social impact theory thesis

Social Impact Theory In Psychology

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Bibb Latané created social impact theory in 1981, and he is also credited as one of the psychologists who brought the bystander effect to light.

Latané’s theory suggests that we are greatly influenced by the actions of others. We can be persuaded, inhibited, threatened, and supported by others.

Latané’s theory proposes that individuals can be the sources or targets of social influence . Social impact theory is a model that conceives of other people’s influence as the result of social forces acting on the individual.

The likelihood that someone will respond to social influence is thought to increase with the source’s strength, the event’s immediacy, and the number of sources exerting the impact.

What is a division of impact?

A division of impact means that the social impact gets spread out between all the people it is directed at. If all the influence is targeted at a single individual, this puts a huge pressure on them to conform or obey.

However, if the influence is directed at two people, the influence is halved.

The more targets there are, the more pressure is shared. This idea is known as diffusion of responsibility. This can explain how the bystander effect can occur in a situation where one person needs help, and a group of people can watch and not feel responsible for helping, compared to if they were the only other person present.

Social Impact Theory’s Three Variables

This is how important influencing an individual or group of people is to the person. There are thought to be two categories of strength that determine a source’s impact:

Trans-situational strength – this exists no matter what the situation is, including the source’s age, physical appearance, authority, and perceived intelligence.

Situation-specific – this looks closer at the situation at hand and the behavior that the target is being asked to perform.

For instance, you may be more likely to listen to a doctor when seeking medical advice but may be less likely to take on their interior design advice.

Someone is more likely to influence another if they are close to each other at the time of the influence attempt. There are three types of immediacy:

Physical immediacy – how physically close the source is to a target.

Temporal immediacy  – a target is more likely to be influenced immediately after a source has asked them to do so.

Social immediacy – if the source is close friends or family members with the target, they may be more likely to influence them.

Moreover, if someone is of the same gender, sexual orientation, or religion, they can likely influence each other as they relate to each other.

Simply, this involves the number of people there is in a group. There is a rule called psychosocial law which states that at some point, the number of influencers has less of an effect on the target.

Influence tends to significantly increase up until about 5 or 6 sources are attempting to influence.

Once past 5 or 6 people, the difference in impact increases but at a decreasing rate, meaning it is not as strong.

Numerous studies support the social impact theory. Below are some examples of famous studies:

Sedikides & Jackson (1990)

This was a field experiment that took place at the birdhouse at a zoo. A confederate told groups of visitors not to lean on the railings near the cages that held the birds to see whether the visitors would obey.

It was found that if the confederate was dressed in a zookeeper uniform, obedience was high. If they were dressed casually, obedience was lower.

This demonstrates social impact, especially the strength aspect, because of the perceived authority of the confederate.

As time went on, more visitors started ignoring the instruction not to lean on the railings.

This demonstrates immediacy because as the instruction gets less immediate, it has less of an impact. It was also found that the larger the group of visitors, the more disobedience was observed, which supports the idea of a division of impact.

Darley & Latané (1968)

This experiment involved participants sitting in booths with the purpose of discussing health issues over an intercom.

One of the speakers was a confederate who would pretend to suffer a heart attack during their talk. It was then observed whether the participants would help the confederate.

It was found that if there was one other participant present, they went for help 85% of the time. This dropped to 62% if there were two other participants and dropped further to 31% if there were 4+ participants.

This study supports Latané’s idea of numbers affecting social impact and the diffusion of responsibility.

You are more likely to help someone if you are the only person present, but there is less responsibility when there are more people present.

Milgram (1965)

Milgram completed many variations on his original famous experiment wherein ‘teacher’ participants were instructed to administer electric shocks to a ‘learner’ confederate who did not actually receive any shocks.

One variation experiment had two peer confederates in the room with the teacher, who refused to continue the experiment.

The results showed that obedience dropped from 65% to 10% with the presence of two rebelling confederates. This supports that social impact can be influenced by the number of individuals present.

What is dynamic social impact theory?

Social impact theory predicts how sources can influence a target, but a criticism is that it neglects how the target may influence the source.

Social impact theory is now often called dynamic social impact theory as it considers the target’s ability to influence the source. It views influence as a two-way exchange rather than a one-way street.

How does social impact theory relate to social media?

Social impact theory was obviously developed long before social media platforms existed. Nevertheless, social impact theory can be observed and utilized by people and brands to influence others.

If we have friends, family, and co-workers who post on social media, we are more likely to be influenced by their opinions if they are trusted people who are close to us (strength and social immediacy).

Likewise, the number of people who share the same opinion on social media is likely to influence others.

Brands can utilize social impact theory to sell their products on social media platforms. Brands and companies can get people of high status to help promote their products and get people to buy them.

For instance, if we see a celebrity that we like promoting a product on social media, saying how good it is, we may be more influenced to buy the product because of the strength of their influence.

This influence often works best if the influencer is of high status, the influencing statement is more immediate, and there are multiple influencers sharing the same message (strength, immediacy, and number).

Latane, B., & Darley, J. M. (1968). Group inhibition of bystander intervention in emergencies.   Journal of personality and social psychology ,  10 (3), 215.

Latané, B. (1981). The psychology of social impact.  American Psychologist, 36 (4), 343.

Latané, B., & Wolf, S. (1981). The social impact of majorities and minorities.  Psychological Review ,  88 (5), 438.

Milgram, S. (1965). Some conditions of obedience and disobedience to authority .  Human relations ,  18 (1), 57-76.

Sedikides, C., & Jackson, J. M. (1990). Social impact theory: A field test of source strength, source immediacy and number of targets.  Basic and applied social psychology ,  11 (3), 273-281.

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Social Impact Assessment as a vehicle to better understand and improve stakeholder participation within urban development planning: The Maltese Case

Vella, Steven (2018) Social Impact Assessment as a vehicle to better understand and improve stakeholder participation within urban development planning: The Maltese Case. Doctoral thesis, Birmingham City University.

Environmental decision-making situations are typically complex and chaotic, with confused political messages, conflicting agendas and limited account taken of the wider social contexts in which decisions are made and play out. Many different types of knowledges from diverse social actors, sometimes with different epistemological and ontological backgrounds, must be taken into account. In environmental and urban planning, these challenges are increasingly being addressed through the integration of public participation in Social Impact Assessments (SIA) to inform Environmental Impact Assessments (EIA). Research on environmental governance suggests that direct public participation and integration of stakeholder concerns in the environmental decision-making process could reduce the potential for conflict and lead to “better” decisions. However, the mechanisms through which participation benefits decision-making processes are unclear and contested. Previous attempts to understand “what works” in participation have been confounded by the multifaceted interactions that exist between the different components of social-ecological systems and the often-unacknowledged influence of context. The context of participation includes the social norms of society at large, and of different social units or communities of practice, the political context in which participation is performed and integrated into practice in urban planning, and the environmental context in which decisions will play out. Most of the disciplines that have traditionally sought to understand stakeholder engagement in environmental decisions struggle to recognize or analyse the role of these underlying dynamics and context. However, without a better understanding of these deep dynamics and the contexts in which participation takes places, it becomes very difficult to explain why some processes meet their objectives while others fail, or produce unintended consequences. This doctoral thesis makes empirical contributions to our understanding of stakeholder participation in urban development in Malta, and uses this case study research to generate methodological insights into best practices in stakeholder and public engagement and inter-professional collaboration in SIAs. Grounded in the analysis of the empirical data produced from the ethnographic experience of an applied anthropologist working as an SIA practitioner on three proposed urban development projects in Malta, the thesis differentiates between descriptive and explanatory factors to develop a typology and a theory of stakeholder and wider public engagement. The typology describes different types of public and stakeholder engagement based on agency (who initiates and leads engagement) and mode of engagement (from communication to co-production), while the theory explains much of the variation in outcomes from different types of engagement. This typology and theory is tested using empirical evidence from three Maltese SIA case studies, and then is further developed based on insights from case study findings and literature. It emphasises the roles of context and scale (especially temporal) in determining the initial choice of engagement type, and moves from an initial linear theoretical framework to one where the factors determining the outcomes of participation are framed as an interdependent, loosely nested set of factors, influencing one another along the planning life-cycle. This stresses the dynamic nature of the planning and decision-making process over time and across changing macro, meso and micro socio-cultural, political and geo-spatial contexts. Finally, the thesis shows how applied anthropology and its practitioners can effectively combine critical social theory of complex systems with its application and pragmatic engagement with the contemporary problems of the social and physical environment, working and collaborating across disciplinary borders and blurring the lines between theory and practice. Anthropology and its methods can offer an alternative way to look at the world and the range of methodological approaches that anthropologists are trained in, especially qualitative data collection based on participant observation and ethnography provide that extra ‘edge’ to the analysis of the complex systems that urban and environmental conservation projects investigate, while building relationships that help increase positive outcomes of stakeholder involvement within such initiatives and projects.

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Social Impact Theory

Social impact theory definition.

Social impact theory differs from other models of social influence by incorporating strength and immediacy, instead of relying exclusively on the number of sources. Although criticisms have been raised, the theory was (and continues to be) important for the study of group influence. Reformulating social impact theory to accommodate the influence of targets on sources (i.e., dynamic social impact theory) has further increased its validity and range of explainable phenomena. Furthermore, pushing social impact theory into applied areas in social psychology continues to offer fresh perspectives and predictions about group influence.

Tests of Social Impact Theory

Number of sources.

Social impact theory predicts multiple sources will have more influence on a target than will a single source. Research has generally supported this prediction: Many studies have shown a message presented by multiple people exerts more influence than does the same message presented by a single person. However, the effect of multiple sources only holds true under three conditions. First, the influencing message must contain strong (rather than weak) arguments. Weakly reasoned arguments, whether given by multiple sources or not, result in little attitude change. Second, the target must perceive the multiple sources to be independent of one another. The effect of multiple sources disappears if the target believes the sources “share a single brain.” The colluding party will in such cases be no more effective than will a single source. Third, as the number of sources grows large, adding additional sources will have no additional effect. For example, the effect of 4 independent sources substantially differs from the effect of 1 source, but the effect of 12 independent sources does not substantially differ from the effect of 15 independent sources.

Strength and Immediacy

The inclusion of strength and immediacy as variables is unique to social impact theory; no other social influence theory includes these variables. Defining strength and immediacy in research studies is less straightforward than is defining the number of sources, but the operational definitions have been relatively consistent across studies. Researchers usually vary the source’s strength with differences in either age or occupation (adults with prestigious jobs presumably have more strength than do young adult college students). Researchers usually vary the source’s immediacy either with differences in the physical distance between the source and the target (less distance means more immediacy) or, in cases of media presentation, with differences in the size of the visual image of the source (a larger image focused more on the face relative to the body means more immediacy).

Surprisingly, however, these two components of the model have received considerably less empirical investigation than has the number of sources; therefore, the effects of strength and immediacy on influence are less clear. A statistical technique called meta-analysis, which allows researchers to combine the results of many different studies together, has helped researchers draw at least some conclusions. Across studies, meta-analyses on these two variables indicate statistically significant effects of low magnitude (i.e., the effects, though definitely present, are not very strong). Furthermore, strength and immediacy appear to only exert influence in studies using self-report measures; the effects of strength and immediacy wane when more objective measures of behavior are examined.

Dynamic Social Impact Theory

In its traditional form, social impact theory predicts how sources will influence a target, but neglects how the target may influence the sources. Dynamic social impact theory considers this reciprocal relationship. The theory predicts people’s personal attitudes, behaviors, and perceptions will tend to cluster together at the group level; this group-level clustering depends on the strength, immediacy, and number of social influence sources. Day-to-day interaction with others leads to attitude change in the individual, which then helps contribute to the pattern of beliefs at the group level. In support of the immediacy component of dynamic social impact theory, for example, studies have shown randomly assigned participants were much more likely to share opinions and behaviors with those situated close to them than with those situated away from them, an effect which occurred after only five rounds of discussion.

New Directions for Social Impact Theory

Recently, researchers have pushed social impact theory outside the areas of persuasion and obedience into more applied areas of social psychology. For example, recent studies have examined social impact theory in the context of consumer behavior. In one study, researchers varied the size and proximity of a social presence in retail stores, and examined how this presence influenced shopping behavior. Furthermore, several tenets of social impact theory seem to predict political participation. One study found as the number of people eligible to vote increases, the proportion of people who actually vote asymptotically decreases. This finding accords with social impact theory, which predicts an increasingly marginal impact of sources as their number grows very large.

Social impact theory has enjoyed great theoretical and empirical attention, and it continues to inspire interesting scientific investigation.

References:

• Harkins, S. G., & Latane, B. (1998). Population and political participation: A social impact analysis of voter responsibility. Group Dynamics: Theory, Research, and Practice, 2, 192-207.
• Latane, B., & Wolf, S. (1981). The social impact of majorities and minorities. Psychological Review, 88, 438-453.

Social Impact Theory (Definition + Examples)

In Milgram’s Obedience experiment, participants delivered deadly electric shocks simply because they were told. In the Stanford Prison Experiment, participants got violent with each other, even though they were in a simulation that was meant to last only a few days. Outside of social psychology experiments, we have witnessed people do unimaginable and questionable things.

The Social Impact Theory attempts to bring some clarity to this confusion.

Latané’s Social Impact Theory suggests that individuals can be sources or targets of social influence. It attempts to answer why we perform behaviors in certain situations. The source influences the target depending on various factors. Latané demonstrates these factors using an equation: i = f(S * I * N).  

History of Social Impact Theory

In 1981, psychologist Bibb Latané created the idea of Social Impact Theory. If Bibb Latané’s name looks familiar to you, it’s because he is often credited as one of the psychologists who brought the Bystander Effect to light.

In Social Impact Theory, “i” is the impact. It’s a function of three variables: strength (s,) immediacy (i,) and the number of sources (n.) If any of these are significantly high or low, it will have a serious effect on the impact on the target. As you read about social impact theory, you are going to dive deeper into these factors and how they can impact someone’s behavior.

Social Impact Theory's Three Variables

Strength simply refers to the importance of the source. If the source appears to be an authority on a subject (or any subject,) the target is more likely to perform the behaviors that they suggest.

Two categories of strength determine a source’s impact: trans-situational strength and situation-specific strength.

Trans-situational Strength

The first type of strength exists no matter what the situation is. Trans-situational strength includes:

• The source’s age
• Physical appearance or other characteristics
• Held authority
• Perceived intelligence

Of course, society and culture may influence whether the source has trans-situational strength. In some countries, royalty has more strength than any other government figure. In other countries, the government leaders are the most important. Context will shape the source’s strength regardless of the situation.

Situation-specific Strength

This type of strength looks closer at the situation at hand and the behavior that the target is being asked to perform. Let’s say you were reading an article online about COVID in the early days of the pandemic. You see a quote from a medical doctor telling you to stay inside. You’re more likely going to listen to that doctor because they have authority in the situation.

Now let’s say you’re reading an article about fashion. You see a quote from a medical doctor telling you that the color pink is out, and the color orange is in. Not only are you unlikely to listen to the doctor’s advice, but you are also likely to be confused as to why this person is making a comment on something so out of their expertise.

Other factors under the umbrella of situation-specific strength include:

• Peer pressure
• Intoxication
• Political climate

Immediacy also makes a difference. But when psychologists talk about immediacy, they aren’t just talking about time.

Three forms of immediacy may impact a target: physical immediacy, temporal immediacy, and social immediacy.

Physical Immediacy

Immediacy may also be defined as “the quality of bringing one into direct and instant involvement with something.” If a source is physically close to a target, they are more likely to have an impact.

Think about this. If someone sends you an email asking you to answer a survey, you may take them up on it. (This is when strength really comes into play.) But if someone comes up to you at the grocery store and asks you to answer a survey, you are more likely to say yes (or at least respond to the person who is asking.)

