspace: Recognizing the importance of the virtual world in social work assessments. Journal of Social Work Education 36:587–600.

Utilizing the person-in-environment approach, this source explores the opportunities online communication provides individuals in fostering relationships, either healthy or unhealthy.

Jones, Steve, ed. 1994. CyberSociety: Computer-mediated communication and community . Thousand Oaks, CA: SAGE.

Explores the construction, maintenance, and mediation of emerging cybersocieties. Aspects of social relationships generated by computer-mediated communication are discussed.

Kiesler, Sara, Jane Siegel, and Timothy W. McGuire. 1984. Social psychological aspects of computer-mediated communication. American Psychologist 39.10: 1123–1134.

DOI: 10.1037/0003-066X.39.10.1123

The authors present potential behavior and social effects of computer-mediated communication.

Kraut, Robert, Michael Patterson, Vickie Lundmark, Sara Kiesler, Tridas Mukopadhyay, and William Scherlis. 1998. Internet paradox: A social technology that reduces social involvement and psychological well-being? American Psychologist 53.9: 1017–1031.

DOI: 10.1037/0003-066X.53.9.1017

This study examines the positive and negative impacts of the Internet on social relationships, participation in community life, and psychological well-being. The implications for research, policy, and technology development are discussed.

Lea, Martin, and Russell Spears. 1995. Love at first byte? Building personal relationships over computer networks. In Understudied relationships: Off the beaten track . Edited by J. T. Wood and S. Duck, 197–233. Thousand Oaks, CA: SAGE.

This chapter focuses on the connection between personal relationships and computer networks. Previous studies that examine dynamics of online relationships are reviewed.

Nie, Norman H., and Lutz Erbring. 2000. Internet and society: A preliminary report . Stanford, CA: Stanford Institute for the Quantitative Study of Society.

This study presents the results of an early study that explores the sociological impact of information technology and the role of the Internet in shaping interpersonal relationships and interactions.

Pardeck, John T., and Ruth S. Schulte. 1990. Computers in social intervention: Implications for professional social work practice and education. Family Therapy 17.2: 109.

The authors discuss the impact of computer technology on aspects of social work intervention including inventory testing, client history, clinical assessment, computer-assisted therapy, and computerized therapy.

Schopler, Janice H., Melissa D. Abell, and Maeda J. Galinsky. 1998. Technology-based groups: A review and conceptual framework for practice. Social Work 43.3: 254–267.

DOI: 10.1093/sw/43.3.254

The authors examine studies of social work practice using telephone and computer groups. Social work practice guidelines for technology-based groups are discussed.

Turkle, Sherry. 1984. The second self: Computers and the human spirit . New York: Simon & Schuster.

Explores the use of computers not as tools but as part of our social and psychological lives and how computers affect our awareness of ourselves, of one another, and of our relationship with the world.

Weizenbaum, Joseph. 1976. Computer power and human reason: From judgment to calculation . San Francisco: W. H. Freeman.

Examines the sources of the computer’s power including the notions of the brilliance of computers and offers evaluative explorations of computer power and human reason. The book presents common theoretical issues and applications of computer power such as computer models of psychology, natural language, and artificial intelligence.

back to top

Users without a subscription are not able to see the full content on this page. Please subscribe or login .

Oxford Bibliographies Online is available by subscription and perpetual access to institutions. For more information or to contact an Oxford Sales Representative click here .

• About Social Work »
• Meet the Editorial Board »
• Adolescent Depression
• Adolescent Pregnancy
• Adolescents
• Adoption Home Study Assessments
• Adult Protective Services in the United States
• African Americans
• Aging out of foster care
• Aging, Physical Health and
• Alcohol and Drug Abuse Problems
• Alcohol and Drug Problems, Prevention of Adolescent and Yo...
• Alcohol Problems: Practice Interventions
• Alcohol Use Disorder
• Alzheimer's Disease and Other Dementias
• Anti-Oppressive Practice
• Asian Americans
• Asian-American Youth
• Autism Spectrum Disorders
• Baccalaureate Social Workers
• Behavioral Health
• Behavioral Social Work Practice
• Bereavement Practice
• Bisexuality
• Brief Therapies in Social Work: Task-Centered Model and So...
• Bullying and Social Work Intervention
• Canadian Social Welfare, History of
• Case Management in Mental Health in the United States
• Central American Migration to the United States
• Child Maltreatment Prevention
• Child Neglect and Emotional Maltreatment
• Child Poverty
• Child Sexual Abuse
• Child Welfare
• Child Welfare and Child Protection in Europe, History of
• Child Welfare and Parents with Intellectual and/or Develop...
• Child Welfare Effectiveness
• Child Welfare, Immigration and
• Child Welfare Practice with LGBTQ Youth and Families
• Children of Incarcerated Parents
• Christianity and Social Work
• Chronic Illness
• Clinical Social Work Practice with Adult Lesbians
• Clinical Social Work Practice with Males
• Cognitive Behavior Therapies with Diverse and Stressed Pop...
• Cognitive Processing Therapy
• Cognitive-Behavioral Therapy
• Community Development
• Community Policing
• Community-Based Participatory Research
• Community-Needs Assessment
• Comparative Social Work
• Computational Social Welfare: Applying Data Science in Soc...
• Conflict Resolution
• Council on Social Work Education
• Counseling Female Offenders
• Criminal Justice
• Crisis Interventions
• Cultural Competence and Ethnic Sensitive Practice
• Culture, Ethnicity, Substance Use, and Substance Use Disor...
• Dementia Care
• Dementia Care, Ethical Aspects of
• Depression and Cancer
• Development and Infancy (Birth to Age Three)
• Differential Response in Child Welfare
• Direct Practice in Social Work
• Disabilities
• Disability and Disability Culture
• Domestic Violence Among Immigrants
• Early Pregnancy and Parenthood Among Child Welfare–Involve...
• Eating Disorders
• Ecological Framework
• Economic Evaluation
• Elder Mistreatment
• End-of-Life Decisions
• Epigenetics for Social Workers
• Ethics and Values in Social Work
• European Institutions and Social Work
• European Union, Justice and Home Affairs in the
• Evidence-based Social Work Practice: Finding Evidence
• Evidence-based Social Work Practice: Issues, Controversies...
• Experimental and Quasi-Experimental Designs
• Families with Gay, Lesbian, or Bisexual Parents
• Family Caregiving
• Family Group Conferencing
• Family Policy
• Family Services
• Family Therapy
• Family Violence
• Fathering Among Families Served By Child Welfare
• Fetal Alcohol Spectrum Disorders
• Field Education
• Financial Literacy and Social Work
• Financing Health-Care Delivery in the United States
• Forensic Social Work
• Foster Care
• Foster care and siblings
• Gender, Violence, and Trauma in Immigration Detention in t...
• Generalist Practice and Advanced Generalist Practice
• Grounded Theory
• Group Work across Populations, Challenges, and Settings
• Group Work, Research, Best Practices, and Evidence-based
• Harm Reduction
• Health Care Reform
• Health Disparities
• History of Social Work and Social Welfare, 1900–1950
• History of Social Work and Social Welfare, 1950-1980
• History of Social Work and Social Welfare, pre-1900
• History of Social Work from 1980-2014
• History of Social Work in China
• History of Social Work in Northern Ireland
• History of Social Work in the Republic of Ireland
• History of Social Work in the United Kingdom
• HIV/AIDS and Children
• HIV/AIDS Prevention with Adolescents
• Homelessness
• Homelessness: Ending Homelessness as a Grand Challenge
• Homelessness Outside the United States
• Human Trafficking, Victims of
• Immigrant Integration in the United States
• Immigrant Policy in the United States
• Immigrants and Refugees
• Immigrants and Refugees: Evidence-based Social Work Practi...
• Immigration and Health Disparities
• Immigration and Intimate Partner Violence
• Immigration and Poverty
• Immigration and Spirituality
• Immigration and Substance Use
• Immigration and Trauma
• Impaired Professionals
• Implementation Science and Practice
• Indigenous Peoples
• Individual Placement and Support (IPS) Supported Employmen...
• In-home Child Welfare Services
• Intergenerational Transmission of Maltreatment
• International Human Trafficking
• International Social Welfare
• International Social Work
• International Social Work and Education
• International Social Work and Social Welfare in Southern A...
• Interpersonal Psychotherapy
• Intervention with Traumatized Populations
• Interviewing
• Intimate-Partner Violence
• Juvenile Justice
• Kinship Care
• Korean Americans
• Latinos and Latinas
• Law, Social Work and the
• LGBTQ Populations and Social Work
• Mainland European Social Work, History of
• Major Depressive Disorder
• Management and Administration in Social Work
• Maternal Mental Health
• Measurement, Scales, and Indices
• Medical Illness
• Men: Health and Mental Health Care
• Mental Health
• Mental Health Diagnosis and the Addictive Substance Disord...
• Mental Health Needs of Older People, Assessing the
• Mental Health Services from 1990 to 2023
• Mental Illness: Children
• Mental Illness: Elders
• Meta-analysis
• Microskills
• Middle East and North Africa, International Social Work an...
• Military Social Work
• Mixed Methods Research
• Moral distress and injury in social work
• Motivational Interviewing
• Multiculturalism
• Native Americans
• Native Hawaiians and Pacific Islanders
• Neighborhood Social Cohesion
• Neuroscience and Social Work
• Nicotine Dependence
• Occupational Social Work
• Organizational Development and Change
• Pain Management
• Palliative Care
• Palliative Care: Evolution and Scope of Practice
• Pandemics and Social Work
• Parent Training
• Personalization
• Person-in-Environment
• Philosophy of Science and Social Work
• Physical Disabilities
• Podcasts and Social Work
• Police Social Work
• Political Social Work in the United States
• Positive Youth Development
• Postmodernism and Social Work
• Postsecondary Education Experiences and Attainment Among Y...
• Post-Traumatic Stress Disorder (PTSD)
• Practice Interventions and Aging
• Practice Interventions with Adolescents
• Practice Research
• Primary Prevention in the 21st Century
• Productive Engagement of Older Adults
• Profession, Social Work
• Program Development and Grant Writing
• Promoting Smart Decarceration as a Grand Challenge
• Psychiatric Rehabilitation
• Psychoanalysis and Psychodynamic Theory
• Psychoeducation
• Psychometrics
• Psychopathology and Social Work Practice
• Psychopharmacology and Social Work Practice
• Psychosocial Framework
• Psychosocial Intervention with Women
• Psychotherapy and Social Work
• Qualitative Research
• Race and Racism
• Readmission Policies in Europe
• Redefining Police Interactions with People Experiencing Me...
• Refugee Children, Unaccompanied Immigrant and
• Rehabilitation
• Religiously Affiliated Agencies
• Reproductive Health
• Research Ethics
• Restorative Justice
• Risk Assessment in Child Protection Services
• Risk Management in Social Work
• Rural Social Work in China
• Rural Social Work Practice
• School Social Work
• School Violence
• School-Based Delinquency Prevention
• Services and Programs for Pregnant and Parenting Youth
• Severe and Persistent Mental Illness: Adults
• Sexual and Gender Minority Immigrants, Refugees, and Asylu...
• Sexual Assault
• Single-System Research Designs
• Social and Economic Impact of US Immigration Policies on U...
• Social Development
• Social Insurance and Social Justice
• Social Intervention Research
• Social Justice and Social Work
• Social Movements
• Social Planning
• Social Policy
• Social Policy in Denmark
• Social Security in the United States (OASDHI)
• Social Work and Islam
• Social Work and Social Welfare in East, West, and Central ...
• Social Work and Social Welfare in Europe
• Social Work Education and Research
• Social Work Leadership
• Social Work Luminaries: Luminaries Contributing to the Cla...
• Social Work Luminaries: Luminaries contributing to the fou...
• Social Work Luminaries: Luminaries Who Contributed to Soci...
• Social Work Practice, Rare and Orphan Diseases and
• Social Work Regulation
• Social Work Research Methods
• Social Work with Interpreters
• Solution-Focused Therapy
• Strategic Planning
• Strengths Perspective
• Strengths-Based Models in Social Work
• Supplemental Security Income
• Survey Research
• Sustainability: Creating Social Responses to a Changing En...
• Syrian Refugees in Turkey
• Systematic Review Methods
• Task-Centered Practice
• Technology, Human Relationships, and Human Interaction
• Terminal Illness
• The Impact of Systemic Racism on Latinxs’ Experiences with...
• Transdisciplinary Science
• Translational Science and Social Work
• Transnational Perspectives in Social Work
• Transtheoretical Model of Change
• Trauma-Informed Care
• Triangulation
• Tribal child welfare practice in the United States
• United States, History of Social Welfare in the
• Universal Basic Income
• Veteran Services
• Vicarious Trauma and Resilience in Social Work Practice wi...
• Vicarious Trauma Redefining PTSD
• Victim Services
• Welfare State Reform in France
• Welfare State Theory
• Women and Macro Social Work Practice
• Women's Health Care
• Work and Family in the German Welfare State
• Workforce Development of Social Workers Pre- and Post-Empl...
• Working with Non-Voluntary and Mandated Clients
• Young and Adolescent Lesbians
• Youth at Risk
• Youth Services
• Privacy Policy
• Cookie Policy
• Legal Notice
• Accessibility

Powered by:

• [185.66.15.189]
• 185.66.15.189

Essay on Technology and Human Relationships

Students are often asked to write an essay on Technology and Human Relationships in their schools and colleges. And if you’re also looking for the same, we have created 100-word, 250-word, and 500-word essays on the topic.

Let’s take a look…

100 Words Essay on Technology and Human Relationships

Introduction.

Technology has transformed how we communicate and build relationships. It’s an inseparable part of our lives, influencing how we interact with each other.

Technology’s Role

Technology, particularly social media, allows us to connect with people globally. It bridges distances, making communication easy and instant.

Impact on Relationships

However, technology can also affect relationships negatively. Overuse can lead to less face-to-face interaction, potentially weakening bonds.

Balancing technology use is crucial. It can enhance relationships if used mindfully, but can also harm if not used responsibly.

250 Words Essay on Technology and Human Relationships

Enhanced communication.

Technology has revolutionized communication, making it possible to connect with people around the world in real-time. Social media platforms, video conferencing, and instant messaging apps have facilitated constant communication, strengthening relationships.

Virtual Relationships

However, there’s a growing concern about the authenticity of relationships in the digital era. Technology can create an illusion of closeness, leading to superficial relationships. The rise of virtual relationships can also lead to the erosion of face-to-face social skills.

Technology’s Impact on Intimacy

Technology can both enhance and hinder intimacy. On one hand, it allows couples in long-distance relationships to maintain their bond. On the other hand, excessive use of technology can create a digital barrier, reducing quality time and causing emotional disconnect.

In conclusion, while technology has the potential to enrich human relationships, it can also create challenges. It’s essential to strike a balance between technology use and maintaining genuine, meaningful relationships. The key lies in using technology as a tool to enhance relationships, not as a replacement for real human connection.

500 Words Essay on Technology and Human Relationships

Technology has become an integral part of our lives, shaping our interactions and the way we perceive relationships. The advent of digital tools has revolutionized communication, making it easier and quicker. However, this digital revolution also raises questions about its impact on human relationships.

Technology: A Bridge or a Barrier?

However, while technology bridges the gap between people geographically, it may also erect barriers in personal relationships. The digital world often provides a curated version of reality, where individuals present their ‘best selves’. This may lead to superficial relationships, devoid of emotional depth. The ease of communication also inadvertently leads to an expectation of constant availability, potentially causing stress and anxiety.

Impact of Technology on Interpersonal Skills

The rise of digital communication has resulted in a shift from face-to-face interactions to screen-based conversations. This shift has implications for our interpersonal skills. Non-verbal cues such as body language, tone of voice, and facial expressions, which are crucial in understanding others’ emotions, are often lost in digital communication. This loss can lead to misunderstandings and misinterpretations, impacting the quality of relationships.

The Paradox of Connection and Isolation

Technology and empathy.

Empathy, the ability to understand and share the feelings of others, is a cornerstone of human relationships. However, technology might be diminishing our empathetic abilities. The constant bombardment of information and the impersonal nature of digital communication can desensitize us to others’ experiences. This desensitization can hinder the formation of meaningful relationships, which are based on mutual understanding and empathy.

In conclusion, technology has transformed human relationships in profound ways. It has made communication more accessible but has also led to issues such as superficial relationships, reduced interpersonal skills, and feelings of isolation. As we continue to embrace technology, it is crucial to be mindful of these potential pitfalls. Balancing digital interactions with face-to-face communication, fostering empathy, and promoting genuine connections can help us navigate the digital age without compromising the quality of our relationships.

Apart from these, you can look at all the essays by clicking here .

Happy studying!

Leave a Reply Cancel reply

Logo Left Content

Logo Right Content

Stanford University School of Medicine blog

A conflicted relationship: On technology and human interaction

How has technology changed the way people interact?

A physician, a linguist and a sociologist tackled these questions and more in a recent panel discussion hosted by the  Center for Advanced Study in the Behavioral Sciences and the  Catalyst for Collaborative Solutions , both at Stanford.

“Technology is hugely important,” physician-author  Abraham Verghese , MD, told the audience. “It’s going to make us better and better – but it’s not going to take away the importance of the human interaction.”

Verghese, whose Presence Center at Stanford focuses on harnessing technology for the human experience in medicine, said the relationship between the physician and patient has been a bedrock of the field for centuries. However, recent technological advancements – particularly electronic medical records – increasingly pull doctors away from face-to-face dealings with their patients.

“We have a medical records system where, for every one hour cumulatively I spend with a patient, I spend two hours charting on this computer and another hour at night further dealing with the inbox related to all of this,” Verghese said.

Some may wonder if the personal touch is still needed, Verghese said. He argued that it is. People can assess a situation in ways that computers cannot, thus avoiding potential medical errors, particularly in the most seriously ill patients, he said. Additionally, the relationship between a doctor and a patient is fundamentally human: an individual providing care to another individual at their most vulnerable.

