research support for informational social influence

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1 Introduction and Overview

Department of Psychology, Northeastern University

Psychological Sciences, Purdue University

• Published: 05 December 2016
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The study of social influence has been central to social psychology since its inception. In fact, research on social influence began in the 1880s, predating the coining of the term social psychology. However, by the mid-1980s, interest in this area had waned. Now the pendulum is swinging back, as seen in growing interest in non-cognitive, motivational accounts. Our hope is that the publication of this volume will aid this movement. The chapters, written by leading scholars, cover a variety of topics in social influence, incorporating a range of levels of analysis (intrapersonal, interpersonal, and intragroup) and both source and target effects. The book also includes chapters on theories that are most relevant to social influence, as well as a set of chapters on social influence in applied settings. Finally, we include a section that considers the future of social influence in social psychology.

Social influence lies at the heart of social psychology. In fact, in his classic Handbook chapter, E. E. Jones (1985) noted that social psychology “can almost be defined as the study of social influence” (1985, p. 79). If anything, this is an understatement, as is shown by a comparison of their definitions. Social influence has been defined as the process “wherein one person’s attitudes, cognitions, or behaviors are changed through the doings of another” ( Cialdini & Griskevicius, 2010 , p. 385) and as “the myriad ways that people impact one another, including changes in attitudes, beliefs, feelings and behavior, that result from the comments, actions, or even the mere presence of others” ( Gilovich, Keltner, & Nisbett, 2011 , p. 276). Social psychology has been defined as “ an attempt to understand and explain how the thought, feeling, and behavior of individuals are influenced by the actual, imagined, or implied presence of other human beings ” ( Allport, 1954 , p. 3, italics in original).

The fact that the first experimental work in what would come to be termed “social psychology” ( Allport, 1924 ) was conducted on topics that would later be placed under the rubric of social influence also demonstrates the crucial role that it has played in the history of social psychology. For example, Binet and Henri’s (1894 , as cited by Prislin & Crano, 2012 ) research on suggestibility prefigured Asch’s (1951) work on conformity. Féré (1887 , as cited by Prislin & Crano, 2012 ) and Triplett (1898) examined a phenomenon that Allport (1924) would later term “social facilitation.” In the 1880s, Ringelmann conducted research on the effect of working with others (published in 1913, as cited by Kravitz & Martin, 1986 ), which was later extended in work on social loafing ( Latané, Williams, & Harkins, 1979 ).

Given the centrality of social influence in the field, it would be no exaggeration to say that virtually any topic in social psychology, aside, perhaps, from hard-core social cognition, could be included in a handbook of social influence. Certainly there are some topics that form the core of social influence, such as conformity (e.g., Asch, 1951 ), compliance (e.g., Freedman & Fraser, 1966 ), and obedience (e.g., Milgram, 1963 ) that would be included in any such volume. Work in these areas exhibits many of the hallmarks of research on social influence: a high degree of experimental realism; behavioral measures; and real-world settings. As can be seen from the dates of these citations, work in these areas predates the cognitive revolution, and interest in these areas continues to the present day, even in the popular media, as is suggested by the recent release of two films, The Experimenter (2015), which depicts Milgram’s obedience research, and The Experiment (2015), which portrays Zimbardo’s prison simulation.

On the other hand, there are other topics that likely would not be included, even though they obviously involve social influence. For example, persuasion clearly represents a means of social influence, but it is typically not included under the rubric of social influence. In fact, research on social influence and attitudes is not even published in the same subsections of the top journal of the field, the Journal of Personality and Social Psychology . Research on persuasion is published in the Attitudes and Social Cognition section, whereas research on social influence is published in Interpersonal Relations and Group Processes .

Prislin and Crano (2012) argue that in the early part of the 20th century, likely as a result of the theoretical influence of people like Dewey (1922/1930) and Mead (1934) , attitudes were thought of in terms of group-shared norms. However, they argue that this orientation ran afoul of efforts by psychologists such as Floyd Allport ( 1919 , 1924 ) to “establish social psychology as a science that uses the individual as a unit of analysis” (p. 332), and “consequently, attitudes were defined in strictly individualistic terms without reference to social influence” (p. 333). Prislin and Crano argue that the domination of the field for the past 50 years by the cognitive orientation has reinforced this divorce of attitudes from social influence.

In addition, persuasion research also tends to use paper-and-pencil paradigms (or, more appropriately in this era, computer-presented paradigms) in laboratories that allow for the measurement of intervening processes. These measures can then be used to assess mediation, the testing of which has been de rigeur in social psychology for the last several decades. Field studies, however, which account for many studies in social influence, are not amenable to the use of intervening, intrusive questionnaires aimed at assessing possible mediation. In this sense, classic social influence methods have fallen out of favor in our field, possibly further separating persuasion research and theory from social influence research and theory.

Although these arguments have merit, there may be a simpler explanation. In his 1935 Handbook chapter, Gordon Allport noted that “the concept of attitude is probably the most distinctive and indispensable concept in contemporary American social psychology” (p. 798), and, in the 1954 Handbook , he noted that the construct of attitude is the “primary building stone in the edifice of social psychology” (p. 63). Perhaps the sheer volume of work on attitudes and its centrality to the field require that it stand alone. Certainly it would be hard to argue that persuasion is not a form of social influence.

In any event, in the current volume we follow historical precedent and devote chapters to topics such as conformity, compliance, and obedience, but we will not include a chapter on persuasion. In fact, consistent with the earlier argument, a treatment of attitudes and attitude change would easily be worth a volume of its own. For the rest of the chapters, we have selected a range of topics that fall under the rubric of social influence. Unlike some reviews that include only research that focuses on the target of social influence attempts (e.g., Cialdini & Trost, 1998 ), we also include some chapters that focus on influence sources—their motivation and differential effectiveness. The chapters incorporate a range of levels of analysis: intrapersonal, interpersonal, and intragroup. We include two chapters that cover the theories most relevant to social influence effects: social comparison and social identity. We have also included a set of chapters that examine social influence effects in applied settings. Finally, we include a section that considers the future of social influence in social psychology.

We begin with a chapter that covers ethical issues in social influence research from Milgram’s work in the 1960s, the conduct of which, at the least, served to increase concern about ethical behavior in research, through current work conducted on the Internet (Kimmel). We then move to a pair of chapters on Intrapersonal Processes , one on gender and one on personality. In her chapter, Carli compares females and males both as sources and targets of influence. However, as will be seen, although gender differences in influenceability were reported in the 1980s (e.g., Eagly & Carli, 1981 ), current research shows little evidence of overall differences. The same is not true for gender differences in source effects: Women are less effective sources of social influence than men. Carli provides evidence for this claim and suggests reasons for it.

Nezlek and Smith’s chapter is devoted to the intersection of personality and individual differences with research and theory on social influence. Strangely, the two domains have not enjoyed much overlap; so in addition to covering the existing research in which researchers ask what traits are associated with influence and influenceability, Nezlek and Smith speculate as to why the two areas are not highly interwoven, and they implore future researchers to consider their mutual interaction.

In the next section of the volume we look at social influence as it is reflected in interpersonal processes . We begin with a chapter in which Suls and Wheeler review the development of social comparison theory. We do not mean to suggest that social comparison theory ( Festinger, 1954 ) is directly relevant to all of the chapters in this section, but it was a dominant theory in the period of time in which much of the work in this section was begun, and it helped to shape the thinking in this period. For example, the distinction between normative and informational influence in conformity that was made by Deutsch and Gerard (1955) echoes the two aims of social comparison: self-enhancement and self-evaluation. Normative influence stems from our concern about how we appear to others. People want their opinions, beliefs, and attributes to reflect positively on themselves (self-enhancement), but they are also motivated to learn the truth, as is reflected by the effect of informational influence (self-evaluation).

In the next chapters in this section, we cover conformity, compliance, and obedience. We present these topics in this order because this organization reflects an underlying dimension of social control that begins with behavior that is produced by the simple observation of others engaging in the target behavior (conformity), then goes to behavior that is produced by requests (compliance), and ends with behavior that is produced by the orders or demands of someone in authority (obedience).

In his chapter, Hodges describes conformity as a crucial dimension of culture and of human survival. Without it, we would not have survived. However, Hodges argues that equally important is the fact that people have a tendency to diverge. Hodges proposes that traditional experimental social psychology has focused on the former without giving enough weight to the latter. Hodges also argues that experimental social psychology has focused too much on the molecular level of behavior without situating this behavior in the larger cultural context. In his chapter, he incorporates work from anthropology, as well as social, developmental, and cognitive psychology, arguing for a broader view of the complex interplay of conformity and divergence.

In Guadagno’s chapter on compliance, she focuses primarily on how social influence tactics influence others to change their behavior. To many, this focus comprises the core of social influence—a preponderance of field studies in which individuals buy products, commit time, sign petitions, erect posters, or vote, for things, people, and ideas that they were unlikely to buy or endorse before. Relying on Cialdini’s (2009) six principles of influence—reciprocity, commitment and consistency, authority, social validation or social proof, and liking and similarity—she organizes her chapter around an examination of the extant research on these six principles, their underlying mechanisms, especially the mindlessness hypothesis, and their application to influence within the newest realm of social influence: social media.

In his chapter, Burger reviews research on how individuals respond to orders or demands from a person or institution in a position of authority. Although there has been some other work, for more than 50 years, research and discussion in this area have been dominated by Milgram’s work on destructive obedience. In fact, it would be hard to find any other topic in social influence in which a single program of research has exerted so much influence on the work on that topic dating from its inception to the present. This is particularly surprising given that Milgram’s interpretation of his own findings did not find wide acceptance even at the time that it was proposed. Burger provides an account of the development of this area of research and provides suggestions as to how it can move beyond Milgram’s pioneering work.

In the next chapter, Nolan discusses social norms as a source of social influence. Although social norms and conformity are closely related topics, there is a key difference. In his chapter on conformity, Hodges notes that conformity is nearly always understood in terms of matching one’s behavior with that of others because one observed others engaging in that behavior. In her chapter, Nolan defines social norms as “rules and standards that are understood by members of a group, and that guide morally relevant social behavior by way of social sanctions, instead of the force of laws.” Thus, although closely related to conformity, social norms can operate without direct observation of others engaging in the target behavior. In her chapter, Nolan argues that her perspective represents a new way of looking at social norms that emphasizes norm enforcement as an essential component of understanding and defining a behavior as normative. Thus, in addition to the typical review of the literature on social norms as sources of social influence, the chapter discusses how and when individuals enforce norms. Nolan also discusses methods of maximizing the effects of social norm interventions, paying particular attention to combining descriptive and injunctive norms, reference groups, personal relevance, and cognitive resources.