Temporal Immediacy

Time also makes an impact on a target. A target is more likely to act immediately after a source asked them to do so. If the target waits five, ten, or twenty minutes, the likelihood that they are going to ask will decrease with time.

Marketers often consider this as they build campaigns or write ads. People are more likely to “buy now” rather than “buy at some point.” If you give potential customers too much time to weigh their options, they are less likely to act.

Social Immediacy

The last form of immediacy is social immediacy. This ties in with the strength of a source. If the source and target are “close,” the source is more likely to make an impact. The target may see themselves in the source because of the source’s race, gender, sexual orientation, religion, etc. The idea that we are more comfortable with people who are “like us” is not new in psychology. But the Social Impact Theory suggests that are also more likely to be influenced by people who are “like us.”

Number is arguably the most important of these three factors. Even if a single source is not physically close or particularly “strong,” they will still be impactful if they are surrounded by many other sources.

One example of this comes from Stanley Milgram . Milgram is known for conducting one of the most controversial experiments in social psychology history. But not all of his experiments involved electric shocks. In 1968, Milgram asked a group of actors to stand on the street and stare up at the sky for 60 seconds at a time. As the actors did this, Milgram recorded how many passersby followed the gaze of the actors.

Milgram found that the number of actors influenced how many people followed their gaze. One person standing on the street and looking at the sky didn’t influence too many passersby. But a group of five or ten are more likely to turn some heads.

Psychosocial Law

How much influence does that fifth or tenth source have on a target? Well, not a lot. The Psychosocial Law of the Social Impact Theory states that each subsequent source has less and less impact on the target. The third person to look at the sky is not going to be as impactful as the first or second. But they will still contribute to the overall impact.

Dynamic Social Impact Theory 

Latané and his colleagues continued their research on social impact theory by “zooming out.” They looked at how different groups of people interact and influence each other. These observations, and the patterns within them, became the basis for dynamic social impact theory. 

When compared to Latané’s larger body of work, social impact theory is seen as a “static” theory. The theory focuses on predicting how one person may be influenced on one topic or element by a larger group. Dynamic social impact theory looks at how this one person or group, who is influenced, may also influence a group or person as well. For example, celebrities influence the media, and media influences celebrities. The common people influence politicians and politicians influence the common people. This reciprocity is considered in dynamic social impact theory. 

What Is Dynamic Social Impact Theory? 

Dynamic social impact theory (DSIT) describes how groups create culture. Latané and colleagues picked up on four patterns that they believe have an impact and shape culture among groups. These four patterns are clustering, correlation, consolidation, and continuing diversity. At the heart of culture, the theory suggests, is communication between people

What are these four patterns, exactly? Let’s explore. 

Clustering is the presence of regional differences in attitudes, behaviors, and preferences. People influence other people who are close to them. For example, people in the United States are more likely to drink coffee in the morning to wake themselves up. People in the United Kingdom prefer tea. People in Austin might reach for a breakfast taco in the morning, while people in New York City prefer to order bagels. The connections between food and drink preferences and their regional locations happen due to clustering. This makes sense. If you are not sure what to have for breakfast, you are likely to check out what is being ordered by people around you, rather than people across the globe. 

Correlation

When you spend time around a group of people, the group tends to adopt a similar mindset and perspective. As the group forms and opinions are shared, you all start to convince each other to believe the same things. We see this often in political parties. When a group of people belongs to a political party, they are more likely to carry all the beliefs of that party. This may happen because they only listen to commentators from that party, or are not exposed to the opposing side’s argument. 

Consolidation and Continuing Diversity 

Cultures change. The beliefs that Americans have had on same-sex or interracial marriage, for example, have changed over time. In this way, the people influence the politicians, and politicians influence the people. Consolidation (and continuing diversity) are two patterns that address how groups of people change.

These two processes are typically at odds with each other. Consolidation describes the minority influence giving way to the majority influence. The majority “takes over” the minority influence until it is largely eradicated. This doesn’t always happen completely. In many cases, minority groups within a larger culture remain isolated but alive. That isolation allows them to hold onto their minority influence. This is continuing diversity. 

Minority opinion can also “take over” majority opinion. 

The Importance of Communication in DSIT

As mentioned before, communication is central to all of these patterns. Without communication, culture would remain stagnant. Communication isn’t just two neighbors having a conversation over their fences. Political signs on front lawns are communication. Storefronts advertising “the best bagels in Brooklyn” are communicating. Recalling moments in regional history is communication. Communication may be intentional or unintentional, but either way, it can shape culture and the attitudes of people within a group. 

This perspective made social impact theory and DSIT significant ideas of their time. If communication can have such a big impact on culture and majority opinion, then scholars might have a better idea of how large swaths of people can be influenced to meet certain goals. 

How Is Social Impact Theory Reductionist?

"Reductionist" theories attempt to simplify very complicated topics in psychology. Many critics of social impact theory believe that it is reductionist. Strength, immediacy, and number of sources do not cover every factor that goes into decisions or behaviors. There may be other factors at play. Credibility, the target’s ability to perform a behavior, and the actual behavior may also influence whether or not someone performs a behavior. But despite this criticism, Social Impact Theory is still important when thinking about the impact a person can have on another.

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Social Impact Theory of Human Communication Essay

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Introduction

Works cited.

The social impact theory is the theory that is applied to individual and inter-group relations as well as tendencies that may be viewed in the course of human communication. In general, the theory investigates the influence of society on individuals and the opposite influence an individual may produce on society. it has been long ago proved that the interrelations of two individuals and influence they may produce on each other highly differ from the distribution of power that occurs between a group and an individual. Together with this, it is a fact that the more the size of the group is, the more effective the influence produced by it on a individual is. The opposite influence is also reducing with the extent of increasing the number of the group on which influence is produced. Scientifically speaking, it is “a model that conceives of influence from other people… acting on individuals, much as physical forces can affect an object” (Leslie).

There are two types of the social impact theories – first of all, it is the general theory that claims that all forms of social influence, whatever the specific social process, will be proportional to a multiplicative function of the strength, immediacy, and number of people who are the sources of influence, and inversely proportional to the strength, immediacy, and number of people being influenced (Latane and Drigotas).

In contrast to the general theory, or as its consequence, there appeared a dynamic social impact theory that explores the relations between people within and between groups, juxtaposing the influence that may be produced by an individual on a group and vice versa. According to the opinion of Mabry and Sudweeks, the dynamic social impact theory has a disadvantage of not considering the relationd of space, time and communication modality, which makes it limited to a certain extent (Mabry and Sudweeks, p. 2).

The social impact theory has found much support and became very interesting for research as its functionality has been proved in many spheres. There is even a method of utilizing the theory in order to manipulate people and achieve one’s goal: people who are knowledgeable in manipulating people may do it much quicker and easier if they involve some person into their influence thus creating a group, and then continue producing influence on other people with the help of this group. As a result, the number of people in the group enlarges, becomes more powerful and fulfills the initial aim of the initiator. Together with this, the members of the group may not even realize that the follow not their aims but the initially stipulated aim of the person who was the creator of the group.

The discussed theory has been successfully applied in many social events such as massive gathering, propaganda, group work in an organization, learning the principles of convincing people in something. Group pressure is also a common phenomenon in any place where an individual may come across a need to persuade some people thus entering the relations of mutual influence. It is absolutely commonplace to see the examples of the theory in action. “In meetings in the workplace, few will speak out if their opinion differs from the majority” (Social Impact Theory).

The theory has been developed recently; there were many findings represented in the sphere of psychology, furthering its findings. For example, it is interesting to note some of Latane’s findings, for example, that each individual can influence others; but the more people are present, the less influence any one individual will have. Thus, we are more likely to listen attentively to a speaker if we are in a small group than if we were in a large group (Theory of Social Impact).

• Latane, Bibb, and Drigotas, Stephen. Social Impact Theory. 2009.
• Leslie. Social Impact Theory, 2008.
• Mabry, Edward, and Sudweeks, Evelyn. “Assessing the Impacts of Dynamic Social Impact Theory in Asynchronous Groups” Paper presented at the annual meeting of the International Communication Association, Dresden International Congress Centre, Dresden, Germany, 2009.
• Social Impact Theory , 2009. Web.
• Theory of Social Impact, 2009.
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• 2 MRC/CSO Social and Public Health Sciences Unit, Institute of Health & Wellbeing , University of Glasgow , Glasgow , UK
• Correspondence to Dr Emily Long, MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow G3 7HR, UK; emily.long{at}glasgow.ac.uk

This essay examines key aspects of social relationships that were disrupted by the COVID-19 pandemic. It focuses explicitly on relational mechanisms of health and brings together theory and emerging evidence on the effects of the COVID-19 pandemic to make recommendations for future public health policy and recovery. We first provide an overview of the pandemic in the UK context, outlining the nature of the public health response. We then introduce four distinct domains of social relationships: social networks, social support, social interaction and intimacy, highlighting the mechanisms through which the pandemic and associated public health response drastically altered social interactions in each domain. Throughout the essay, the lens of health inequalities, and perspective of relationships as interconnecting elements in a broader system, is used to explore the varying impact of these disruptions. The essay concludes by providing recommendations for longer term recovery ensuring that the social relational cost of COVID-19 is adequately considered in efforts to rebuild.

• inequalities

Data availability statement

Data sharing not applicable as no data sets generated and/or analysed for this study. Data sharing not applicable as no data sets generated or analysed for this essay.

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https://doi.org/10.1136/jech-2021-216690

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Introduction

Infectious disease pandemics, including SARS and COVID-19, demand intrapersonal behaviour change and present highly complex challenges for public health. 1 A pandemic of an airborne infection, spread easily through social contact, assails human relationships by drastically altering the ways through which humans interact. In this essay, we draw on theories of social relationships to examine specific ways in which relational mechanisms key to health and well-being were disrupted by the COVID-19 pandemic. Relational mechanisms refer to the processes between people that lead to change in health outcomes.

At the time of writing, the future surrounding COVID-19 was uncertain. Vaccine programmes were being rolled out in countries that could afford them, but new and more contagious variants of the virus were also being discovered. The recovery journey looked long, with continued disruption to social relationships. The social cost of COVID-19 was only just beginning to emerge, but the mental health impact was already considerable, 2 3 and the inequality of the health burden stark. 4 Knowledge of the epidemiology of COVID-19 accrued rapidly, but evidence of the most effective policy responses remained uncertain.

The initial response to COVID-19 in the UK was reactive and aimed at reducing mortality, with little time to consider the social implications, including for interpersonal and community relationships. The terminology of ‘social distancing’ quickly became entrenched both in public and policy discourse. This equation of physical distance with social distance was regrettable, since only physical proximity causes viral transmission, whereas many forms of social proximity (eg, conversations while walking outdoors) are minimal risk, and are crucial to maintaining relationships supportive of health and well-being.

The aim of this essay is to explore four key relational mechanisms that were impacted by the pandemic and associated restrictions: social networks, social support, social interaction and intimacy. We use relational theories and emerging research on the effects of the COVID-19 pandemic response to make three key recommendations: one regarding public health responses; and two regarding social recovery. Our understanding of these mechanisms stems from a ‘systems’ perspective which casts social relationships as interdependent elements within a connected whole. 5

Social networks

Social networks characterise the individuals and social connections that compose a system (such as a workplace, community or society). Social relationships range from spouses and partners, to coworkers, friends and acquaintances. They vary across many dimensions, including, for example, frequency of contact and emotional closeness. Social networks can be understood both in terms of the individuals and relationships that compose the network, as well as the overall network structure (eg, how many of your friends know each other).

Social networks show a tendency towards homophily, or a phenomenon of associating with individuals who are similar to self. 6 This is particularly true for ‘core’ network ties (eg, close friends), while more distant, sometimes called ‘weak’ ties tend to show more diversity. During the height of COVID-19 restrictions, face-to-face interactions were often reduced to core network members, such as partners, family members or, potentially, live-in roommates; some ‘weak’ ties were lost, and interactions became more limited to those closest. Given that peripheral, weaker social ties provide a diversity of resources, opinions and support, 7 COVID-19 likely resulted in networks that were smaller and more homogenous.

Such changes were not inevitable nor necessarily enduring, since social networks are also adaptive and responsive to change, in that a disruption to usual ways of interacting can be replaced by new ways of engaging (eg, Zoom). Yet, important inequalities exist, wherein networks and individual relationships within networks are not equally able to adapt to such changes. For example, individuals with a large number of newly established relationships (eg, university students) may have struggled to transfer these relationships online, resulting in lost contacts and a heightened risk of social isolation. This is consistent with research suggesting that young adults were the most likely to report a worsening of relationships during COVID-19, whereas older adults were the least likely to report a change. 8

Lastly, social connections give rise to emergent properties of social systems, 9 where a community-level phenomenon develops that cannot be attributed to any one member or portion of the network. For example, local area-based networks emerged due to geographic restrictions (eg, stay-at-home orders), resulting in increases in neighbourly support and local volunteering. 10 In fact, research suggests that relationships with neighbours displayed the largest net gain in ratings of relationship quality compared with a range of relationship types (eg, partner, colleague, friend). 8 Much of this was built from spontaneous individual interactions within local communities, which together contributed to the ‘community spirit’ that many experienced. 11 COVID-19 restrictions thus impacted the personal social networks and the structure of the larger networks within the society.

Social support

Social support, referring to the psychological and material resources provided through social interaction, is a critical mechanism through which social relationships benefit health. In fact, social support has been shown to be one of the most important resilience factors in the aftermath of stressful events. 12 In the context of COVID-19, the usual ways in which individuals interact and obtain social support have been severely disrupted.

One such disruption has been to opportunities for spontaneous social interactions. For example, conversations with colleagues in a break room offer an opportunity for socialising beyond one’s core social network, and these peripheral conversations can provide a form of social support. 13 14 A chance conversation may lead to advice helpful to coping with situations or seeking formal help. Thus, the absence of these spontaneous interactions may mean the reduction of indirect support-seeking opportunities. While direct support-seeking behaviour is more effective at eliciting support, it also requires significantly more effort and may be perceived as forceful and burdensome. 15 The shift to homeworking and closure of community venues reduced the number of opportunities for these spontaneous interactions to occur, and has, second, focused them locally. Consequently, individuals whose core networks are located elsewhere, or who live in communities where spontaneous interaction is less likely, have less opportunity to benefit from spontaneous in-person supportive interactions.

However, alongside this disruption, new opportunities to interact and obtain social support have arisen. The surge in community social support during the initial lockdown mirrored that often seen in response to adverse events (eg, natural disasters 16 ). COVID-19 restrictions that confined individuals to their local area also compelled them to focus their in-person efforts locally. Commentators on the initial lockdown in the UK remarked on extraordinary acts of generosity between individuals who belonged to the same community but were unknown to each other. However, research on adverse events also tells us that such community support is not necessarily maintained in the longer term. 16

Meanwhile, online forms of social support are not bound by geography, thus enabling interactions and social support to be received from a wider network of people. Formal online social support spaces (eg, support groups) existed well before COVID-19, but have vastly increased since. While online interactions can increase perceived social support, it is unclear whether remote communication technologies provide an effective substitute from in-person interaction during periods of social distancing. 17 18 It makes intuitive sense that the usefulness of online social support will vary by the type of support offered, degree of social interaction and ‘online communication skills’ of those taking part. Youth workers, for instance, have struggled to keep vulnerable youth engaged in online youth clubs, 19 despite others finding a positive association between amount of digital technology used by individuals during lockdown and perceived social support. 20 Other research has found that more frequent face-to-face contact and phone/video contact both related to lower levels of depression during the time period of March to August 2020, but the negative effect of a lack of contact was greater for those with higher levels of usual sociability. 21 Relatedly, important inequalities in social support exist, such that individuals who occupy more socially disadvantaged positions in society (eg, low socioeconomic status, older people) tend to have less access to social support, 22 potentially exacerbated by COVID-19.