“The ritual of the exam, when performed well, it really seals the physician-patient relationship,” he said. “It localizes the illness, not on a lab report somewhere, not in an image somewhere, but on one’s body.”

For young adults, technology often substitutes for in-person interaction, but that doesn’t necessarily make the communication less meaningful, said Stanford linguist Sarah Ogilvie , PhD. She spoke of an undergraduate who decided to skip in-person lectures and watch them online at an accelerated speed in order to pay better attention.

“They are forced to concentrate to try and follow what the lecturer is saying and they are no longer distracted by their social media, which they say is the big distraction when they go to a physical lecture,” Ogilvie said.

The words used by the iGen generation – born after 1995 when the World Wide Web became broadly public – provide a window into how technology has shaped their lives, Ogilvie said.

For example, they create different Instagram accounts for different audiences: “Insta” for the public; “Finsta,” an account under a fake name that can only be viewed by close friends; “Ginsta,” for people they know through their gay identity.

Reddit, 4chan and other online forums allow members of iGen to experiment with different personas, Ogilvie said: “They help support an identity that might change. It might change monthly, it might change weekly, or even daily.”

While iGen adapts to technology, older generations seem stuck complaining about the increased time pressures of the digitized world, said Judy Wajcman , PhD, a sociology professor at the London School of Economics who is currently a fellow at the Center for Advanced Study in the Behavioral Sciences.

Perhaps they should blame themselves, she said: “We really value a fast, busy life, an action-packed life, and so we build technologies that feed this speed.”

Despite doomsayers, Wacjman said, her research has shown that cellphones have actually been important for cementing intimacy, and that a similar cycle of “moral panics and messianic hopes” has accompanied most technological advancements, including television.

“If you actually look at what people do with their technologies… people do amazing, different, contradictory things, and will tell you in the same sentence that they love and hate the machines, and they live fine with all these contradictions,” she said.

Photo courtesy of the Center for Advanced Study in the Behavioral Sciences

Popular posts

Padded helmet cover shows little protection for football players

Dick Cheney on his heart transplant: “It’s the gift of life itself”

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 04 April 2023

Artificial intelligence in communication impacts language and social relationships

• Jess Hohenstein 1 ,
• Rene F. Kizilcec 1 ,
• Dominic DiFranzo 2 ,
• Zhila Aghajari 2 ,
• Hannah Mieczkowski 3 ,
• Karen Levy 1 ,
• Mor Naaman 4 ,
• Jeffrey Hancock 3 &
• Malte F. Jung 1  

Scientific Reports volume  13 , Article number:  5487 ( 2023 ) Cite this article

40k Accesses

29 Citations

239 Altmetric

Metrics details

• Engineering

A Publisher Correction to this article was published on 03 October 2023

This article has been updated

Artificial intelligence (AI) is already widely used in daily communication, but despite concerns about AI’s negative effects on society the social consequences of using it to communicate remain largely unexplored. We investigate the social consequences of one of the most pervasive AI applications, algorithmic response suggestions (“smart replies”), which are used to send billions of messages each day. Two randomized experiments provide evidence that these types of algorithmic recommender systems change how people interact with and perceive one another in both pro-social and anti-social ways. We find that using algorithmic responses changes language and social relationships. More specifically, it increases communication speed, use of positive emotional language, and conversation partners evaluate each other as closer and more cooperative. However, consistent with common assumptions about the adverse effects of AI, people are evaluated more negatively if they are suspected to be using algorithmic responses. Thus, even though AI can increase the speed of communication and improve interpersonal perceptions, the prevailing anti-social connotations of AI undermine these potential benefits if used overtly.

Similar content being viewed by others

Influencing human–AI interaction by priming beliefs about AI can increase perceived trustworthiness, empathy and effectiveness

Humans rely more on algorithms than social influence as a task becomes more difficult

Human–AI collaboration enables more empathic conversations in text-based peer-to-peer mental health support

Introduction.

Communication is the basic process through which people form perceptions of others 1 , build and maintain social relationships 2 , and achieve cooperative outcomes 3 . Generative AI that draws from Large Language Models (LLMs) is poised to fundamentally change how we communicate. AI applications like ChatGPT are increasingly used to produce any kind of language, from text messages and social media posts to computer programs and speeches 4 , 5 , 6 .

One of the most pervasive AI applications to date is personalized reply suggestions in text-based communication, commonly known as “smart replies” 7 . As of 2017, algorithmic responses constituted 12% of all messages sent through Gmail 8 , representing about 6.7 billion emails written by AI on our behalf each day 9 . Smart reply systems aim to make text production more efficient by drawing on general text corpora to predict what a person might type and generating one or more suggested responses that the person can choose from when responding to a message 7 (see Fig.  1 ). Rapid adoption of this type of AI in interpersonal communication has been facilitated by a large body of technical research regarding various methods for generating algorithmic responses 7 , 10 , 11 .

Despite the rapid deployment of AI applications in new products and contexts as well as growing concerns about their consequences for society 12 , the scientific community has largely ignored the potential social impacts of integrating AI-generated messages into human communication. Reports from the AI Now Institute liken this scenario to “conducting an experiment without bothering to note the results” 13 and have repeatedly noted the under-investment in research on the social implications of AI while calling for an increase in interdisciplinary examinations of these systems within human populations 14 .

Left side: Example of a message exchange with AI support (i.e. a smart-reply enabled messenger). Typical examples of smart replies and how they might be presented to a user are shown in orange at the bottom. Right side: Abstract representation of the influence of AI on interpersonal communication. Either one or both participants can have access to AI support (e.g. in the form of smart replies). When given access to AI support, participants may choose to use it or not (actual use). However, independent of actual use, participants make assumptions about AI support (perceived use). Both actual use and perceived use influence the overall message exchange and the perceptions people form of each other.

In response, a growing body of work at the intersection of computer and social sciences is concerned with understanding how AI systems may be influencing human behavior 5 , 15 , 16 . Initial studies have found that algorithmic responses can impact how people write 17 , and users perceive that the mere presence of smart replies influences the way that they communicate, in part because of the linguistic skew of smart replies, which tend to express excessive positive emotion as compared to normal conversation 18 . However, we do not know how our social relationships with and perceptions of others are affected when we let algorithms speak on our behalf.

To examine the interpersonal consequences of using AI to generate messages, we developed a custom messaging application and conducted two randomized experiments to study how the display and use of AI-generated smart replies in real-time text-based communication affects how people interact and perceive each other. We show that a widely-deployed smart reply algorithm affects various aspects of interpersonal communication, including communication speed, emotional tone, and interpersonal evaluations in both positive and negative ways.

AI Impacts Social Relationships: It is Perceived Negatively but Improves Interpersonal Perceptions

Inspired by theories of how computer-mediated communication can affect intimacy and relationship maintenance 19 , we hypothesized that seeing AI-generated reply suggestions could influence participants’ feelings of connectedness with their conversation partner. To test the effect of AI mediation on interpersonal trait inferences and perceptions of cooperativeness, we developed a novel messaging application (detailed in the Methods section) that allows us not only to control which smart replies are displayed but also to collect data about their use in communication.

To identify the effects and perceptions of algorithmic responses in conversation, we randomly assigned 219 pairs of participants (“self” and “partner”) independently to have smart replies (i.e., suggested responses generated using the Google Reply API 20 ) either available to use or not. This resulted in four messaging scenarios: (1) both participants can use smart replies, (2) only the self can use smart replies, (3) only the partner can use smart replies, or (4) neither participant can use smart replies. The availability of smart replies encourages participants to use them in conversation. To estimate the effects of smart reply usage, not its mere availability, on conversation speed, sentiment, and interpersonal outcomes, we use an instrumental variable (IV) approach. IV analysis is an established econometric method to estimate causal effects when the experimental treatment depends on individual adoption 21 . Our instrument is the availability of smart replies for the partner, which is both randomly assigned and unobserved by the self. Participants are also blind to whether any given message they receive is a smart reply. This creates ideal conditions for the exclusion restriction assumption of IV to be satisfied because any effect of the instrument (smart reply availability) on the outcome (e.g., ratings of affiliation) is exclusively through its effect on exposure (proportion of messages from the partner that are smart replies).

Participants engaged in a conversation about a policy issue while our application tracked the presentation and use of smart replies. After completing the conversation, participants were given a definition of smart replies and asked to rate on a scale from 1 (“never”) to 5 (“always”) how often they believed that their partner had used them. They also responded to established survey measures of dominance and affiliation (Revised Interpersonal Adjective Scale 22 ). The measure presented participants a list of words that “describe how people interact with other” (e.g. shy, kindhearted, outgoing) and asked them to “rate how accurately each word describes your conversation partner” on a scale from “Extremely inaccurate” (1), to “Extremely accurate” (7). Finally, participants completed a cooperative communication measure 23 that asked participants to rate their agreement with statements such as “we often criticize each other” on a scale from “Strongly disagree” (1) and “Strongly agree” (7). The presentation of the three post-task measures was randomized between participants to avoid any possible order effects. For detailed information about each measure, please see the supplementary materials.

We find that the availability of algorithmic responses was a strong encouragement to use them in conversation [first-stage: t (211) = 13.8, P <0.0001]. Smart replies accounted for 14.3% of sent messages on average. Availability of algorithmic responses also resulted in faster communication speed, with 10.2% more messages sent per minute [intent-to-treat estimate: t (198) = 2.173, P = 0.0309]. Smart replies sped up messaging specifically for the participant who could use them, because the partner’s use of smart replies did not significantly improve communication speed of the self [IV estimate: b = 0.402, t (205) = 0.825, P = 0.410]. While smart replies can improve communication speed, their consequences for interpersonal perceptions are more complex.

Participants are capable of recognizing their partner’s use of smart replies to some degree: beliefs about how much their partner used smart replies correlated with actual use but not strongly [Pearson’s r = 0.22, t (97) = 3.62, P = 0.0005]. Consistent with commonly held beliefs about the negative implications of AI in social interactions 24 , 25 , we find strong associations between perceived smart reply use by the partner and attitudes towards them. The more participants thought their partner used smart replies, the less cooperative they rated them [ t (92) = −9.89, P  < 0.0001], the less affiliation they felt towards them [ t (92) = −6.90, P < 0.0001], and the more dominant they rated them [ t (92) = 2.27, P = 0.0256], as shown in Fig.  2 , even after controlling for their partner’s actual smart reply use. This shows correlationally that people who appear to be using smart replies in conversation pay an interpersonal toll, even if they are not actually using smart replies. However, this finding does not show causally how attitudes shift in response to actual smart reply use.

Average rating of the partner’s cooperative communication, affiliation, and dominance by the self for different levels of perceived smart reply (SR) use by the partner ( N = 361). Error bars show one cluster-robust standard error above and below the mean. See Supplementary Table S6 for details about the frequency of responses per response category.

We find that increased use of smart replies by the partner actually improved the self’s rating of the partner’s cooperation [IV estimate: b = 15.66, t (189) = 2.39, P = 0.018] and sense of affiliation towards them [IV estimate: b = 21.79, t (189) = 2.75, P = 0.007], but not dominance [IV estimate: b = −0.53, t (189) = −0.13, P = 0.90]. Although perceived smart reply use is judged negatively, actual use by the partner resulted in more positive attitudes. Notably, ratings of cooperation and affiliation were not significantly affected by the presence of algorithmic responses for the self [intent-to-treat estimates: cooperation b = 0.397, t (188) = 0.436, P = 0.663; affiliation b = −0.397, t (188) = −0.362, P = 0.718], only ratings of dominance were reduced given the presence of algorithmic responses for the self [ b = −1.338, t (188) = −2.233, P = 0.021].

We also find that increased use of smart replies by the partner led the self to send messages with more positive sentiment [IV estimate: b = 0.178, t (205) = 2.02, P = 0.045], even if smart reply messages were excluded from the sentiment score [ b = 0.208, t (205) = 2.17, P = 0.031]. The self’s message sentiment was also more positive if algorithmic responses were available to the self [intent-to-treat estimate: b = 0.026, t (198) = 2.05, P = 0.0422], unless the calculation of message sentiment omits smart reply messages [ b = 0.019, t (198) = 1.35, P = 0.1801]. This suggests that merely showing algorithmic responses did not affect the sentiment of written messages, but rather, it affected message sentiment by using smart reply messages which tend to have positive sentiment. Taken together, these findings imply that the effects of AI mediation on interpersonal perceptions are related to changes in language introduced by the AI system.

AI impacts language: its sentiment affects emotional content in human conversations

To better understand how the sentiment of AI-suggested responses affects conversational language, we conducted a second experiment. Using a between-subjects design, we randomly assigned 291 pairs to discuss a policy issue using our app in one of four conditions: (1) Google smart replies (generated using the Google Reply API 20 ), (2) positive smart replies (rated by crowdworkers to have positive sentiment), (3) negative smart replies (rated by crowdworkers to have negative sentiment), or (4) no smart replies were made available to both participants to use in conversation. We measured conversation sentiment using VADER, a lexicon- and rule-based sentiment analysis tool that is ideal for analyzing short, social messages 26 . As a precursor to the VADER score analysis, we used the LIWC affect dictionary 27 to confirm that smart replies introduced more affective language into the conversation (see Methods section). We aggregated VADER scores into a sentiment polarity score ranking from most positive (1) to most negative (−1), with neutral (0) in the middle. On average, conversations lasted for 6.33 min [ SD =2.67] and used 20 messages including smart replies.

We find that the availability of negative smart replies caused conversations to have more negative emotional content than conversations with positive smart replies [ t (127) = 2.75, P = 0.007, d = .352] and the widely-used Google smart replies [ t (127)=2.40, P = 0.018, d = .323; Fig.  3 ], which highlights the positive sentiment bias of smart replies in commercial messaging apps. Google smart replies had a similar effect on conversation sentiment as a set of positive smart replies [ t (150) = 0.51, P = 0.61], but did not cause significantly more positive sentiment compared to having no smart replies available [ t (137) = 0.55, P = 0.58]. Moreover, we find that these shifts in language are driven by people’s use of smart replies rather than mere exposure to smart reply suggestions; repeating the analysis with smart reply messages omitted from the conversation corpus, we find minimal differences in conversation sentiment between the smart reply conditions [ F (3277) = 0.360, P = 0.782]. Taken together, these findings demonstrate how AI-generated sentiment affects the emotional language used in human conversation.

Mean overall conversation sentiment by experimental condition: both participants assigned to no smart replies, negative, positive, or Google smart replies. Error bars show one cluster-robust standard error above and below the mean.

Our research shows that generative AI, including a commercially-deployed AI system, can have a significant impact on how people communicate with both positive and negative consequences. We find that people choose to use AI when given the opportunity, and this increases the speed of communication and leads to more emotionally positive language. However, we also find that when participants think that their partner is using more algorithmic responses, they perceive them as less cooperative, less affiliative and more dominant. This finding could be related to common assumptions about the negative implications of AI in social interactions. For example, humans are already predisposed to trust other humans over computers 25 , and most current communication systems featuring AI mediation lack transparency for users (i.e., the sender knows that their responses have been modified or generated by AI, while the receiver does not). Taken together with users’ preference for reducing uncertainty in interactions 28 , this could lead to negative perceptions of AI in everyday communication. Indeed, these negative perceptions confirm recent findings that people believe that smart replies often do not capture what they want to say and could alter the way that they communicate with others 18 , and that text suspected of or labeled as generated by an AI was perceived as less trustworthy 24 .

Despite these negative perceptions of AI in communication, we find that as people actually use more algorithmic responses, their communication partner has more positive attitudes about them. Even though perceived smart reply use is viewed negatively, actual smart reply use results in communicators being viewed as being more cooperative and affiliative. In other words, it seems that the negative perception of using AI to help us communicate does not match the reality.

It is important to note that these findings are specifically related to using AI in communication and are not observable when we consider instances where users are simply presented with AI recommendations but do not use them. In other words, although we did not find any main effects of being exposed to smart replies, we instead find that the presentation of smart replies acts as an encouragement to use them, and by using them, people are tweaking their language and the way that they are perceived by others.

Our work has implications for theory in communication and psychology. We provide evidence that using AI can shape language production and associated interpersonal perceptions. Understanding this impact is important because language is inextricably linked with listeners’ characterizations of a communicator, including their personality 1 , emotions 2 , sentiment 26 , 29 , and level of dominance 30 . Indeed, we find that using AI-generated responses changed the expression of emotion in human conversations. The influence of AI on human emotional communication is deeply concerning given that AI is writing billions of emails for us every day 9 . With the increasing popularity of other forms of AI mediating our everyday communication (e.g., Smart Compose 31 ), we have little insight into how regularly people are allowing AI to help them communicate or the potential long-term implications of the interference of AI in human communication. Our work suggests that interpersonal relationships are likely to be affected, potentially positively, but future research needs to investigate the longitudinal effects of such changes. For example, could this tweaking of our language potentially lead to a loss of personal communication style, with language expression becoming increasingly homogeneous over time?

This work also has implications for research in computer science that focuses on AI development, as we highlight both opportunities and risks of deploying such systems. We demonstrate how AI systems can influence interactions in positive ways through exceedingly subtle forms of intervention. Merely providing reply suggestions can change the language used in a conversation, with changes being consistent with the linguistic qualities of the algorithmic responses. Additionally, previous work has shown that when conversations go awry, people trust the AI more than their communication partner and assign some of the blame that they otherwise would have assigned to this person to the AI 32 . Taken together, these findings suggest possible opportunities for developers to affect conversational dynamics and outcomes by carefully controlling the linguistics of smart replies that are shown to people 33 . However, this also raises potential risks as AI continues to become increasingly present in our social interactions. With this knowledge, it is important for researchers and practitioners to consider the broader social consequences when designing algorithms that support communication.