McCarty and Karau define social inhibition as the tendency for behaviors that are typically exhibited to be minimized in the presence of others. In some ways, social inhibition is similar to conformity, in that there need be no intention to influence on the part of the sources: simple awareness of (or belief in) their presence is sufficient to lead to the cessation of the target’s behavior. McCarty and Karau argue that, despite its long history, research on social inhibition does not form a cohesive literature. In their chapter, they address this issue by integrating research from several different traditions, including helping behavior, emotional expression, and behaviors that elicit social disapproval. They go on to discuss moderators and mediators of these effects (e.g., arousal, ambiguity, pluralistic ignorance, diffusion of responsibility, feelings of capability, evaluation apprehension, and confusion of responsibility), and to distinguish social inhibition from other related concepts. By so doing, they hope to facilitate the integration of future research on this topic.

In their chapter on social facilitation, Seitchik, Brown, and Harkins note that research conducted for more than a century has shown that the presence of others improves performance on simple tasks and debilitates it on complex tasks, whether these others are audience members or coactors. In their chapter, Seitchik et al. review theories offered to account for how two features of these others, their mere presence and/or the potential for evaluation they represent, produce these effects, and they conclude that we are no closer now to isolating the relevant process(es) than we were 100 years ago. They then consider the molecular task analysis proposed by Harkins (2006) as an approach to attacking this problem, followed by a review of the work testing the mere effort account suggested by this analysis. Finally, they place the mere effort account in the larger context represented by the threat-induced potentiation of prepotent responses model, which aims to account for the effect of threat on task performance. Seitchik et al. argue that this approach shows great promise, having the potential to integrate the different lines of research that focus on motivated task performance, such as social loafing, goal setting, intrinsic motivation/creativity, achievement goal theory, social facilitation, and stereotype threat.

Hales, Ren, and Williams’s chapter examines the universal phenomenon of ostracism—ignoring and excluding—and how it works to guide and influence behavior. A relative newcomer on the social influence research block, this chapter summarizes over 20 years of social psychological research that demonstrates ostracism’s power. The authors argue that ostracism—or the threat of it—serves to account for the potency of normative influence. Targets of ostracism experience pain, threatened fundamental needs, and worsened mood. As a consequence, they think and act in ways to fortify their threatened needs, including going along to get along, lashing out, and seeking solitude. The authors also review the research on what motivates sources to use ostracism, as well as examining the impact of ostracizing on the sources who use it. The sources’ motives are tied to the functions they think ostracism serves. The authors propose three functions of ostracism: (1) to protect —shielding groups from threatening members; (2) to correct —signaling to individuals that their behavior needs to be modified to remain in the group; and (3) to eject —permanently removing deviant individuals who resist correction.

In a chapter that focuses on source effects, Tyler and Adams consider self-presentation as a social influence tactic. They suggest that past research has characterized people’s self-presentations as resulting from conscious and deliberate strategic efforts to influence the impressions that others form of them, and note that this approach results in both theoretical and empirical problems. For example, following this approach has limited the research designs that have been used, thereby ignoring the issue of context cuing. Tyler and Adams argue that the results of the few studies that have examined automatic self-presentation are promising, and they provide the foundation for future work.

In his chapter, Van Kleef examines the interpersonal, rather than the intrapersonal, effects of emotional expression. That is, in contrast to the existing models that focus on the effects that a person’s emotions has on him/herself, Van Kleef’s theory (emotions as agents of social influence theory) focuses on the effects that one person’s emotional expressions has on another person’s attitudes, cognitions, and/or behavior. Van Kleef proposes a dual process model, which suggests that emotional expressions can exert social influence through an inferential process or through affective reactions. The inferential pathway is used to the extent that the target is motivated and able to engage in thorough information processing and perceives the emotional expression to be appropriate. To the extent that the target’s ability or motivation to process the information is reduced and s/he perceives the emotional expression as inappropriate, the affective pathway is used. Van Kleef goes on to discuss differences and commonalities between this interpersonal framework and the more traditional intrapersonal approach.

In the next section of the volume we look at social influence processes as an intragroup process . In their chapter on social identity/self-categorization processes, Gaffney and Hogg note that, traditionally, social psychologists have examined social influence processes at an interpersonal level, focusing on the relationship between the individual target and the individual source. However, influence can occur within a group context as well; as a result, accounting for group membership and group identification allows for a more comprehensive understanding of social influence. As was the case for social comparison theory, we do not mean to suggest that social identity/self-categorization is directly relevant to all of the chapters in this section, but it has been highly influential since the early 1990s. In their chapter, adopting a social identity/social categorization perspective, Gaffney and Hogg provide an overview of group-based motivations for influence, including leadership, minority group influence, and political and social movements as examples of social influence taking place in a group context. In later chapters in this section, some of these topics are examined in greater detail (e.g., leadership, minority influence). The Gaffney and Hogg chapter provides an overall context into which these treatments can be placed.

In his chapter on deindividuation, Spears argues that this phenomenon occupies a central place in social influence’s pantheon. However, he notes that deindividuation theory’s central premise that people lose their sense of self in the group, becoming more likely to engage in mindless aggression, has not received consistent support over the years. In his chapter, Spears makes the case for a social identity account of crowd behavior—that the individual self gives way to the social self; but he also points out that techniques for tapping unconscious states and processes that are now available also provide the opportunity to test the original predictions in more compelling ways than were possible when the original research was conducted.

Hornsey and Jetten, in their chapter on stability and change within groups, examine the psychological tension produced by defending the status quo within groups and engaging in intragroup change. They begin with the same foundational research on conformity covered elsewhere in this volume (e.g., Hodges), but they extend their analysis to the processes at work in interacting groups. They then go on to provide a counterpoint to the analysis that emphasizes pressures toward stability by showing that intragroup change and reform is also an integral part of group life. Finally, they examine the process of change within the group: who is most likely to seek it; who is more effective at seeking it; and what strategies are most effective in producing it.

The next chapter, by Butera, Falomir-Pichastor, Mugny, and Quiamzade, also covers minority influence, but it sets out to explain the ways in which minority points of view may, or may not, influence society at large. Whereas social influence research in the United States tended to focus on the power of majorities, European research reflected their experiences with the power of a persistent minority in effecting important changes. The authors examine the early findings of Moscovici, Nemeth, and others, as well as the critiques leveled against these groundbreaking studies. Always present was a tension between points of view that saw minority influence as derivative of, or qualitatively different from, majority influence. Newer models have sparked a great deal of research that makes this particular topic a lively and growing domain within social influence.

Adopting a social identity perspective, Platow, Haslam, and Reicher propose that leadership, the process of enhancing the contribution of group members to the realization of group goals, essentially represents group-based social influence, emerging from psychological in-group members, particularly highly in-group prototypical ones. The authors argue for a difference between failed and successful leadership, not so much in terms of influence, but in terms of the source of influence. Power over resources can allow a leader to control the behavior of followers, but this is a sign of failed leadership, because in these instances behavior is geared toward gaining reward and avoiding punishment, rather than pursuing a collective vision. Successful leadership depends upon shared social identity, and it is conferred on would-be leaders as much as created by the leaders themselves. This differentiation between successful and failed leadership provides insight into examining research, theory, and application of leadership processes and outcomes.

In the next section of this volume, we include chapters that describe the use of social influence techniques in four applied fields : clinical psychology, health, law, and consumer behavior.

Heesacker organizes his chapter on social influence and clinical intervention using Kelman’s (1958) tripartite model: compliance, identification, and internalization. In this model, compliance is not defined as behavior in accord with a request, but rather as cooperation motivated by the desire for social acceptance. People are influenced to comply because they wish to avoid negative social consequences or to gain social approval. Heesacker’s review suggests that under some circumstances, normative feedback has changed behaviors like drug abuse and gambling. Of course, this work is directly relevant to the work on social norms described by Nolan in her chapter of that name in the current volume, as well as Hodges’s chapter on conformity. The second process, identification, is relevant for understanding the effect of the therapeutic alliance between the therapist and client. This alliance strengthens the extent to which the client identifies with the therapist, which Heesacker argues is causally related to clinical outcomes. Finally, Heesacker argues that although a great deal of basic social influence research has been conducted on internalization, this work has had little influence on clinical practice. Much more research has been generated by a clinician-developed approach to internalization: motivated interviewing. Heesacker then bemoans the lack of basic social influence research relevant to clinical practice produced in recent years and describes a number of promising directions that this research could take.

In their chapter on social influence and health, Martin and DiMatteo examine the effects of different sources of social influence on health behavior: parents; peers once one enters adolescence; social networks as one enters adulthood; health care providers; and system-level influences like public health programs, health-related media messages, and educational interventions. Social comparison processes ( Suls & Wheeler , this volume), supportive social networks, and social norms ( Nolan , this volume) serve as sources of these effects.

In their review, Demaine and Cialdini conceptualize social influence and the law as consisting of three parts: social influence in the legal system; the legal regulation of social influence in our everyday lives; and law as an instrument of social influence. Their review suggests that most psychological research has focused on the first part. For example, the suggestibility of eyewitnesses recounting events underlying litigation and false confessions by crime suspects resulting from police interrogations have each benefitted from long-term, programmatic study, as have several facets of juror decision making. In contrast, the study of the effects of legal regulation of social influence in our everyday lives has received sparse and scattershot attention, as has the third part, law as an instrument of social influence. Demaine and Cialdini argue that the lopsidedness of the attention paid to the three areas does not reflect the importance of or differences in the number of empirical questions in these domains, and that these neglected areas hold great promise for future inquiry.

Kirmani and Ferraro tackle the burgeoning research on social influence in marketing. Marketing and consumer behavior are a perfect fit for social influence because the ultimate goals are behavioral: purchases for self and others, and telling others of their purchases and evaluations of the products. Truly one of the domains in which social influence research has accelerated, the authors focus their review on the research published only in the top journals in marketing and consumer behavior. Recognizing that the research on social influence in marketing borrows from many other disciplines, Kirmani and Ferraro nicely weave theory and research from social psychology, economics, sociology, anthropology, and communication into the field of marketing. The authors also point to some groundbreaking research by consumer researchers—gift giving and word of mouth—that will provide theory and be of interest to these other fields.

In the final section, informed by the chapters of the volume, we speculate about the future of research and theory in social influence. We also include two final chapters: Sagarin and Henningsen’s chapter focuses on resistance to persuasion and shows how this research can help to shape the future work that will be required to understand resistance to behavioral social influence attempts; and, in recognition of the profound changes coming to the study of social influence, the final chapter, by David Byrne of the Talking Heads, provides an example of how social media can affect the social influence process.