Social and interactional norms

Interactional norms are key relational mechanisms which build trust, belonging and identity within and across groups in a system. Individuals in groups and societies apply meaning by ‘approving, arranging and redefining’ symbols of interaction. 23 A handshake, for instance, is a powerful symbol of trust and equality. Depending on context, not shaking hands may symbolise a failure to extend friendship, or a failure to reach agreement. The norms governing these symbols represent shared values and identity; and mutual understanding of these symbols enables individuals to achieve orderly interactions, establish supportive relationship accountability and connect socially. 24 25

Physical distancing measures to contain the spread of COVID-19 radically altered these norms of interaction, particularly those used to convey trust, affinity, empathy and respect (eg, hugging, physical comforting). 26 As epidemic waves rose and fell, the work to negotiate these norms required intense cognitive effort; previously taken-for-granted interactions were re-examined, factoring in current restriction levels, own and (assumed) others’ vulnerability and tolerance of risk. This created awkwardness, and uncertainty, for example, around how to bring closure to an in-person interaction or convey warmth. The instability in scripted ways of interacting created particular strain for individuals who already struggled to encode and decode interactions with others (eg, those who are deaf or have autism spectrum disorder); difficulties often intensified by mask wearing. 27

Large social gatherings—for example, weddings, school assemblies, sporting events—also present key opportunities for affirming and assimilating interactional norms, building cohesion and shared identity and facilitating cooperation across social groups. 28 Online ‘equivalents’ do not easily support ‘social-bonding’ activities such as singing and dancing, and rarely enable chance/spontaneous one-on-one conversations with peripheral/weaker network ties (see the Social networks section) which can help strengthen bonds across a larger network. The loss of large gatherings to celebrate rites of passage (eg, bar mitzvah, weddings) has additional relational costs since these events are performed by and for communities to reinforce belonging, and to assist in transitioning to new phases of life. 29 The loss of interaction with diverse others via community and large group gatherings also reduces intergroup contact, which may then tend towards more prejudiced outgroup attitudes. While online interaction can go some way to mimicking these interaction norms, there are key differences. A sense of anonymity, and lack of in-person emotional cues, tends to support norms of polarisation and aggression in expressing differences of opinion online. And while online platforms have potential to provide intergroup contact, the tendency of much social media to form homogeneous ‘echo chambers’ can serve to further reduce intergroup contact. 30 31

Intimacy relates to the feeling of emotional connection and closeness with other human beings. Emotional connection, through romantic, friendship or familial relationships, fulfils a basic human need 32 and strongly benefits health, including reduced stress levels, improved mental health, lowered blood pressure and reduced risk of heart disease. 32 33 Intimacy can be fostered through familiarity, feeling understood and feeling accepted by close others. 34

Intimacy via companionship and closeness is fundamental to mental well-being. Positively, the COVID-19 pandemic has offered opportunities for individuals to (re)connect and (re)strengthen close relationships within their household via quality time together, following closure of many usual external social activities. Research suggests that the first full UK lockdown period led to a net gain in the quality of steady relationships at a population level, 35 but amplified existing inequalities in relationship quality. 35 36 For some in single-person households, the absence of a companion became more conspicuous, leading to feelings of loneliness and lower mental well-being. 37 38 Additional pandemic-related relational strain 39 40 resulted, for some, in the initiation or intensification of domestic abuse. 41 42

Physical touch is another key aspect of intimacy, a fundamental human need crucial in maintaining and developing intimacy within close relationships. 34 Restrictions on social interactions severely restricted the number and range of people with whom physical affection was possible. The reduction in opportunity to give and receive affectionate physical touch was not experienced equally. Many of those living alone found themselves completely without physical contact for extended periods. The deprivation of physical touch is evidenced to take a heavy emotional toll. 43 Even in future, once physical expressions of affection can resume, new levels of anxiety over germs may introduce hesitancy into previously fluent blending of physical and verbal intimate social connections. 44

The pandemic also led to shifts in practices and norms around sexual relationship building and maintenance, as individuals adapted and sought alternative ways of enacting sexual intimacy. This too is important, given that intimate sexual activity has known benefits for health. 45 46 Given that social restrictions hinged on reducing household mixing, possibilities for partnered sexual activity were primarily guided by living arrangements. While those in cohabiting relationships could potentially continue as before, those who were single or in non-cohabiting relationships generally had restricted opportunities to maintain their sexual relationships. Pornography consumption and digital partners were reported to increase since lockdown. 47 However, online interactions are qualitatively different from in-person interactions and do not provide the same opportunities for physical intimacy.

Recommendations and conclusions

In the sections above we have outlined the ways in which COVID-19 has impacted social relationships, showing how relational mechanisms key to health have been undermined. While some of the damage might well self-repair after the pandemic, there are opportunities inherent in deliberative efforts to build back in ways that facilitate greater resilience in social and community relationships. We conclude by making three recommendations: one regarding public health responses to the pandemic; and two regarding social recovery.

Recommendation 1: explicitly count the relational cost of public health policies to control the pandemic

Effective handling of a pandemic recognises that social, economic and health concerns are intricately interwoven. It is clear that future research and policy attention must focus on the social consequences. As described above, policies which restrict physical mixing across households carry heavy and unequal relational costs. These include for individuals (eg, loss of intimate touch), dyads (eg, loss of warmth, comfort), networks (eg, restricted access to support) and communities (eg, loss of cohesion and identity). Such costs—and their unequal impact—should not be ignored in short-term efforts to control an epidemic. Some public health responses—restrictions on international holiday travel and highly efficient test and trace systems—have relatively small relational costs and should be prioritised. At a national level, an earlier move to proportionate restrictions, and investment in effective test and trace systems, may help prevent escalation of spread to the point where a national lockdown or tight restrictions became an inevitability. Where policies with relational costs are unavoidable, close attention should be paid to the unequal relational impact for those whose personal circumstances differ from normative assumptions of two adult families. This includes consideration of whether expectations are fair (eg, for those who live alone), whether restrictions on social events are equitable across age group, religious/ethnic groupings and social class, and also to ensure that the language promoted by such policies (eg, households; families) is not exclusionary. 48 49 Forethought to unequal impacts on social relationships should thus be integral to the work of epidemic preparedness teams.

Recommendation 2: intelligently balance online and offline ways of relating

A key ingredient for well-being is ‘getting together’ in a physical sense. This is fundamental to a human need for intimate touch, physical comfort, reinforcing interactional norms and providing practical support. Emerging evidence suggests that online ways of relating cannot simply replace physical interactions. But online interaction has many benefits and for some it offers connections that did not exist previously. In particular, online platforms provide new forms of support for those unable to access offline services because of mobility issues (eg, older people) or because they are geographically isolated from their support community (eg, lesbian, gay, bisexual, transgender and queer (LGBTQ) youth). Ultimately, multiple forms of online and offline social interactions are required to meet the needs of varying groups of people (eg, LGBTQ, older people). Future research and practice should aim to establish ways of using offline and online support in complementary and even synergistic ways, rather than veering between them as social restrictions expand and contract. Intelligent balancing of online and offline ways of relating also pertains to future policies on home and flexible working. A decision to switch to wholesale or obligatory homeworking should consider the risk to relational ‘group properties’ of the workplace community and their impact on employees’ well-being, focusing in particular on unequal impacts (eg, new vs established employees). Intelligent blending of online and in-person working is required to achieve flexibility while also nurturing supportive networks at work. Intelligent balance also implies strategies to build digital literacy and minimise digital exclusion, as well as coproducing solutions with intended beneficiaries.

Recommendation 3: build stronger and sustainable localised communities

In balancing offline and online ways of interacting, there is opportunity to capitalise on the potential for more localised, coherent communities due to scaled-down travel, homeworking and local focus that will ideally continue after restrictions end. There are potential economic benefits after the pandemic, such as increased trade as home workers use local resources (eg, coffee shops), but also relational benefits from stronger relationships around the orbit of the home and neighbourhood. Experience from previous crises shows that community volunteer efforts generated early on will wane over time in the absence of deliberate work to maintain them. Adequately funded partnerships between local government, third sector and community groups are required to sustain community assets that began as a direct response to the pandemic. Such partnerships could work to secure green spaces and indoor (non-commercial) meeting spaces that promote community interaction. Green spaces in particular provide a triple benefit in encouraging physical activity and mental health, as well as facilitating social bonding. 50 In building local communities, small community networks—that allow for diversity and break down ingroup/outgroup views—may be more helpful than the concept of ‘support bubbles’, which are exclusionary and less sustainable in the longer term. Rigorously designed intervention and evaluation—taking a systems approach—will be crucial in ensuring scale-up and sustainability.

The dramatic change to social interaction necessitated by efforts to control the spread of COVID-19 created stark challenges but also opportunities. Our essay highlights opportunities for learning, both to ensure the equity and humanity of physical restrictions, and to sustain the salutogenic effects of social relationships going forward. The starting point for capitalising on this learning is recognition of the disruption to relational mechanisms as a key part of the socioeconomic and health impact of the pandemic. In recovery planning, a general rule is that what is good for decreasing health inequalities (such as expanding social protection and public services and pursuing green inclusive growth strategies) 4 will also benefit relationships and safeguard relational mechanisms for future generations. Putting this into action will require political will.

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Twitter @karenmaxSPHSU, @Mark_McCann, @Rwilsonlowe, @KMitchinGlasgow

Contributors EL and KM led on the manuscript conceptualisation, review and editing. SP, KM, CB, RBP, RL, MM, JR, KS and RW-L contributed to drafting and revising the article. All authors assisted in revising the final draft.

Funding The research reported in this publication was supported by the Medical Research Council (MC_UU_00022/1, MC_UU_00022/3) and the Chief Scientist Office (SPHSU11, SPHSU14). EL is also supported by MRC Skills Development Fellowship Award (MR/S015078/1). KS and MM are also supported by a Medical Research Council Strategic Award (MC_PC_13027).

Competing interests None declared.

Provenance and peer review Not commissioned; externally peer reviewed.

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• Published: 30 May 2024

The impact of self-determination theory: the moderating functions of social media (SM) use in education and affective learning engagement

• Uthman Alturki 1 &
• Ahmed Aldraiweesh 1  

Humanities and Social Sciences Communications volume  11 , Article number:  693 ( 2024 ) Cite this article

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• Development studies

This study attempts to explore the relationship between the two mediator variables effective learning engagement and educational social media (SM) usage and the study’s outcome measures, which include student satisfaction and learning performance. The distribution of a self-determination theory questionnaire with external factors to 293 university students served as the primary data collection method. King Saud University used a poll to personally collect data. Partial least squares structural equation modeling was then used to examine the data and assess the model in Smart-PLS. Students’ academic success and contentment at colleges and universities seem to be positively correlated, and their active involvement in learning activities and educational use of SM. It was shown that important factors influencing affective learning participation and the instructional use of SM for teaching and learning include perceived competence, perceived autonomy, perceived relatedness, information sharing, and collaborative learning environments. It was discovered that these connections were important. The self-determination theory provided confirmation that this model is appropriate for fostering students’ feelings of competence, autonomy, and relatedness in order to increase their affective learning involvement. This, in turn, improves students’ satisfaction and achievement in higher education.

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Introduction.

Numerous academics are examining students’ desire to participate in social media (SM) tools and the impact of their use on the educational environment due to the growing popularity of SM and the extensive use of these tools by students in their daily lives. Teachers are keen to grasp the educational importance of SM, even though researchers are still in the investigative stage, trying to gather definitive information about whether or not using SM platforms is suitable (Al-Adwan et al., 2021 ; Al-Rahmi et al., 2022a ). Nonetheless, earlier studies in the literature (Sabah, 2022 ) mainly examined the causes of SM tool acceptance or use and the ways in which important elements influence this kind of educational usage (Sayaf et al., 2022 ; Ullah et al., 2021 ). Educational SM use is the deliberate integration of SM platforms and tools into the classroom to enhance the overall pedagogical experience. This approach considers the contemporary digital landscape and leverages the SM platform’s communicative and collaborative potential to foster students’ active learning, engagement, and knowledge (Al-Rahmi et al., 2022b ; Yin & Yuan, 2021 ). Websites like blogs, wikis, discussion forums, and microblogging sites can be used to promote student participation, peer learning, and cooperative projects (Sayaf et al., 2022 ). Nevertheless, despite the potential benefits of SM use in education, a number of considerations need to be made carefully, including fostering a supportive learning environment and privacy issues (Sayaf et al., 2022 ). Most SM research in Saudi Arabia has concentrated on the motivations behind and actions connected to SM use.

According to Yin and Yuan ( 2021 ) and Sabah ( 2022 ), students’ attitudes toward using SM in a blended learning environment, like Facebook and WhatsApp, as well as their acceptance of using SM for educational purposes, have all been investigated through experimental research in a few studies. Additionally, Facebook and Twitter have been used to support student-centered learning. Previous studies have examined the role that cultural norms play in preserving social relationships on Facebook as well as the risks that excessive Facebook use poses to students’ mental health (Remedios et al., 2017 ). Furthermore, investigations into the potential educational benefits of SM platforms are still underway; numerous studies have evaluated the extent to which SM usage enhances learning (Hameed et al., 2022 ; Nti et al., 2022 ). However, because these studies ignored the particular correlations between the use of SM and learning outcomes, they were unable to deepen our understanding of these explanatory relationships. As a result, the prior literature found inconsistent findings about these platforms’ ability to improve students’ learning outcomes. A few of these studies (Alhussain et al., 2020 ), for example, found that using SM enhanced students’ academic achievement. However, other research has shown that SM use has a negative impact on academic performance (Al-Maatouk et al., 2020 ) and that students’ use of SM reduces their study time and effort. It’s noteworthy that other research has revealed SM platforms might not be advantageous for students.

According to them, these platforms have little to no impact on academic achievement (Al-Rahmi et al., 2022c ) and are improper for improving learning performance (Foroughi et al., 2022 ). The majority of this study (Alamri et al., 2020b ) has not taken into consideration the important function that mediator variables play in examining the relationship between the use of SM and satisfaction as well as learning outcomes. Few studies have explicitly examined the moderator factor (such as cyberstalking) that affects the association between academic performance and SM use (Al-Rahmi et al., 2022d ), and how, while using SM for learning, cyberbullying plays a moderating effect in the relationship between academic accomplishment and cooperative learning in higher education (Alyoussef et al., 2019 ). Understanding how and when SM use might increase or decrease results requires a sophisticated viewpoint because there are more complex links between SM use and its outcomes than merely a positive or negative influence. Actually, learning’s ability to make knowledge widely available, accessible, and flexible is what really distinguishes it from other conventional kinds of education (Han & Shin, 2016 ).

Because of these benefits, SM has drawn a sizable initial user base, particularly in the setting of higher education. In fact, because they typically possess their own mobile devices, college learners are more likely to accept and employ SM learning than students in a classroom (Al-Qaysi et al., 2021 ). Nevertheless, attracting the initial user base is just half the battle for the success of SM sites. How to retain students using SM is a crucial question that many educators and providers of SM technology are posing (Chawinga, 2017 ). While a lot of scholarly research has been done on the acceptance and use of mobile learning, most of these studies have identified the factors that led to its development from a technological standpoint (Pham & Dau, 2022 ). Perceived utility, effort expectations, and performance expectations are some of the factors that affect the early uptake of SM use in higher education (Chugh et al., 2021 ).

It has been demonstrated that increased well-being and intrinsic motivation are significantly impacted by the fulfillment of the three general psychological aims of SDT—autonomy, competence, and relatedness (Ryan & Deci, 2000 ). Garn defines relatedness as the need to feel socially connected in a classroom environment, competence as the process of growing one’s own abilities and skills, and autonomy as the basic need to regulate one’s behavior and organize oneself according to one’s self-awareness (Garn et al., 2019 ). SDT is applied in a variety of settings, including the commercial world, the workplace, and educational institutions. According to Sun et al. ( 2019 ), it is regarded as one of the “most supported by evidence incentive theories” in use today. SDT has several uses in the field of research in education as well. The goal of SDT, a macro-level theory concerning human incentive, is to make clear the relationships that exist between motivation, growth, and well-being.