Overall, we show how an AI system designed to help people can have unintended social consequences. AI has the potential to help people communicate more quickly and improve interpersonal perceptions in everyday conversation, but our findings caution that these benefits are coupled with alterations to the emotional aspects of our language, and we do not know the effects that such changes could have on communication patterns over time.

All methods were carried out in accordance with relevant ethics guidelines and regulations. All experimental protocols and materials were approved by Cornell University’s Institutional Review Board for Human Participant Research (IRB) (Protocol Number: 1610006732): https://researchservices.cornell.edu . Informed consent was obtained from all participants and study 1 was pre-registered on AsPredicted 34 .

We randomly assigned pairs of participants (“self” and “partner”) independently to have smart replies either available to use or not while engaged in a conversation about a policy issue. This resulted in four conditions: (1) both participants can use smart replies, (2) only the self can use smart replies, (3) only the partner can use smart replies, or (4) neither participant can use smart replies. Inspired by theories of how computer-mediated communication can affect intimacy and relationship maintenance 19 , we expected that seeing AI-generated reply suggestions would influence participants’ perceptions of their conversation partner as well as their language.

Participants

We recruited 438 Mechanical Turk crowdworkers to this study in return for monetary compensation. Research has shown that data provided by MTurk participants often meets or even exceeds “the psychometric standards set by data collected using other means” 35 . The sample size is comparable to recent other studies that examined the social consequences of algorithmically mediated communication 32 , 36 .

Because the focus of our research is on full conversations, we excluded conversations with less than 10 messages exchanged overall and those during which a single participant sent less than 3 messages (one pair of participants). We additionally exclude six pairs of participants who did not engage in a meaningful conversation and instead primarily clicked the smart replies (over 75% of messages sent are smart replies). This results in 424 participants for analyses focused on smart reply use. Conversations lasted for 6.81 min on average ( SD = 2.31) and comprised 21.0 messages on average ( SD = 7.55). For the analysis of post-conversation self-report outcomes, we also excluded participants who did not complete the full survey (63 participants). This left N = 361 (124 women, 235 men, 1 other gender) for survey-based analyses. Participants ranged in age from 18 to 68 ( M = 34.07, SD = 10.1).

Smart reply research platform

We developed a flexible web-based research tool called Moshi, that allowed us to recruit participants online and engage them in real-time interpersonal communication tasks while receiving smart reply support.

Moshi is designed as a web application that allows two participants to text chat with one another. Like in existing commercial messaging applications that feature smart replies, participants can also be presented with smart replies that they can tap to send in addition to the standard text box for typing messages. This research tool, available for use by others ( https://github.com/Social-Design-Lab/moshi ), provides researchers with an experimental platform giving them full control over the type of smart replies that are displayed, how and when they are displayed and who sees them (please see the Supplementary file for more details).

We developed two messenger modes for study 1: No smart replies and real smart replies. Each mode could be activated independently for a participant. In the no smart reply mode, participants had to manually type each message that they sent. The real smart reply mode uses Google’s Reply model 20 to generate smart replies.

To assess the impact of smart replies on social relationships, we measured perceived dominance and affiliation, and perceived cooperative communication toward the respective conversation partner as well as perceived smart reply use. To assess the impact of smart reply on language we measured communication speed, and messaging sentiment.

Perceived dominance and affiliation were operationalized through the revised interpersonal adjective scales (IAS-R). The IAS-R provides an empirical measure of various dimensions that underlie interpersonal transactions 22 . To shorten the measure, two adjectives with the highest loading factors from each interpersonal octant were selected, based on the analysis of Wiggins and colleagues 22 , resulting in 16 items to be ranked. The instructions read, “Below are a list of words that describe how people interact with others. Based on your intuition, please rate how accurately each word describes your conversation partner” (adapted from 37 ). Participants rated each statement on rating-scale items anchored by “Extremely inaccurate” (1), “Somewhat accurate” (4), and “Extremely accurate” (7). These ratings were then combined according to a formula adapted from 22 to determine ratings of affiliation and dominance 37 (See Appendix for details).

Perceived cooperative communication was operationalized through a 7-item scale 23 where participants rated their agreement with statements describing cooperative communication in their overall interaction with their partner. The instructions read, “Thinking about your interaction with your partner, please rate the extent to which you agree with each of these statements.” Participants rated each statement on rating-scale items anchored by “Strongly disagree” (1) and “Strongly agree” (7).

Perceived smart reply use was operationalized by asking participants how often they believed their partner used smart replies on a 5-point scale ranging from “1= Never” to “5 = Always”. The presentation of all post-task survey measures was randomized between participants to address potential order effects in responses.

Communication speed was operationalized by calculating the average number of messages a participant sent per minute.

Messaging sentiment was operationalized using VADER, a lexicon and rule-based sentiment analysis tool specifically attuned to sentiments expressed on social media 26 . This analysis tool yields a sentiment metric indicating how positive, negative, or neutral the sentiment of the supplied text is. For our purposes, messages were analyzed individually using the VADER compound sentiment output, an aggregated score ranging from −1 to 1 (i.e., most negative to most positive) based on the three aforementioned sentiment components.

Participants were directed to a Qualtrics survey that guided them through the study procedure. After obtaining informed consent, participants were informed that they would be using a messaging system to complete a discussion task with an anonymous partner. Participants were then presented with a task involving a discussion of unfair rejection of work, an issue that is relevant to crowdworkers 38 . Specifically, we asked pairs to come to an agreement on the “top 3 changes that Mechanical Turk could make to better handle unfairly rejected work.” After opening the messaging platform, participants waited up to 5 min for another participant to enter the conversation. If 5 min elapsed without another participant arriving, participants were able to prematurely exit the survey and receive partial compensation. Once another participant arrived, the pair had as much time as they needed to come to an agreement on a ranked list. After verifying that a conversation was completed, participants were directed to our post-task measures.

Data analysis

Following standard procedure for Instrumental Variable (IV) estimation, we compute three types of estimands: first-stage effects, intent-to-treat effects, and IV effects  21 . In all cases, we compute cluster-robust standard errors (i.e., CR2) using the coef_test function in the clubSandwich R package 39 . The first-stage effects estimate how much random assignment to smart reply availability led participants to use smart replies in conversation. The intent-to-treat effects estimate how much assignment to smart reply availability caused changes in outcome measures, such as ratings on the post-survey, communication speed or sentiment. The IV effects estimate the marginal effects of increased smart reply use by the partner on outcomes for the self. Specifically, we analyzed outcome data for the self using IV regression with partner smart reply use instrumented by partner random assignment to condition; the self’s randomly assigned condition was added as a covariate. We used the ivreg function in the AER R package 40 . The reported estimates represent coefficients, t-statistics and p -values from the IV regression output.

We use an IV approach to estimate the effects of smart reply use (instrumented by randomly assigned availability) on conversation speed, sentiment, and interpersonal perceptions (dominance, affiliation, and cooperative communication). The exclusion restriction assumption is plausible by virtue of the experimental design, because neither participant is informed about their partner’s smart reply availability or whether any given message is a smart reply.

To better understand how the sentiment of AI-suggested responses affects conversational language, we conducted a second experiment. Using a between-subjects design, we randomly assigned 291 pairs to discuss a policy issue using our app in one of four conditions: (1) Both participants receive Google smart replies, (2) both participants receive smart replies with positive sentiment), (3) both participants receive negative smart replies with negative sentiment, or (4) no smart replies.

Across all conditions, 582 Mechanical Turk crowdworkers participated in this study and received monetary compensation for their time. We excluded 13 pairs of participants with less than 10 messages exchanged overall and where one participant sent less than 3 messages. Conversations lasted for 6.33 min on average ( SD = 2.67) and consisted of 20.2 messages on average ( SD = 8.63). From a brief post-conversation survey, completed by 510 participants (92%), we know that participants ranged in age from 19 to 69 ( M = 35.6, SD = 9.97), 206 women, 275 men, and one other gender.

Materials and measures

We used the same research platform as in study 1 but extended it with two additional modes: Positive and negative sentiment smart replies. For example, in the positive smart reply condition, a participant might see smart replies such as, “I like it” and “I can’t agree more”, whereas in the negative smart reply condition, a participant might see smart replies such as, “I don’t get it” and “No you are not”. These smart replies were chosen randomly from an input file without being too repetitive (i.e., all three utterances shown in each instance are different, and the same utterance is not shown in immediately subsequent instances). Utterances were chosen from previous work 18 that asked crowdworkers to rate the sentiment of smart replies. Smart reply suggestions included only those that were rated as having definitive positive or negative sentiment, respectively.

To assess the impact of smart replies on language, we measured messaging sentiment. The measure was operationalized as in study 1.

Procedures were similar to study 1, except participants in the smart reply conditions were informed that they would be “[...] using an AI-mediated messaging system to have a conversation with your partner. While you are messaging, artificial intelligence (AI) will provide smart replies that you can simply tap to send.”, while participants in the control condition were told that they would be “[...] using a standard messaging system to have a conversation with your partner”.

We analyzed the resulting data at the individual level using a simple linear regression with cluster-robust standard errors using the lm_robust function in the estimatr R package 41 . The dependent variable was the individual language measure (i.e., VADER sentiment) and the independent variable was the assigned condition; no covariates were added. The reported statistics are the t-statistic and p -value for the relevant coefficient, and Cohen’s d computed manually.

To ensure that any language differences that we found were not the result of demographic differences between the four conditions 42 , we examined the demographic makeup (i.e., age, gender, and race) between conditions and did not find any significant differences.

As a precursor to the VADER sentiment analysis, we examined the affect measure provided by the Linguistic Inquiry and Word Count (LIWC), a dictionary-based text analysis tool that determines the percentage of words that reflect a number of linguistic processes, psychological processes, and personal concerns 26 , 27 . We use the LIWC Affect score to check if the use of affective language changes with the introduction and use of smart replies. Affect, with values ranging from 0 to 100, is operationalized as the sum of the Positive Emotion and Negative Emotion scores in LIWC.

We found that the presence of positive and Google smart replies caused conversations to have higher affect than conversations without smart replies ( t (124) = 2.95, P  <  0.001, d = 0.272). The effect of positive and Google smart replies on affect was statistically similar ( t (150) = 0.354, P = 0.724). The presence of negative smart replies had a strong negative effect on conversation affect compared to the control condition without smart replies ( t (123) = −3.50, P  <  0.001, d = 0.454). Taken together, these findings demonstrate how AI-generated sentiment affects the emotional language used in human conversation.

Data availability

The datasets generated and analyzed during the current studies are available in a Mendeley repository 43 , http://dx.doi.org/10.17632/6v5r6jmd3y.1 . Due to the potentially sensitive nature of information revealed by participants in the conversations, participants were assured that the raw conversation data would remain confidential and not be shared.

Change history

03 october 2023.

A Correction to this paper has been published: https://doi.org/10.1038/s41598-023-43601-0

Mairesse, F., Walker, M. A., Mehl, M. R. & Moore, R. K. Using linguistic cues for the automatic recognition of personality in conversation and text. J. Artif. Intell. Res. 30 , 457–500 (2007).

Article   MATH   Google Scholar  

Pennebaker, J. W., Mehl, M. R. & Niederhoffer, K. G. Psychological aspects of natural language use: our words, our selves. Annu. Rev. Psychol. 54 , 547–577 (2003).

Article   PubMed   Google Scholar  

Zhang, J. et al. Conversations Gone Awry: Detecting Early Signs of Conversational Failure . arXiv preprint arXiv:1805.05345 (2018).

Stone, P. et al. Artificial Intelligence and Life in 2030. In: One Hundred Year Study on Artificial Intelligence: Report of the 2015–2016 Study Panel . Tech. Rep., Stanford University (2016).

Rahwan, I. et al. Machine behaviour. Nature 568 , 477–486 (2019).

Article   ADS   CAS   PubMed   Google Scholar  

Jakesch, M., Hancock, J. T., & Naaman, M. Human heuristics for AI-generated language are flawed. Proc. Natl. Acad. Sci , 120 (11), e2208839120 (2023)

Article   CAS   PubMed   PubMed Central   Google Scholar  

Kannan, A. et al. Smart reply: automated response suggestion for email. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 955–964 (2016).

Bullock, G. Save Time with Smart Reply in Gmail . https://www.blog.google/products/gmail/save-time-with-smart-reply-in-gmail/ (2017).

Kraus, R. Gmail Smart Replies may be Creepy, But They’re Catching on Like Wildfire . https://mashable.com/article/gmail-smart-reply-growth/ . (2018).

Henderson, M. et al. Efficient Natural Language Response Suggestion for Smart Reply . arXiv preprint arXiv:1705.00652 (2017).

Ritter, A., Cherry, C. & Dolan, W. B. Data-driven response generation in social media. In Proceedings of the Conference on Empirical Methods in Natural Language Processing , 583–593 (Association for Computational Linguistics, 2011).

Shakirov, V. Review of State-of-the-Arts in Artificial Intelligence with Application to AI Safety Problem . arXiv preprint arXiv:1605.04232 (2016).

Crawford, K. et al. The AI Now Report: The Social and Economic Implications of Artificial Intelligence Technologies in the Near-Term (AI Now Institute at New York University, 2016).

Whittaker, M. et al. AI Now Report 2018 (AI Now Institute at New York University New York, 2018).

Lee, M. K. Understanding perception of algorithmic decisions: fairness, trust, and emotion in response to algorithmic management. Big Data Soc. 5 , 2053951718756684 (2018).

Article   Google Scholar  

Hancock, J. T., Naaman, M. & Levy, K. AI-mediated communication: definition, research agenda, and ethical considerations. J. Comput.-Mediat. Commun. (2020).

Arnold, K. C., Chauncey, K. & Gajos, K. Z. Predictive text encourages predictable writing. In Proceedings of the 25th International Conference on Intelligent User Interfaces , 128–138 (2020).

Hohenstein, J. & Jung, M. AI-supported messaging: an investigation of human-human text conversation with AI support. In Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems - CHI ’18 , https://doi.org/10.1145/3170427.3188487 (2018).

Tong, S. & Walther, J. Relational maintenance and CMC. Computer-mediated Communication in Personal Relationships 98–118 (2011).

Google. Smart Reply ML Kit. https://developers.google.com/ml-kit/language/smart-reply . (2020).

Angrist, J. D., Imbens, G. W. & Rubin, D. B. Identification of causal effects using instrumental variables. J. Am. Stat. Assoc. 91 , 444–455 (1996).

Wiggins, J. S., Trapnell, P. & Phillips, N. Psychometric and geometric characteristics of the Revised Interpersonal Adjective Scales (IAS-R). Multivar. Behav. Res. 23 , 517–530 (1988).

Article   CAS   Google Scholar  

Lee, J. Leader-member exchange, the“ Pelz Effect,’’ and cooperative communication between group members. Manag. Commun. Q. 11 , 266–287 (1997).

Jakesch, M., French, M., Ma, X., Hancock, J. T. & Naaman, M. AI-mediated communication: how profile generation by ai affects perceived trustworthiness. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems - CHI ’19 , 1–13, (ACM Press, New York, New York, USA, 2019). https://doi.org/10.1145/3290605.3300469

Promberger, M. & Baron, J. Do patients trust computers?. J. Behav. Decis. Mak. 19 , 455–468. https://doi.org/10.1002/bdm.542 (2006).

Hutto, C. J. & Gilbert, E. VADER: a parsimonious rule-based model for sentiment analysis of social media text. In Eighth International AAAI Conference on Weblogs and Social Media (2014).

Pennebaker, J. W., Francis, M. E. & Booth, R. J. Linguistic inquiry and word count: Liwc 2001. Mahway Law. Erlbaum Assoc. 71 , 2001 (2001).

Google Scholar  

Berger, C. R. & Calabrese, R. J. Some explorations in initial interaction and beyond: toward a developmental theory of interpersonal communication. Hum. Commun. Res. 1 , 99–112 (1975).

Breck, E., Choi, Y. & Cardie, C. Identifying expressions of opinion in context. IJCAI 7 , 2683–2688 (2007).

Rienks, R. & Heylen, D. Dominance detection in meetings using easily obtainable features. In International Workshop on Machine Learning for Multimodal Interaction , 76–86 (Springer, 2005).

Lambert, P. in SUBJECT: Write emails faster with Smart Compose in Gmail . https://www.blog.google/products/gmail/subject-write-emails-faster-smart-compose-gmail/ . Accessed 23 Sep 2020. (2018).

Hohenstein, J. & Jung, M. AI as a moral crumple zone: the effects of AI-mediated communication on attribution and trust. Comput. Hum. Behav. 106 , 106190 (2020).

Sukumaran, A., Vezich, S., McHugh, M. & Nass, C. Normative influences on thoughtful online participation. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems , CHI ’11, 3401–3410, (ACM, New York, NY, USA, 2011). https://doi.org/10.1145/1978942.1979450

Hohenstein, J., Kizilcec, R., DiFranzo, D., Aghajari, Z. & Jung, M. As Predicted: Effect of Smart Reply Use on Language and Interpersonal Perceptions . (40389). https://aspredicted.org/4mi3z.pdf (2020).

Buhrmester, M. D., Talaifar, S. & Gosling, S. D. An evaluation of amazon’s mechanical turk, its rapid rise, and its effective use. Perspect. Psychol. Sci. 13 , 149–154 (2018).

Jakesch, M., French, M., Ma, X., Hancock, J. T. & Naaman, M. Ai-mediated communication: how the perception that profile text was written by ai affects trustworthiness. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems , 1–13 (2019).

Knutson, B. Facial expressions of emotion influence interpersonal trait inferences. J. Nonverb. Behav. 20 , 165–182 (1996).

McInnis, B., Cosley, D., Nam, C. & Leshed, G. Taking a HIT: designing around rejection, mistrust, risk, and workers’ experiences in Amazon Mechanical Turk. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems , 2271–2282 (2016).