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Social Influence as Socially Distributed Information Processing

• First Online: 27 February 2020

Cite this chapter

• Andrzej Nowak 27 ,
• Robin Vallacher 28 ,
• Agnieszka Rychwalska 29 ,
• Magdalena Roszczyńska-Kurasińska 29 ,
• Karolina Ziembowicz 30 ,
• Mikołaj Biesaga 29 &
• Marta Kacprzyk-Murawska 29  

Part of the book series: SpringerBriefs in Complexity ((BRIEFSCOMPLEXITY))

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Social influence is arguably the most fundament and pervasive social process. The majority of research on this topic adopts the perspective of the source of influence, investigating how the he or she can overcome the resistance or passivity of the target of influence. In this view, social influence is tantamount to control and often involves strategies and tactics of manipulation. This chapter presents Regulatory Theory of Social Influence (RTSI), which examines social influence from the perspective of the target. RTSI holds that rather than always resisting social influence, the target often plays an active role in controlling the influence process. In particular, the target optimizes decision-making and judgment by delegating information processing to potential sources, thereby conserving his or her cognitive resources and improving the quality of his or her decisions and judgments. The interaction of four factors— trust , coherence , issue importance , and own expertise —determine the target’s choice of sources and the level of abstraction in the information sought from these sources. The chapter briefly discusses each factor and their interactive effects on the choice of sources and the level of information that is sought from them. Beyond maximizing the cognitive efficiency of the target and the quality of his or her outcomes, the processes specified by RTSI enhance the functioning of the social group in which the target embedded. The model also has practical applications in various domains, including the design of rules for interaction among AI agents in techno-social groups.

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Nowak, A. et al. (2019). Social Influence as Socially Distributed Information Processing. In: Target in Control. SpringerBriefs in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-30622-9_1

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Social Influence Theory (SIT)

Social influence theory: a review, introduction.

Social Influence Theory (SIT) was originally formulated by Herbert Kelman (1953) in the early 1950s as the framework explaining the conditions under which social influence induces attitude or behaviour change. The theory was developed against the backdrop of significant social and political upheavals, exemplified by civil rights movements and anti-war protests. The socio-political climate change brought to the fore the contradictory phenomena in which people could conform to social norms and rules whether or not they believed in their legitimacy (Kelman, 1953). However, prior research on social influence was inadequate to explain the underlying processes of collective persuasion and the translation of social compliance into attitude and behaviour change. Although evidence existed about the predictors of conformity (Kelman, 1953; Sherif, 1935), the intricate nature of attitude changes stimulated by a particulate type of communication lacked a theoretical explanation. Dissatisfied with the ability of the preceding theories to explain contradictory responses to social pressures and social norms, in Social Influence Theory, Kelman (1953; 1958;1974) analysed different types of influence situations and associated responses. 

Social Influence Theory was inspired by the intellectual foundations of three streams of research laying the basis for analysing different motivational grounds for attitude change (Kelman, 1974). These streams included the literature on conformity and social dynamics (Asch, 1961; Sherif, 1935), cognitive dissonance/consistency research (Festinger, 1954; Festinger, 1962) and the functional theories of attitude (Katz, 1960; Smith, Bruner & White, 1956). Kelman built upon research on conformity and social dynamics (Asch, 1961; Luchins, 1945; Sherif, 1935) that had investigated the personal conditions that make people conform or resist pressures in social groups, such as political propaganda and general public opinion. The guiding principles borrowed from those observations that informed the development of SIT were that: (1) attitude is a societal product, regulated by social conventions, values, norms, and rules, as well as the reflection of personal predisposition, (2) social influence has a differential impact on people’s acceptance of the influence, and (3) the acceptance of the influence depends on power, the characteristics of an influencing persona/group, and the message that they communicate (Asch, 1961; Festinger, 1954; Sherif, 1935). Cognitive dissonance/consistency research provided a point of reference to suggest that attitude and behaviour change happens against the backdrop of discrepancies between the existing attitudes and new cognitions (Kelman, 1979). In a pursuit to downplay these discrepancies, individuals would either resist or make a change in the existing system of beliefs and values (Festinger, 1954). The observations on the consistency of cognitions helped formulate the two opposing forces necessary for influence acceptance. These forces include new information communicated by an influencing agent, manifested as the exertion of influence, and an inherent inclination of people towards stability, which counterforces the permeation of the new information in the existing system of beliefs and values (Kelman, 1979). Finally, Kelman drew on the functionalists’ views of Katz (1960) and Smith et al. (1956), which stated that attitude has a functional significance for “ the goals we are pursuing, the values we are hoping to maximise, the coping processes in which we are engaged ” (Kelman, 1979:128). The functionalist approach emphasises that an individual changes attitudes not only to reduce the discrepancy between existing beliefs and the induced behaviour, but also because the individual is motivated to engage in that behaviour (Kelman, 1974). This is because the induced behaviour has implications for self-evaluation (e.g., self-image, self-esteem) and the ability to maintain social relations. As such, the motivational power of the induced behaviour depends on the degree to which the individual is oriented towards self-development, growth, new learning and self-utilisation (Kelman, 1979).

To go beyond a mere understanding of the motivation for attitude and behaviour change, Kelman’s Social Influence Theory aimed to expand knowledge on several fronts. First, it aimed to shed light on the structure of social situations to explain the generic processes that are required for the induction of behaviour. Second, the theory aimed to distinguish between the different modes of influence acceptance by drawing on the differences in situational premises in which influence is attempted. As such, through Social Influence Theory, Kelman sought to explain the qualitative distinctions of different modes of individuals’ responses to a social influence and the relative importance of those modes for long-term behaviour change (Kelman, 1979).

Social Influence Theory postulates that there are three modes of social influence acceptance, namely compliance, identification, and internalisation (Kelman, 1958). Kelman (1974) defined social influence as a behaviour change within the social settings induced by one person or a group of people. Compliance, identification, and internalisation represent the responses to social influences that can be best viewed as the outcomes of the dynamic processes of interaction with an influencing agent(s) in an informational and motivational context (Kelman, 1979). To understand the difference between compliance, identification, and internalisation, Kelman (1974) proposed a structure of generic social influence situations from the perspective of the person towards whom the influence is elicited (hereafter referred to as an individual or a person), as illustrated in Figure 1. The left-hand side of the model demonstrates the three steps of the evaluation of the stimulus elements: the definition of the situation, the presentation of the agent and the specification of the response. These are required for an influence to have an effect on its recipient. Exposure, positive orientation and induced behaviour represent responses to the stimulus elements, leading to influence acceptance. The right-hand side of the model depicts the psychological prerequisites for positive responses to stimulus elements, namely the perception of the conditions for goal achievement and an expected goal of induced behaviour. The interrelation between the stimulus assessment stages, associated psychological processes and the consequent responses to the stimulus are illustrated by arrows. “O” refers to an influencing person or a group of people (hereafter referred to as an influencing agent) that individuals interact with to derive the meaning about an agent, specify a response to influence exposure and assess the conditions for induced behaviour (Kelman, 1974).

Definition of situation refers to the state that follows after an influencing agent provides information in a deliberate or unintentional attempt to induce behaviour (Kelman, 1974). Deliberate induction of behaviour takes the form of direct commands, such as threats, requirements, orders or persuasion. Unintentional induction can be an indirect expression of expectations, thoughts and norms (Kelman, 1979). Induced behaviour is discrepant to some extent from the behaviour and attitudes of the individual upon whom influence is exerted, which typically entails some degree of the individual’s resistance to change. This is because people are inherently inclined to cognitive stability, which reduces personal receptivity to new information. If receptivity to new information is weak, the influence of the new information will probably be neutralised by the recipient (Pelinka & Suedfeld, 2017). Therefore, the chances are that the definition of situation will lead to individuals’ willingness to expose themselves to the influence, depending on the degree to which the corresponding psychological prerequisite (an expected goal of induced behaviour) and the situational condition (importance of induced behaviour) are met. To minimise resistance to social influence, the information about induced behaviour should convince an individual that the adoption of induced behaviour, although challenging an existing system of beliefs and attitudes, is conducive to the achievement of an individual’s goals (Kelman, 1974; Kelman, 1979). The definition of situation activates the willingness to expose oneself to the induced behaviour if its consequences are perceived as relevant for goal achievement. Consequently, the strength of motivation to respond to the influence depends on the degree of importance of the induced behaviour to one’s own goals (Kelman, 1974). Presentation of agent is the stage when a person evaluates the characteristics of an influencing agent, which may be social status, expertise, prestige, values, available resources and other attributes (Kelman, 1953; Kelman, 1974). The relationship between this stage of stimulus evaluation with the corresponding psychological prerequisite means that the influencing agent is evaluated in terms of how powerful the agent is in creating the required conditions for goal achievement. By deriving meaning from the attributes and communication of influencing agents, individuals can assess the relevance of the behaviour of the influencing agents to the achievement of personal goals (DeShields, Kara & Kaynak, 1996; Kelman, 1958; Kelman, 1974). The awareness of the characteristics of the influencer that are salient for a person reduces the psychological distance and resistance to the elicited information (Kelman, 1979). Characteristics that resonate with the individual signal favourable conditions for goal achievement, ensure a positive orientation toward the influencing agent and enhance predisposition towards the adoption of the induced behaviour. Hence, the factor amplifying the likelihood that agent evaluation will translate into positive orientation towards the agent is the strength of their perceived power (Kelman, 1974). Specification of response is the process preceding the adoption of behaviour, whereby an individual forms an understanding of a precise course of action for the behaviour to be implemented (Kelman, 1974). The induction of behaviour is conditioned by its prepotency. Prepotency is the extent to which induced behaviour is considered the most clearly relevant that has been activated in the influence situation. In other words, the stronger and the more relevant the induced behaviour compared to other available alternatives, the more chances are that behaviour will be induced (Kelman, 1953; Kelman, 1974).

The difference in the importance of induced behaviour, the power sources of an influencing agent, and the manner in which the induced behaviour has become prepotent contribute to the variance in behavioural responses to the social influence (Pelinka & Suedfeld, 2017; O'Keefe, 2016). The resulting changes in behaviour may be overt or covert in the form of new attitudes and beliefs (Kelman, 1979). Overt and covert behaviour change (i.e. influence acceptance) is manifested through either of the three routes, i.e. compliance with the influence , identification with an influencing agent or internalisation of the induced behaviour. The induction of the behaviour through the compliance, identification and internalisation routes is associated with different emotions and individuals’ expectations of the embeddedness of induced behaviour in the social system (Table 1).  Specifically, compliance is the acceptance of influence for individuals to receive rewards and avoid punishments for non-compliant behaviour (Kelman, 1958; Pelinka & Suedfeld, 2017). Compliance is a response to external demands motivated by concerns about the social ramifications of that behaviour (Pelinka & Suedfeld, 2017). Examples of such behaviour include abiding by laws and the prescriptions of a doctor. Such behaviour helps reduce the discrepancy in rules and norms cultivated and imposed by the external social environment, and, consequently, eliminates the feelings of social fear and embarrassment that noncompliance may cause (Kelman, 1974). The induced behaviour is likely to be performed under the surveillance of the influencing agent (Pelinka & Suedfeld, 2017). In the patient-doctor dynamics, compliance is higher when doctors have close interaction with patients and the latter tend to seek doctors’ approval (Davis, 1971). Similarly, the enforcement of laws is merely possible due to the existence of formal institutions controlling compliance with laws in different spheres of life (Jackson et al., 2012). Therefore, compliance is likely to occur if an influencing agent has the means of control. The agent would exert power and authority in relation to the recipients of communication, while the latter would have restricted behavioural choices to act otherwise (Bagozzi & Lee, 2002; Cialdini & Goldstein, 2004).