It emphasizes how important fundamental psychological requirements like relatedness, competence, and autonomy have been. The application of SDT in virtual learning environments has been the subject of numerous research investigations. According to Hsu et al.‘s 2019 study, for instance, satisfying basic mental needs enhanced self-regulated learning, which in turn was associated with higher reported knowledge transfer and greater course-end accomplishment. The significance of students’ continuous self-regulation for effective learning in an online setting was the subject of another study conducted in 2021. It achieved this by explaining the connections between students’ motivation, fundamental psychological requirements, and ongoing desire to participate in online self-regulated learning using SDT (Luo et al., 2021 ). Thus, by applying the SDT principles to create online learning that supports students’ autonomy, competence, and relatedness, educators can improve students’ motivation and learning outcomes through the use of SM (Chiu, 2023 ; Al-Rahmi et al., 2022d ).

SDT can conduct additional studies on SM platforms by examining how intrinsic motivation as well as conduct during learning are influenced by the three fundamental psychological requirements of independence, skill, and connection (Sun et al., 2019 ). Researchers utilizing SDT also looked at how learner motivation, success, and well-being were affected by mobile learning tools (Jeno et al., 2019 ). As learners themselves, teachers make use of a range of educational resources. SDT (Jansen in de Wal et al., 2020 ) states that when a person’s basic psychological needs are met, learning actions and goals also become more obvious.

Early studies on SM and education have shown how important trust and the kind of organization are in fostering knowledge sharing (Singh et al., 2019 ; Ahmad & Huvila, 2019 ). Mutambik et al. ( 2022 ) state that people regard collaborative learning highly. SM’s function in promoting education and information sharing still has advantages and disadvantages because there is a lack of research demonstrating how SM affects information sharing in the field of education. Though self-determination theory (SDT) with affective learning involvement and educational usage of SM factors (Platts, 1972 ; Sørebø et al., 2009 ) can be used to study the basic processes by which student motivation to learn develops and how they affect SM tools, very little research has been done on these topics, especially in the post-adoption phase (Nti et al., 2022 ).

SM in higher education

Lately, lifelong learning in terms of skills has taken precedence over knowledge in higher education (Abdullah Moafa et al., 2018 ).

Cooperation abilities are highly valued by employers, which is how they are in this compilation (Raza et al., 2020 ). Considering the broad meaning of “SM,” it is not surprising that most research has focused on websites such as Facebook, Twitter, and MySpace as educational successes. As stated by Alisaiel et al. (2022c), the broadest definition of active collaborative learning is when two or more people collaborate to study or attempt to learn new. According to Stockdale and Coyne ( 2020 ), SM websites aim to facilitate various tasks for their users, such as sending and receiving emails, adding friends, creating personal profiles, joining groups, creating apps, and finding other users.

Compared to Web 1.0, which was less active and more static, Web 2.0 allows for greater user engagement, collaboration, and personalization (Tajvidi & Karami, 2021 ). It combines active collaborative learning with the use of Facebook, blogs, and YouTube, amongst other platforms, as noted in Al-Rahmi et al. ( 2021a ).

Furthermore, it has historically been challenging for faculty personnel in higher education to communicate with students effectively (Alenazy et al., 2019 ). They have access to SM technologies, which makes it possible to communicate with students quickly and create more engaging lessons. SM is used by students to raise their academic standing. Teachers and students can both use social networks to improve and expedite the teaching and learning process.

King Saud University medical students stated that they used SM for academic studies much less frequently (40%) but that their primary objectives were enjoyment (95.8%), keeping up with the news (88.3%), and socializing (85.5%) (Lahiry et al., 2019 ). A different study found that students with lower GPAs use Facebook more normally than their higher-achieving peers. They explained this by filling out that students often use Facebook as a way to divert attention away from social or academic issues they are having (Michikyan et al., 2015 ).

The impact of SM on students’ academic achievement is a controversial subject. For college students, there have supposedly been conflicting results. Therefore, generalizations on the impact of SM are untrue (Lepp et al., 2014 ). It’s noteworthy to note that more than half of students admitted the negative effects that excessive social media use had on both their personal and academic lives (64.6.1%) and students (77.1%). Of them, 69% claimed that social media prevented them from attending classes. Parallel to this, Able to Encourage (Alwagait et al., 2015 ) examined how SM affected the academic achievement of 108 Saudi students. According to 60% of individuals, using social media excessively interfered with their capacity to function (Sarfraz et al., 2022 ). An additional survey included university students in Ghana who said WhatsApp negatively affected their academic performance. They blamed it on their inability to focus in class and their difficulty juggling their online activities with their homework responsibilities (Sarfraz et al., 2022 ).

However, some studies (Alturki & Aldraiweesh, 2022 ; Capriotti & Zeler, 2023 ) discovered that SM might be used as an instructional tool to promote communication, facilitate cooperative learning, and boost student engagement (Al-Rahmi et al., 2022c ). Through this study, we hope to close a knowledge gap in the literature and provide further insight into the connections between students’ satisfaction with their academic progress and their use of SM.

The current study intends to investigate the moderating effects of affective learning involvement and instructive SM use to facilitate concurrently the students’ satisfaction and learning performance, which were measured by the students’ approval and learning performance, in order to address the aforementioned limitations. This investigation, therefore, has two goals. The first objective is to evaluate the impact of SM use as a teaching tool on students’ enactment and satisfaction.

The second objective is to assess how the connection between student happiness and academic success is influenced by specific mediator characteristics, such as the use of educational SM and emotional learning engagement. Consequently, this study tackles the following essential research questions:

What effects on learning and usage of SM platforms for education do academic students’ needs for SM-used autonomy (perceived competence, perceived autonomy, and perceived relatedness) have? When SM and affective learning engagement are employed as instructional strategies in the classroom, do students’ happiness and academic achievement increase?

Development of research models and hypotheses

Six mini-theories make up the SDT, a larger framework of human inspiration and well-being that explains the connection between motivation and fundamental psychological needs (Ryan & Deci, 2000 ). One of these is the SDT’s Basic Psychological Needs Theory, which promotes motivation and well-being (Ryan & Deci, 2000 ). It has been demonstrated that increased well-being and intrinsic motivation are significantly impacted by the fulfillment of the three general psychological aims of SDT—autonomy, competence, and relatedness (Ryan & Deci, 2000 ). The use of SM tools in education has theoretical ramifications for technology, society, behavior, and education. Learning outcomes during the COVID-19 pandemic, pupil involvement, perceived value, perceived simplicity of use, perceived enjoyment, perception of using SM, intention of behavior to use SM, and using SM for collaborative education are just a few of the variables that were used in the study conducted in Saudi Arabia that is reported by Alismaiel et al. ( 2022b ).

Concentrated on various elements that influence students’ learning outcomes and satisfaction, including information sharing, a collaborative learning environment, affective learning involvement, perceived competence, perceived autonomy, perceived relatedness, and the use of SM in education. Furthermore, a study carried out in Malaysia (Al-Rahmi et al., 2022a ) used some elements from the previous research, including student satisfaction and collaborative learning, but not all of the elements. Furthermore, a study conducted in Saudi Arabia by Alturki and Aldraiweesh ( 2022 ) used a number of variables, such as task-technology fit, perceived utility, perceived ease of use, behavioral intention to use, and actual use of mobile M-learning. On the other hand, concentrated on various elements that influence students’ learning outcomes and satisfaction, including information sharing, a cooperative learning environment, affective learning involvement, perceived competence, perceived autonomy, perceived relatedness, and the use of SM in education. Consequently, Yildiz Durak ( 2019 ) made use of a Turkish study. Social impact, performance expectations, effort expectations, behavior to utilize these devices, and actual use were some of the factors used in this study.

Moreover, concentrated on various elements that influence students’ learning outcomes and satisfaction, including information sharing, a collaborative learning environment, affective learning involvement, perceived competence, perceived autonomy, perceived relatedness, and the use of SM in education. Last but not least, a study conducted in Greece (Troussas et al., 2021 ) used a number of variables, such as social features, social gamification, perceived utility, usage of social networks, perceived ease of use, attitude toward utilizing social networks, and intention to use. Furthermore, concentrated on various elements that influence students’ learning outcomes and satisfaction, including information sharing, a collaborative learning environment, affective learning involvement, perceived competence, perceived autonomy, perceived relatedness, and the use of SM in education.

The research model incorporates exogenous variables of educational use of SM platforms that support endogenous variables of student satisfaction and learning performance impact, as well as mediator variables of affective learning involvement and educational use of SM, as illustrated in Fig. 1 . We developed a model (Fig. 1 ) to better understand how the educational use of SM at King Saud University in Saudi higher education influences the collaborative learning environment, knowledge sharing, affective learning participation, and educational usage of SM. The association between students’ academic achievement, level of satisfaction with their education, and affective engagement when using SM for learning is depicted in Fig. 1 .

Conceptual framework.

Perceived competence

Students must be adept at online learning if they can use the online tool to improve their academic status. Zhao et al. ( 2011 ) claim that the degree to which an ability demand is satisfied affects motivation levels. Pupils who believe they are capable of handling online SRL will have the confidence to manage their own involvement. Affective learning involvement is expected to be linked to this sense of competence. Competency and self-efficacy are related concepts. Self-efficacy, as defined by Hew and Sharifah ( 2017 ), is people’s opinion of their ability to organize and complete tasks to produce desired outcomes (Bandura, 2012 ). In an SM-related scenario, Yang and Brown ( 2015 ) illustrated the relationship between students’ use of SM for education and their social competency. The IS literature has connected computer proficiency to affective learning involvement (Rezvani et al., 2017 ). As per Sørebø et al. ( 2009 ), the supposed ease of use of e-learning technology is associated with its perceived benefits and collaborative nature. We hypothesise:

H1: Affective learning involvement is positively impacted by perceived competence .

H2: The use of SM for education is positively impacted by perceived competency .

Perceived autonomy

Students must be willing to self-regulate their own decisions and learning processes in order to be considered autonomous when utilizing SM in educational settings. Several studies have shown the connection between success and self-governance. According to Bedenlier et al. ( 2020 ), autonomy support has a significant effect on employee satisfaction and business trust in the context of an organization. Adoption of organizational changes is encouraged, according to Trenshaw et al. ( 2016 ), when managers support the changes. Indeed, a sum of studies revealed that autonomy promoted both intrinsic and extrinsic motivation, which had positive outcomes (Williams & Deci, 1996 ). The perceived support from supervisors affects how beneficial and simple a system is viewed to be in the IS domain (Zhang et al., 2015 ). In the framework of e-learning, perceived independence affects perceived value and perceived playfulness (Hew & Sharifah, 2017 ). These theories which maintain that the degree of perceived freedom in SM in classrooms is probably positively correlated with the use of SM for academic goals as well as emotional learning are supported by the findings of the current study. As a result, we recommend the following:

H3: Perceived autonomy has a positive impact on Affective learning involvement .

H4: Perceived autonomy has a positive impact on educational SM use .

Perceived relatedness

SDT had maintained that competence and autonomy were the most important motivators of motivation, despite the fact that relatedness was found to be a substantial antecedent of motivation (Williams & Deci, 1996 ). According to Rogers ( 2017 ), people participate in activities even when they are not especially enjoyable because they need to interact with other people. Put another way, when people feel connected to something, they are more likely to be motivated and invested in it. Numerous research scenarios have supported the association between felt relatedness and motivation (Rogers, 2017 ). According to Luo et al. ( 2021 ), students’ usage of social media for educational purposes is significantly influenced by their peers and acquaintances. Students spend a great deal of time on campuses with their fellow students and exchange modern learning experiences frequently, which may strengthen their both intrinsic and external incentives (Zhang et al., 2015 ). Thus, we can hypothesize that:

H5: Perceived relatedness has a positive impact on Affective learning involvement .

H6: Perceived relatedness has a positive impact on educational SM use .

Information sharing

Through information sharing, people tell one another, their family, and other contacts about various topics. The quality and content of information exchange determine its effectiveness, and these factors have significant practical implications (Muliadi et al., 2022 ). Chang and Chuang ( 2011 ) believe that knowledge is produced through the integration of facts, theory, and observation.

Learning is enhanced when individuals get together in groups and engage with each other, as they exchange experiences and knowledge (Eid & Al-Jabri, 2016 ). Research on content and education in an organizational setting indicates that information sharing through the use of SM tools aids organizational learning. According to Alyouzbaky et al. ( 2022 ), student use of SM improves educational results and academic performance.

Aslam et al. ( 2013 ) examined the impact of information sharing on academic achievement with Khuram, Syed, Ramish, and Aslam. To gauge students’ performance in learning, they employed the cumulative GPA (CGPA) (Alalwan et al., 2019 ). However, they were unable to find any significant relationships between them. Their success was attributed to the use of a relative (norm-referenced) grading scheme such as the CGPA (Alalwan et al., 2019 ). Students find it more difficult to communicate their knowledge in the classroom when there is a higher level of competition due to CGPA (Salimi et al., 2022 ). Examined the relationship between student grades and the volume and caliber of information exchanged. She found that the only relationship between the quantity of information shared and students’ grades (or learning performance) was that there was no association between learning quantity and quality. He and Gunter ( 2015 ) state that among the student learning experiences that comprise the learning performance in our study are discussion, self-reliance, leadership, problem-solving, and creativity. We contend that both student achievement and high-quality education are impacted by knowledge sharing. Consequently, we put up the following theory:

H7 Information sharing has a positive impact on Affective learning involvement .

H8: Information sharing has a positive impact on educational SM use .

Collaborative learning environment

Two or more students working together to accomplish a similar learning objective is known as collaborative learning (CL), as defined by Astherhan & Schwarz (2016) and Islamy et al. ( 2020 ). Constructive student interaction (CL) is highly valued (Johnson & Johnson, 2009 ); students are encouraged to ask questions, offer extensive justifications, exchange counterarguments, generate novel concepts and problem-solving strategies, and so forth. Prior research has demonstrated that a crucial element in determining whether or not students gain from CL is student contact (Al-Rahmi et al., 2020 ). These studies demonstrate the need for supportive surroundings for student interaction as well as the importance of collaborative contact, or the way in which students work together. Research conducted by Alalwan et al. ( 2019 ) and Hamadi et al. ( 2022 ) highlights the importance of student participation throughout task completion. On-task interaction is the term used to describe interaction that is relevant to the current task. It is noteworthy that this encompasses not only cognitive tasks that aid in the group’s achievement of its goals but also social-affective and regulative activities (Qureshi et al., 2021 ). Explaining, summarizing, or having direct conversations about the concepts at hand are examples of cognitive processes (Almusharraf & Bailey, 2021 ). One example of how the instructor is essential to fostering positive student involvement during CL is when they diagnose the group’s progress and step in when necessary (Van Leeuwen et al., 2015 ). (Almusharraf & Bailey, 2021 ). There is a risk of positive student involvement if there is insufficient instructor leadership. When teachers fail to identify problems early on and take the necessary action to address them, the collaborative process may not be as effective as it may be, and learning results may suffer as a result (Van Leeuwen et al., 2015 ). The findings indicate that it can be challenging for educators to guide groups of students engaged in cooperative learning (Van Leeuwen et al., 2015 ). One example is that it requires teachers to have a range of higher-level competencies (De Hei et al., 2016 ). This is because teachers are required to oversee multiple groups at the same time during CL, provide support with task content and cooperation strategies, and assess whether intervention is required and, if so, what kind of intervention is most suitable (De Hei et al., 2016 ). Consequently, we put up the following theory:

H9: Collaborative learning environment has a positive impact on Affective learning involvement .