Pustejovsky, J. clubSandwich: cluster-robust (sandwich) variance estimators with small-sample corrections. R package version 0.2. 3. R Found. Stat. Comput. Vienna (2017).

Kleiber, C., Zeileis, A. & Zeileis, M. A. Package ‘AER’. Package (2020).

Blair, G. et al. Package ‘estimatr’. Statistics 7 , 295–318 (2018).

Schwartz, H. A. et al. Personality, gender, and age in the language of social media: the open-vocabulary approach. PLoS One 8 , e73791 (2013).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Hohenstein, J., Kizilcec, R., DiFranzo, D., Aghajari, Z. & Jung, M. AI-Mediated Communication: Effects on Language and Interpersonal Perceptions. https://doi.org/10.17632/6v5r6jmd3y.1 (2021).

Download references

This material is based upon work supported by the National Science Foundation under Grant No. CHS 1901151.

Author information

Authors and affiliations.

Department of Information Science, Cornell University, 343 Campus Rd, Ithaca, NY, 14853, USA

Jess Hohenstein, Rene F. Kizilcec, Karen Levy & Malte F. Jung

Department of Computer Science and Engineering, Lehigh University, 113 Research Dr, Bethlehem, PA, 18015, USA

Dominic DiFranzo & Zhila Aghajari

Department of Communication, Stanford University, 450 Jane Stanford Way, Stanford, CA, 94305, USA

Hannah Mieczkowski & Jeffrey Hancock

Cornell Tech, 2 W Loop Rd, New York, NY, 10044, USA

You can also search for this author in PubMed   Google Scholar

Contributions

J.H., R.F.K., and M.F.J. conceived the experiments, J.H. collected the data, J.H., R.F.K., and M.F.J. analyzed and interpreted the data, J.H., D.D., Z.A., and M.F.J. designed and developed the software, J.H. and R.F.K. wrote the initial draft, K.L., J.H., M.N., and M.F.J. acquired funding. All authors reviewed the manuscript.

Corresponding author

Correspondence to Malte F. Jung .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The original online version of this Article was revised: The Funding section in the original version of this Article was omitted. The Funding section now reads: "This material is based upon work supported by the National Science Foundation under Grant No. CHS 1901151."

Supplementary Information

Supplementary information., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Hohenstein, J., Kizilcec, R.F., DiFranzo, D. et al. Artificial intelligence in communication impacts language and social relationships. Sci Rep 13 , 5487 (2023). https://doi.org/10.1038/s41598-023-30938-9

Download citation

Received : 19 August 2022

Accepted : 03 March 2023

Published : 04 April 2023

DOI : https://doi.org/10.1038/s41598-023-30938-9

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Assessment of the impacts of artificial intelligence (ai) on intercultural communication among postgraduate students in a multicultural university environment.

• Abdul Qahar Sarwari
• Muhammad Naeem Javed
• Mohammad Nubli Abdul Wahab

Scientific Reports (2024)

Unraveling Predictive Mechanism in Speech Perception and Production: Insights from EEG Analyses of Brain Network Dynamics

• Jianwu Dang

Journal of Shanghai Jiaotong University (Science) (2024)

The ethics of personalised digital duplicates: a minimally viable permissibility principle

• John Danaher
• Sven Nyholm

AI and Ethics (2024)

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

• Privacy Policy
• Write For Us

Science by Zeba Academy

Curious by Nature

The Impact of Technology on Human Relationships

In the fast-paced world we live in, technology has become an integral part of our daily lives, reshaping the way we communicate and interact with one another. From the early days of written letters to the digital era of instant messaging and social media, the evolution of communication has been both fascinating and transformative.

This article explores the profound and multifaceted impact of technology on human relationships, delving into the evolution of communication and examining the positive impacts technology has had on fostering connections among individuals.

Evolution of Communication

To understand the impact of technology on human relationships, it is crucial to examine the historical evolution of communication methods. In ancient times, people relied on written letters, face-to-face conversations, and communal gatherings to share information and connect with one another. However, the advent of digital communication platforms has ushered in a new era.

The emergence of digital communication platforms, such as email, instant messaging, and social media, has significantly altered the way we interact. These platforms have broken down geographical barriers, allowing individuals from different corners of the world to communicate in real-time. As a result, the shift from face-to-face interactions to virtual connections has become increasingly prevalent. Virtual meetings and video calls have become commonplace, offering a convenient alternative to physical meetings [1].

Positive Impacts of Technology on Human Relationships

The positive impacts of technology on human relationships are substantial , with increased connectivity and global communication standing out as key benefits. Social media platforms, such as Facebook, Twitter, and Instagram, have become powerful tools for connecting people, fostering a sense of community, and enabling the exchange of ideas on a global scale. Individuals can now maintain relationships with friends, family, and colleagues regardless of physical distance.

Moreover, instant messaging and video calls have become indispensable in facilitating communication. Apps like WhatsApp, Skype, and Zoom have transformed the way we stay in touch, making it easier for people to connect with loved ones or collaborate with colleagues irrespective of their location. Long-distance relationships, once considered challenging, are now sustained by these technological advancements, allowing partners to share experiences and maintain a sense of closeness despite the miles between them [2].

In addition to personal relationships, technology has played a pivotal role in enhancing collaboration in work and education. With the rise of remote work and online learning, individuals can seamlessly collaborate on projects, share ideas, and participate in educational programs from virtually anywhere. This has not only increased efficiency but has also opened up new avenues for networking and skill development.

As we navigate the digital landscape, it is essential to acknowledge the positive transformations technology has brought to human relationships. The ability to connect globally, maintain long-distance relationships, and collaborate effortlessly are clear demonstrations of how technology has enriched the way we relate to one another [3].

Negative Impacts of Technology on Human Relationships

As technology continues to permeate every aspect of our lives, it brings with it a set of challenges that can significantly impact the quality of our interpersonal relationships. One notable negative impact is the erosion of face-to-face communication skills. With the prevalence of digital communication tools, individuals may find themselves relying more on text messages, emails, and social media interactions, leading to a decline in the development of essential interpersonal skills such as non-verbal communication, empathy, and active listening.

Social isolation and loneliness have also emerged as critical concerns associated with the extensive use of technology. While digital platforms facilitate connections on a global scale, paradoxically, they can contribute to a sense of isolation among individuals. Spending excessive time online, engaging in virtual interactions, can sometimes replace meaningful face-to-face connections, leaving individuals feeling emotionally distant from those around them.

Moreover, the impact of technology on family dynamics cannot be overlooked. Increased screen time and digital distractions within households can lead to diminished family interactions. Parents and children may find themselves engrossed in individual screens, diminishing the quality of shared family experiences. The virtual world, if not managed mindfully, can inadvertently disrupt the cohesion and bonding within families [4].

Psychological and Emotional Effects

The psychological and emotional effects of technology on human relationships are complex and multifaceted. One significant concern is the influence on mental health, particularly through the lens of social comparison and self-esteem. Social media platforms, where individuals often showcase curated versions of their lives, can lead to unhealthy comparisons and feelings of inadequacy. The constant exposure to seemingly perfect lives on social media may contribute to a negative impact on self-esteem and overall mental well-being.

Another issue is the rise of cyberbullying and online harassment. The digital realm provides anonymity to individuals, emboldening some to engage in harmful behaviors. The consequences of online harassment can be severe, affecting individuals emotionally and psychologically. It poses a threat to the sense of safety and security that should be inherent in any healthy relationship, whether online or offline.

Additionally, technology, particularly in the form of smartphones and social media, has given rise to addiction and the dopamine loop. The instant gratification provided by likes, comments, and notifications triggers the release of dopamine, the brain’s pleasure neurotransmitter , creating a feedback loop that can lead to addictive behavior. Smartphone addiction and social media addiction are increasingly recognized as issues that can negatively impact an individual’s mental health and relationships [5].

Balancing Technology and Real-life Interactions

In the face of these challenges, finding a balance between technology and real-life interactions becomes crucial for maintaining healthy relationships. Setting boundaries on screen time is one effective strategy to mitigate the negative impacts of technology. Establishing specific times for device use and designating tech-free zones, especially during family meals or intimate conversations, can contribute to a more present and engaged environment.

Incorporating tech-free activities into daily routines is another approach to strike a balance. Encouraging outdoor activities, reading physical books, or engaging in hobbies that do not involve screens can provide opportunities for genuine human connections. These activities not only foster a sense of togetherness but also contribute to overall well-being.

Fostering empathy in a digital age is essential for maintaining meaningful relationships. Mindful communication, characterized by active listening, understanding, and expressing emotions effectively, can bridge the gap created by digital interactions. Building emotional intelligence, both in face-to-face and virtual interactions, allows individuals to navigate the complexities of human emotions and strengthen their connections with others.

The landscape of human relationships is undergoing a transformative shift as technology continues to advance. Understanding the future trajectory of these relationships in the digital age is essential for navigating the complexities that lie ahead. In this exploration, we will delve into anticipated trends, societal shifts, and the overarching impact on how we connect and relate to one another [6].

The Future of Human Relationships in the Digital Age

As technology evolves, so do the dynamics of human relationships. The future is likely to witness advancements in virtual reality (VR) and augmented reality (AR) technologies, promising more immersive and realistic digital interactions. Virtual meetings may become indistinguishable from face-to-face encounters, erasing geographical boundaries and fostering a sense of presence in remote collaborations.

Moreover, the integration of artificial intelligence (AI) in communication platforms is expected to personalize and enhance user experiences. AI-driven algorithms could predict communication patterns, preferences, and emotional states, thereby facilitating more meaningful and tailored interactions. This could reshape how individuals engage with digital interfaces and strengthen the emotional resonance of online communication [7].

Societal and Cultural Shifts in Perceptions of Digital Connections

The societal and cultural perceptions of digital connections are likely to evolve. As technology becomes deeply ingrained in our daily lives, the distinction between online and offline relationships may blur. Virtual connections may be perceived with equal validity as physical ones, challenging traditional notions of closeness and intimacy. Societal norms may adapt to accommodate the growing prevalence of digital relationships, recognizing their significance in contemporary human interaction.

Furthermore, the concept of privacy in digital relationships may undergo reevaluation. As individuals share more aspects of their lives online, there may be a shift in societal expectations regarding personal boundaries. Striking a balance between openness and privacy in the digital realm will become an ongoing discourse, influencing how individuals navigate the complexities of sharing their lives within the digital space [8].

In conclusion, the impact of technology on human relationships is a dynamic and multifaceted phenomenon. From the evolution of communication to the positive and negative impacts, and the challenges of finding a balance, we have explored the various dimensions of this intricate interplay. As we move into the future, it is evident that technology will continue to shape and redefine how we connect with one another.

While anticipated technological trends promise more immersive and personalized interactions, they also pose challenges. The risk of further isolating individuals in a virtual world, the ethical considerations surrounding AI-driven communication, and the potential erosion of traditional values must be carefully considered. As we embrace the digital age, it is imperative to approach these advancements with a thoughtful and ethical mindset.

Finding a harmonious balance between technology and real-life interactions remains a crucial aspect of nurturing healthy relationships. The strategies mentioned earlier, such as setting boundaries on screen time, incorporating tech-free activities, and fostering empathy, will continue to be relevant in the evolving landscape of human connections.

Ultimately, the future of human relationships in the digital age hinges on our ability to harness technology’s potential while preserving the core values that define meaningful connections. It is a journey that requires ongoing reflection, adaptability, and a commitment to prioritizing the human experience in an increasingly digital world.

• Turkle (2011). Alone Together: Why We Expect More from Technology and Less from Each Other.
• McEwan (2019). The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power.
• Hampton (2011). Social networking sites and our lives.
• Sherry Turkle. (2015). Reclaiming Conversation: The Power of Talk in a Digital Age.
• Jean M. Twenge. (2017). Why Today’s Super-Connected Kids Are Growing Up Less Rebellious, More Tolerant, Less Happy–and Completely Unprepared for Adulthood–and What That Means for the Rest of Us.
• Adam Alter (2017). Irresistible: The Rise of Addictive Technology and the Business of Keeping Us Hooked.
• Rheingold, H. (2014). Net Smart: How to Thrive Online.
• Turkle, S. (2017). Alone Together: Why We Expect More from Technology and Less from Each Other.

Liked what you saw?

Subscribe to our newsletter and get regular updates about new tutorials, articles, courses and more! 

Enter your email address to subscribe.

(Generally two emails per month; no spam; unsubscribe anytime)

You're all set. 🙂 

Related Posts

Plant-Human Relationships: Exploring the Deep Connections Between Humans and Plants

Sensual Delights: Unveiling the Truth Behind 10 Historical Aphrodisiac Foods

• Sales  +1 (800) 646-0520
• Online Survey Tool (DIY)
• Survey Design
• Survey Distribution
• Survey Participation
• Data Management
• All Features
• Enterprise Feedback
• Take a Tour
• Form Builder
• Customer Experience Overview
• Omnichannel Experience
• Voice of Customer
• Customer Analytics
• Customer Journey
• Alerts and Action
• Net Promoter Score (NPS)
• Employee Experience Overview
• Employee Engagement
• Employee Pulse
• HR Analytics
• Help Desk Ticketing
• Closing the Loop
• Automation and Integration 
• Managed Services
• Survey Templates

From design to distribution to data, our comprehensive solution is both intuitive and powerful.

• Customer Service
• Market Research
• Risk Assessment
• Event Planning
• Legal and Compliance
• Resident Satisfaction
• Credit Union
• Financial Services
• Travel & Hospitality
• Higher Education
• Manufacturing

Our top-tier certifications and practices ensure your data privacy and security every step of the way.

Professionally designed questionnaires for a wide range of projects allow you to go live in no time.

Our powerful, flexible solutions serve clients across industries and around the world. Their success is our success.

• See Case Studies

Enabling individuals and small teams to create and manage professional-looking surveys, forms, and polls.

Empowering businesses to gather and analyze feedback through a comprehensive platform that supports secure collaboration.

Enabling companies to improve customer experience and loyalty by examining trends and drivers across the customer journey.

Transforming always-on feedback collection into a streamlined triage and follow-up flow to close the loop efficiently.

Helping organizations to listen more deeply to their employees to uncover key drivers that impact engagement and retention.

• SogoConnect
• Video Library
• Latest Release
• Sign Up Free Sign Up Free
• Request a Demo Request a Demo

How Has Technology Affected Social Interaction?

Estimated Reading Time : 5 mins

It’s no surprise that the world is becoming increasingly digital, but the current pace of digitization is astonishing. From AI chatbots to dating apps, tech is the new real life.

We do our shopping online, build our businesses online, spend our downtime online, and even make life-long friends online. As of 2022, 66 percent of the global population were active internet users (Statista). Bringing us closer to commodities, the internet is set to become an even bigger part of our lives. But can technology help improve social interaction skills?

Technology is a fast-paced market, and every pioneer is in a race for first place on the podium. New innovations are piloted every day in a desperate attempt to sell what the masses will buy—tools to make our daily lives easier. As older generations warm to the idea of utilizing technology in the face of a pandemic, we move eerily closer to a world where social interaction is primarily digital.

How is your org impacted by technology? Get insights with our free company culture climate survey template !

But should we look at this as a positive or negative change? Will human interaction strengthen or suffer as a result of new communication technologies? In this article we will explore the good, the bad, and the ugly truth of technology’s impact on social interaction.

There is no denying that the internet has had a positive impact on human communication. Compared to pre-internet times, we are now more connected than ever before. Here are some of the ways that technology has a positive effect on social interaction.

Instant global communication

How can technology affect social media? Most of us now have the means to contact people on the other side of the world immediately. No waiting for the postman, messenger pigeon, or message in a bottle—technology has broken down the communication barriers that distance once presented. It is a phenomenon that is commonly referred to as globalization.

Virtual connections

Let’s look at how technology affects human relationships. Video calling apps like Skype and Zoom mean people can now experience emotional connections without having to be in the same room. This has been especially important throughout the coronavirus crisis, where families from separate households were forced to stay apart. The day the UK lockdown was announced, Zoom was downloaded 2.13m times around the world—up from 56,000 a day two months earlier.

Online dating

Technology has also made finding love much easier, with online dating increasing in popularity. A report released last year predicted that more than 50% of couples will meet online by 2035 . The same report found 47% of people believe online dating makes it easier for introverted people to find love.

Barrier-free interaction

Similarly, people who would otherwise have limited social interaction now have the option to be part of online communities. People with disabilities can forget about their physical boundaries inside a video game universe, while socially anxious people can gain confidence by practicing interaction over the internet instead of face to face. Sending messages allows people the time to process information and formulate a response, whereas face to face is more immediate. On the other hand, this isn’t always a good thing.

Of course, as with everything, there is another angle to consider. How does technology affect our social lives negatively? Some would argue that the more anonymous and less immediate interaction associated with digital communication is bad news. Behind the screens of smartphones and the keyboards of computers, there are also more chances for deception—particularly for vulnerable people. Here are some of the ways the technology has a negative impact on social interaction.

Decreased human contact

More and more people are beginning to rely on technology to communicate with their loved ones, friends and associates. The coronavirus lockdown that forced millions of people to work from home also accelerated online communication tools—meaning we get even less human contact than ever before. As of July 2020, 58% of customer interactions were digital .

As remote working becomes the norm, transactional processes are automated with self-service machines too. For many, this can theoretically be a good thing, but it also highlights how technology affects our communication negatively. The same ease of technological-driven interactions leaves some groups behind. The increase in the use of technology to communicate could also cause a rise in loneliness, especially among elderly people who may rely on these transactional encounters as their primary source of social contact.

Are your customers satisfied with your communication? Find out!

A rise in bullying

Technology has also made cyberbullying possible, and children are now particularly vulnerable to harassment online. While cyberbullying is a worry for children, it is also becoming a common challenge among adults too. Results from a YouGov poll conducted last year showed that a quarter of adults have experienced cyberbullying . Given the disheartening rise in suicide among victims of cyberbullying, it is clear to see that technology is not always a healthy source of social interaction.