Identification is a social influence with the purpose of achieving self-definite goals (Kelman, 1958; Pelinka & Suedfeld, 2017). Like compliance, the adoption of behaviour following identification does not manifest individuals’ personal values, but it is intrinsically satisfying (Cialdini & Goldstein, 2004; Kelman, 1958; Pelinka & Suedfeld, 2017). The induction of behaviour through identification is based on the expectation that the behaviour helps fulfil particular social roles. It is important for people because it can promote one’s own position in society. The induced behaviour becomes dominant when demands in social roles are clearly delineated (Kelman, 1958; Pelinka & Suedfeld, 2017). The social roles become apparent if individuals affiliate themselves with a particular social category (Kelman, 1974). For example, individuals can be nudged into the consumption of sustainable products and voting for a democratic political party in the United States of America under the expectation that identification with green consumerism and social equality would help sustain membership in respective social and political groups (Bagozzi & Lee, 2002). Identification is more likely to take place, if the relationship to an influencing agent is salient, while the source inducing the behaviour should be attractive enough to foster the desire to affiliate with it (Kelman, 1958; Pelinka & Suedfeld, 2017). Therefore, refusal to identify oneself with a social category with a dominating narrative in society can create feelings of guilt and shame (Kelman, 1974).

Influence internalisation is an act of adopting behaviour because it is construed as being congruent with personal values and views, such as deeply entrenched beliefs and attitudes about social conduct, norms and idealised images (Kelman, 1958; Pelinka & Suedfeld, 2017; O'Keefe, 2016). Such beliefs, values and attitudes usually work as self-guiding principles nurtured early on in people’s lives under the influence of the social environment the person grew up in (Bagozzi & Lee, 2002). The denial of social influence internalisation that goes against deeply rooted values could lead to emotional reactions, such as regret and self-disappointment (Kelman, 1974). For the behaviour to be perceived as congruent, it has to be relevant and easily relatable to personal goals (Kelman, 1979; Pelinka & Suedfeld, 2017). The perception of behavioural congruency and intrinsic value for the individual is facilitated when an influencing agent is perceived as credible (Kelman, 1958). The internalisation of behaviour aligned with the intrinsic needs of a person is expected to result in the restructuring of the existing value system and means-end framework for goal achievement (Kelman, 1958).

The three cognitive modes of influence acceptance can result in different behavioural responses (Bagozzi & Lee, 2002). Out of the three acceptance processes, internalisation can be considered to be the strongest predictor of long-term commitment, as it does not depend on a social and situational context (Kelman, 2006). In the case of internalisation, individuals’ behaviour is consistent with norms and beliefs, which are already internalised and deep-rooted in the existing system of values. As the influence is not dissonant with existing cognitions, the induced behaviour will more likely prevail in any social context and under any circumstances (Kelman, 1958). For example, individuals are willing to engage in pro-environmental behaviour in the long term if pro-environmental values had been deeply entrenched in the individual before influence elicitation took place. In contrast, the commitment to behaviour accepted through identification depends on the context, where the induced behaviour is relevant for maintaining desirable social identity and reciprocal social relationships (Stryker & Serpe, 1982). For example, commitment to organisations based on an employee’s corporate identification will carry on until the employee changes the workplace. The effect of compliance is short-term, as the behaviour is constrained to the context where surveillance is possible and other behavioural alternatives are limited (Kelman, 1958). In other words, the induction of behaviour is compelled by the need to create a favourable short-term impression in specific social conditions (Friedlander & Schwartz, 1985). For example, to provide services, police and healthcare workers require people to enact certain behaviours, which may not be sustained once the service or treatment is complete.

Table 1: Distinctions between the three modes of influence acceptance (Based on Kelman, 1958 and Kelman, 1974)

The development of Social Influence Theory marked a turning point in social influence research, addressing a gap in prior studies, which predominantly focused on instances of conformity (Asch, 1961; Sherif, 1935).  SIT advanced the literature in two ways. Firstly, it offered a nuanced and qualitative analysis of social influence situations. It made it possible to delineate between individuals' responses to persuasion, ranging from superficial conformity to genuine changes in beliefs, in both dual relationships and group dynamics. Secondly, SIT complemented the functionalist approach to attitude (Katz, 1960; Smith, Bruner & White, 1956). In addition to interpreting induced behaviour in functional terms, the theory also provided classifications of the different meanings of situations in which influences occur and the implications of these situations for attitude and behaviour change (Kelman, 1979).

Theory Extension

A model of interpersonal influence characteristics.

SIT has inspired numerous studies in social psychology, with scholars building upon and developing new frameworks. One of the notable modifications of SIT is the development of a new Model of Interpersonal Influence Characteristics, by Levy et al. (Levy, Collins & Nail, 1998). Levy et al. (Levy, Collins & Nail, 1998) reconceptualised the social influence literature produced in 1950, largely dominated by the research of Kelman (1953; 1958; 1974) and newer social influence taxonomies (Raven, 1992). The new conceptual taxonomy was aimed at addressing the challenge in the existing theories on social influence, namely vagueness in the concepts within the sphere of social influence, and the challenge of differentiating social influence from other topics in social psychology.

The model of Levy et al. (1998) was different from traditional approaches in that it did not attempt to offer a mutually exclusive and discrete categorisation of social influence instances for predictive purposes. Instead, it offered a parsimonious model for analysing interpersonal influence cases. The model consists of four fundamental elements: (1) the level of cognitive processing, (2) the intentionality of the influence, (3) social status, and (4) the direction of change. The level of cognitive processing refers to the degree of an individual’s awareness of being exposed to the influence of another person, which is represented as the conscious-unconscious dichotomy (Levy, Collins & Nail, 1998). This dichotomy corresponds to the views of other cognitive psychologists, suggesting that people tend to process information in an automatic or deliberate manner (Petty & Cacioppo, 1986). Automatic and deliberate cognitive processes contrast in the degree to which they are controlled by an individual and the level of attentional resources they require (Wyer & Srull, May 1, 1994; Levy, Collins & Nail, 1998). For example, conscious cognitive processing is prompted by an individual’s awareness of the attempt to induce behaviour. The influencee puts mental effort into considering whether a behavioural or attitudinal response should align with or oppose the induced behaviour. Responses could be the decision to abide by enforced legal regulation or rebel against the imposition of rules by legitimate authorities. Unconscious cognitive processing occurs outside of the influencee’s awareness and control. It happens without cognitive guidance and results in such responses as intuitive alignment to commonly accepted social norms, instinctive social responses (e.g. equity and reciprocity), and spontaneous reactions in the form of attraction or repulsion (Levy, Collins & Nail, 1998).

The intentionality of the influence refers to whether an influencing agent has the intention to exert power on an individual. It is considered only in cases of the conscious processing of influences (Levy, Collins & Nail, 1998). The notion of unintentionality can be traced back to Social Influence Theory, emphasising that identification can result from an unintentional attempt to provoke actions (e.g. self-categorisation to achieve a desired social status) (Kelman, 1958). The degree of intentionality takes the form of a three-level category: intentional, unintentional and irrelevant influences. Intentional influence is the one which is exerted deliberately (e.g. rules of conduct) and inflicts either compliant or non-compliant responses (Levy, Collins & Nail, 1998). Unintentional influence stipulates imitation or anti-imitation, manifested as the increased or decreased similarity in behavioural, cognitive or affective characteristics between individuals and influencers. Examples of imitation could be acting in line with group norms and the adoption of the traits and characteristics of the influencer. Consequently, the instances of anti-imitation could be acting in opposition to social norms and the imitation of traits and characteristics of the opposite social group (Levy, Collins & Nail, 1998; Levy & Nail, 1993). The irrelevant category encompasses the concepts that are theoretically irrelevant, where the intentionality of the influence is impossible to trace. Under such circumstances, the acceptance of influence can hinge upon individuals’ personal experience, observations or exposure to persuasive or impactful communication (Levy, Collins & Nail, 1998).

Social status refers to the relative difference of an individual from an influencing agent (lower vs peer, vs higher vs irrelevant), which can correspond to different forms of social influence acceptance (Levy, Collins & Nail, 1998). Specifically, a higher social status of an influencing agent bestows upon them the power to make individuals obedient or, conversely, can trigger rebellious reactions against influencers (Levy, Collins & Nail, 1998). In the condition of influencers’ lower social status, individuals can also succumb to social pressure if they have responsibility over the influencers. Such a form of compliance is referred to as power-of-the-powerless (Raven, 1993). Negative power dynamics for influencees can also provide them with the grounds to oppose the influence—an act referred to as counter power-of-the-powerless (Levy, Collins & Nail, 1998). Intentional influence from peers is postulated to result in either direct conformity or non-conformity with social expectations and norms. Similarly, an unintentional influence attempt from peers is poised to result in either imitation or anti-imitation of the behaviour and attitudes of a social group (Levy, Collins & Nail, 1998).

The direction of change takes the form of positive, negative and irrelevant responses. The inclusion of the direction of change characteristic in the framework was rooted in the proposition that sometimes influence can be counter-effective and raise an opposite reaction (Levy, Collins & Nail, 1998). Table 2 summarises and defines all types of responses to social influence. The relationships between the four fundamental interpersonal influence characteristics making up the decision tree are presented in Figure 2.

Table 2: Definitions of social influence concepts (Levy, Collins & Nail, 1998, pp. 730-733)

Note: the concept appears in the order in which it is presented in Figure 2

The decision tree of interpersonal influence characteristics served as a novel conceptualisation of Kelman’s Social Influence Theory and other research in the broad domain of social influence (French, 1956; Raven, 1992). Consisting of only four types of characteristics, the framework represents a logical system for differentiating a higher number of interpersonal influence instances, compared to Social Influence Theory. Also, reformulating existing knowledge on social influence opened avenues for theoretical advances, especially around influence intentionality and unconscious processing, which had not been emphasised in prior theories. The decision tree by Levy et al. (Levy, Collins & Nail, 1998) presents distinct subcategories for unintentional influence (i.e. indirect conformity, anti-conformity, identification, disinhibitory contagion, and residual influence types) and unconscious influence processing (i.e. attraction, echo contagion, hysterical contagion, repulsion, social loafing and other influences theoretically irrelevant to the concept category). Consequently, the model not only addressed the knowledge gaps persistent in social influence research at that time, but paved the way for further empirical inquiry into the underexplored dimensions of social influence situations.