H10: Collaborative learning environment has a positive impact on educational SM use .

Affective learning involvement

Referred to as “emotional or hedonistic,” affective participation is triggered by affective or intrinsic impulses (Lim & Richardson, 2021 ). Research has already demonstrated that learning engagement positively affects students’ propensity to continue reading on their mobile devices (Wei et al., 2021 ), learning outcomes (Brom et al., 2017 ), and approval of learning management systems (Klobas & McGill, 2010 ). Participation in cognitive learning encompasses the advanced learning stages connected to a particular learning task. High intellectual engagement in the current study’s setting implies that students actively engage in mobile learning activities and digest the content on digital learning platforms. Consequently, sustained use and other benefits are anticipated from strong engagement in cognitive learning (Sørebø et al., 2009 ). Conversely, heightened emotional experiences associated with a specific learning activity are represented by affective learning participation. These emotional states are present when pupils are learning, and they will most likely have an effect on the learning outcomes or progress. The more emotionally engaged students are in their learning, the more likely it is that they will stick with a mobile learning platform. As a matter of fact, Wei et al. ( 2021 ) found that affective involvement positively influences the intention to continue utilizing mobile reading. Based on the evidence that is currently accessible, we can hypothesize that:

H11: Affective learning involvement has a positive impact on students’ satisfaction .

H12: Affective learning involvement has a positive impact on learning performance .

SM use in education

Online communities, or SM platforms, aim to improve communication, teamwork, and content sharing (Alismaiel et al., 2022c ). These platforms act as an open channel of communication in the social life of today, allowing people to meet and share ideas or expertise. According to Al-Rahmi et al. ( 2021b ), Facebook offers many channels that vary in terms of accessibility, engagement, and speed. Posts are accessible to all followers on the network, while private messages are available exclusively to a specific individual. Exactly studies have found that the use of SM sites (such as Facebook, Twitter, and blogs) in the classroom has enhanced effective learning, enriched online interactions, and increased student engagement (Dumford & Miller, 2018 ). Students’ involvement in the SM collaborative environment enhanced their ability to communicate with one another in groups and their performance in group projects (Saini & Abraham, 2019 ). Users can communicate in groups and exchange experiences thanks to the collaborative SM environment (Almulla & Alamri, 2021 ). The environment in question fosters knowledge-sharing behavior and fortifies reciprocal social links among group members, both of which can be explained by the belief in mutual advantages (del Valle et al., 2017 ). Given this, the following hypotheses are proposed:

H13: Educational SM use has a positive impact on Affective learning involvement .

H14: Educational SM use has a positive impact on students’ satisfaction .

H15: Educational SM use has a positive impact on learning performance .

Students’ satisfaction

perceived usefulness and simplicity of use are two variables that, in the user’s opinion, are critical and significant for the deployment of specific technologies and students’ contentment. These qualities are essential because they make it possible to predict the level of happiness that a technology user will experience (Qureshi et al., 2021 ). Performance can be predicted by user experience, as is well known. The pleasure that technology makes available has an impact on user adoption, future performance, and the desire to socialize (Sayaf et al., 2021 ). (Dumpit & Fernandez, 2017 ) suggested that students’ learning pleasure in this setting might be raised through embedded online interaction. Thus, it stands to reason that a student’s degree of satisfaction will likewise affect how well they do. Performance is impacted by the formal education that facilitators, learners, students, and institutions have acquired as a result of reaching their academic objectives (Sayaf et al., 2021 ). Shayan and Iscioglu ( 2017 ) assert that SM still has an impact on students’ performance in scientific fields. Sayaf et al. ( 2022 ) assert that students’ knowledge and learning are supported by the advantages of a fulfilling use of educational technology, such as Facebook and Twitter (Al-Rahmi et al., 2018 ). To go deeper into this subject, they tried to ascertain how Facebook affected students’ academic performance (Al-Samarraie & Saeed, 2018 ). Furthermore, SM use helps to create a positive relationship between users’ pleasure and learning results (Sayaf et al., 2022 ). Given this, the following hypothesis is proposed:

H16: Students’ satisfaction has a positive impact on learning performance .

Learning performance

According to this research, learning show is the degree to which a student participates in progressive learning, which is critical for meeting learning objectives related to gaining new knowledge and skill development over the course of education. Our study’s main focus, student learning performance, is more on the efficacy of the students’ learning process or experience than it is on academic achievement (Ko, 2012 ). SM sites have educational value and can increase students’ motivation and engagement in their studies (Thorne et al., 2009 ). When using these SM platforms for learning, professionals and students alike benefit (S.Patel et al., 2013 ). According to recent studies, SM platforms offer benefits to both professionals and students (Yin & Yuan, 2021 ). Online SM software enhances the learning environment by encouraging more online engagement and debate among students as compared to traditional learning management solutions (Astatke et al., 2021 ). The time and location constraints of traditional face-to-face teaching techniques are eliminated by SNS. As to Alamri et al. ( 2020a ), Facebook provides a basic means of contact and information exchange for students, teachers, and other students. Students who utilized Facebook more regularly for SM did better academically than their peers, according to Sabah ( 2022 ). However, because they used Facebook more for fun than for discussing and sharing course material, Kirschner & Karpinski ( 2010 ) discovered that college students who utilized the SM platform reported lower academic than non-users. The benefits of using SNS for both teaching and learning are not refuted by the findings of Kirschner & Karpinski ( 2010 ). Emphasizing social contact will promote meaningful learning and student innovation, claim Al-Rahmi, Al-Rahmi, et al. (2022). Our study focuses on performance or experience in relation to the use of SNS.

Research methodology

Participants in the study.

Out of the 300 respondents who received the survey, 293 (97.6%) provided insightful answers. As previously mentioned, 300 students were given samples of questionnaires from King Saud University in Saudi Arabia during the summer semester of 2022, which ran from July to August. After a thorough evaluation, it is evident that seven of the surveys had to be removed since they were not comprehensive. The 293 remaining surveys were then imported into IBM SPSS 26. This meant that, excluding the aforementioned scenarios from the sample surveys, there were just 293 questionnaires left for analysis. Hair et al. ( 2012 ) corroborated this phenomenon by stating that outliers have to be disregarded because they frequently yield erroneous statistical conclusions (refer to Table 1 ).

Measurement tools

The content validity of the measurement scales was demonstrated by modifying the construct items from previous studies. The following were modified from (Ryan & Deci, 2000 ; Sørebø et al., 2009 ; Sun et al., 2019 ; Zhao et al., 2011 ): basic demographic information (gender, age, and specialty); questionnaire items measuring perceived competence, perceived autonomy, perceived relatedness, and affective learning involvement (each with five items); questionnaire items measuring information sharing, collaborative learning environments, and educational use of SM (each with five items). 45 questionnaire items measuring 9 factors (affective learning involvement, collaborative learning environment, educational SM use, information sharing, perceived autonomy, perceived competence, perceived relatedness, academic achievement, and students’ satisfaction) were adopted, and five items measuring each factor were adopted from Alamri et al. ( 2020a ) and Hosen et al. ( 2021 ).

Methodology of the questionnaires (survey)

To assess the research hypothesis, our study is quantitative and uses questionnaire methodologies. To assess the components, we modified and contextualized survey questions from previously approved instruments to fit our research strategy. The poll items were scored using a five-point Likert scale, where 1 represents “strongly disagree” and 5 represents “strongly agree.” Convenience sampling was used to collect the data. The survey’s sample consisted of Saudi Arabian undergraduate students enrolled at King Saud University. Among the targeted classes were foundational courses that are necessary for all students at the university, not just those majoring in management (refer to Table 1 ).

Convergent validity has been identified using average variance extraction (AVE), which must be less than 0.500. To assess discriminant validity, the heterotrait-monotrait ratio (HTMT), cross-loading, and the Fornell-Larcker criterion were applied. In the meantime, the dependability of the data was assessed using an internal dependability and consistency approach. Values greater than 0.700 are necessary for two dependability metrics: composite reliability (CR) and Cronbach’s alpha. For the assessment model, we reported the significance of the association using the path coefficients, t -value, and p -value.

The questionnaire was given to each respondent by hand in order to gather feedback on how social media is used to study as well as the responses they gave regarding how it affects their enjoyment and academic achievement. Methods for analyzing data: This study used IBM SPSS and partial least squares structural equation modeling (PLS-SEM) for data analysis. The measurement methods and structural models utilized in this study were assessed using the Smart PLS 3.3 software. The accuracy and dependability of the data were assessed as they were being used to build the measurement model. To assess the validity of the data, we reported both discriminant and convergent validity.

Data analysis and result

Measurement and modeling of structural equations (PLS-SEM) were used in this work to analyze the data. In this work, partial least squares (PLS) and structural equation modeling, or SEM, were used to examine the proposed hypotheses. PLS enables researchers to evaluate the reliability and validity of the model and look for correlations across theoretical constructs (Hair et al., 2019 ). A practical and trustworthy method for managing the reflecting measures and mediating effects is the use of smart-PLS software (Barnes, 2011). Therefore, the linkages in the structural model were assessed using Smart-PLS (3.3.3), particularly for the confirming factor analysis (CFA).

Using SPSS 23, the preliminary descriptive data and correlation were acquired. The parts that follow include more information. Convergent validity: The average variances retrieved (AVEs) from the concepts were used to assess convergent validity. The difference between the variation attributed to measurement errors and the variance represented by indicators of a deep component is shown by AVEs (Chin, 1998 ). Every one of our constructs has an AVE value greater than 0.5 (Table 2 ). This suggests that each of the components in our model has enough construct validity (Ramajesthan et al., 2012; Komiak & Benbasat, 2006 ).

Discriminant validity

To assess the validity of discrimination, the Fornell-Larcker test and the heterotrait-monotrait ratio of associations (HTMT) were used (Fornell & Larcker, 1981 ). The Fornell-Larcker criterion states that the square root of the AVEs for these latent variables and the factor loadings should be compared in order to confirm discriminant validity. For the latent constructs to exhibit enough discriminant validity, their correlation must be less than the square of the root of the AVEs of the latent variables being studied (Ramakrishnan et al., 2012 ). Table 3 shows the link between the latent parameters and their square root. There is less association between the latent factors (non-diagonal elements) and the square base of the AVEs (diagonal elements). This implies that our model’s constructs all have strong discriminant validity. Table 4 shows the loading and crossover for each indication.

All of the constructs had outside loadings that were higher than the other constructs (bold). In contrast to the loadings of its other constructs, such as the Collaborative Learning Environment (0.489), Educational Usage of SM (0.421), Information Sharing (0.542), Learning Performance (0.521), Perceived Competence (0.509), Perceived Relatedness (0.528), and Students’ Satisfaction (0.496), the indicator AFLN_1 within the construct of Affective Learning Involvement obtained the highest loading of 0.803. In order to ascertain the discriminant validity of the correlations, we also assessed their HTMT ratio (Henseler et al., 2015 ) (Table 5 ). If two constructs were 100% trustworthy, the disattenuated correlation—also referred to as the genuine correlation—between them is determined via the HTMT approach.

Composite reliability

We used the composite reliability metric and Cronbach’s alpha to evaluate the dependability of the internal consistency. All of our constructs had composite dependabilities and Cronbach’s alpha values greater than 0.7 (Table 6 ), showing adequate reliability.

The variance inflating factor

We searched for multicollinearity-related issues in the structural model. Table 7 indicates that the predictor constructs’ variance inflation factor, or VIF, levels are all less than five, suggesting that the model is not multicollinear, in accordance with Hair et al. ( 2012 ) and 2019 (Hair et al.). Researchers could calculate the VIF, or variance inflation factor, to assess redundancy and ascertain the level of convergence in the precursor indicators (Hair et al., 2019 ). A VIF score of 5, which denotes that 80% of an indicator’s variation is explained by the remainder of the initial indicators linked to the same construct, is indicative of possible multicollinearity issues in the context of PLS-SEM.

This is a reconsideration of the setup of the formative measurement paradigm. In light of the prior conversation, we propose removing an indicator to reduce multicollinearity issues. If this indicator’s formative measurement approach coefficient (outer weight) does not differ significantly from zero, (1) multicollinearity is very high (as shown by a VIF value of 5 or higher), and (3) the remaining indicators sufficiently capture the domain of the being studied. A test for sophisticated model skills was integrated with basic model testing.

Nevertheless, “the importance of convergence should be noted by announcing the values to change the extension factor before offering details of the main model” (VIF). Interestingly, the collinearity of the indexing was assessed (Hair et al., 2019 ). Collaborative learning environments, the use of SM in education, information sharing, perceived competence, perceived relatedness, and autonomy are the factors that determine emotional learning involvement (Table 7 ). Multiple problems are frequently viewed as having more than three structures; hence, VIF values must be three. As a predictor of affective learning participation, educational SM use had values of 2.074 and 1.940, respectively, according to Table 7 ’s data test results, which indicate that all VIFs are 3. Therefore, the study’s model does not have a collinearity issue.

Model fit analysis

A model fit analysis was performed to show that the proposed model is valid. Table 6 presents evidence that all parameter estimates were higher than the minimal threshold value, hence confirming the appropriateness of the suggested model. Additionally, bootstrap samples were produced using the revised sample data. To test the model fit in PLS-SEM, a total of seven key indicators were used: SRMR, RMSEA, Chi-square, Degrees of Freedom, ChiSqr/dF, NFI, and CFI. Henseler et al. ( 2015 ) and Bentler & Bonett, ( 1980 ) state that the threshold values for SRMR, RMSEA, and NFI are, respectively, less than 0.08 and greater than 0.90. Moreover, the ChiSqr/dF value is, therefore, less than 3.00. Table 8 shows the SRMR and RMSEA indexes, which are 0.056 and 0.068, respectively. Additionally, 0.913 and 0.918, respectively, are the NFI and CFI indices. All seven primary signals for the model fit indices indicated that the model was typically rather well-matched. Table 8 displays the specifics of the model fit and collinearity evaluations.

Assessment model and hypothesis testing

To test our theories, we used PLS. PLS enables the simultaneous evaluation of multiple interdependent relationships. A structural model in PLS illustrates the connections between the theoretical ideas. Using the bootstrapping technique, 500 recommended random samples were generated with SmartPLS (Hair et al., 2019 ). The hypotheses were evaluated using a one-tailed t -test due to their unidirectional nature. The study model’s route evaluation and the variance that each path supplied were assessed by looking at the connections between all of the hypotheses. Every theory was proven correct.

The path coefficient results are shown in Table 9 and Figs. 2 and 3 . A relationship between affective learning involvement and perceived competence (H1 = 0.122, t  = 2.258), perceived autonomy (H3 = 0.485, t  = 8.649), perceived relatedness (H5 = 0167, t  = 2.556), information sharing (H7 = 0.179, t  = 3.854), and collaborative learning environment (H9 = 0183, t  = 2.512) has been found in the first, third, fifth, seventh, and ninth (H1, H3, H5, H7, and H9). The results showed a significant and positive correlation, supporting H1, H3, H5, H7, and H9. Additionally, the hypothesis numbers two, four, six, eight, and ten (H2, H4, H6, H8, and H10) suggest a connection between perceived competence (H2 β = 0.147, t  = 2.339), With educational SM use for teaching and learning, there is a correlation between perceived autonomy (H4 β = 0.137, t  = 2.150), perceived relatedness (H6 β  = 0.168, t  = 2.310), information sharing (H8 β  = 0.208, t  = 3.628), and collaborative learning environment (H10 β  = 0.253, t  = 3.475).

Path coefficient results.

Results for path ( t -values).