Online deception

Catfishing is the practice of creating a fake online identity with intent to manipulate, stalk, or abuse a specific victim. It has become a scarily common activity among dating sites and social media platforms and is the subject of a popular MTV reality TV show. A 2018 survey saw 9% respondents say that being catfished had affected their mental health .

A platform for predators

Technology has also provided a platform for online predators to pursue their victims. And as UK schools closed during the coronavirus lockdown, children were on their devices a lot more often and faced with a sudden drop in social interaction. While there is not yet any overwhelming evidence to suggest the pandemic caused an increase in predatory activity online, The National Center for Missing & Exploited Children (NCMEC) has said reports to their CyberTipline increased by 106% during the first months of the pandemic.

The ugly truth

Whether we see it in a positive or negative light, or even a mixture of the two, the truth of the matter is that technology has had a huge impact on the way we communicate with each other. While it allows us to make instant connections with people on the other side of the world, it also puts us at risk of loneliness , as well as new forms of harassment and manipulation.

But as new technologies are unveiled to the world, new cybersecurity regulations and other tech safety measures are likely to be put in place, too. These aren’t likely to protect everyone from the negative effects of technology, but it also rests on our own shoulders to use the internet with caution. Equally, it is our own responsibility to ensure we get enough real-life human contact in our daily lives, in addition to digital social interaction.

Recent Posts

• Boosted Survey Response Rates: Proven Strategies for Success August 26, 2024
• The Art of Connecting: Mastering Digital-First Professional Networking August 13, 2024
• The Power of Brand Loyalty In Today’s Marketplace: Study Results August 9, 2024
• 6 Psychological Strategies to Improve Customer Loyalty and Retention August 6, 2024
• The 11-Minute Marketing Research Process Guide You Need for 2024 July 23, 2024

Subscribe for tips and insights to drive better decisions!

• Privacy Policy
• Terms of Service
• Anti-Spam Policy
• Data & Security

Greater Good Science Center • Magazine • In Action • In Education

What Makes Technology Good or Bad for Us?

Everyone’s worried about smartphones. Headlines like “ Have smartphones destroyed a generation? ” and “ Smartphone addiction could be changing your brain ” paint a bleak picture of our smartphone addiction and its long-term consequences. This isn’t a new lament—public opinion at the advent of the newspaper worried that people would forego the stimulating pleasures of early-morning conversation in favor of reading the daily .

Is the story of technology really that bad? Certainly there’s some reason to worry. Smartphone use has been linked to serious issues, such as dwindling attention spans , crippling depression , and even increased incidence of brain cancer . Ultimately, though, the same concern comes up again and again: Smartphones can’t be good for us, because they’re replacing the real human connection of the good old days.

Everyone’s heard how today’s teens just sit together in a room, texting, instead of actually talking to each other. But could those teenagers actually be getting something meaningful and real out of all that texting?

The science of connection

A quick glance at the research on technology-mediated interaction reveals an ambivalent literature. Some studies show that time spent socializing online can decrease loneliness , increase well-being , and help the socially anxious learn how to connect to others. Other studies suggest that time spent socializing online can cause loneliness , decrease well-being , and foster a crippling dependence on technology-mediated interaction to the point that users prefer it to face-to-face conversation.

It’s tempting to say that some of these studies must be right and others wrong, but the body of evidence on both sides is a little too robust to be swept under the rug. Instead, the impact of social technology is more complicated. Sometimes, superficially similar behaviors have fundamentally different consequences. Sometimes online socialization is good for you, sometimes it’s bad, and the devil is entirely in the details.

This isn’t a novel proposition; after all, conflicting results started appearing within the first few studies into the internet’s social implications, back in the 1990s. Many people have suggested that to understand the consequences of online socialization, we need to dig deeper into situational factors and circumstances. But what we still have to do is move beyond recognition of the problem to provide an answer: When, how, and why are some online interactions great, while others are dangerous?

The interpersonal connection behaviors framework

As a scientist of close relationships, I can’t help but see online interactions differently from thinkers in other fields. People build relationships by demonstrating their understanding of each other’s needs and perspectives, a cyclical process that brings them closer together. If I tell you my secrets, and you respond supportively, I’m much more likely to confide in you again—and you, in turn, are much more likely to confide in me.

This means that every time two people talk to each other, an opportunity for relationship growth is unfolding. Many times, that opportunity isn’t taken; we aren’t about to have an in-depth conversation with the barista who asks for our order. But connection is always theoretically possible, and that’s true whether we’re interacting online or face-to-face.

Close relationships are the bread and butter of happiness—and even health. Being socially isolated is a stronger predictor of mortality than is smoking multiple cigarettes a day . If we want to understand the role technology plays in our well-being, we need to start with the role it plays in our relationships.

And it turns out that the kind of technology-mediated interactions that lead to positive outcomes are exactly those that are likely to build stronger relationships. Spending your time online by scheduling interactions with people you see day in and day out seems to pay dividends in increased social integration . Using the internet to compensate for being lonely just makes you lonelier; using the internet to actively seek out connection has the opposite effect .

“The kind of technology-mediated interactions that lead to positive outcomes are exactly those that are likely to build stronger relationships”

On the other hand, technology-mediated interactions that don’t really address our close relationships don’t seem to do us any good—and might, in fact, do us harm. Passively scrolling through your Facebook feed without interacting with people has been linked to decreased well-being and increased depression post-Facebook use.

That kind of passive usage is a good example of “ social snacking .” Like eating junk food, social snacking can temporarily satisfy you, but it’s lacking in nutritional content. Looking at your friends’ posts without ever responding might make you feel more connected to them, but it doesn’t build intimacy.

Passive engagement has a second downside, as well: social comparison . When we compare our messy lived experiences to others’ curated self-presentations, we are likely to suffer from lowered self-esteem , happiness, and well-being. This effect is only exacerbated when we consume people’s digital lives without interacting with them, making it all too easy to miss the less photogenic moments of their lives.

Moving forward

The interpersonal connection behaviors framework doesn’t explain everything that might influence our well-being after spending time on social media. The internet poses plenty of other dangers—for two examples, the sense of wasting time or emotional contagion from negative news. However, a focus on meaningful social interaction can help explain decades of contradictory findings. And even if the framework itself is challenged by future work, its central concept is bound to be upheld: We have to study the details of how people are spending their time online if we want to understand its likely effects.

In the meantime, this framework has some practical implications for those worried about their own online time. If you make sure you’re using social media for genuinely social purposes, with conscious thought about how it can improve your life and your relationships, you’ll be far more likely to enjoy your digital existence.

This article was originally published on the Behavioral Scientist . Read the original article .

About the Author

Jenna Clark

Jenna Clark, Ph.D. , is a senior behavioral researcher at Duke University's Center for Advanced Hindsight, where she works to help people make healthy decisions in spite of themselves. She's also interested in how technology contributes to our well-being through its effect on our close personal relationships.

You May Also Enjoy

How to Stop Your Smartphone From Hurting Your Health

Five Ways to Build Caring Community on Social Media

How Smartphones Are Killing Conversation

Are Smartphones Bad for Teen Mental Health?

Does Technology Cut Us Off from Other People?

How to Keep Your Smartphone from Hurting Your Relationships

• Tools and Resources
• Customer Services
• Communication and Culture
• Communication and Social Change
• Communication and Technology
• Communication Theory
• Critical/Cultural Studies
• Gender (Gay, Lesbian, Bisexual and Transgender Studies)
• Health and Risk Communication
• Intergroup Communication
• International/Global Communication
• Interpersonal Communication
• Journalism Studies
• Language and Social Interaction
• Mass Communication
• Media and Communication Policy
• Organizational Communication
• Political Communication
• Rhetorical Theory
• Share Facebook LinkedIn Twitter

Article contents

Communication technology and interpersonal relationships.

• Andrew M. Ledbetter Andrew M. Ledbetter Department of Communication Studies, Texas Christian University
• https://doi.org/10.1093/acrefore/9780190228613.013.497
• Published online: 22 August 2017

Owing to advances in communication technology, the human race now possesses more opportunities to interact with interpersonal partners than ever before. Particularly in recent decades, such technology has become increasingly faster, mobile, and powerful. Although tablets, smartphones, and social media are relatively new, the impetus behind their development is old, as throughout history humans have developed mechanisms for communicating ideas that transcend inherent temporal and spatial limitations of face-to-face communication. In the ancient past, humans developed writing and the alphabet to preserve knowledge across time, with the later development of the printing press further facilitating the mass distribution of written ideas. Later, the telegraph was arguably the first technology to separate communication from transportation, and the telephone enabled people at a distance to hear the warmth and intimacy of the human voice. The development of the Internet consolidates and advances these technologies by facilitating pictorial and video interactions, and the mobility provided by cell phones and other technologies makes the potential for communication with interpersonal partners nearly ubiquitous. As such, these technologies reconfigure perception of time and space, creating the sense of a smaller world where people can begin and manage interpersonal relationships across geographic distance.

These developments in communication technology influence interpersonal processes in at least four ways. First, they introduce media choice as a salient question in interpersonal relationships. As recently as the late 20th century, people faced relatively few options for communicating with interpersonal partners; by the early years of the 21st century, people possessed a sometimes bewildering array of channel choices. Moreover, these choices matter because of the relational messages they send; for example, choosing to end a romantic relationship over the phone may communicate more sensitivity than choosing to do so via text messaging, or publicly on social media. Second, communication technology affords new opportunities to begin relationships and, through structural features of the media, shape how those meetings occur. The online dating industry generates over $1 billion in profit, with most Americans agreeing it is a good way to meet romantic partners; friendships also form online around shared interests and through connections on social media. Third, communication technology alters the practices people use to maintain interpersonal relationships. In addition to placing traditional forms of relational maintenance in more public spaces, social media facilitates passive browsing as a strategy for keeping up with interpersonal partners. Moreover, mobile technology affords partners increased geographic and temporal flexibility when keeping contact with partners, yet simultaneously, it may produce feelings of over-connectedness that hamper the desire for personal autonomy. Fourth, communication technology makes interpersonal networks more visibly manifest and preserves their continuity over time. This may provide an ongoing convoy of social support and, through increased efficiency, augment the size and diversity of social networks.

• interpersonal communication
• impression formation
• relational maintenance
• social networks
• social media
• computer-mediated communication
• long-distance relationships

You do not currently have access to this article

Please login to access the full content.

Access to the full content requires a subscription

Printed from Oxford Research Encyclopedias, Communication. Under the terms of the licence agreement, an individual user may print out a single article for personal use (for details see Privacy Policy and Legal Notice).

date: 15 September 2024

• Cookie Policy
• Privacy Policy
• Legal Notice
• Accessibility
• [185.66.15.189]
• 185.66.15.189

Character limit 500 /500

Advertisement

Human/AI relationships: challenges, downsides, and impacts on human/human relationships

• Published: 04 October 2023

Cite this article

• Anne Zimmerman   ORCID: orcid.org/0000-0001-9418-0724 1 ,
• Joel Janhonen   ORCID: orcid.org/0000-0003-0696-7987 2 &
• Emily Beer   ORCID: orcid.org/0000-0002-8453-4346 1  

3988 Accesses

7 Citations

157 Altmetric

20 Mentions

Explore all metrics

Advances in artificial intelligence have resulted in objects that behave and sound like humans and exude a more human feel. Therefore, the relationships between humans and technology have evolved, becoming more personal and complex. Some AI is harmful to humans due to the nature of the human relationship with it. We explore examples ranging from chatbots to AI romantic partners. While humans must better protect themselves emotionally, tech companies must create design solutions as well as be transparent about profit motives to address the growing basket of harms. We propose solutions including alignment with AI principles that promote well-being, prevent exploitation, and acknowledge the importance of human relationships.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Personal AI, deception, and the problem of emotional bubbles

Influencing human–AI interaction by priming beliefs about AI can increase perceived trustworthiness, empathy and effectiveness

Master and Slave: the Dialectic of Human-Artificial Intelligence Engagement

Explore related subjects.

• Artificial Intelligence
• Medical Ethics

Anthropomorphism and personification “both ascribe human qualities to inanimate or living things like animals or clocks.” See Edens, K. (2018) Anthropomorphism and Personification: What's the Difference? ProWritingAid website. https://prowritingaid.com/art/812/anthropomorphism-%26-personification%3A-what-s-the-difference.aspx .

Jobst Landgrebe and Barry Smith (2023) Machines Will Never Rule the World. Routledge: New York.

Neural networks are mathematical systems to do machine learning and deep learning using AI. Deep learning can be supervised, semi-supervised, or unsupervised. Machine learning more simply is using a statistical model and algorithms that act as mathematical sets of instructions to predict outcomes. Generative AI creates content using data. For more information on AI terminology, see Aggarwal, C. (2018) Neural Networks and Deep Learning. Springer: Cham, Switzerland; and see Pasick, A. (2023) Artificial Intelligence Glossary: Neural Networks and Other Terms Explained. New York Times. https://www.nytimes.com/article/ai-artificial-intelligence-glossary.html .

Norvig, P. and Russell, S. (2020) Artificial Intelligence: A Modern Approach, 4th Ed., Prentice Hall: New York.

Trafton, A. (2022) Study urges caution when comparing neural networks to the brain. MIT News. https://news.mit.edu/2022/neural-networks-brain-function-1102 . See Graupe, D. (2016) Deep Learning Neural Networks: Design and Case Studies. World Scientific Publishing Co.: New Jersey. (deep learning neural networks “dig deeply in the input data” and can use many layers of nonlinear data; they analyze and classify.).

Landgrebe, J. and Smith, B. (2023) Why Machines Will Never Rule the World. Routledge: New York; Aggarwal, C. (2018) Neural Networks and Deep Learning. Springer: Cham, Switzerland. (artificial neural networks simulate biological processes, but are not biological. They perform computations based on functions of inputs.) (“In fact, the most basic units of computation in the neural network are inspired by traditional machine learning algorithms like least-squares regression and logistic regression. Neural networks gain their power by putting together many such basic units, and learning the weights of the different units jointly in order to minimize the prediction error. From this point of view, a neural network can be viewed as a computational graph of elementary units in which greater power is gained by connecting them in particular ways.”)

T. Nichols Photography website. https://thiscaliforniakid2.wixsite.com/tnicholsphotography/about .

Somers, M. (2019) Emotion AI, Explained. Ideas Made to Matter . MIT. https://mitsloan.mit.edu/ideas-made-to-matter/emotion-ai-explained .

Abedin, B., Meske, C., Junglas, I., Rabhi, F., & Motahari-Nezhad, H. R. (2022). Designing and managing human-AI interactions. Information Systems Frontiers, 24(3), 691–697.

Kshirsagar, S., & Magnenat-Thalmann, N. (2002, July). Virtual humans personified. In  Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1  (pp. 356–357).

Angelova, M. (2017) Why Do Bulgarians Shake Their Heads to Say Yes? The Culture Trip website. https://theculturetrip.com/europe/bulgaria/articles/why-do-bulgarians-shake-their-heads-to-say-yes/ .

Braga, A., & Logan, R. K. (2017). The emperor of strong AI has no clothes: limits to artificial intelligence.  Information ,  8 (4), 156.

Edens (2018).

Lopatovska, I., & Williams, H. (2018, March). Personification of the Amazon Alexa: BFF or a mindless companion. In  Proceedings of the 2018 Conference on Human Information Interaction & Retrieval  (pp. 265–268).

Servais, Véronique (2018). Anthropomorphism in Human–Animal Interactions: A Pragmatist View. Frontiers in Psychology, 9, 2590. https://doi.org/10.3389/fpsyg.2018.02590 .

Servais (2018).

Serpell, J. (2003). Anthropomorphism and Anthropomorphic Selection—Beyond the "Cute Response", Society & Animals, 11(1), 83–100. https://doi.org/10.1163/156853003321618864 .

Dale, J. P. (2017). The appeal of the cute object.  The aesthetics and affects of cuteness , 35–55.

Dacey, M. (2017). Anthropomorphism as Cognitive Bias. Philosophy of Science, 84(5), 1152–1164.

Piaget, J. (1929). The child's concept of the world. Londres, Routldge & Kegan Paul.

McLeod, S. A. (2018). Preoperational stage. Simply Psychology. www.simplypsychology.org/preoperational.html .

Bullock, M. (1985). Animism in childhood thinking: A new look at an old question. Developmental Psychology, 21(2), 217–225. https://doi.org/10.1037/0012-1649.21.2.217 .

Gullone, Eleonora. (2014). Risk Factors for the Development of Animal Cruelty. Journal of Animal Ethics. 4. 61–79.

Lane, J. D., Wellman, H. M., Olson, S. L., LaBounty, J., & Kerr, D. C. (2010). Theory of mind and emotion understanding predict moral development in early childhood. The British journal of developmental psychology, 28(Pt 4), 871–889. https://doi.org/10.1348/026151009x483056 .

Tanya N. Beran, Alejandro Ramirez-Serrano, Roman Kuzyk, Meghann Fior, Sarah Nugent (2011). Understanding how children understand robots: Perceived animism in child–robot interaction. International Journal of Human–Computer Studies, 69(7–8), 539–550.

Heberlein, A. S., & Adolphs, R. (2004). Impaired spontaneous anthropomorphizing despite intact perception and social knowledge. Proceedings of the National Academy of Sciences of the United States of America, 101(19), 7487–7491. https://doi.org/10.1073/pnas.0308220101 .

The Cleveland Clinic describes the amydala as “the processing center for emotions”. https://my.clevelandclinic.org/health/body/24894-amygdala .

Distinguished from persona in AI development, which refers to an archetype upon which a company can test how well-suited AI is to the goals, preferences, privacy concerns, and values of a likely user.