Applications

For decades, Social Influence Theory guided studies across various disciplines. Initially, its application was predominantly seen in organisational, medical, and socio-political studies. In the discipline of organisational management, the SIT was employed to discern behavioural patterns related to compliance, identification, and the internalisation processes influencing employees' behavioural development. For example, researchers delved into the impact of authority on employee compliance (Freedman, 1981), negative behaviour development among co-workers due to group affiliations (Herold & Conlon, 1981) and the changes in internalised beliefs among demographically (dis)similar individuals (Eagly, Wood & Fishbaugh, 1981). In medical studies, the theory was utilised to explore the factors inherent to social environmental influencing health-destructive and health-protective behaviours, such as resistance to smoking (Covington, 1981) and adherence to treatments advocated by healthcare practitioners (Dembroski, Lasater & Ramirez, 1978; Rodin & Janis, 1979). In the socio-political context, the theory found application in explaining the implications of large-scale interventions for specific nations' economic and political submissiveness (vs dominance) (Richardson, 1976), civil movements and socio-cultural transformations (Ewens & Ehrlich, 1973; Moscovici & Mugny, 1985; Pool, Wood & Leck, 1998), political polarisation (Kelman, 1970; MOR, 2007; Watanabe et al., 2017) and other phenomena. Equally valuable was SIT’s application in understanding social relations at the community level. For instance, researchers investigated the role of the social context in the development of cultural preferences among children (Bunton & Weissbach, 1974; Kochanska, Tjebkes & Fortnan, 1998) and the influence of values on the perception of policies of international financial institutions (Riggs, 1980). At the individual level, the theory was employed to examine various aspects of interpersonal relations, including but not limited to the perception of power and the legitimacy of actions (Bickman, 1974), stigmatisation and dynamics in interracial interactions (Page, 1997).  

In the past two decades, along with the unfaded interest in the social influence phenomenon within organisational and larger social systems (Binyamin, 2020), the application of SIT has also expanded in the domains of marketing and information system management (Baker et al., 2014; Cheung et al., 2022; Huang, 2019). As research on user interaction around information systems has burgeoned, scholars have adapted Kelman's framework to investigate shifts in attitudes toward new technology. For example, SIT was integrated with the Elaboration Likelihood Model developed by Petty and Cacioppo (1986) to understand the effectiveness of influence in the process of information systems acceptance. Modifications in SIT also concerned the integration of context-specific factors associated with influence acceptance. Studies examined the impact of system characteristics (design quality, technology quality, information quality) on identification (Cheung et al., 2022). SIT was also used for examining post-adoption behaviour, whereby the effect of identification processes on continuous usage of IT services was tested alongside the effect of the expectation-confirmation mechanism (i.e. the assessment of the degree to which expectations are confirmed by actual performance) (Huang, 2019).

The adaptability of SIT to various scenarios of interpersonal interactions has led to implications for various sub-streams in marketing and public relations research. In the traditional marketing context, for instance, researchers have delved into consumption behaviour under the influence of communication from referent groups and the alignment of influence with personal goals and values (Yang, Tseng & Lee, 2021). In the business-to-business environment, the focus has extended to examining the impact of brand communications on the behaviour and performance of customer service representatives (Baker et al., 2014). Recently, with more companies venturing into sustainable production, there is a growing body of research utilising SIT for understanding the underpinnings of pro-environmental behaviour. Studies have examined the influence of sustainability advocacy groups and personal norms on sustainable consumption choices (Confetto et al., 2023; Elgaaied-Gambier, Monnot & Reniou, 2018). The applications of Social Influence Theory have been instrumental in designing persuasive messages and campaigns that align with different company objectives, whether aiming for momentary compliance or the enduring internalisation of influence. In the digital marketing context, SIT has been used to explain user behaviour on social media platforms (Oliveira, Garcia & Vivacqua, 2021; Sánchez-Fernández & Jiménez-Castillo, 2021; Santiago, Magueta & Dias, 2020) and the attitudinal and behavioural implications of online influencer marketing (Fan & Chan, 2023; Tafesse & Wood, 2021). In particular, studies found that social media influencers’ personality characteristics (e.g. closeness, interactivity, homophily and originality) drive imitation and the adoption of induced behaviour ((Chloe) Ki, Park & Kim, 2022; Li & Peng, 2021). Researchers have applied the theory loosely, however, primarily focusing on the characteristics of an influencing agent. This emphasis often occurs without due consideration to the relation of the examined characteristics to a particular route of influence acceptance.

Limitations

Social Influence Theory garnered critique around its lack of acknowledgement of contextual factors, its inability to differentiate the intentionality of influences and associated behavioural responses, and insufficient attention to the role of power dynamics in social influence situations and cognitive processes of persuasion (Latané, 1981; Levy, Collins & Nail, 1998; Lisha et al., 2017; Petty & Cacioppo, 1986; Wang et al., 2022). In particular, researchers emphasised the need to explore the contextual differences of situations affecting variance in social influence effects. To that end, s ome critics argued that Kelman's theory may not be adequate to address the role of situational factors in social influence processes (Dong et al., 2021). It was found that temporal-spatial factors (i.e. time difference and the crowdedness of the environment where influence is elicited) affect the evaluation of the characteristics of an influencing agent and the persuasiveness of communication consequently (Dong et al., 2021). Other scholars contended that the theory does not account for the variations in social influence processes across different cultures (Lisha et al., 2017; Wang et al., 2022). Evidence from cross-cultural research has suggested that social influence dynamics can be influenced by cultural norms and values (Wang et al., 2022). Therefore, cultural differences should be considered to eliminate flawed generalisations. Second, the notion of intentionality was implicit in Social Influence Theory, suggesting that identification did not require an influencing agent to deliberately exert power upon an individual to stimulate the induction of behaviour to secure membership in a particular social group (Kelman, 1953; Levy, Collins & Nail, 1998). The lack of explicit distinctions between intentional and unintentional influences limited the understanding of the types of potential behavioural responses that unintentional influences may entail (Levy, Collins & Nail, 1998). Drawing on this limitation of SIT, Levy et al. (Levy, Collins & Nail, 1998) developed the Model of Interpersonal Influence Characteristics and provided an explicit categorisation and scenarios under which unintentional influence can be exerted and the responses it entails. Third, it has also been highlighted that SIT did not pay necessary consideration to power dynamics in social influence situations. In that regard, the Social Impact Theory by Latané (1981) emphasises the role of power and its distribution. Social Impact Theory postulates that the strength of the influencing group of people, the immediacy (proximity) of the agent to the individual and the number of people in the group determine how people respond to social influence. Finally, it has been argued that, in an attempt to cover the broader scope of responses to social influences, SIT provides an insufficient explanation of the cognitive processes underscoring communication persuasion (Petty & Cacioppo, 1986).  To address that limitation, the Elaboration Likelihood Model developed by Petty and Cacioppo (1986) delves into the motivational and ability factors impacting information processing and persuasion.

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Dinara Davlembayeva (Business School, Cardiff University, UK) & Savvas Papagiannidis (Business School, Newcastle University, UK)

How to Cite

Davlembayeva, D. & Papagiannidis, S. (2024) Social Influence Theory: A review . In S. Papagiannidis (Ed), TheoryHub Book . Available at https://open.ncl.ac.uk / ISBN: 9781739604400

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Operationalised Qualitatively / Quantitatively Level Micro-level

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ISBN: 978-1-7396044-0-0

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Research Article

The effects of information and social conformity on opinion change

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Project administration, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation School of Public Affairs, The Pennsylvania State University - Harrisburg, Middletown, Pennsylvania, United States of America

Roles Conceptualization, Data curation, Methodology, Supervision, Validation, Writing – original draft, Writing – review & editing

Affiliations Department of Biochemistry, The Pennsylvania State University, University Park, Pennsylvania, United States of America, Department of Molecular Biology, The Pennsylvania State University, University Park, Pennsylvania, United States of America, Department of Political Science, The Pennsylvania State University, University Park, Pennsylvania, United States of America

• Daniel J. Mallinson, 
• Peter K. Hatemi

• Published: May 2, 2018
• https://doi.org/10.1371/journal.pone.0196600
• Reader Comments

18 Mar 2020: Mallinson DJ, Hatemi PK (2020) Correction: The effects of information and social conformity on opinion change. PLOS ONE 15(3): e0230728. https://doi.org/10.1371/journal.pone.0230728 View correction

Extant research shows that social pressures influence acts of political participation, such as turning out to vote. However, we know less about how conformity pressures affect one’s deeply held political values and opinions. Using a discussion-based experiment, we untangle the unique and combined effects of information and social pressure on a political opinion that is highly salient, politically charged, and part of one’s identity. We find that while information plays a role in changing a person’s opinion, the social delivery of that information has the greatest effect. Thirty three percent of individuals in our treatment condition change their opinion due to the social delivery of information, while ten percent respond only to social pressure and ten percent respond only to information. Participants that change their opinion due to social pressure in our experiment are more conservative politically, conscientious, and neurotic than those that did not.

Citation: Mallinson DJ, Hatemi PK (2018) The effects of information and social conformity on opinion change. PLoS ONE 13(5): e0196600. https://doi.org/10.1371/journal.pone.0196600

Editor: Yong Deng, Southwest University, CHINA

Received: August 17, 2017; Accepted: April 16, 2018; Published: May 2, 2018

Copyright: © 2018 Mallinson, Hatemi. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data are available from the corresponding author’s Harvard Dataverse ( http://dx.doi.org/10.7910/DVN/YVCPDT ).

Funding: This project was supported by a $1,000 internal grant from the Penn State Department of Political Science (awarded to DJM). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Information and persuasion are perhaps the most important drivers of opinion and behavioral changes. Far less attention, however, has been given to the role of social pressure in opinion change on politically-charged topics. This lacuna is important because humans have a demonstrated proclivity to conform to their peers when faced with social pressure. Be it in the boardroom or on Facebook, Solomon Asch and Muzafer Sherif’s classic studies hold true today. Individuals conform based on a desire to be liked by others, which Asch [ 1 , 2 ] called compliance (i.e., going along with the majority even if you do not accept their beliefs because you want to be accepted), or a desire to be right, which Sherif et al. [ 3 ] termed private acceptance (i.e., believing that the opinions of others may be more correct or informed than their own). These two broad schemas encompass many specific mechanisms, including, motivated reasoning, cognitive dissonance, utility maximization, conflict avoidance, and pursuit of positive relationships, among others. Information-based social influence and normative social influence (i.e., conformity pressure) both play important, albeit distinct, roles in the theories of compliance and private acceptance (see [ 4 ]). In both cases, humans exhibit conformity behavior; however only in private acceptance do they actually update their beliefs due to the social delivery of new information.

Extensions of Asch and Sherif’s path-breaking works have been widely applied across a number of behavioral domains [ 5 – 9 ], to include political participation. For example, significant attention has been focused on the import of conformity on voter turnout and participatory behaviors [ 10 ], including the effects of social pressure on the electoral behavior of ordinary citizens [ 11 – 15 ]. This body of work points to both the subtle and overt power of social influence on electoral behavior, yet little is known about the import of social conformity for politically charged topics in context-laden circumstances, particularly those that challenge one’s values and opinions.