The results showed a significant and positive correlation, supporting H2, H4, H6, H8, and H10. Furthermore, the eleventh and twelfth hypotheses (H11 and H12) demonstrate a relationship between students’ happiness on SM platforms (H12 β  = 0.262, t  = 4.076) and affective learning participation (H11 β  = 0.326, t  = 6.367). Consequently, H11 and H12 are endorsed. The thirteenth, fourteenth, and fifteenth hypotheses (H13, H14, and H15) demonstrate a relationship between the use of SM in education and students’ satisfaction (H14 β  = 0.532, t -value = 11.373), learning performance (H15 β  = 0.238, t -value = 3.622), and affective learning involvement (H13 β  = −0.156, t -value = 2.222). The results showed a significant and positive correlation, supporting the three hypotheses (H13, H14, and H15). Lastly, the hypothesis is that the impact of learning performance on student satisfaction is positively and strongly correlated with it (H16 β  = 0.191, t  = 2.560). The results showed a significant and positive correlation; as a result, H16 is validated; refer to Figs. 2 and 3 .

Discussion and implications

The relationships between student satisfaction, academic achievement, and views of competence, autonomy, relatedness, information sharing, collaborative learning settings, affective learning engagement, and SM use in the classroom are clarified by the results of our study. Furthermore, the effective learning environment that is described throughout the educational use of SM may be helpful to users and students.

This makes it easier to switch to depictions of the necessities for education and instruction that are more accurate. The findings and observations demonstrate how using social networks to foster a supportive and upbeat environment is beneficial for learning, teamwork, and information sharing. Furthermore, by enhancing views of competence, independence, and relatedness, it can enhance affective engagement with learning in educational settings. By encouraging student interaction and teamwork, as well as supporting discussion groups and the finishing of assignments or research projects, improves the environment, which has a greater effect on students’ performance (Al-Qaysi et al., 2020 ; Al-Rahmi et al., 2022a ; Al-Rahmi et al., 2022c ).

Additionally, SM is demonstrated by improvements in research skills among teachers and the sharing of ideas among students for use in education. SM is more useful for educational purposes than in-person interactions (Rodriguez-Triana et al., 2020 ). The determination of the education was to determine whether self-determination requirements had an impact on college students’ emotional learning engagement. The study’s conclusions demonstrate that when using a certain SM platform, users are more likely to use it for learning if they receive social influence and encouragement for doing so.

The current study discovered that perceived competence had a substantial impact on affective learning involvement or educational usage of SM in the online learning environment, which is consistent with the results of Chiu ( 2022 ) and Hsu et al. ( 2019 ). According to one theory, college students who possess great self-management abilities are better at acquiring self-discipline than other students and view SM as one of the most crucial tools for self-learning. Learners are inclined to participate in mobile learning when they experience social effects and support from the SM platform they are utilizing for learning.

Moreover, the current study discovered that perceived competence had a substantial impact on affective learning involvement or educational usage of SM in the online learning environment, which is consistent with the results of Chiu ( 2022 ) and Hsu et al. ( 2019 ). According to one theory, college students who possess great self-management abilities are better at acquiring self-discipline than other students and view SM as one of the most crucial tools for self-learning. The current study discovered that attitudes about relatedness, independence, and learning support have a major impact on students’ college affective learning engagement when using educational SM.

According to the hypotheses (H1, H2, H3, H4, H5, and H6), relatedness, competence, and autonomy are related to affective learning participation and with using SM platforms for educational purposes. The hypotheses are validated because the findings indicate a positive and substantial association. The results align with earlier studies (Nikou & Economides, 2017 ; Zhao et al., 2011 ) that examined the impact of relatedness, competence, and autonomy on psychological learning involvement. This demonstrates that college students would use SM in learning contexts more when their education activity was supported by the SDT.

The research findings (H7, H8, H9, and H10) associate the sharing of information and collaborative environments for learning with emotional engagement with learning and instructional use of SM platforms. The results demonstrate that information sharing and interactive classrooms greatly enhance the use of social media for educational purposes, and these two elements are identified as the most important cursor signals for this purpose. This finding’s practical explanation is that students do not view social media platforms as traditional learning environments; rather, they use them for multitasking. As a result, the investigation’s findings confirm previous findings about the correlations between the factors (Yilmaz, 2016; Alalwan et al., 2019 ).

Hypotheses (H11, H12, H13, H14, and H15) that are based on the research’s findings suggest that SM use, students’ pleasure, affective learning involvement, and academic success are positively and significantly correlated. The hypotheses are validated because the findings indicate a positive and substantial association. Table 9 (affective learning participation and educational usage of SM) shows that students’ academic progress and satisfaction are greatly improved by SM use. It’s interesting to notice that student contentment and SM use in the classroom are somewhat more correlated than academic accomplishment.

This is probably due to the fact that students were more aware of the ways in which using SM for teaching and learning could improve their education. The findings also suggest that three factors are critical to students’ academic success: affective learning engagement, SM use for education, and student satisfaction. Furthermore, previous research has demonstrated that provided that the type and appropriateness of the utilization are taken into consideration, system utilization is often associated with gains in learning performance (DeLone & McLean, 2003 ), which provides indirect support for the observed results.

Moreover, the general results align with earlier empirical studies that demonstrate a robust positive correlation between students’ academic performance and their utilization of social media for education (Alamri et al., 2020b , 2020a ; Sobaih et al., 2022 ) and the influence of social media on developing (Al-Rahmi et al., 2021a ; Al-Rahmi et al., 2022d ; Sayaf et al., 2022 ).

In the end, the results clearly validate hypothesis (H16) and show that learning success in educational institutions has a beneficial influence on students’ satisfaction. Hypothesis 16 is validated by the study’s findings, which demonstrate a substantial relationship between students’ happiness and their academic performance in higher education. Conversely, it is more probable to be applied in educational settings if a greater number of students are satisfied with their learning outcomes. Numerous scholars have investigated the significance of students’ contentment in the classroom. Thus, the results of the analysis corroborate previous conclusions on the associations among the variables being studied (Al-Rahmi et al., 2022b ; Alhussain et al., 2020 ). For this reason, this study employs distinct methodologies compared to previous studies.

The study first suggests that there may be a relationship, mediated by educational SM usage, between affective learning participation, self-determination theory (SDT), and SM usage for education. With this information, students may create and implement strategies that promote SM use in the classroom that are both responsible and productive. One way to promote student participation and teamwork in the classroom would be to integrate SM platforms. Secondly, it is imperative to attend to the academic achievement and contentment of pupils.

This is demonstrated by comprehending the role that SDT plays as a mediator between emotional learning and educational SM use. By emphasizing autonomy, competence, and relatedness, students can develop curriculum frameworks and instructional practices that enable self-directed learning (SDT). This entails promoting knowledge exchange and cultivating a cooperative learning atmosphere. This tactic might promote increased drive and participation in the educational process. Thirdly, the findings demonstrate how crucial it is for students to grasp SDT and how different teaching approaches are impacted by this understanding. Through cooperative information exchange, students can acquire the skills necessary to integrate SDT concepts into their instructional strategies.

This could mean offering workshops, seminars, and ongoing support to students in order to provide them with the knowledge and skills needed to create a motivating and supportive learning environment. As a result, the study emphasizes the need to promote effective learning engagement and demonstrates how crucial student emotional engagement is to achieving effective learning outcomes.

Education establishments can focus on creating student-centered learning environments that prioritize the needs of each individual, encourage collaboration, and provide opportunities for self-expression in accordance with SDT principles. By putting these findings into practice, pupils as well as stakeholders can contribute to the development of more interesting and successful teaching strategies that raise student satisfaction and overall learning success. Politicians should consider how SM usage impacts students’ participation in emotional learning and how it is used in the classroom. To promote responsible and educational SM use on college campuses, policies and guidelines can be developed.

This means addressing issues related to digital literacy and privacy in addition to creating a framework that promotes the beneficial integration of SM into the educational process. Compared to earlier research in these areas, which was unable to determine the influence of affective learning engagement on the usage of SM in the classroom, our findings offer a substantial theoretical advance (Alismaiel et al., 2022a ; Ryan & Deci, 2020 ).

Prior to presenting the study’s conclusions, it is important to recognize its limitations. First of all, real-use behavior among college students is not taken into consideration by the model that is suggested in this study. The mentioned limitation should not be construed as negating our findings since other instances of empirical study have substantiated the causal relationship between conduct and intention (Alismaiel et al., 2022a ; Ryan & Deci, 2020 ; Venkatesh & Davis, 2000 ).

However, the accuracy with which they predict outcomes may still vary depending on how different evaluations of intention are made. More research will be possible because of the study model’s incorporation of real-world ongoing activity. A further potential limitation could be found in the diverse educational and cultural settings. For example, Saudi Arabia, a nation that emphasizes collectivism, is where the current study was conducted. There may be various patterns in the effects of SDT demands on students’ motivation and behavior in individualistic societies where people function more freely. It is recommended that future research expand on this paradigm by analyzing the findings in diverse cultural contexts.

Secondly, the study only included students from one Saudi Arabian university who had previously used SM platforms as teaching aids, so the sample size was somewhat limited because only students from those four academic subjects were chosen for participation. Future research could replicate this study at other academic institutions with more participants, but caution should be exercised because the results may not be generalizable to other academic institutions. Lastly, the suggested model was validated using self-reported measures. The truth is that privacy issues prevented the writers from obtaining objective information on GPA (such as student grades).

Therefore, to test the proposed paradigm, future research may employ objective metrics and concentrate on one or more courses. In order to provide a more comprehensive model (such as gender, specialization, and experience), future research may look into a wide range of determinants for SM use predictors (such as instructors and students with peers, ease of use, characteristics, and perceived enjoyment), as well as other mediating factors.

The usage of SM in the classroom and how it impacts students’ engagement with emotional learning have drawn attention in recent years. SM platforms, including SM websites (SNS) like Facebook, Twitter, and Instagram, can be used in a range of educational settings to support cooperative argumentation, equitable participation, and affective learning (Näykki et al., 2019 ). Several significant insights about the roles of SM use in education and emotional learning involvement are as follows:

Both positive and negative effects: Using SM might have an impact on students’ emotional states. Stress, worry, and depression are just a few of the unpleasant emotions that can arise from it, but it can also help learners develop positive emotions and build their emotional repertoire. This aligns with the findings of Chen & Xiao ( 2022 ).

Moderating Roles: The association between SM use in educational settings and learning outcomes can be weakened by task-technology fit (TTF) as well as perceived risk (PR).

If educators and legislators are aware of these moderator factors, they can make more informed decisions on how to include SM in the classroom. This aligns with the findings of Sabah and Altalbe ( 2022 ). SM affordances: SM platforms can offer students a number of advantages, such as better chances for distance learning, enhanced literacy, and extended communication abilities. This aligns with the findings of Chen and Xiao ( 2022 ).

Social comparison theory: Students may experience negative emotions and emotive learning engagement as a result of comparing their experiences and successes on SM, which can lead to a variety of issues. This aligns with the findings of Bui et al. ( 2023 ). SM Integration in Education: Educational institutions may integrate technological advances such as learning games, SM sites, and digital manufacturing into their learning activities to enhance emotional learning involvement. This aligns with the findings of Näykki et al. ( 2019 ).

Both positive and negative effects on emotional learning involvement might result from the use of SM in the classroom. Teachers, legislators, and researchers need to understand SM’s affordances as well as the moderating effects of PR and TTF in order to make well-informed judgments about SM integration in the tutorial room. In the context of digital education, addressing social comparison theory and its potential negative effects on students’ emotional states might help improve affective learning involvement.

The study’s findings are consistent with the idea that factors that promote students’ academic performance and happiness include competence, autonomy, relatedness, information sharing, and a collaborative learning environment. The findings also showed how students’ views of competence, autonomy, and relatedness affected their enjoyment and academic performance, which encouraged affective learning engagement and the instructional use of SM for teaching and learning.

Similarly, the results demonstrated that students’ information-sharing and collaborative learning settings are impacted by the use of SM in the classroom, which in turn influences their satisfaction and academic performance. The results also supported the application of SDT in combination with exogenous variables to investigate students’ emotional learning engagement and the pedagogical advantages of SM instruction to improve students’ happiness and achievement in postsecondary education.

In general, SM promotes students’ academic activities, knowledge sharing, information exchange, and peer communication through information sharing and collaborative learning environments. Subsequent studies using one or more courses and objective measurements may test the proposed model, depending on the restrictions of the research design and the quantitative approach selected. Subsequent studies might look at a variety of antecedents for students using SM for academic purposes.

Data availability

The datasets generated during the analysis of this research were shared in the supplementary files.

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Measuring, analyzing, and evaluating social, environmental, and economic impact is crucial to aligning the sustainable development strategies of international organizations, governments, and businesses. In this sense, society has been a determining factor exerting pressure for urgent solutions. The main objective of this paper is to provide an exhaustive analysis of the literature about the tools for measuring social impact and their evolution over the last 50 years. The search was conducted in the main academic databases (Scopus and Web of Science), where 924 articles were found from 1969 to 2020 related to the topic. The results of the quantitative analysis show that 71% of the publications were in the last ten years and the most productive countries were the USA and the United Kingdom. The relational analysis identifies 4 large clusters that fragment the literature into different subfields. The most used keywords are linked to the term "Social" in measurement methods, new concepts, and participants. This article contributes to the literature by giving the researcher an insight into the current state of art, trends, categories within the field, and future lines of research.

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1 Introduction

Measuring and evaluating social aspects has been gaining importance over the years. There has been a growing interest in knowing the impact that an action, activity, or decision has on society and the environment. The work of Finsterbusch and Wolf ( 1981 ) comments that social impacts are dynamic processes or non-static conditions and therefore must be constantly measured. Becker ( 2001 ) defined social impact as the process of identifying the future consequences of current or proposed action, which are related to individuals, organizations, and social macrosystems. Private organizations seek to ensure that their mission and the impact they generate are consistent (Ormiston et al., 2011 ), and the public sector knows that they are necessary to guide new policies (Reeves, 2016 ). These tools are better known as Social impact assessment (SIA) and play a key role in this environment. A U.S. study presented by the Department of Health, Education, and Welfare (Cohen, 1969 ) showed the shortcomings and lack of methodology for measuring social issues. Later, development economists called for more accurate social indicators to measure the quality and well-being of their citizens' lives (Hicks and Streeten, 1979 ), the gross national product (GNP) was no longer enough as a measure of growth.

Every day, society demands clear answers from world leaders on social, environmental, and equality issues. This pressure is important and is reflected in the increase in sustainability reports (Cubilla-Montilla et al., 2019 ). The need for more precise indicators adapted to the sector will be reflected in better, more accurate, and reliable results (Hutchins et al., 2018 ). In 1976, with the growth of impact measurement issues, a U.S. study sought to classify them by social impact areas (social security, health, labor) (Fry, 2006 ). After 50 years, other methods of measuring social impact have been introduced, proposed mainly by academics and in some cases by international organizations and governments. Public institutions and organizations worldwide have begun to listen to society. It has been reflected in agreements and regulations for SIA as a basis for monitoring their impact. For example, the United States Environmental Protection Agency ( 2017 ) indicates that sustainable manufacturing is based on at least two of these three elements: economic, social, or environmental. Moreover, the European Commission ( 2014 ) proposes methods and defines the benefits of measuring social impact. In this sense, the Horizon 2020 program seeks to meet social challenges, health and welfare, sustainable energy, and other issues related to generating a positive social and environmental impact. Moreover, the 17 sustainable development goals proposed by the ONU ( 2015 ) seek to address different resources involving all sectors to be part of it. Finally, impact measurement is crucial for achieving these goals and creating better methods, information processing, presentation of results, and analyze how they influence decision-making.

This paper is constructed as follows. In section two, we review the theory and previously published reviews on the subject. In section three, we describe the methodology used and the steps to reach the sample to be analyzed. In section four, we will make a descriptive analysis of the collected material and an assessment of the databases used applying quantitative methods. Section five will analyze the relationship between keywords and their behavior over time using the Vosviewer tool (van Eck and Waltman, 2010 ). And finally, by identifying gaps and future lines of research, we hope to encourage other researchers to continue their research in the field of social sustainability.