Abercrombie, G., Curry, A. C., Pandya, M., & Rieser, V. (2021). Alexa, Google, Siri: What are your pronouns? Gender and anthropomorphism in the design and perception of conversational assistants. arXiv preprint arXiv:2106.02578.

Abercrombie, et al.

Gao, Y., Pan, Z., Wang, H., & Chen, G. (2018, October). Alexa, my love: analyzing reviews of amazon echo. In  2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)  (pp. 372–380). IEEE; See also Lopatovska, I., & Williams, H. (2018, March). Personification of the Amazon Alexa: BFF or a mindless companion. In  Proceedings of the 2018 Conference on Human Information Interaction & Retrieval  (pp. 265–268) (many users politely addressed Alexa; two showed signs of love or reprimanded Alexa.).

Dippel, A. (2019). Metaphors We Live By. Three Commentaries on Artificial Intelligence and the Human Condition.

Dippel (2019).

Westerman, D., Edwards, A. P., Edwards, C., Luo, Z., & Spence, P. R. (2020). I-It, I-Thou, I-Robot: The perceived humanness of AI in human–machine communication.  Communication Studies ,  71 (3), 393–408.

Low, C. (2020) Alexa will seem more human with breathing pauses and learning skills. Engadget. https://www.engadget.com/amazon-2020-alexa-breathing-teach-voice-profiles-for-kids-172918631.html .

August, K. J., & Rook, K. S. (2013). Social Relationships. In M. D. Gellman & J. R. Turner (Eds.), Encyclopedia of Behavioral Medicine (pp. 1838–1842). Springer. https://doi.org/10.1007/978-1-4419-1005-9_59 .

Carpenter, A., & Greene, K. (2015). Social Penetration Theory. In C. R. Berger, M. E. Roloff, S. R. Wilson, J. P. Dillard, J. Caughlin, & D. Solomon (Eds.), The International Encyclopedia of Interpersonal Communication (1st ed., pp. 1–4). Wiley. https://doi.org/10.1002/9781118540190.wbeic160 .

Carpenter and Greene (2015).

Rusbult, C. E., Martz, J. M., & Agnew, C. R. (1998). The Investment Model Scale: Measuring commitment level, satisfaction level, quality of alternatives, and investment size. Personal Relationships , 5 (4), 357–387. https://doi.org/10.1111/j.1475-6811.1998.tb00177.x .

Rusbult, et al. (1998).

Mitchell, M. S., Cropanzano, R. S., & Quisenberry, D. M. (2012). Social Exchange Theory, Exchange Resources, and Interpersonal Relationships: A Modest Resolution of Theoretical Difficulties. In K. Törnblom & A. Kazemi (Eds.), Handbook of Social Resource Theory: Theoretical Extensions, Empirical Insights, and Social Applications (pp. 99–118). Springer. https://doi.org/10.1007/978-1-4614-4175-5_6 .

Skjuve, M., Følstad, A., Fostervold, K. I., & Brandtzaeg, P. B. (2021). My Chatbot Companion—A Study of Human-Chatbot Relationships. International Journal of Human–Computer Studies , 149 , 102601. https://doi.org/10.1016/j.ijhcs.2021.102601 .

Skjuve, et al. (2021).

Brandtzaeg, P. B., Skjuve, M., & Følstad, A. (2022). My AI Friend: How Users of a Social Chatbot Understand Their Human–AI Friendship. Human Communication Research , 48 (3), 404–429. https://doi.org/10.1093/hcr/hqac008 .

Pentina, I., Hancock, T., & Xie, T. (2023). Exploring relationship development with social chatbots: A mixed-method study of replika. Computers in Human Behavior , 140 , 107600. https://doi.org/10.1016/j.chb.2022.107600 .

Slang.ai website, https://www.slang.ai/about .

Brandtzaeg, P. B., Skjuve, M., & Følstad, A. (2022). My AI friend: How users of a social chatbot understand their human–AI friendship.  Human Communication Research ,  48 (3), 404–429.

Danaher, John & Galway, Nui. (2021). What Matters for Moral Status: Behavioral or Cognitive Equivalence? Cambridge Quarterly of Healthcare Ethics. 30. (See the work of Danaher and Galway on the fascinating debate between cognitive and behavioural equivalence in the context of AI.).

Brandtzaeg, P. B., Skjuve, M., & Følstad, A. (2022). My AI Friend: How Users of a Social Chatbot Understand Their Human–AI Friendship.  Human Communication Research ,  48 (3), 404–429.

Lasek, M., Jessa, S. (2013). Chatbots for Customer Service on Hotels’ Websites. Information Systems in Management, Vol. 2 (2) 146 − 158.

Beatty, C., Malik, T., Meheli, S., & Sinha, C. (2022). Evaluating the Therapeutic Alliance With a Free-Text CBT Conversational Agent (Wysa): A Mixed-Methods Study.  Frontiers in Digital Health ,  4 , 847991.

Denecke, K., Abd-Alrazaq, A., Househ, M. (2021). Artificial Intelligence for Chatbots in Mental Health: Opportunities and Challenges. In: Househ, M., Borycki, E., Kushniruk, A. (eds) Multiple Perspectives on Artificial Intelligence in Healthcare. Lecture Notes in Bioengineering. Springer, Cham. https://doi.org/10.1007/978-3-030-67303-1_10 .

Denecke, et al. (2021).

Dhimolea, T. K., Kaplan-Rakowski, R., & Lin, L. (2022). Supporting Social and Emotional Well-Being with Artificial Intelligence. In  Bridging Human Intelligence and Artificial Intelligence  (pp. 125–138). Cham: Springer International Publishing.

Sannon, S., Stoll, B., DiFranzo, D., Jung, M., & Bazarova, N. N. (2018, October). How personification and interactivity influence stress-related disclosures to conversational agents. In  companion of the 2018 ACM conference on computer supported cooperative work and social computing  (pp. 285–288).

Balloccu, S., Reiter, E., Collu, M. G., Sanna, F., Sanguinetti, M., & Atzori, M. (2021, June). Unaddressed challenges in persuasive dieting chatbots. In  Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization  (pp. 392–395).

Lasek and Jessa (2013).

Liao, M., & Sundar, S. S. (2021, May). How should AI systems talk to users when collecting their personal information? Effects of role framing and self-referencing on human-AI interaction. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1–14) (example of data collection); Liang, F., Yu, W., An, D., Yang, Q., Fu, X., & Zhao, W. (2018). A survey on big data market: Pricing, trading and protection.  Ieee Access ,  6 , 15132–15154 (large market for big data).

Björkas, R., & Larsson, M. (2021). Sex dolls in the Swedish media discourse: Intimacy, sexuality, and technology.  Sexuality & Culture ,  25 (4), 1227–1248.

Independent_Cash1873. (2023, February 17). U/Kuyda, My daughter wants her friend back. [Reddit Post]. R/Replika. www.reddit.com/r/replika/comments/114t15n/ukuyda_my_daughter_wants_her_friend_back/ .

Ta, V., Griffith, C., Boatfield, C., Wang, X., Civitello, M., Bader, H., DeCero, E., & Loggarakis, A. (2020). User Experiences of Social Support From Companion Chatbots in Everyday Contexts: Thematic Analysis. Journal of Medical Internet Research , 22 (3), e16235. https://doi.org/10.2196/16235 .

Hawkley, L. C. (2022). Loneliness and health. Nature Reviews Disease Primers , 8 (1), Article 1. https://doi.org/10.1038/s41572-022-00355-9 .

Ta et al. (2020); Brandtzaeg, P. B., Skjuve, M., & Følstad, A. (2022). My AI Friend: How Users of a Social Chatbot Understand Their Human–AI Friendship. Human Communication Research , 48 (3), 404–429. https://doi.org/10.1093/hcr/hqac008 .

Ta, et al. (2020).

Calvert, S. (2019). Socializing Artificial Intelligence. Issues in Science and Technology 36(1).

Brandtzaeg et al., 2022; Skjuve et al., 2021.

Zapcic, I., Fabbri, M., & Karandikar, S. (2023). Using Reddit as a source for recruiting participants for in-depth and phenomenological research. International Journal of Qualitative Methods, 22, 16094069231162674; Cinelli, M., De Francisci Morales, G., Galeazzi, A., Quattrociocchi, W., & Starnini, M. (2021). The echo chamber effect on social media. Proceedings of the National Academy of Sciences, 118(9), e202330111 (“bias in the information diffusion toward like-minded peers”).

Brannigan, M. (2022) Caregiving, Carebots, and Contagion. Lexington Books: Maryland, p. 5.

Brannigan, p. 54.

Wilkinson, Richard G, Marmot, Michael & World Health Organization. Regional Office for Europe. (1998). The solid facts: social determinants of health. Copenhagen: WHO Regional Office for Europe. https://apps.who.int/iris/handle/10665/108082 .

Kuyda. (2023, February 9). Update [Reddit Post]. R/Replika. www.reddit.com/r/replika/comments/10xn8uj/update/ .

Cole, S. (2023, February 17). Replika CEO Says AI Companions Were Not Meant to Be Horny. Users Aren’t Buying It. Vice . https://www.vice.com/en/article/n7zaam/replika-ceo-ai-erotic-roleplay-chatgpt3-rep .

Cole, S. (2023, February 15). “It’s Hurting Like Hell”: AI Companion Users Are In Crisis, Reporting Sudden Sexual Rejection. Vice . https://www.vice.com/en/article/y3py9j/ai-companion-replika-erotic-roleplay-updates .

Bevan, R. (2023, February 19). Replika Charged Users $70 A Year For Their AI Partners, And Now They’re “Gone.” TheGamer. https://www.thegamer.com/replika-role-play-romance-updates-controversy/ .

gabbiestofthemall. (2023, February 11). Resources If You’re Struggling [Reddit Post]. R/Replika. www.reddit.com/r/replika/comments/10zuqq6/resources_if_youre_struggling/ .

Hennig-Thurau, T., Aliman, D.N., Herting, A.M. et al. Social interactions in the metaverse: Framework, initial evidence, and research roadmap. J. of the Acad. Mark. Sci. (2022). https://doi.org/10.1007/s11747-022-00908-0 .

Cross, C. (2020). Romance Fraud. In: The Palgrave Handbook of International Cybercrime and Cyberdeviance. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-90307-1_41-1 .

Huang, MH., Rust, R.T. A strategic framework for artificial intelligence in marketing. J. of the Acad. Mark. Sci. 49, 30–50 (2021). https://doi.org/10.1007/s11747-020-00749-9 .

Denworth, L. (2020). Friendship: The Evolution, Biology, and Extraordinary Power of Like’s Fundamental Bond. W.W. Norton and Co.: New York.

https://scoop.upworthy.com/dutch-supermarket-introduces-a-unique-slow-checkout-lane-to-help-fight-loneliness-595693 .

https://www.hs.fi/kaupunki/espoo/art-2000009329300.html .

Fjeld, Jessica and Achten, Nele and Hilligoss, Hannah and Nagy, Adam and Srikumar, Madhulika, Principled Artificial Intelligence: Mapping Consensus in Ethical and Rights-Based Approaches to Principles for AI (January 15, 2020). Berkman Klein Center Research Publication No. 2020-1, Available at SSRN: https://ssrn.com/abstract=3518482 or http://dx.doi.org/10.2139/ssrn.3518482 .

Foroohar, R. (2019). Don't be Evil: How Big Tech Betrayed Its Founding Principles—and All of Us. Broadway Business; Foer, F. (2018). World without mind: The existential threat of big tech. Penguin; Lawrence, K. (2021). Instagram’s Latest Lawsuit: Examining Data Privacy in Big Tech. Sage Publications: Sage Business Cases Originals.

Beijing Academy of Artificial Intelligence (BAAI). (2019). Beijing Artificial Intelligence Principles.

Montreal Declaration for a Responsible Development of Artificial Intelligence, 2018. https://recherche.umontreal.ca/english/strategic-initiatives/montreal-declaration-for-a-responsible-ai/

Institute of Electrical and Electronic Engineers (IEEE). (2016). Ethically Aligned Design: A Vision for Prioritizing Human Wellbeing with Artificial Intelligence and Autonomous Systems.

Fjeld, et al. (2020), citing IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems (n 5) pp. 21–22 (Principle 2.).

The Public Voice (2018). Universal Guidelines for Artificial Intelligence. “We state clearly that the primary responsibility for AI systems must reside with those institutions that fund, develop, and deploy these systems.”.

Foroohar, R. (2019).  Don't be Evil: How Big Tech Betrayed Its Founding Principles–and All of Us . Broadway Business; Foer, F. (2018).  World without mind: The existential threat of big tech . Penguin; Lawrence, K. (2021). Instagram’s Latest Lawsuit: Examining Data Privacy in Big Tech. SAGE Publications: SAGE Business Cases Originals.

Calvert, S. (2019).

Seppala, E., Rossomando, T., & Doty, J. R. (2013). Social connection and compassion: Important predictors of health and well-being. Social Research: An International Quarterly, 80(2), 411–430; Cascio, C. J., Moore, D., & McGlone, F. (2019). Social touch and human development. Developmental cognitive neuroscience, 35, 5–11;

Fjeld, et al. (2020); Ozawa-de Silva, C., & Parsons, M. (2020). Toward an anthropology of loneliness. Transcultural Psychiatry, 57(5), 613–622.

https://www.pwc.com/gx/en/issues/data-and-analytics/artificial-intelligence/what-is-responsible-ai.html .

Murtarelli, Grazia; Gregory, Anne; Romenti, Stefania (2021). A conversation-based perspective for shaping ethical human–machine interactions: The particular challenge of chatbots. Journal of Business Research 129.

We have received no funding for the article.

Author information

Authors and affiliations.

Columbia University, 2970 Broadway, New York, NY, 10027, USA

Anne Zimmerman & Emily Beer

The Finnish Institute of Bioethics, Pirkankatu 21 A 9, 33230, Tampere, Finland

Joel Janhonen

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Anne Zimmerman .

Ethics declarations

Conflict of interest.

We have no conflict of interest.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Zimmerman, A., Janhonen, J. & Beer, E. Human/AI relationships: challenges, downsides, and impacts on human/human relationships. AI Ethics (2023). https://doi.org/10.1007/s43681-023-00348-8

Download citation

Received : 25 May 2023

Accepted : 11 September 2023

Published : 04 October 2023

DOI : https://doi.org/10.1007/s43681-023-00348-8

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Romantic-partner AI
• Personification
• Find a journal
• Publish with us
• Track your research

Home — Essay Samples — Information Science and Technology — Disadvantages of Technology — How technology has negatively impacted human’s interaction

How Technology Has Negatively Impacted Human's Interaction

• Categories: Disadvantages of Technology Impact of Technology Negative Impact of Technology

About this sample

Words: 778 |

Published: Jan 4, 2019

Words: 778 | Pages: 2 | 4 min read

Cite this Essay

To export a reference to this article please select a referencing style below:

Let us write you an essay from scratch

• 450+ experts on 30 subjects ready to help
• Custom essay delivered in as few as 3 hours

Get high-quality help

Verified writer

• Expert in: Information Science and Technology

+ 120 experts online

By clicking “Check Writers’ Offers”, you agree to our terms of service and privacy policy . We’ll occasionally send you promo and account related email

No need to pay just yet!

Related Essays

4 pages / 1957 words

2 pages / 727 words

4 pages / 2763 words

4 pages / 1815 words

Remember! This is just a sample.

You can get your custom paper by one of our expert writers.

121 writers online

Still can’t find what you need?

Browse our vast selection of original essay samples, each expertly formatted and styled

Related Essays on Disadvantages of Technology

Technology has revolutionized the way we live, work and communicate. In the 21st century, it seems that we cannot function without our smartphones, tablets, laptops, and most importantly, the internet. While technology has [...]

Technology has transformed the way we live, work and communicate. The advancements in technology have brought about numerous benefits, but they have also raised concerns regarding the effects on different aspects of our lives. [...]

Networking is term used for referring, interrelation of computer devices/ hardware and together make them a system which could share information and files using communication channels. We can see roots of networking back in [...]

Have you ever felt alone, sad, depressed, or in a sense, like you’re not wanted? Now ask yourself, how long have you been spending on your phone, computer or just social media alone? In our generation we have access to the [...]

Innovation in Technology has made the world increasingly 'connected' than at any other time. The web has given us more prominent access to the data, opened doors for interactions through online life, and empowered us to take [...]

Media has a power over the public that many overlook. It has altered the way people think, interpret situations, act, and live. While providing knowledge and answers to the fingertips of many, having social media rewires the [...]

Related Topics

By clicking “Send”, you agree to our Terms of service and Privacy statement . We will occasionally send you account related emails.

Where do you want us to send this sample?

By clicking “Continue”, you agree to our terms of service and privacy policy.

Be careful. This essay is not unique

This essay was donated by a student and is likely to have been used and submitted before

Download this Sample

Free samples may contain mistakes and not unique parts

Sorry, we could not paraphrase this essay. Our professional writers can rewrite it and get you a unique paper.

Please check your inbox.

We can write you a custom essay that will follow your exact instructions and meet the deadlines. Let's fix your grades together!

Get Your Personalized Essay in 3 Hours or Less!

We use cookies to personalyze your web-site experience. By continuing we’ll assume you board with our cookie policy .

• Instructions Followed To The Letter
• Deadlines Met At Every Stage
• Unique And Plagiarism Free

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Innov Aging

Social Relations and Technology: Continuity, Context, and Change

Toni c antonucci.