Testing conformity pressure in the ideological and political identity domain may explicate whether the pressure to align with an otherwise unified group is different when dealing with politically charged topics versus context-free topics such as the size of a line or the movement of a ball of light [ 2 , 16 ]. Opinions on politically charged topics are complex, value laden, aligned with cultural norms, and not easily changed [ 17 – 21 ]. It remains unknown if the effects of social conformity pressures on political opinions are conditioned by the personal nature of the locus of pressure. To be sure, social conformity is a difficult concept to measure without live interaction. An observational approach makes it difficult to untangle if or how social pressure independently affects behaviors given these variegated casual mechanisms, and whether changes in opinion that result from social interaction are due to compliance or private acceptance. Nevertheless, experiments provide one means to gain insight into how and why opinion change occurs. Here, we undertake an experiment to test the extent to which opinion change is due to persuasion through new information, social conformity pressure, or a combination of the two in a more realistic extended discussion environment.

Conformity and political behavior

Both observational and experimental research has addressed different aspects of the impact of socially-delivered information on individual behavior. Observational analyses of social networks form the backbone of much of the recent research on social influence and political behavior. Sinclair [ 22 ], for instance, demonstrates that citizen networks convey a bounded set of political information. Individuals may turn to highly informed peers [ 23 ] or aggregate information from trusted friends and family [ 24 ] in order to reduce the cost of gathering the information required to engage in political behavior (e.g., voting). In turning to their network, they are open to privately accepting this useful information. Political information, however, is not the only type of information transmitted through personal networks. Social pressure helps the network induce compliance with desired social norms [ 25 – 27 ]. In this case, members of the network provide information regarding the group’s expectations for appropriate engagement in politics. Individuals that are concerned about whether or not the group will continue to accept them therefore conform out of a desire to be liked, broadly defined. Norms are often self-enforcing, with merely the perceived threat of potential sanctions being enough to regulate behavior through compliance and self-sanctioning [ 28 , 29 ].

The debate over the practicality and reality of deliberative democracy further highlights the importance of understanding the role of political conformity in public and elite discourse. Scholars and theorists argue that political decisions are improved and legitimized under a deliberative process [ 30 – 34 ], even though deliberation does not necessarily result in consensus [ 35 ]. The crux of democratic deliberation is that participants are engaging in a rational discussion of a political topic, which provides the opportunity for each to learn from the others and thus privately update their preferences (i.e., out of a desire to be right). It results in a collectively rational enterprise that allows groups to overcome the bounded rationality of individuals that would otherwise yield suboptimal decisions [ 36 ]. This requires participants to fully engage and freely share the information that they have with the group.

Hibbing and Theiss-Morse [ 37 ], however, raise important questions about the desirability of deliberation among the public. Using focus groups, they find that citizens more often than not wish to disengage from discussion when they face opposition to their opinions. Instead, they appear averse to participation in politics and instead desire a “stealth democracy,” whereby democratic procedures exist, but are not always visible. In this view, deliberative environments do not ensure the optimal outcome, and can even result in suboptimal outcomes. In fact, the authors point directly to the issue of intra-group conformity due to compliance as a culprit for this phenomenon. The coercive influence of social pressure during deliberation has been further identified in jury deliberations [ 38 , 39 ] and other small group settings [ 40 ].

Beyond politics, there is experimental evidence of the propensity to conform out of a desire to either be liked or to be right [ 25 , 41 – 45 ]. Using a simple focus group format and pictures of lines, Asch [ 1 , 2 ] demonstrated that individuals would comply with the beliefs of their peers due to a desire to be accepted by the group, even if they disagree and even when they believe the group opinion does not match reality. To do this, Asch asked eight members of a group to evaluate two sets of lines. The lines were clearly either identical or different and group members were asked to identify whether there was a difference. Unknown to the participant, the seven other group members were confederates trained to act in concert. At a given point in the study, the confederates began choosing the wrong answer to the question of whether the lines were equal. Consequently, the participant faced social pressure from a unified group every time they selected their answer. Asch varied the behavior of the group, including the number of members and number of dissenting confederates. Participants often exhibited stress and many eventually complied with the group consensus, even though the group was objectively wrong and participants did not agree with them privately.

Using a much more complex and context-laden format—a youth summer camp with real campers—Sherif et al. [ 3 ] demonstrated private acceptance whereby humans internalize and conform to group norms because consensus suggests that they may have converged on a right answer. In this case, the boys in the camp quickly coalesced into competing factions and initial outliers in the groups conformed out of a desire to win competitions (i.e., be right). While the groundbreaking Robbers Cave experiments revealed a great deal about group behavior well beyond conformity, we focus specifically on this particular aspect of the findings, which have stood the test of time in numerous replications and extensions across a wide variety of social domains [ 46 – 52 ].

Replication of Asch’s experimental work, in particular, has met varying degrees success. Lalancette and Standing [ 53 ]found that Asch’s results were mixed when using a prompt more ambiguous than unequal lines. Further, Hock [ 54 ] critiques the Asch design for not replicating a real life situation. Focusing on divorce attitudes, Kenneth Hardy provided an early application of Asch’s public compliance and Sherif’s private acceptance theories to political opinions using a similar small-group format with six confederates and one participant. Confederates offered not only their opinions, but also reasons for their opinions, which provided a methodological innovation by introducing more information than just the confederates’ votes. Hardy’s work provided an important starting point for identifying the process of conformity in the political realm, but it remains limited. He only utilized men in his study and did not allow for repeated discussion to assess how long participants hold up to conformity pressure. In a more recent study, Levitan and Verhulst [ 55 ]found persistence in political attitude change after interaction with a unanimously-opposing group, but they did not incorporate any discussion.

Our experiment builds on these works by examining the micro-process underlying opinion change for a politically charged topic discussed in a real context. We bridge between studies that allow for no discussion with those that study day-long deliberations in order to determine if group influence has a stronger effect, even when the discussion centers on an attitude closely tied with social identity. Our interaction of about an hour simulates a likely real-world example of dialogue. More importantly, our design allows us to speak to the debate over social influence by pulling apart the desires to be right (private acceptance) and liked (compliance). Our main goal is not to completely predict the general public’s behavior, but rather to identify the independent causal role of social pressure on opinion change, given the known import of information effects. We expect conformity pressure and information to have joint and independent effects on opinion change.

Variation in conformity behavior

While our primary interest is in identifying the average effects of information and conformity pressure on opinion change, we nevertheless recognize that there is variation in humans’ responses to social pressure, depending on observed and unobserved individual characteristics. Thus the average treatment effect recovered can mask substantively important heterogeneity [ 56 , 57 ]. For instance, not all of Asch’s or Hardy’s subjects complied with group opinion and there was a great deal of variation in how willing Sherif et al.’s campers coalesced into cohesive and functioning groups. In order to address this possibility we test three factors that have been previously identified as covarying with the average propensity to conform: personality traits, self-esteem, and ideology. The most consistent evidence points towards those who change their opinions as being generally more agreeable, neurotic, and having lower self-esteem [ 58 ].

Generating hypotheses regarding the import of other personality and ideological dispositions on opinion change for political, moral and identity-laden topics is more complicated. Extant research indicates support for both stability and change for these traits and differs in the source of that change, i.e., whether it is informational or social. For example, on the one hand we might expect those who are more politically conservative to be more likely to conform to the group overtly, given extant studies showing conservatives think less negatively toward conformity and comply more often to group pressure and norms [ 59 – 61 ]. In addition, conservatives are also higher on the Conscientiousness personality trait, and this trait both reflects and is related to more conformist behavior [ 62 – 64 ].

On the other hand, conservatism, by definition, advocates the status quo and is related to resistance to change and greater refusal to privately accept new information, specifically if that information contradicts one’s values [ 65 , 66 ], leading to a greater likelihood of internal stability. In a similar manner, those high in openness and more politically liberal, while more likely to take in new information, and thus possibly more likely to privately accept it, are also less prone to restrictive conformity, and thus possibly less likely to conform publicly [ 67 ]. We treat these propositions as secondary hypotheses, and explore their import in a limited manner given restrictions in the data.

Materials and methods

In order to explicate the independent and joint effects of compliance and private acceptance, we designed an experiment, conducted at the Pennsylvania State University from May to December of 2013, which placed participants in a deliberative environment where they faced unified opposition to their expressed opinion on a political topic that is relevant to their local community. We assessed participants’ privately-held opinions, absent the group, before and after the treatment in order to determine whether those who expressed a change in opinion during the discussion only did so verbally in order to comply with the group and gain acceptance or if they privately accepted the group’s opinion and truly updated their own values. The group discussed the topic openly, for approximately 30–45 minutes, also allowing us to assess participant behavior throughout the discussion. We discuss the specifics in more detail below.

In designing the experiment, we leveraged a unique time in Penn State’s history, the aftermath of the Jerry Sandusky child abuse scandal and the firing of longtime Head Coach Joe Paterno. The firing provided an ideal topic of discussion and a hard test of conformity pressure given the fact that it exhibited high salience on campus, was politically charged, and connected to the participants’ identities as Penn State students. The question posed to our participants was whether or not they felt that Coach Paterno should have been fired by the University’s Board of Trustees in November 2011. Previous research demonstrates that undergraduates may not have as clearly defined political attitudes as older adults on many topics and thus may be more susceptible to conformity pressure from peers due to non-attitudes [ 68 ]. This informed our choice of the discussion topic, as Paterno’s role in the abuse was not only highly salient on the Penn State campus, but typically invoked strong and diametrically opposed opinions in the undergraduate population and the general Penn state community. We begin by providing some background on this issue and its connection to identity and politics.

Firing of Penn State football Head Coach Joe Paterno

The first week of November 2011 was a whirlwind for students at Penn State. Police arrested former defensive coach Jerry Sandusky on charges of child sexual abuse following the release of a grand jury report by the Pennsylvania Office of the Attorney General. In the midst of a national media firestorm and with evidence mounting that the University President, Athletic Director and Head football Coach had been aware of Sandusky’s activities, Penn State President Graham Spanier resigned and the Board of Trustees relieved Paterno of his duties. They also placed the Athletic Director, Tim Curley, and Vice President, Gary Schultz, on administrative leave after being indicted for perjury regarding their testimony about their knowledge of Sandusky’s sexual assaults of young boys. Immediately after the firings and suspensions, students poured into campus and downtown State College, causing damage and flipping a news van [ 69 ]. Various student protests persisted for weeks. The following summer brought Sandusky’s conviction, but controversy has not subsided, especially in Pennsylvania. The firing is continually alive at Penn State, as lawsuits against the university and the trials of Spanier, Curley, and Shultz continue to progress as Paterno’s family and supporters seek to restore his legacy.

While the real-life context of our design adds to its external validity, the discussion topic’s high salience and likelihood of evoking a strong opinion also improves the internal validity of the experiment. Paterno was more than an employee; he was the image of Penn State, “an extension of [the students’ and alumni’s] collective self” ([ 70 ], 154), and thus tied to students’ identities as members of the community [ 71 ]. As reported at the time of the scandal:

“More than any other man, Mr. Paterno is Penn State–the man who brought the institution national recognition… Paterno is at the core of the university’s sense of identity.” [ 72 ].