2 Literature Review

In the '60s Social Indicators (SI) were positioned as tools to measure social impact and gain more prominence to economic indicators (Bauer, 1966 ). The SIA, first through the SI make their appearance (Olson, 1969 ). For a correct and precise interpretation of these actions, several studies arise initially linked to the social welfare of employees and society (Drewnowski, 1972 ). Besides, the focus and relationship with sustainability issues grow (Psacharopoulos and Patrinos, 1994 ). Table 1 identifies some of the main tools of measuring social impact and its main objective.

Over time, its application has been multiple and has gradually become relevant in economic, commercial, and comparative aspects between nations (Thorelli, 1983 ), in national policy planning (Krendel, 1971 ; Press, 2008 ), the impact of tourism (Perdue et al., 1999 ) or to evaluate the management of companies concerning social issues (Gallego, 2006 ).

In the mid-1970s, research specialized and sought more precise information from those involved who were directly affected by their impact. These first questions arose about the SI since there was no confidence in the veracity of their results (Krieger, 1972 ). For this reason, a methodology was created to evaluate the quality of the indicators (Malizia, 1972 ). Over the years, studies were published to continue correcting and improving these methodologies (Drewnowski, 1972 ), to better understand social welfare and thus help formulate policies that favor the social sector through laws and proper planning (Owens, 1976 ).

Previous studies have examined issues related to measuring social impact, the first compiling the theory generated in those years (Fox, 1986 ). They discussed how these tools are a basic necessity to help measure the development of society (Diener and Suh, 1997 ; Hicks and Streeten, 1979 ) and how they can contribute to reporting (Adams and Frost, 2008 ). Other authors have studied the areas where these measurement tools can contribute, both in the Political, Economic, Society, and Ecological (Brouwer and Van Ek, 2004 ). Others examine in-depth how specific organizations behave in the face of a social evaluation (Arvidson and Lyon, 2014 ). They also investigated the implementation of tools for measuring social impact (Umair et al., 2015 ).

Besides, other authors focused their research on creating new methods and tools to measure social impact (Becker and Sanders, 2006 ). Finally, some research was conducted reviewing and evaluating previous developed tools (Malizia, 1972 ; Sieber, 1979 ), warning about their difficulty measuring them and the data quality used in evaluation (Strauss and Thomas, 1996 ).

As shown in Table 2 , the most extensive review is Josa and Aguado, which ends in 2019. We cover a more extended period from 1960 to 2020. We intend to reach a more global view with our review since most of these previous reviews have a more specific focus on a measurement tool. By doing so, we want to discover new growth sectors in social measurement, such as reporting tools.

3 Methodology

To provide guidance and direction for future research on the topic of SIA, the study followed the PRISMA methodology (Moher et al., 2009 ). PRISMA means Preferred Reporting Items for Systematic reviews and Meta-Analyses. It is a methodology that acts as a guard against arbitrary decision-making during review conduct. It serves as a guideline to improve the transparency, accuracy, completeness, and frequency of documented systematic review and meta-analysis protocols (Shamseer et al., 2015 ). This type of analysis allows qualitative and quantitative evaluation for research on a specific topic (Brewerton and Millward, 2001 ). Moreover, it is a clear and transparent process to achieve our objectives, making them reproducible for other researchers (Kitchenham and Charters, 2007 ). This same methodology has recently been used in publications related to sustainability issues (Ferreira Gregorio et al., 2018 ; Merli et al., 2018 ; Niñerola et al., 2020 ) and social indicators (Kühnen and Hahn, 2018 ).

The next section details the steps followed in this review. How the data sources, were chosen, the filtering (Table 3 ), the screening process, and how the final sample was reached (Fig. 1 ).

PRISMA workflow

3.1 3.1 Data Sources and Search Strategy

The main sources of information to carry out the review were the Web of Science (WoS) and the Scopus database. They were chosen due to the extension and impact of their publications in different scientific fields (Falagas et al., 2008 ).

Following previous works (Merli et al., 2018 ), the article search engine was made up of the following keywords: “Impact Measurement” OR “Social Impact Assessment” OR “Social Indicators” OR “Social Return on Investment”.

The initial search was done on title, abstract, author keywords, and keyword plus in WoS. And in title, abstract, and keywords concerning Scopus. The first results in Scopus were 6,980 documents and, in WoS 4,063 documents (last search 20/06/2021). Later, four filters were set: period, language, document type, and research domain. Only academic peer review papers were considered until 2020, written in English and Spanish. Moreover, they should be included in one of the following research areas: Business, Management and Accounting, and Economics, Econometrics and Finance, in Scopus. In WoS, the areas were: Economics, Management, Business, and Business Finance (Table 3 ) .

In addition, the PRISMA workflow (Moher et al., 2009 ) is presented in Fig. 1 showing the searching process agglutinating both databases.

3.2 3.2 Databases’ Comprehensiveness for SIA

The final result of each database reflects that Scopus has 600 unique records in their 817 documents. On the other hand, WoS, on its 324 papers, only accounts for 107 unique records. 217 duplicated documents were found comparing both databases. So, the final sample for analyzing was 924 records (Fig. 2 ).

Material collection process

With the information obtained, both databases are analyzed quantitatively and not by the quality or impact of the documents to see the similarity of the two chosen databases. Three analyses were done: (1) the Meyer Index of Uniqueness (Meyer et al., 1983 ), (2) Traditional Overlap, and (3) Overlap.

3.2.1 Meyer's Index

The result obtained through the Meyer Index, according to Pulgarín and Escalona ( 2007 ) will serve us to evaluate the coverage of a database on a given topic. In Meyer's index, the result is valued over 1, and each database is given a weight of 0.5 for the duplication that may exist (Meyer et al., 1983 ; Sánchez et al., 2017 ). The results of this indicator will show us how unique and singular the documents are.

In formula 2 , relative to Scopus, the results indicate that this database has a uniqueness of 77%. On the other hand, in WoS, only 23% of its documents will be only found there (Formula 3 ).

3.2.2 Traditional Overlap

On the other hand, the Traditional Overlap indicates that the higher the percentage, the grter the similarity between the documents published in both databases (Pulgarín and Escalona, 2007 ). This measure is interesting because it justifies using two databases together by getting a complete picture of the field of study.

The result of formula 5 indicates that 23.48% of the total articles identified for this study are in both databases, which does not show excessive overlap and reaffirms the decision to use both Scopus and WoS to conduct the review.

3.2.3 Overlap

Finally, to show the percentage of participation or coverage that one database has over the other, formula 6 proposed by Bearman and Kunberger ( 1977 ) was applied.

The results indicate that Scopus covers a broad extension of the document source, including 66.98% of WoS publications (Formula 8 ). This value represents only 26,56% in WoS (Formula 7 ).

4 Descriptive Analysis

The following sections will develop our analysis to visualize who, where, and when has written about the topic of study.

4.1 Evolution of the Publications

The first record found is from 1969. From that point on, its growth has gradually increased in the first 40 years. During the last decade, the research topic takes more importance in the academic world, almost 71% of the total publications occur during this period. Besides, the year 2019 marks a peak of more than 93 publications which means 10%. Figure 3 illustrates the trend that the social impact metrics literature has had throughout its 50 years.

Distribution of the literature over time

4.2 4.2 Main Authors

The study identifies more than 2040 authors who have published documents on SIA. However, 1919 authors only have published a single document which not making this field their main field of expertise.

As can be seen in Table 4 , only 4 authors have published 5 or more documents. In terms of productivity, the main author is George Serafiem, with seven publications directly related to the topics analyzed. His work is related to the measurement, management, and communication of corporate sustainability performance, environmental, social, and governance. The seconds, Sanjeev Gupta and Frank Vanclay, each with six publications. Most of Gupta's studies analyze public spending related to education, health, and poverty, areas where social indicators try to measure impact. Frank Vanclay has maintained his research on measuring the effect that the creation and implementation of large projects have on society, e.g. mining.

On the other hand, Table 5 shows the most cited authors noting that they are not the most prolific, except for Serafeim. Serafiem G., Ioannou I., and Cheng B. are the co-authors of the most cited paper in our sample (Cheng et al., 2014 ). It is about the importance of demonstrating that improving stakeholder engagement and transparency of CSR outcomes are essential for reducing capital constraints and improving finance access.

Baker F. and Intagliata J. work, aimed at improving the quality of life of chronic patients, has more than 276 citations. On it, they evaluate the effectiveness of the Community Support System (CSS) program by showing society's concern and the government in making decisions regarding social issues.

4.3 4.3 Geographical Distribution

The diversity found in the authors' affiliation is shown in Fig. 4 . 80 countries have contributed to this topic in these 50 years.

Geographical origin of literature according to the affiliation of the authors (number of articles)

The United States, being the pioneer in the subject, has maintained its interest and its publications reach 237 documents, representing more than 25% of the sample. Obviously, they are placed at the top of the list. The UK and Australia follow them with 106 and 84 documents, respectively.

By geographical area, Brazil is the leading South American country with 26 documents. On the Asian side, China with 17, India with 42, and Russia with 17 are the countries that have contributed most to the study of SIA. Focusing on Europe, it could be said that the countries of the old continent have had more interest in these topics. They have published almost half of the articles.

Finally, it should be noted that the group of emerging economies that make up the so-called BRICs have shown great interest in researching social impact metrics, with a total of 113 publications.

4.4 4.4 Main Sources

Three journals have made the majority of publications. They have as their primary research topics sustainability, environment, and society issues. They seek to promote solutions to current problems through their publications.

The Journal Cleaner Production occupies the first position with 5.63% of the published articles. The Evaluation and Program Planning journal is second with 2.9% of the papers, and the World Development with 2.16% is third. These journals represent 11% of the total of 924 published documents.

As a great variety of authors were found in the previous section, the study also identifies 470 sources that have published at least one article related to the topic.

On the other hand, looking at the number of citations, which can be an indicator of the quality of the publications, the first one continues to be The Journal of Cleaner Production with 960, followed by Evaluation and Program Planning and World Development with 784 and 690 citations respectively (Table 6 ). These three journals have become the main and most important source of information about SIA for researchers. In addition, information on the current situation, ranking, quartile, and impact factor is included. The International Journal of Social Economics is new in the Category of Economics in the JCR. Therefore some information is not available.

5 Relational Analysis

Vosviewer software is a tool that allows us to build and visualize bibliometric maps (van Eck & Waltman, 2010 ). It has become the most widely used and fastest-spreading tool in the scientific world (Pan et al., 2018 ). This section will identify the most used keywords, their level of relationship, the proximity between them, and trends over time. Finally, new participants in the SIA environment and their growth in recent years were discovered, resulting in the creation of other categories.

5.1 5.1 Keywords Analysis

Authors use keywords to identify their work. These keywords give us an idea of the content, topic, or methodology of the article. The study identifies 1,593 different keywords. In Table 7 , the ten most used were detailed. As expected, SI and SIA appeared at the top of the list. Among the main measurement tools, only Social Return on Investment is highly used as a keyword.

A threshold of 10 occurrences and a minimum of 10 connections were established in the study to visualize more clearly the relationships between these main keywords. These values led to the identification of 51 keywords.

The node size shows the number of repetitions (occurrences) of each keyword (Fig. 5 ). On the other hand, the lines represent the number of times the keywords appear together. Moreover, the thickness of this line represents the intensity of this relationship, thicker lines more times appearing together.

Keyword network visualization.

One of the biggest nodes is "social indicators", which occupied first place in Table 7 . It should be emphasized that the word "sustainability" appears very close to "Social Indicators", but that is also related to all the most repeated keywords. Moreover, “sustainability” is next to "corporate social responsibility" which contains terms related to the company and its activities. Another important remark is that "social return on investment" (SROI) appears together in the same cluster that "impact measurement" and they are the ones that have more distance to the "social indicators" keyword.

The software groups the items by color. Each color is a cluster, and each keyword can only belong to one. Keywords in the same cluster indicate that they are strongly related to each other. The clusters found are compiled in Table 8 .

Cluster #1 in red has 16 elements, being the largest group. They can relate to economic, development, and social policy issues, highlighting sustainability issues and methods to measure them. Cluster #2, in green with 13 items, with more generic keywords and related to social and economic indicators that sought to know aspects of the welfare of society.

On the other hand, cluster #3 in blue highlights the terms of measurement and social evaluation, several nodes have a considerable thickness and are very close, both companies, investments, ventures, and entrepreneurs, involved with social terms. Finally, cluster #4 in yellow is located in the middle due to the close relationship with all the clusters. Its primary node "social impact assessment" interacts with various sectors such as business, social, and projects that have a great environmental impact. This node is located in this position, showing us that the interest in knowing the social impact has been the subject of research by various sectors, government, environment, and companies.

On the other hand, to see the evolution of these keywords over time is an interesting second analysis of our sample. Through the VOSviewer, it is possible to see if the keywords appearing in older papers or, on the contrary, their use is more recent with a color scale. Figure 6 shows this information.

Keyword trend visualization

The term "social indicator" mainly was employed in the early decades because it is in dark blue. "Social Indicators" was one of the first keyword used since the 60s and has been gaining interest until the 90s. It had great prominence, especially in publications related to politics and the welfare of society. Related keywords were appearing such as "quality life", "poverty" and "public issues" during the first period.

Following the chronological axis from 2011, in green appear themes related to the environment, new terms to measure the impact, such as "social impact assessment". The private sector is included in this topic by the researchers since their activities are the ones that generate more impact, and they seek to evaluate it through their studies of corporate social responsibility. Another major field of research is sustainability in terms of development, reporting, and evaluation.

Currently, as shown in yellow the keywords associated with "social" dominate the graphic. They seek to evaluate businesses and all the agents involved, the investment, its source, or how it is used. On the other hand, also recognize the entrepreneur and social innovations and the social value that this can generate. So, in the end, two terms stand out, which in turn are the tools that seek to measure the current impact in various sectors, both public and private and with particular emphasis on the social. The first is "social life cycle assessment," whose main objective is to provide information on the life cycle activity of your product that can have a social effect on people (Dreyer et al., 2006 ). The second is "social return on investment," which seeks to understand how the value of an investment can generate a social benefit (Nicholls et al., 2012 ).

5.2 5.2 Categorization by Researched Topics

The study identified the main categories that could cover the most relevant and important trends in the publications and know the behavior of each topic in these 50 years. Sixteen categories were established (Table 9 ) and, through an analysis of the content of the publications, each paper was assigned to one category.

According to the results (Table 10 ), the first three categories agglutinate 31% of the publications. They are the most important and related to creating new methodologies and their application in social issues. It is worth noting that the first six categories represent more than 57%. On the other hand, the last five categories do not represent even 20% of the total, but most of them have been published in the last two decades, so a potential growth may be expected.

5.3 5.3 Trends in Publication

The histogram shown in Fig. 7 (see appendix for more detail) shows the evolution of the main categories in the last decade. This analysis includes the first four categories in Table 10 as the most important because of the number of publications. However, SROI's category was added because 92% of its publications appear in the last eight years, being larger than those published by the 3rd and 4th categories in the same period.

Evolution of main categories.

5.3.1 New Measurement Methods

The first paper contributing to this category was published in 1972. The interest and importance of finding new measurement methods are reflected in the number of publications. It indicates that organizations find tools helpful for better strategic planning (Alireza et al., 2017 ). Other studies have considered sustainability, environmental, economic, and social indicators for product redesign (Lacasa et al., 2016 ). During the first years, it was already considered a complement to better measure the quality of life. In this sense, social and human indicators were taken into account (Hicks and Streeten, 1979 ). The study of Wachs and Kumagai ( 1973 ) regarding accessibility indicators helps to elaborate more coherent policies.