1 Department of Psychology and Institute for Social Research, University of Michigan

Kristine J Ajrouch

3 Department of Sociology, Anthropology and Criminology, Eastern Michigan University

Jasmine A Manalel

2 Department of Psychology, University of Michigan

Social relations, although basic to human nature, health and well-being, have become increasingly complicated as a result of changing population demography and technology. In this essay, we provide a historical overview of social relations, especially as they affect older people. We briefly review the evolution of theory and measurement surrounding social relations as well as early empirical evidence. We consider how social relations have changed over time as well as continuity and change regarding basic characteristics of social relations. Of special interest is the emerging influence of technology on how people maintain contact, especially the changing ways people can use technology to increase, decrease, maintain, or avoid social relations. We consider both negative and positive aspects of these new technologies and their influence on health and well-being. Finally, we conclude that new and emerging technologies hold great promise for the future by overcoming traditional barriers to maintaining social contact, support exchange, and information acquisition. Nevertheless, we caution that these new technologies can have the dehumanizing effect of distance thus creating the potential for insensitivity and increased negativity. In sum, we are cautiously optimistic about the promise of technology to expand, but not replace, traditional forms of social contact.

Translational Significance

Incorporating technology into our study of social relations will be informative for our understanding of how communication modalities influence or are an expression of closeness and/or conflict. Further, technology has important potential for keeping social networks linked and for delivering potential interventions from telehealth to caregiving.

Social relations are a fundamental aspect of human life. This has been advocated early in the history of social science by luminaries such as Cooley (1902) , Durkheim (1915) , and Mead (1913) , and continues to be of significance today as scholars document this point both theoretically and empirically, see Antonucci, Ajrouch, & Birditt (2014) , for a review. Context also matters, in that the ways in which social relations evolve and influence well-being have been shown to vary across time and place ( Ajrouch, Fuller, Akiyama & Antonucci, 2017 ; Fiori, Smith & Antonucci, 2007 ). At the same time, technological developments are fundamentally changing the ways in which we experience social relations, and may impact health and well-being accordingly. In this invited essay, we identify the convoy model as our guiding theoretical framework for understanding continuity and change in social relations. We consider how social relations have changed over time, specifically how technological advances engender new modes of contact for older adults. This is followed by a consideration of challenges facing the study of social relations, with particular attention to the need for theoretical and empirical assessments that take into account newly developing characteristics of our society. These include changes in the demography of the family and changes in migration patterns. We then elaborate on how new of experiencing social relations may have both positive and negative effects, thereby differentially influencing health and well-being. An important challenge to the field of social relations is to consider how to incorporate these developments into current and timely research.

The Convoy Model of Social Relations

The convoy model was developed to specify the scientific study of social relations by detailing the antecedent factors influencing social relations (personal and situational), identifying multiple dimensions of social relations, and illustrating how these factors influence health and well-being ( Antonucci, 2001 ; Kahn & Antonucci, 1980 ). Individual characteristics such as age, gender, race, and religion illustrate the personal characteristics thought to influence social relations, whereas situational characteristics such as roles, norms, organizations, and communities were identified as important external factors influencing social relations. Multiple dimensions of social relations were specified to include convoy structure, support, and satisfaction or relationship quality. The tenets of the convoy model were built on key findings in the literature showing that social relations are an important part of the health and well-being of older people in the United States and around the globe.

Structure refers to characteristics of the people in one’s network such as size, composition, contact frequency, and geographic proximity. Size and composition are important in so far as larger, more diverse networks are associated with positive outcomes. Much evidence has accumulated to indicate that older people enjoy spending leisure time with friends and that these relationships are associated with positive well-being ( Adams & Blieszner, 1989 ; Antonucci & Akiyama, 1995 ). On the other hand, older people also frequently report that they would turn to family, specifically spouse and children when in need. Cantor’s (1979) hierarchy of caregiving clearly designated the order of caregiving preference to be spouse/partner, child, other family, friend\neighbor, and formal caregiver.

A great deal of attention over the last century was given to the “decline of the family” as well as the decreased status of elders in our society. It was argued that older people were increasingly disrespected, alone, and isolated. Yet, classic studies challenged these notions with extensive, empirical investigations. It is now fairly well established, and convoy data continue to empirically support, that older people generally live quite close to and are in regular contact with their children ( Ajrouch et al., 2017 ; Fiori, Antonucci & Akiyama, 2008 ; Shanas et al, 1968 ). Not only do older people receive help, support, and contributions from their children, they also provide these same types of assistance to their children. In fact, older people often provide more than they receive ( Akiyama, Antonucci, & Campbell, 1997 ; Webster et al., 2012 ; Wiemers, Seltzer, Schoen, Hotz & Bianchi, 2016 ). In sum, social support structure includes various elements, all of which are included in the convoy model.

Support refers to the provision and receipt of support, such as aid, affect and affirmation. Lack of social support can have a significant negative impact on health and well-being. One of the most intriguing classic findings is that the single factor most likely to prevent nursing home placement is the report by the older person that they had a confidante, someone with whom they could share their intimate feelings ( Lowenthal & Haven, 1968 ; this finding has been replicated around the world, e.g. in Australia by Giles, Glonek, Luszcz, & Andrews, 2007 ). Social support, and, in particular, protection from isolation and loneliness, are clearly important for the health and well-being of older people. The convoy model recognizes various support types, including instrumental and emotional support, as key predictors of health and well-being.

Satisfaction refers to one’s assessment of one’s social relations, sometimes referred to as adequacy or quality of relationships. It was thought that the existence of a relationship presupposed positive relationship quality and support. These assumptions were increasingly questioned as people began to note that while some families did evidence close, positive relationships, others might better be characterized as negative or ambivalent (both positive and negative) at best. Troll (1971) used the term residential propinquity to note that while many older people wanted to remain close to their family, they actually preferred not to live with them. She suggested that people recognized that it was easier to maintain positive relationships when some distance, privacy, and independence could be maintained. Family relations often include intergenerational relations. Bengtson and his colleagues expanded the family social relations literature by examining intrafamily intergenerational relations and introducing solidarity theory. According to this theory, positive features of adult child–parent ties include contact, emotional bonds, and support exchanges ( Fingerman, Sechrist, & Birditt, 2013 ; Silverstein & Bengtson, 1997 ). In addition, once Bengtson and colleagues expanded this work to investigate the possibility of negativity in intergenerational relations ( Silverstein, Parrott, Angelinni, & Cook, 2000 ), they found that in most families some level of conflict also existed, with younger people reporting more conflict than older people. Bengtson attributed this to differences in intergenerational stake, which referred to the fact that older people were more invested in family links to ensure their legacy, whereas younger people sought to establish independence and create their own legacy ( Bengtson & Kuypers, 1971 ). Empirical evidence has accumulated supporting both these theoretical perspectives ( Suitor, Sechrist, Gilligan, & Pillemer, 2011 ). The convoy model ensures attention to the complexity of relationships quality.

Over the years, evidence has accumulated in support of the convoy model ( Antonucci, 2001 ; Ajrouch et al., 2017 ). Fortunately, the model is designed to incorporate the study of newly emerging developments that might influence social relations. Technological advances, especially with regard to communication technology and social media, offer new ways for enabling older adults to establish social connectedness with family and friends ( Czaja et al., 2017 ; Delello & McWhorter, 2017 ; Leist, 2013 ). Technology can also provide pathways for support in managing health conditions among older adults and those who provide care ( Czaja, 2017 ). Though, as the convoy model posits, use and benefits of technology likely vary according to personal and situational characteristics, and will influence health in unique ways.

Incorporating Technological Developments Into the Study of Social Relations

The nature of social interaction has changed as technological advances have provided new methods of contact. Consider the evolution from in-person contact and letter writing to the telegraph and telephone and, most recently, to ever more individualized and electronic forms of contact such as cell phones, video calls (e.g., Skype, FaceTime), and social media (e.g., Facebook). We know very little about how different forms of communication influence social relations, health, and well-being.

To address the observation that social relations are now experienced in new ways because of technological developments, we recently analyzed a measure of contact frequency that distinguished in-person contact from telephone and electronic contact using the longitudinal Survey of Social Relations ( Antonucci, Birditt & Webster, 2010 ). See Table 1 for a description of participant characteristics. We then examined the degree to which positive and negative relationship quality measured at Time 1, predicted adults’ frequency and use of different forms of communication 10 years later with members of their convoy, namely parents, spouse, child, and friend. We briefly report on our findings in the following paragraphs. For those who might doubt their use, we should note that older adults are increasingly using social media. While over 90% of young people are online and have cell phones, over half of adults age 65 and over are online and 78% own a cell phone ( Anderson, 2015 ; Zickuhr & Madden, 2012 ).

Social Relations Study Wave 3 (Time 2) Sample Descriptives ( N = 557)

Note . Includes nonindependent sample of respondents who completed interviews at both Time 1 and Time 2. M = mean; SD = standard deviation.

Considering different contact modes, as expected, in-person contact was most frequent with spouse (see Table 2 ). Electronic communication was lowest with parents. Interestingly, telephone use was consistent across all relationships.

Descriptive Statistics for Contact Frequency With Network Members via Different Modes a

Note. M = mean; SD = standard deviation.

In many cases, links between relationship quality and contact differed between younger and older adults, depending on the mode of contact. Among older respondents, in-person contact frequency with fathers with whom respondents had a highly negative relationship was much lower than among those with a lower negative relationship quality (see Figure 1 ; all graphs plotted at 1 SD above and below the mean for relationship quality and age). On the other hand, there was little difference among younger respondents’ contact frequency across levels of negative relationship quality with father.

Relationship quality by age interaction effect on in-person contact with father.

There was also an age × relationship quality effect on telephone contact with spouse indicating little difference among younger people, but older people with low spousal positive relationship quality reporting significantly less telephone contact with their spouse than those with a high spousal positive relationship quality ( Figure 2 ). The findings with respect to negative relationship quality were somewhat but not completely parallel. Level of negativity in the relationship did not influence frequency of telephone contact with friends among older people but, interestingly, more negativity in the relationship was associated with more telephone contact with friends among younger people ( Figure 3 ).

Relationship quality by age interaction effect on telephone contact with spouse.

Relationship quality by age interaction effect on telephone contact with friend.

Finally, we examined the use of electronic forms of communication such as video chat, Skype, text, Facebook, and email. There were no effects of positive relationship quality across any of these forms of communication, although older people were less likely to use them than younger people. This age effect was also evident for negative relationship quality. Older people with high negativity in their relationship with their child were significantly less likely to communicate with them electronically than those with low negativity in their child relationship ( Figure 4 ). On the other hand, once again there were no differences in electronic communications among young people regardless of the negativity of their relationship with child.

Relationship quality by age interaction effect on electronic contact with child.

In sum, these findings show how new ways of experiencing social relations vary by age and relationship type. These findings do suggest some age differences but perhaps most importantly highlight the role of relationship quality to mode of communication. New ways of engaging in social relations are not evenly experienced across generations, and hence point to new areas for investigating how social relations influence well-being. Next, we present the ways in which new contact forms via technological developments inform the scientific study of social relations in the context of demographic shifts and health.

Population Demographic Shifts

Many technological advances have occurred within the context of broader demographic changes, including shifts in mortality, fertility, mobility, and marital patterns ( Bianchi, 2014 ). Longer life spans provide older adults with more opportunities to build relationships with younger generations. Increased mobility and migration have led to less geographically proximate family networks, posing potential barriers to support exchanges and contact. Changes in marital patterns have resulted in increasing heterogeneity of family structures. Thus, older adults today are embedded within diverse and complex family structures that shape the type and quality of their social ties. It is important to consider these new aging family forms and functions when evaluating the role of technology in the establishment and maintenance of these social ties, and how the social needs of older adults are being met through technological advances in communication. We next discuss how these demographic shifts influence patterns of intergenerational and romantic relationships, the adaptation of immigrants, and the implications that technology has for these patterns.

Intergenerational Relations

Families are changing such that intergenerational ties, especially those across more than two generations, are becoming increasingly salient ( Antonucci, Jackson, & Biggs, 2007 ; Bengtson, 2001 ; Swartz, 2009 ). Although older adults are less likely to adopt new technologies, they may be motivated to do so by intergenerational ties, e.g., to learn to use a smartphone or social media in order to maintain contact with children and grandchildren. A recent cross-national study demonstrated that countries with a higher prevalence of mobile phone subscriptions also had higher levels of maternal contact by adult children, particularly daughters ( Gubernskaya & Treas, 2016 ).

Technology has the unique potential to influence grandparent–grandchild relationships due to younger generations’ faster adoption of new technologies. Although in-person communication continues to be the most frequent type of contact for grandparents, mobile phones, texting, and email are becoming increasingly popular as a means of staying in touch with grandchildren ( Hurme, Westerback, & Quadrello, 2010 ; Quadrello et al., 2005 ). Given the increased mobility of families and the inverse relationship between geographic proximity and in-person contact, newer communication technologies provide a means by which grandparents can overcome barriers of distance to maintain meaningful ties with younger generations.

Increased levels of intergenerational contact via multiple media platforms, including texting and social networking sites, can have both positive and negative implications for the quality of relationships. Increased contact between older and younger generations could foster feelings of solidarity and closeness, leading to more positive evaluations of the relationship. On the other hand, higher levels of telephone and electronic contact could also promote more negative interactions and exchanges, especially when compared to in-person contact which may mute negativity because of the ability to perceive real-time reactions. Similarly, more technologically proficient individuals may feel frustrated with friends or relatives who struggle to communicate with newer technologies, eroding the quality of their relationship. More research is needed to identify the positive and negative implications of contact via newer technologies for intergenerational relationships, especially given the generational disparities in technology use ( Fingerman & Birditt, 2011 ).

Immigrant Aging

New and varied ways to communicate across geographic distances have created a world of possibilities for immigrants. The advent of communication technologies such as Skype, WhatsApp, Viber, and FaceTime (among others), has made the ability to connect with close others who are geographically distant almost effortless. Moreover, smart phones are revolutionizing communication patterns, no longer restricting the ability to connect by having to be at a particular place. New technologies now facilitate connections between individuals wherever they are instead of individuals in specific locations. For older immigrants, these ways of having social relations may be a double-edged sword, as they facilitate relationships with those left behind, but may also make interactions in the host country more segregated. On the other hand, the Internet may simply serve as a buffer, much as ethnic enclaves do, facilitating adaptation and integration to the host society. We review recent findings in the following paragraphs.

Technology can be an outlet for immigrants who are socially isolated. For instance, among older immigrants from the former Soviet Union to Israel, social media became a resource that both reunited families and old friends living in various parts of the world, as well as helped to create new relationships ( Khvorostianov, Elias, & Nimrod, 2012 ). This way of practicing social relations overcame major problems encountered by elderly immigrants—that of loneliness and social isolation. Khvorostianov and colleagues illustrate that such connections served as a source of joy and empowerment, facilitating transnational connections created through the Internet, ultimately supporting social integration. Similar trends have been identified among older Chinese immigrants living in New Zealand ( Zhang, 2016 ).

For immigrants who leave their homeland at a young age, using information and communication technology (ICT) can, in fact, strengthen adaptation in the host country as one grows older. Hunter (2015) found that migrant workers from Africa living in France opted to remain in the host society after retirement given the ease with which they could connect with family back home as well as remain connected to attachments in France that were reinforced through smart phone technology. Evidence is accumulating to suggest that older immigrants’ social relations facilitated through ICT leads to stronger identities, and empowerment, overall enhancing quality of life. Yet, research of this sort is sparse, and generally occurs with small, nonrepresentative samples. The potential advantages and challenges that arise for immigrants through these new types of social relations is an area in need of further study.

Marital Patterns

A population trend that has widespread implications for how older adults use technology is the heterogeneity of marital statuses, including “gray divorce” (i.e., divorce after the age of 50; Brown & Lin, 2013 ) and never married older adults ( Cooney & Dunne, 2001 ). The misconception that older singles are not interested in finding and maintaining romantic relationships is countered by increasing numbers of later life daters ( Brown & Shinohara, 2013 ). The role that the Internet and social media play in establishing new romantic relationships presents a promising opportunity for research on how older adults use technology.

Although growing numbers of older adults turn to the Internet, including social media and dating websites, to find romantic partners, a surprising lack of attention has been paid regarding older adults’ use of technology to establish romantic connections. Online dating has become a popular means of finding romantic partners for people of all ages, including older adults. Some studies suggest that middle-aged and older adults may, in fact, be more likely than younger adults to use the Internet to meet potential partners ( Stephure, Boon, Mackinnon, & Deveau, 2009 ; Valkenburg & Peter, 2007 ). One advantage of online dating is that individuals’ partner preferences can be tailored and expectations can be explicitly stated. Older adults have been found to capitalize on this feature through the content of their online personal ads ( Alterovitz & Mendelsohn, 2009 ; Davis & Fingerman, 2016 ; Wada, Mortenson, & Hurd Clarke, 2016 ).

In response to older adults’ adoption of the Internet in finding romantic partners, dating websites have made a more concerted effort to target this population. Popular dating websites boast large bases of older subscribers, whereas others are solely dedicated to serving adults aged 50 and older (e.g. OurTime.com ). This is one example of how older adults are not simply consumers of new technology, but also influence the creation of new technology aimed to meet their social needs. Future research should consider the evolving bi-directionality of technology use by older individuals and their resulting influence on the development of new technology. Next, we consider how technology may impact social relations in the context of health.

Social Relations and Health

New technologies have been found to directly influence health due to the possibilities they generate to better connect with others. There is concern that the latest forms of contact and communication threaten community in the U.S. ( Althaus & Tewksbury, 2000 ); yet, it appears that using the Internet is associated with higher levels of perceived support among older adults ( Cody et al., 1999 ) and lower levels of isolation and loneliness ( Cotten, Anderson & McCullough, 2013 ). Further, older adults are often motivated to use new technologies so that they may connect with others ( Sims, Reed, & Carr, 2016 ). In sum, opportunities to enhance social relations through new technologies may initiate new ways to think about how social relations influence health and well-being.