Given the emotion surrounding this issue, it is not unlike morality policies that evoke strong responses from individuals [ 73 ], thereby providing a hard test of conformity pressure on value- and identity-laden opinions. There is no better example of this than the ongoing pursuit of justice by the children subjected to abuse by Catholic priests and the mounting evidence of systematic concealment and enablement of such abuse by the Catholic Church. The similarities between Penn State and the Church persist on nearly every level, including the scandals threatening an important aspect of its members’ identities. In this way, the experience of students following the child abuse scandal at Penn State generalizes to politically relevant circumstances where organizational power and personal identities are challenged.

In addition to being a highly salient and identity-laden topic of discussion, the Paterno firing is a social and political issue. It weighed heavily on the 2012 Board of Trustees elections, when many candidates campaigned on their support for Paterno. Furthermore, Pennsylvania Governor Tom Corbett was a de facto member of the Board and originally launched the Sandusky investigation while serving as the state Attorney General. As a board member, Corbett advocated for Paterno’s firing and faced both praise and criticism across the Commonwealth. As a result of the scandal, Pennsylvania passed legislation that clarifies responsibilities for reporting child abuse and heightens penalties for failures to report. The abuse received national recognition. When asked for his reaction to the firing, President Obama called on Americans to search their souls and to take responsibility for protecting children [ 74 ]. Thus, there is recognition by elites, the public, the media, and the academy that Paterno’s firing is an inherently political issue. Furthermore, the topic has personal importance to the participants, is identity laden, and relevant at the local, state, and national-levels. Having described the context of the topic of discussion, we now turn to describing the experimental protocol.

Participant recruitment

The experiment was advertised as a study on political discussion in upper- and lower-level social science courses, as well as through campus fliers and a university research website. As an incentive, participants were entered into a raffle for one of eight $25 gift cards to Amazon. The first participants completed the study in May 2013 and data collection closed in December 2013. There were no major developments in the Sandusky scandal during our data collection phase, thus we believe that no outside events threaten the validity of the study. The firing of the four university officials, Joe Paterno’s death, Jerry Sandusky’s conviction, issuance of the Freeh Report, and the National Collegiate Athletic Association’s sanctions all occurred prior to the start of data collection. This study was approved by the Pennsylvania State University Office for Research Protections Institutional Review Board (Study# 41536) on February 20, 2013. All participants in the treatment group signed a written informed consent form prior to participating in the study. Participants in the control group supplied implied consent by completing the online survey after reading an informed consent document on the first web page of the survey. Penn State’s IRB approved both methods of consent. Consent materials can be found with other study reproduction materials at the corresponding author’s dataverse ( http://dx.doi.org/10.7910/DVN/YVCPDT ). Thus, all participants provided informed consent and all procedures contributing to this work complied with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975.

A total of 58 students participated in either the treatment or control groups. Compared to observational studies, this may appear a small number, but it comports with current research norms that require high participant involvement and a substantial amount of their time [ 75 , 76 ] and is consistent with the sample sizes for the foundational work in this area [ 2 , 6 ]. The pre- and post-test, discussion session and debriefing required at least 1.5 hours of each participant’s time. Researchers spent, on average, at least eight hours per participant recruiting, coordinating, and scheduling discussion groups, running discussion sessions, and coding behavioral data. The study generally targeted current undergraduates, but three graduate students and one recent graduate also participated. Upon volunteering to take part in the study, participants were randomly assigned to either the treatment (n = 34) or control (n = 24) group using a coin flip. The total sample includes an un-randomized 16 person pilot of the experimental protocol. See S3 File for additional information on this pilot group, its characteristics, and analyses showing their inclusion does not affect the main findings.

Pre-test survey

Fig 1 presents the study design including information provided to the treatment and control groups (in black) and the points at which we measured their opinion regarding Paterno’s firing (in red). Both groups were administrated a pre-test survey using Qualtrics. The treated group completed this survey before attending a discussion session. In addition to basic demographic characteristics, we collected a number of psychological and behavioral traits for every participant. Ideology was measured by an attitudinal measurement of ideology, a Liberalism-Conservatism scale [ 77 ] widely used to prevent measurement error that arises from the difficulty in accurately collapsing a complex view of politics into a single dimension. This measure of ideology is well validated (e.g., Bouchard et al. 2003) and serves as the basis for modern definitions of ideology across disciplines [ 78 , 79 ]. The measure relies on respondents simply agreeing or disagreeing with a broad range of political and social topics, from evolution to taxes. In this case, we used 48 different topics, which generate an additive scale of conservatism ranging from 0 (very low) to 48 (very high). In addition to measuring our participant’s political ideology, we assessed their self-esteem using Rosenberg’s [ 80 ] scale and personality using McCrae and John’s [ 81 ] 44-question Big 5 dimensions of personality: openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

This figure presents each phase of the study, including information provided to treated and control groups (in black) and the points at which we measured their opinion of the Paterno firing (in red).

https://doi.org/10.1371/journal.pone.0196600.g001

Finally, all participants were asked their opinion on five policies that affect undergraduates at Penn State: alcohol possession on campus; government oversight of academic performance; the firing of Paterno; prevention of State Patty’s Day celebrations; and use of the student activities fee. Participants recorded their opinion using a five-point Likert scale from “strongly agree” to “strongly disagree.” We included five different topics on the survey so that treatment group participants would be unsure as to which topic they would be discussing.

Discussion group

After completion of the online survey, participants in the treatment group were scheduled individually for a discussion session. Each discussion group was comprised of a single participant and two to four trained confederates (we compare differences in the number of experimenters and find no effects; for more information see S4 File ). A total of five unique confederates, three females and two males, were used across the length of the study. Among them were four political science Ph.D. candidates of varying experience and one recent graduate who majored in political science. The confederates looked young and dressed informally, and were not distinguishable from our undergraduate students. In terms of training, the confederates were not strictly scripted so that the discussion would not appear forced or scripted. Instead, the experimenter and other volunteers took part in pre-experiment tests as mock participants so that the confederates could argue both sides of the Paterno firing and develop the consistent points they used for the duration of the study (see S2 File ). Fig 2 shows a typical discussion session. Discussion sessions were held in a conference room with all of the group members sitting around a table. There was no fixed seating arrangement.

Clockwise from bottom left: Experimenter, confederate, confederate, participant, and confederate. Note the participant’s seemingly disengaged body language. This participant ultimately changed their opinion.

https://doi.org/10.1371/journal.pone.0196600.g002

At the beginning of each discussion session, the experimenter reminded the group that the general purpose of the experiment is to understand political decision-making and how individuals form political opinions. They were told that a topic was randomly selected for each discussion group from the five included in the pre-test survey, with their topic being the firing of Paterno. Prior to the start of open discussion, group members were provided a sheet of excerpts from the Freeh Report [ 82 ] regarding Paterno’s involvement in the Sandusky scandal at Penn State (see S1 File ). They were told that the information was drawn from independent investigations and was meant to refresh their memories, given that two years had passed since the firing.

After providing time to read the information sheet, the group was polled verbally regarding whether or not they believed Paterno should have been fired (yes or no). The participant was always asked to answer first. This allowed the confederates to subsequently express the opposite opinion throughout the discussion. Though very little time passed between completion of the pre-test surveys and participation in the discussion groups, we did not rely on the opinions expressed in the pre-test surveys as the basis of our confederates’ opinion. We recorded and used the verbal response as the respondents’ opinion. This also ensures that our confederates were responding to the precise opinion held by the participant at the start of the discussion session. This way we could track the effect of conformity pressure on their opinion throughout the session.

The group was then provided 30 minutes for open discussion; however, discussion was allowed to go beyond 30 minutes in order allow participants to finish any thoughts and reflect a more natural interaction. During this discussion, up to four confederates argued the opposition position to greater or lesser degrees depending on the confederate, including responding to and interacting with the participant and even agreeing with the participant on certain points. At the conclusion of the discussion time, group members were told that researchers wished to understand their true opinion at that moment and that we would be aggregating the individual opinions from our groups in order to gain a sense of overall student opinion on each of the five topics. Thus, they were instructed to complete an anonymous ballot with their final opinion. The anonymous ballot allowed us to measure whether their opinion had actually changed during the discussion, conforming to other people’s behavior due to private acceptance that what they are saying is right, or were only publicly complying with other people’s behavior, without necessarily believing in what they are doing or saying.

Each discussion session was video recorded for the purposes of coding both verbal and non-verbal indications of their opinion. Two coders were hired to review each discussion session video and record a series of behavioral characteristics of the participants (not reported in this paper) as well as their impression regarding whether the participants verbally changed their opinion during the course of the discussion (a binary yes/no). The principal investigators also coded each video. We used the modal code from all four coders, with the principal investigators re-reviewing the videos to break six ties. Fleiss’s Kappa [ 83 ] indicates moderate agreement among raters on the verbally expressed opinion (0.54, p < 0.001).

The combination of anonymous balloting and video recording for verbal cues is an important aspect of the study design that allows us to pull apart whether participants conformed out of a desire to be right, liked, or a combination of the two. Finally, we debriefed each participant to explain the full purpose of the study, including any and all possible points of deception, and to gather information about their personal feelings on being in the minority during the discussion.

Control group

We utilized a control group in order to identify the independent effect of social pressure on opinion change. Their behavior established a baseline expectation for the amount of opinion change we could expect with just the introduction of new information and no interpersonal interaction. This baseline then allows us to compare the two groups, social influence treatment and control, in order to tease apart the independent and joint effects of social conformity pressure and information on opinion change.

To this end, the control group took the same pre-test survey as the treatment group. However, after completion of the survey, instead of being in a deliberative session, control group participants read additional information on a topic that was “randomly” selected from the five opinion questions. Based on their opinion regarding the firing of Paterno, we presented them with the same sheet of information provided to the treated as well as a summary of the same pro- and counter-arguments used by the actual confederates during the discussion group sessions (see S1 and S2 Files). After reading these, control group participants were asked whether they believe Paterno should have been fired (yes or no) and the strength of that opinion (very strongly, somewhat strongly, neutral). If they changed their opinion at this juncture, we consider they did so only because of the introduction of new information, as there was an absence of social pressure. Thus, our design allows us to parse out the effect of the discussion group and the social pressure emerging from an unanimity of opinion opposite to the participants.