5.3.2 Social Issues

The impact created in society has also brought the attention of researchers, with a total of 87 publications. Almost 58% of them were in the last ten years, reaching the maximum in 2017. Papers in this category are focused on knowing how more specific groups are affected by different situations: racial, labor discrimination (Fryer, 2011 ), regional or world poverty (Aturupane et al., 1994 ; Hall & Patrinos, 2006 ), as well as studies oriented to human and social welfare, mental health (Graham and Nikolova, 2015 ) and how an economic crisis can seriously affect it (Mohseni-Cheraghlou, 2016 ). It is vital to measure the impact of those situations for a guarantee that the most vulnerable groups were not the most affected.

5.3.3 Theoretical

Publications aimed at defining, creating concepts, describing, classifying, or applying and interpreting them are included in this category (Becker, 2001 ). It had its most significant contribution in the first 27 years. Until 1999, it was the category that more public had 46% of its production in the last seven years. In 1973 a study highlighted the importance of social policies and programs and their complexity (Moser, 1973 ). Fedderke and Klitgaard ( 1998 ) show a relationship with economic growth. They said that the private sector could also benefit from applying a measurement method and presenting its impact (Doane, 2005 ). Therefore, it was important to provide a theoretical framework for companies (Costa and Pesci, 2016 ).

5.3.4 High Impact sector

There were not many papers in this category in the first years. In fact, until 1999 only 10% of the paper in this category were published. On the other hand, in the last ten years, this score grows to 72%. 2018 was the year with the highest number of papers focused on this subtopic, 11 articles. Australia, in particular, has focused several studies on marine protected areas (McNeill et al., 2018 ; Voyer et al., 2014 ), considered a high impact sector. Papers regarding the fisheries sector have also been a research area included in this category (Bradshaw et al., 2001 ; Brooks, 2010 ). The mining sector and its influence on social and economic development ((Lagos and Blanco Edgar, 2010 ) or the oil sector (Jacob et al., 2013 ) are other examples of industries where the social impact has incidence.

SROI's category has become important in the last seven years of the study. These years include 82% of the articles. 2015 was the most productive year with 12 publications. If only the last 20 years of the study were taken into account, SROI's category would be one of the first five primary categories, given the high volume of participation. From the beginning, some authors comment that social impact assessment can generate financial and social benefits to the company (Lingane and Olsen, 2004 ). Despite its popularity and acceptance in several sectors as a measurement tool, there are barriers and obstacles in the interpretation and lack of training (Millar and Hall, 2013 ). Other aspects such as financial accounting can provide valuable information that, together with social tools, can generate future change in public policy (Nicholls, 2017 ).

6 Discussion

Measuring social impact has been mostly related to issues of politics, welfare, quality of life, and tourism in the first 20 years. There was a need for studies that, through social indicators, sought to measure the influence of business practices in society (Baker and Intagliata, 1982 ; Hicks and Streeten, 1979 ).

In 2000, 8-millennium goals were launched, and several resources were allocated for their achievement. Environmental issues, social development, and the private sector take centre stage in the publications (Gupta et al., 2002 ). Management and policies were further evaluated (Brouwer and Van Ek, 2004 ). Besides, more initiatives have been added, such as the Sustainable Development Goals, which now aim to achieve 17 goals by 2030 (ONU, 2015 ). These objectives increase research, publications, and their variety. The last five years have been a reflection of this effort to improve sustainable practices. At least 7 of the SDG goals are related to the research topic since they claim tools to measure and evaluate them (Schönherr et al., 2017 ).

The international organizations propose that everyone must necessarily participate in achieving the goals. They design several manuals and guides, all to stimulate collaboration and information flow. Therefore, methodologies and models have been developed to help in this regard (Mota et al., 2015 ). The debate is oriented towards exposing the advantages of measuring social impact (Gibbon and Dey, 2011 ) and formulating a criticism or recommendation to improve the methodology.

Previous studies have already shown that companies with better social performance and better stakeholder engagement are essential for reducing capital constraints (Cheng et al., 2014 ). As well as the use of relevant indicators and appropriate language can improve reporting and increase stakeholder interest (Moore et al., 2003 ). The category of companies and enterprises highlights the importance of company objectives and strategies being aligned with social impact measurement (Ormiston et al., 2011 ). Among the categories, it was also possible to detect that some studied established relationships between countries, or in turn regions or cities by their social indicators (Hashimoto et al., 2009 ), either to measure growth or decline between two or more places.

The work of Rawhouser et al. ( 2019 ) also remarked a recent academic interest in focusing on small companies and entrepreneurs. Despite the review of Kühnen and Hahn ( 2018 ), our results indicate that the three leading journals were related to sustainability, environmental, and societal issues. The same happened in the identification of the study subtopics in the field observed through the cluster analysis. Although the search keywords used were exclusively oriented towards social impact, the other dimensions of sustainability appeared (the triple bottom line). It may suggest that many articles do not address social sustainability isolated or authors use very generic keywords to increase their scope.

Finally, another factor to be highlighted is the tremendous growth of the literature reviews over the last two years. 60% of the documents of this category were published since 2018. The increase shows the importance of analyze previous literature for identifying new fields of research or gaps to cover.

7 Conclusion, Limitations, and Future Research Lines

People often work independently, but today's reality depends on being efficient not only at the individual or business level but on a broader scale. Useful tools when measuring social impact in academia and practice may help to improve the whole environment. In this sense, SIA has become a key tool for many governments and public organizations that must obtain precise results for decision-making and create policies that can benefit their population. They are crucial for maintaining and evaluating whether the progress or measures taken are optimal or should be reformulated.

Some of these measurement tools have become a part of operating or even creating new projects and businesses as new funding sources increasingly require a positive social impact. Many non-profit organizations are choosing to provide loans that help develop the social environment. Little by little, public and private sector, even banks are giving more value on the social impact, although there is still a long way to work on. The pressure from society is reflected in global objectives and commitments that nations are willing to achieve in the short term.

Our objective was to show an overview of the content published in the last 50 years on metrics for measuring social impact. The study shows that these metrics produce a bulk of literature not exclusively focused on social practices or implications. The authors chose very generic keywords, which difficult to identify the important documents in the field. This represents a limitation of conducting a study based on keywords searched. Moreover, the categorization has been conducted, giving priority to the main objectives of the article. Some papers could be included in other categories, but the most relevant was chosen by reading the introduction and conclusions.

It should be highlighted that despite "Financing and Investment" and "Reports and tools for measurement" categories do not represent a significant number of our sample papers, they are growing in the last few years. There is an active participation of academics and practitioners in projects considering the objectives included in those categories. Therefore, it is expected that they will continue capturing the attention of researchers as it is essential to disseminate results that include social metrics and to attract financing.

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Alomoto, W., Niñerola, A. & Pié, L. Social Impact Assessment: A Systematic Review of Literature. Soc Indic Res 161 , 225–250 (2022). https://doi.org/10.1007/s11205-021-02809-1

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DOI : https://doi.org/10.1007/s11205-021-02809-1

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Understanding Human Behavior through the Lens of Albert Bandura’s Social Learning Theory

This essay about Albert Bandura’s Social Learning Theory explains how human behavior is learned through observing and imitating others. It highlights key processes such as attention, retention, reproduction, and motivation. The essay discusses the impact of role models, vicarious reinforcement, and self-efficacy on behavior. It also explores applications in education, therapy, and moral development, while addressing criticisms and the concept of reciprocal determinism.

How it works

Understanding human behavior is a complex and multifaceted endeavor that has captivated scholars for centuries. One of the most influential theories in this domain is Albert Bandura’s Social Learning Theory. This theory posits that human behavior is learned through the observation of others, a process known as observational learning or modeling. Bandura’s theory diverges from traditional behavioral theories, which primarily emphasize direct reinforcement and punishment, by highlighting the significance of social influences and cognitive processes in learning.

At the core of Social Learning Theory is the concept that individuals, especially children, learn by watching others and imitating their actions.

This observational learning involves four key processes: attention, retention, reproduction, and motivation. Attention is the first step, where the observer must pay attention to the model. The more interesting or relevant the model’s behavior, the more likely it is to grab the observer’s attention. Retention involves remembering the observed behavior. This process is influenced by the observer’s ability to encode the behavior into memory and retain it for future use. Reproduction is the ability to replicate the behavior that was observed. This process depends on the observer’s physical and mental capabilities. Finally, motivation determines whether the observer has a reason to imitate the behavior, which is influenced by the anticipated consequences of the behavior, such as rewards or punishments.

Bandura’s famous Bobo doll experiment in 1961 provided empirical support for Social Learning Theory. In this study, children observed adults interacting with a Bobo doll, either aggressively or non-aggressively. The children were then given the opportunity to play with the doll themselves. Those who had observed aggressive behavior were more likely to act aggressively toward the doll, demonstrating that children can learn and imitate behaviors simply by watching others, without any direct reinforcement or punishment.

One of the critical aspects of Social Learning Theory is the idea of vicarious reinforcement and punishment. Vicarious reinforcement occurs when an individual observes another person being rewarded for a behavior, increasing the likelihood that the observer will imitate that behavior. Conversely, vicarious punishment occurs when an individual observes another person being punished for a behavior, decreasing the likelihood that the observer will imitate that behavior. This concept explains why people can learn from the experiences of others without directly experiencing the consequences themselves.

Bandura also introduced the notion of self-efficacy, which refers to an individual’s belief in their ability to succeed in specific situations. High self-efficacy can enhance learning and performance, as individuals are more likely to engage in tasks where they feel confident in their abilities. Conversely, low self-efficacy can hinder learning and performance, as individuals may avoid tasks where they feel incompetent. Self-efficacy is shaped by various factors, including personal experiences, social modeling, verbal persuasion, and physiological states.

Social Learning Theory has profound implications for understanding various aspects of human behavior, including aggression, prosocial behavior, and moral development. For instance, the theory helps explain how exposure to violent media can increase aggressive behavior in children and adolescents. By observing aggressive models in media, children may learn that aggression is an acceptable way to resolve conflicts and achieve goals. Conversely, exposure to prosocial models can promote altruistic and cooperative behavior.

In educational settings, Social Learning Theory underscores the importance of role models and collaborative learning. Teachers and parents serve as primary models for children, and their behavior can significantly influence students’ attitudes and actions. By demonstrating positive behaviors and reinforcing desirable actions, educators can foster a conducive learning environment. Additionally, peer modeling can be an effective strategy in classrooms, where students learn from observing and interacting with their classmates.

The theory also has applications in therapy and behavior modification. In cognitive-behavioral therapy (CBT), for example, therapists use modeling to teach clients new skills and behaviors. Clients learn by observing the therapist or others, practicing the behaviors, and receiving feedback. This process helps clients develop new coping mechanisms and change maladaptive behaviors. Similarly, in organizational settings, modeling is used in training programs to teach employees new skills and improve performance.

Bandura’s Social Learning Theory also provides insights into the development of moral behavior and ethical decision-making. Through observing the actions of others, individuals learn about societal norms, values, and ethical principles. Parents, teachers, and other authority figures play a crucial role in modeling moral behavior and shaping the moral development of children. By consistently demonstrating ethical behavior and reinforcing moral values, they can instill a strong sense of morality in the younger generation.

Critics of Social Learning Theory argue that it may oversimplify the complexities of human behavior by focusing predominantly on observational learning and neglecting other factors, such as genetic influences and innate predispositions. However, Bandura acknowledged that behavior is the result of a dynamic interplay between personal, behavioral, and environmental factors, a concept known as reciprocal determinism. This perspective recognizes that while individuals are influenced by their environment, they also have the capacity to shape and alter their environment through their actions.

In conclusion, Albert Bandura’s Social Learning Theory offers a comprehensive framework for understanding human behavior through the lens of observational learning. By emphasizing the role of social influences and cognitive processes, the theory provides valuable insights into how individuals acquire new behaviors, develop self-efficacy, and navigate the complexities of social interactions. Whether in educational settings, therapeutic contexts, or everyday life, the principles of Social Learning Theory continue to inform practices and strategies for promoting positive behavior change and fostering human development.

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SEEDS OF CHANGE: EXPLORING THE POTENTIAL FOR GREENER SCHOOLYARDS IN INDIANAPOLIS

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Seeds of Change: Exploring the Potential for Greener Schoolyards in Indianapolis

Existing literature suggests that having gardens and trees in schoolyards has proven to be positive for student health. Tree canopies in schoolyards provide shade, mitigate urban heat island effects, reduce air, and noise pollution, and even improve mental well-being. Edible schoolyards can enhance children’s hands-on learning experience, foster stronger environmental stewardship, offer fresh food opportunities, and help develop healthier eating habits. However, the implementation of green and edible schoolyards in Indiana remains relatively low. This thesis aims to explore the current tree canopy and garden coverage within the school grounds to understand how these green spaces correlate with demographic factors such as race, income, and population density, aiming to identify potential inequities in the school environment creation. Moreover, it gathers green feature coverage data and staff perspectives to further investigate the potential of expanding different edible green features in schoolyards of the Indianapolis region.

This study included 167 public schoolyards in the research process. Geospatial data analytic and social science methods were utilized in this research. First, ArcGIS was used to analyze the spatial distribution pattern of school Tree Canopy Coverage (TCC) and garden existence. We also examined the relationships between TCC and garden existences to other demographic factors using R language to understand impact criteria and summarize future hurdles and opportunities. In the second method, online surveys were distributed to the same schools to understand the attitudes of school staff towards edible schoolyards. Some preliminary challenges were identified with the 35 responses collected, including funding mechanisms, collaboration limitations, and lack of integration into curriculums to allow valuable education. This research concludes with 2 case studies to represent two common typologies of schoolyards in Indianapolis, using interviews to gain a deeper understanding of further concerns and future working directions for green schoolyard advocates.

Degree Type

• Master of Science
• Horticulture

Campus location

• West Lafayette

Advisor/Supervisor/Committee Chair

Additional committee member 2, additional committee member 3, additional committee member 4, usage metrics.

• Landscape architecture

Using Infrastructure Gaps as Social Sensors for Informing Equity Aims in Policymaking

 My dissertation work aims to assess the feasibility of using established gaps in equity due to infrastructure provision as a mechanism for , rather than a byproduct of , infrastructure investment policy to address issues of social equity created and perpetuated by infrastructure systems. To explore this, I start by assessing the potential of using large-scale infrastructure networks as social sensors to detect aspects of inequity to better inform investment policy. I focus my exploration on broadband infrastructure to begin with, exploring the possibility of using U.S. county-level broadband penetration rates as a social sensor to predict rates of unemployment amidst the COVID-19 pandemic (Chapter 2). This work specifically asks, “How can infrastructure serve as a social sensor that allows for sharper detection of those groups which are most vulnerable to disruption?”. I find that broadband can serve as an effective social sensor which is sharpened when applied to employment contexts relevant to broadband, but, as with any sensor, is prone to error (either false positives or false negatives). I then shift my interest from the macro-system to a more micro-focused approach of how to incorporate preferences from end-users into the investment process. To do this, I develop an innovative approach to incorporating qualitative interview responses into a multi-criteria decision-making process (Chapter 3). I find that hauled system water users in Alaska have a strong preference for the aesthetic properties of their water provision which they balance against the need for reliable water system delivery. I end my investigation by understanding the role that skills play as a sensor for detecting effective and equitable use of infrastructure. To do this, I explore broadband connectivity throughout Rwanda and its impact on a critical aspect of development, public health (Chapter 4). To explore this question, I ask to what degree are wireless biomedical devices (specifically EKGs) available and used in the public hospital system in Rwanda? And what impact does broadband access have on the kinds of services which are offered? I find that the first tier of the digital divide influences the ability of offer telehealth services and propose additional future work on the compounded impacts of this access on both second and their tier access.  

Degree Type

• Dissertation
• Engineering and Public Policy

Degree Name

• Doctor of Philosophy (PhD)

Usage metrics

• Engineering not elsewhere classified

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The Effect of Anticipated Embarrassment on Consumer Preference for Using Chatbots

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