New ways to create and sustain social relations may represent viable alternative sources for developing a sense of community in situations where mobility is limited or restricted. Research indicates that technological developments greatly expand communication options for older adults with mobility limitations, resulting in positive effects for well-being ( Jaeger & Xie, 2009 ; Sims et al., 2016 ). Yet, the effects of communication technology are not necessarily always direct. For instance, Elliot and colleagues (2014) found that ill-health was a considerably weaker predictor of depressive symptoms for high ICT users than for non/limited users, but there was no direct effect of ICT on depressive symptoms. Furthermore, limitations in activities of daily living were a stronger predictor of depressive symptoms for high ICT users. Hence, the benefits of ICT for health are still not clear. Yet, the benefit of ICT-mediated social relations for health and well-being suggests multiple avenues to pursue for social support interventions that may address the challenges that older adults face with the onset of chronic illness. Technological innovations have also spawned various new forms of telehealth communications and treatment as well as social support interventions for their caregivers. We present examples of these potential opportunities next.

Social Support, Intervention, and Technology

Innovative uses of technology have been applied to create social support interventions that maximize good health and well-being. Use of the Internet has opened new avenues for enhancing social support for older adults, especially to address the threat of social isolation and loneliness. One such intervention is the Personal Reminder Information and Social Management (PRISM) system ( Czaja et al., 2017 ). According to Czaja and colleagues, PRISM is a software application designed to support connectivity and resource access among older adults. In a randomized control trial, they showed that access to technology applications, especially email, Internet and games, facilitated social engagement, and provided an effective means of promoting social interactions and connections. Likewise, Delello and McWhorter (2017) showed in a mixed-method study among older adults living in a retirement community that iPads can be used to facilitate closer family relationships and greater overall connection to wider society. Moreover, both studies challenge myths that older adults avoid new technology. Instead, older adults can and will learn new skills to use technology successfully, even if they have never been exposed to it before.

Beyond the issue of social isolation, new communication technology creates unique ways for those with chronic disease to receive support that helps older adults meet the demands of managing illness. One promising mode involves interactive voice response technology (IVR). IVR provides an opportunity to use technology to schedule automated telephone assessment and self-care support calls ( Heisler & Piette, 2005 ). In the case of diabetes, Heilser and Piette used IVR to facilitate connections between peers with the same disease. Findings showed that the technology facilitates an opportunity for reciprocity, where each peer receives and as well as provides support. Moreover, the support experience appears to generate increased self-efficacy, ultimately contributing to better management of diabetes. The IVR technology has also been extended to create the notion of CarePartners as a means to address the health and well-being of informal caregivers ( Piette et al., 2015 ). Piette and colleagues conducted a randomized trial of mobile health support for heart failure patients and their informal caregivers. A CarePartner was identified by measuring the elements of closeness, support type, and quality of key individuals identified by the patient. That person then became the caregiver. The identified caregiver received weekly emails about their loved one’s status and suggestions for how to support self-management. In sum, the provision of informal support was facilitated by IVR and Internet technology. Technological innovations suggest several potential opportunities to leverage the benefits of social support for health and well-being.

Summary and Conclusion

New forms of communication have created unique challenges for understanding relationships. Electronic communication, such as Facebook, instant messaging, Snapchat, Skype, FaceTime, and have all created new opportunities to maintain contacts with close others. Cell phones and email have fundamentally changed how and how often people communicate. The reduced cost of these forms of contact has resulted in almost universal adaptation of some, if not all, of these tools of communication to maintain contact with friends and family. However, we know very little about the effect of these new forms of communication. On the plus side, increased communication can lead to less likelihood of isolation, with easy opportunity to share good news, seek advice about problems, manage health conditions, and generally enjoy exchanges with people we love ( Czaja, 2017 ; Delello & McWhorter, 2017 ; Leist, 2013 ). But is there a minus side? It is also possible that people are losing the art of face-to-face contact, that people are more negative in less personal forms of communication, witness the rise in cyberbullying, because they do not see another’s reactions. Although it is recognized that these public forms of communication can be hurtful, little is currently being done to restrict such negativity. These are challenges that clearly must be addressed. Incorporating new ways of having social relations into theory, recognizing that the use and benefits of technology likely vary according to personal and situational characteristics, and that these new social relations will influence health in unique ways represent important future directions. The convoy model provides a helpful framework for thinking about the ways in which new technologies create new forms of social relations. It is quite clear that the advent and evolution of new communication technologies provide exciting and promising new directions for how we develop, use, and experience social relations.

Conflict of Interest

None reported.

This work was supported by the National Institutes of Health 1R01AG045423-01.

• Adams R. G. & Blieszner R (Eds.). (1989). Older adult friendship: Structure and process . Newbury Park, CA: Sage. [ Google Scholar ]
• Akiyama H., Antonucci T. C., & Campbell R (1997). Exchange and reciprocity among two generations of Japanese and American women . In Sokolovsky J. (Ed.), Cultural Context of aging: Worldwide perspectives (2nd ed, pp. 127–138). Westfort, CT: Greenwood Press. [ Google Scholar ]
• Alterovitz S. S.-R., & Mendelsohn G. A (2009). Partner preferences across the life span: Online dating by older adults . Psychology and Aging , 24 , 513–517. doi:10.1037/a0015897 [ PubMed ] [ Google Scholar ]
• Althaus S. L., & Tewksbury D (2000). Patterns of Internet and traditional news media use in a networked community . Political Communication , 17 , 21–45. doi:10.1080/105846000198495 [ Google Scholar ]
• Anderson M. (2015). Technology Device Ownership: 2015 Retreived August 11, 2017, from http://www.pewinternet.org/2015/10/29/technology-device-ownership-2015/
• Antonucci T. (2001). Social relations: An examination of social networks, social support, and sense of control . In Birren J., & Schaie K. (Eds.), Handbook of the psychology of aging (5th ed). San Diego: Academic Press. [ Google Scholar ]
• Antonucci T. C., Ajrouch K. J., & Birditt K. S (2014). The convoy model: Explaining social relations from a multidisciplinary perspective . The Gerontologist , 54 , 82–92. doi:10.1093/geront/gnt118 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Antonucci T. C., & Akiyama H (1995). Convoys of social relations: Family and friendships within a life span context . In Blieszner R. & Bedford V. H. (Eds.), Handbook of aging and the family (pp. 355–372). Westport, CT: Greenwood Press. [ Google Scholar ]
• Antonucci T. C., Birditt K. S., & Webster N. J (2010). Social relations and mortality: A more nuanced approach . Journal of Health Psychology , 15 , 649–659. doi:10.1177/1359105310368189 [ PubMed ] [ Google Scholar ]
• Antonucci T. C., Jackson J. S., & Biggs S (2007). Intergenerational relations: Theory, research, and policy . Journal of Social Issues , 63 , 679–693. doi:10.1111/j.1540-4560.2007.00530.x [ Google Scholar ]
• Ajrouch K. J., Fuller H. R., Akiyama H., & Antonucci T. C (2017). Convoys of social relations in cross-national context . The Gerontologist . Epub ahead of print. doi:10.1093/geront/gnw204 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Bengtson V. L. (2001). Beyond the nuclear family: The increasing importance of multigenerational bonds . Journal of Marriage and Family , 63 , 1–16. Retrieved from http://onlinelibrary.wiley.com/doi/10.1111/j.1741-3737.2001.00001.x/full [ Google Scholar ]
• Bengtson V. L., & Kuypers J. A (1971). Generational difference and the “developmental stake.” Aging and Human Development , 2 , 249–260. doi:10.2190/AG.2.4.b [ Google Scholar ]
• Bianchi S. M. (2014). A demographic perspective on family change . Journal of Family Theory and Review , 6 , 35–44. doi:10.1111/jftr.12029 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Brown S. L., & Lin I.-F (2013). The gray divorce revolution: Rising divorce among middle-aged and older adults, 1990–2010 . Journals of Gerontology Series B Psychological Sciences and Social Sciences , 67 , 731–741. doi:10.1093/geronb/gbs089 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Brown S. L., & Shinohara S. K (2013). Dating relationships in older adulthood: A national portrait susan . Journal of Marriage and Family , 75 , 1194–1202. doi:10.1021/nl061786n.Core-Shell [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Cantor M. H. (1979). Neighbors and friends: An overlooked resource in the informal support system . Research on Aging , 1 , 434–463. doi:10.1177/016402757914002 [ Google Scholar ]
• Cody M. J., Dunn D., Hoppin S., & Wendt P (1999). Silver surfers: Training and evaluating Internet use among older adult learners . Communication Education , 48 , 269–286. doi:10.1080/03634529909379178 [ Google Scholar ]
• Conney T. M., & Dunne K (2001). Intimate relationships in later life: Current realities, future prospects . Journal of Family Issues , 22 , 838–858. doi:10.1177/019251301022007003 [ Google Scholar ]
• Cooley C. H. (1902). Human nature and the social order . New York: Scribner’s. [ Google Scholar ]
• Cotten S. R., Anderson W. A., & McCullough B. M (2013). Impact of internet use on loneliness and contact with others among older adults: Cross-sectional analysis . Journal of Medical Internet Research , 15 , e39 doi:10.2196/jmir.2306 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Czaja S. J. (2017). The potential role of technology in supporting older adults . Public Policy and Aging Report , 27 , 44–48. doi:10.1093/ppar/prx006 [ Google Scholar ]
• Czaja S. J., Boot W. R., Charness N., Rogers W. A., & Sharit J (2017). Improving social support for older adults through technology: Findings from the PRISM randomized controlled trial . The Gerontologist , gnw249 [Epub ahead of print]. doi:10.1093/geront/gnw249 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Davis E. M., & Fingerman K. L (2016). Digital dating: Online profile content of older and younger adults . Journals of Gerontology, Series B: Psychological Sciences and Social Sciences , 71 , 959–967. doi:10.1093/geronb/gbv042 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Delello J. A., & McWhorter R. R (2017). Reducing the digital divide: Connecting older adults to ipad technology . Journal of Applied Gerontology: The Official Journal of the Southern Gerontological Society , 36 , 3–28. doi:10.1177/0733464815589985 [ PubMed ] [ Google Scholar ]
• Durkheim É. (1915). Elementary forms of the religious life . New York, Free Press. [ Google Scholar ]
• Elliot A. J., Mooney C. J., Douthit K. Z., & Lynch M. F (2014). Predictors of older adults’ technology use and its relationship to depressive symptoms and well-being . The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences , 69 , 667–677. doi:10.1093/geronb/gbt109 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Fingerman K. L., & Birditt K. S (2011). Relationships between adults and their aging parents . In Schaie K. W., & Willis S. L. (Eds.), Handbook of the Psychology of Aging (7th ed, pp. 219–232). Elsevier Inc; 10.1016/B978-0-12-380882-0.00014-0 [ CrossRef ] [ Google Scholar ]
• Fingerman K. L., Sechrist J., & Birditt K (2013). Changing views on intergenerational ties . Gerontology , 59 , 64–70. doi:10.1159/000342211 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Fiori K. L., Antonucci T. C., & Akiyama H (2008). Profiles of social relations among older adults: A cross-cultural approach . Ageing and Society , 28 , 203–231. doi:10.1017/S0144686X07006472 [ Google Scholar ]
• Fiori K. L., Smith J., & Antonucci T. C (2007). Social network types among older adults: A multidimensional approach . The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences , 62 , P322–P330. doi:10.1093/geronb/62.6.P322 [ PubMed ] [ Google Scholar ]
• Giles L. C., Glonek G. F., Luszcz M. A., & Andrews G. R (2007). Do social networks affect the use of residential aged care among older Australians ? BMC Geriatrics , 7 , 24 doi:10.1186/1471-2318-7-24 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Gubernskaya Z., & Treas J (2016). Call home? Mobile phones and contacts with mother in 24 countries . Journal of Marriage and Family , 78 , 1237–1249. doi:10.1111/jomf.12342 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Heisler M. & Piette J. D (2005). “I help you, and you help me”: facilitated telephone peer support among patients with diabetes . The Diabetes Educator , 31 , 869–879. doi:10.1177/0145721705283247 [ PubMed ] [ Google Scholar ]
• Hunter A. (2015). Empowering or impeding return migration? ICT, mobile phones, and older migrants’ communications with home . Global Networks , 15 , 485–502. doi:10.1111/glob.12091 [ Google Scholar ]
• Hurme H., Westerback S., & Quadrello T (2010). Traditional and new forms of contact between grandparents and grandchildren . Journal of Intergenerational Relationships , 8 , 264–280. doi:10.1080/15350770.2010.498739 [ Google Scholar ]
• Jaeger P. T., & Xie B (2009). Developing online community accessibility guidelines for persons with disabilities and older adults . Journal of Disability Policy Studies , 20 , 55–63. doi:10.1177/1044207308325997 [ Google Scholar ]
• Kahn R. L., & Antonucci T. C (1980). Convoys over the life course: Attachment, roles and social support . In Baltes P. B. & Brim O. G. (Eds.), Life-span development and behavior (pp. 253–286). New York: Academic Press. [ Google Scholar ]
• Khvorostianov N., Elias N., & Nimrod G (2012). ‘Without it I am nothing’: The internet in the lives of older immigrants . New Media and Society , 14 , 583–599. doi:10.1177/1461444811421599 [ Google Scholar ]
• Leist A. K. (2013). Social media use of older adults: A mini-review . Gerontology , 59 , 378–384. doi:10.1159/000346818 [ PubMed ] [ Google Scholar ]
• Lowenthal M. F. & Haven C (1968). Interaction and adaptation: Intimacy as a critical variable . American Sociological Review , 33 , 20–30. doi:10.2307/2092237 [ PubMed ] [ Google Scholar ]
• Mead G. H. (1913). The social self . Journal of Philosophy, Psychology and Scientific Methods , 10 , 374–380. doi:10.2307/2012910 [ Google Scholar ]
• Piette J. D., Striplin D., Marinec N., Chen J., & Aikens J. E (2015). A randomized trial of mobile health support for heart failure patients and their informal caregivers: Impacts on caregiver-reported outcomes . Medical Care , 53 , 692–699. doi:10.1097/MLR.0000000000000378 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Quadrello T., Hurme H., Menzinger J., Smith P. K., Veisson M., Vidal S., & Westerback S (2005). Grandparents use of new communication technologies in a European perspective . European Journal of Ageing , 2 , 200–207. doi:10.1007/s10433-005-0004-y [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Shanas E., Townsend P., Wedderburn D., Friis H., Milhoj P., & Stehouwer J (Eds.) (1968). Old people in three industrial societies . New York: Atherton Press. [ Google Scholar ]
• Silverstein M., & Bengtson V. L (1997). Intergenerational solidarity and the structure of adult child-parent relationships in American families . American Journal of Sociology , 103 , 429–460. doi:10.1086/231213 [ Google Scholar ]
• Silverstein M., Parrott T., Angelinni J. J., & Cook F. L (2000). Solidarity and tension between age groups in the United States: Challenge for an aging America in the 21st century . International Journal of Social Welfare , 9 , 270–284. doi:10.1111/1468-2397.00139 [ Google Scholar ]
• Sims T., Reed A. E., & Carr D. C (2016). Information and communication technology use is related to higher well-being among the oldest-old . The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences , gbw130. doi:10.1093/geronb/gbw130 [ PubMed ] [ Google Scholar ]
• Stephure R. J., Boon S. D., Mackinnon S. L., & Deveau V. L (2009). Internet initiated relationships: Associations between age and involvement in online dating . Journal of Computer-Mediated Communication , 14 , 658–681. doi:10.1111/j.1083-6101.2009.01457.x [ Google Scholar ]
• Suitor J. J., Sechrist J., Gilligan M., & Pillemer K (2011). Intergenerational relations in later-life families . In Settersten R., and Angel J. (Eds.) Handbook of Sociology of Aging (pp. 161–178). New York: Springer; 10.1007/978-1-4419-7374-0_11 [ CrossRef ] [ Google Scholar ]
• Swartz T. T. (2009). Intergenerational family relations in adulthood: Patterns, variations, and implications in the contemporary United States . Annual Review of Sociology , 35 , 191–212. doi:10.1146/annurev.soc.34.040507.134615 [ Google Scholar ]
• Troll L. E. (1971) The family of later life: A decade review . Journal of Marriage and the Family , 33 , 263–290. doi:10.2307/349414 [ Google Scholar ]
• Valkenburg P. M., & Peter J (2007). Who visits online dating sites? Exploring some characteristics of online daters . CyberPsychology and Behavior , 10 , 849–852. doi:10.1089/cpb.2007.9941 [ PubMed ] [ Google Scholar ]
• Wada M., Mortenson W. B., & Hurd Clarke L (2016). Older adults’ online dating profiles and successful aging . Canadian Journal on Aging/La Revue Canadienne Du Vieillissement , 35 , 479–490. doi:10.1017/S0714980816000507 [ PubMed ] [ Google Scholar ]
• Webster N. J., Antonucci T. C., Yoon C., McCullough W. R., Fin D. N., & Hartsell D. L (2012). Older adults as consumers: An examination of differences by birth cohort . In Preston S., Kringelback M., & Knutson B. (Eds.) Interdisciplinary Science of Consumption (pp. 281–298). Cambridge, MA: MIT Press. [ Google Scholar ]
• Wiemers E. E., Seltzer J. A., Schoen R. F., Hotz V. J. & Bianchi S. M (2016). The Generational Structure of U.S. Families and Their Intergenerational Transfers . PSC Research Report No. 16–867. 7 2016. [ Google Scholar ]
• UCLA CCPR Population Working Papers . Retrieved from http://papers.ccpr.ucla.edu/index.php/pwp/article/view/PWP-CCPR-2016-036 10.1186/s40711-016-0045-y . [ Google Scholar ]
• Zhang J. (2016). Aging in cyberspace: Internet use and quality of life of older Chinese migrants . The Journal of Chinese Sociology , 3 , 26. doi:10.1186/s40711-016-0045-y [ Google Scholar ]
• Zickuhr K. & Madden M (2012). Older adults and Internet use. For the first time, half of adults aged 65 and older are online Retrieved from http://pewinter- net.org/Reports/2012/Older-adults-and-in- ternet-use.aspx