Results and discussion

The core finding of this study revolves around the question to what extent will people conform to an opposing opinion on a topic that is salient, politically charged, and informs some aspect of their identity? Furthermore, can we evoke deviation rates similar to the foundational studies that relied on less complex aspects of one’s psychology [ 1 ]? And most important, what type of change is occurring? For those participants who changed their opinions, was it due to new information (i.e., private acceptance), social pressure (i.e., public compliance), or some combination of the two? To answer these questions, we first examined the degree of opinion change in both the treatment and control groups. For the control group, we compared their initial opinion from the pre-test survey with the opinion they provided after reading the information sheet and counter-arguments. Fig 3 displays the percentage of each group that did and did not change their opinion. Within the control group, which received the same information as the discussion group, but had no social interaction, only 8 percent of the participants changed their opinion. The information-based change we observed is consistent with extant research [ 84 , 85 ]. In addition, though a large proportion of the control group did not change their opinion, some did moderate it (i.e., strengthened or weakened) based on the receipt of new information alone. See S5 File for a further breakdown of these changes.

https://doi.org/10.1371/journal.pone.0196600.g003

Turning to the treatment group, 38 percent of our treated participants changed their opinion between the initial vote (after receiving information and prior to the discussion) and the final secret ballot. Our complex, identity, and value-laden topic returned findings that comport remarkably close to the deviation rates of Asch [ 2 ] and those that follow (for a meta-analysis, see [ 6 ]). If we consider all other things equal, the 30 percent increase in opinion change is dependent on the treatment of participating in the group discussion (χ 2 = 5.094, p < 0.05). This finding remains unchanged if the 16 non-randomized members of the pilot study are removed from the treatment group (though the p-value of the chi-square declines to 0.10, due to the smaller n, see S3 File ). As further evidence, Table 1 presents logistic regression results demonstrating the treatment effect. Namely, being in the treatment condition increases the odds of opinion change by 581 percent. Meaning, social pressure and/or the personal delivery of information, as opposed to simple exposure to new information, had a profound influence on either true opinion change through private acceptance or conformity through public compliance. Due to the small sample size, we are hesitant to include additional covariates in this model, but instead use t-tests below to examine differences in the characteristics of participants who changed their opinion and those who did not.

https://doi.org/10.1371/journal.pone.0196600.t001

Sources of change

Moving to our secondary analyses, the research design also allowed us to parse out the specific sources of change within the treatment group. Recall we accounted for both true opinion change (i.e., the anonymous ballot at the end of discussion) and verbal opinion change (i.e., declared opinion change during group discussion captured in video and coded by independent raters) for those in the treatment condition. Therefore, we divided those in the treatment group into four subgroups in order to better understand why they changed their opinion. Table 2 shows the percentages of participants in the treatment group who changed their opinion overtly, covertly, or not at all. In sum, 47 percent did not change their opinion between the start and end of the discussion session. A total of 33 percent changed both overtly and covertly, meaning they verbally expressed an opinion change and wrote a changed opinion on their secret ballot. We argue that this group responded to a combination of the desires to be right and liked. Of the remaining participants, 10 percent changed due to a desire to be liked (overtly, but not covertly) and 10 percent due to a desire to be right (covertly, but not overtly). Though only anecdotal, one of the participants in the desire to be right category went so far as to tell the experimenter that he agreed with the group but adamantly refused to agree openly. Such participants were swayed by the introduction of new information out of a strong desire to be right, but apparently did not want to look like they were changing their opinion. Thus, our first set of analyses confirms that information plays an important role in opinion change, but social pressure also has a substantive and, at least in this context, a larger effect. For even a topic so important to one’s identity, participants changed their previously held opinions.

https://doi.org/10.1371/journal.pone.0196600.t002

Psychological differences

Having established the main findings of our study and the relative import of the two causal mechanisms for why participants changed their opinion, we now turn to examining how underlying traits, including ideology, personality, age and sex, differ between those that changed their opinion and those that did not. Demographic differences are included for descriptive purposes. First, we assessed differences between pro- and anti-firing participants. Second, we examined the relationship between direction of opinion change and trait differences between participants that changed their opinion and those that held firm. Due the nature of the experiment and specific focus on the question of causality, these tests are secondary to the main findings in the paper. For the following analyses, the sample sizes are small and in some cases and the findings only speculative.

Across both the treatment and control groups, the pre-test survey showed almost two-to-one support for Paterno keeping his job (i.e., against the firing). As mentioned earlier, “JoePa” was not only a symbol of Penn State, but also an icon to its students, and to some degree seen as a reflection of them. Table 3 displays the average demographic and psychological measures for those for and against the firing, based on the pre-test survey. The only statistically significant difference between the groups is their political ideology. The group opposed to Paterno’s firing is, on average, more conservative in their attitude positions than those that called for his firing. It is important to note that these are college students, and thus the overall distribution of ideology exhibits a liberal skew. However, Fig 4 demonstrates that the pro-firing group is not only less conservative, on average, but is also more ideologically narrow, whereas those that did not support the firing are more conservative, but also drawn from a wider ideological span. This finding suggests that ideology is a substantial factor for individuals that supported the firing. Whereas support for Paterno may have a less pronounced ideological dimension, those supporting his firing may focus more narrowly on the issue of child abuse and the responsibility of those in leadership to protect vulnerable citizens. Given that ideology is the only difference we could identify among participants’ opinions prior to the start of the experiment, we next examined whether there were differences between participants who changed their opinion and those that did not in both the treatment and control conditions.

https://doi.org/10.1371/journal.pone.0196600.t003

https://doi.org/10.1371/journal.pone.0196600.g004

Tables 4 and 5 provide a sense of how demographic and psychological characteristics differ between participants who changed their opinion and those who did not. Table 4 includes both treatment and control participants, whereas Table 5 focuses solely on the treatment group. We found evidence both supporting and refuting our hypotheses presented above. There were consistent significant differences ( p < 0.05) in conservatism and conscientiousness. Namely, participants who changed their opinion are less conservative and less conscientious. Given the reported relationships between these two traits, this finding makes sense. Additionally, when all subjects are pooled ( Table 4 ), there is also a significant difference in neuroticism, with opinion changers registering higher on this scale. Both suggest that political and psychological traits may play a role in the mean shift demonstrated above. There were no differences based on the number of confederates. Meaning, participants were no more or less effected by social pressures from greater (4) or fewer (2) opponents in the discussion environment. These results demonstrate that individual differences exist across individuals that change their opinion and those that do not. Additional research will be required to both confirm and expand upon these findings. What we do find, however, is in line with expectations derived from past research and points to useful areas of future inquiry.

https://doi.org/10.1371/journal.pone.0196600.t004

https://doi.org/10.1371/journal.pone.0196600.t005

All participants were debriefed upon completion of the discussion and informed to all aspects of the study. Participants were asked during the debriefing how they felt about being the only dissenting voice. Forty-seven percent of the treatment group participants freely offered that they felt pressured or intimidated. Twenty-nine percent also freely said that they felt like they had to dig in and defend their position during the discussion. This included six people that ultimately changed their minds. One said, “I’m not getting any support in this room. Alright I’ll defend my own position.” Another said, “I feel extra pressure to explain myself.” For some, their defensiveness continued into the debriefing. In particular, some students that did not change their opinion continued defending themselves when talking one-on-one with the experimenter, even after it was explained no matter which position they took, they would face opposition. This demonstrates that some participants are put on the defensive when faced with a unified opposition. Of those that expressed feeling defensive, some dug-in deeply and did not budge at all, while others opened up to the influence of their peers as the discussion progressed. This behavior comports the foundational work of Asch [ 1 , 2 ] and Milgram [ 86 ] and strongly suggests that our participants indeed experienced social pressure in the treatment condition, but differs in that it highlights the variance in how individual’s react to such pressure.

Limitations

We wish to call attention to two specific limitations of this study that are discussed above and in the supplementary materials, but warrant further mention. The first limitation is the inclusion of a meaningful, relative to the overall sample size, non-randomized pilot of the treatment condition. While this had no substantive effect on the results, it is important to recognize and we discuss this in more detail in the S3 File . Second, Fig 1 makes apparent that we use two similar, but slightly different scales for opinion throughout the study. Namely, pre-test opinion is measured on a five-point Likert scale and the remaining opinion measures are dichotomous (yes/no), with an additional strength question for the control group. Our primary analyses, however, rely on the comparison of the two yes/no answers in the treatment group; the verbal designation of yes/no at the beginning of the discussion section and the yes/no in the post discussion ballot. We further discuss this in the S5 File .

Finally, to some the small sample size of the study may be a limitation, especially those concerned about a replication crisis in Social Psychology [ 87 ]. We would respond, however, that the intensive nature of this study in terms of researcher hours and treatment condition makes it difficult to scale-up. Thus, a multi-site replication is likely the best approach to assessing the veracity of these findings [ 88 , 89 ]. We encourage such replication and have provided all materials necessary on the corresponding author’s Dataverse ( http://dx.doi.org/10.7910/DVN/YVCPDT ). Additional lessons relevant to replication work and laboratory experiments in political science can be found in Mallinson (2018) [ 90 ].

Conclusions

While researchers have examined the roles of social influence (public compliance) and new information (private acceptance) on opinion change, the two are less often examined concurrently and the explicit causal arrows are more often assumed than tested through an experiment. Furthermore, social conformity is a complex concept to measure through surveys or interviews alone. Live interaction provides an optimal means to understand social pressures. Our experiment was designed specifically to further unpack the causal mechanisms underlying opinion change and test whether a person’s values and identity are subject to social pressure. Furthermore, the selection of the topic of study, the firing of an important symbol of Penn State, also allowed us to explicate the extent to which information and social pressure challenge a person’s deeply held values and identity. We find that while information has an important role in changing people’s opinions on a highly salient topic that is attached to a group identity, the social delivery of that information plays a large and independent role. Most individuals that changed their opinion did so out of some combination of the two forces, but there were people who only changed their opinion overtly in order to gain social acceptance as well as those who did not want to give the appearance of changing their mind, but still wanted to be right.

These findings have important implications for research on social and political behavior. They reinforce the understanding that citizens and elites cannot be simply viewed as rational utility maximizers independent of group dynamics. Yet, at the same time, the desire to be right and information remain critical components of opinion change. Furthermore, there are important individual differences such as ideology, self-esteem, and personality that appear to have a role in conformity. Exposure to politics and political discussion are fundamentally social, and therefore behavior is conditioned on the combination of the information one receives, and the social influence of the person or group providing that information interacting with one’s disposition. All should be considered when examining any inter-personal, social or political outcome. Be it a deliberative setting like a jury or a town hall meeting or informal gatherings of citizens, or political elites for that matter, changes in behavior are not simply due to rational information-driven updating, and even when they are, that updating may be pushed by the social forces that we experience in our interactions with other humans in variegated ways dependent upon the characteristics of the individual (for example, see [ 91 ]). This was the case for simple and objective stimuli, like Asch’s lines, and it is also the case in our context-laden experiment that focuses on the complexities of personal identity and opinion. That is, the conformity of social and political values relies on the same psychological mechanisms underlying general conformity.

Beyond theoretical and empirical importance for the study of social and political behavior, these findings also hold normative importance for democratic society. The normative implications are perhaps best exemplified by the organizational and personal turmoil that followed the revelation of child abuse by priests in the Catholic Church. Politics forms important aspects of the social and personal identities of elites and citizens, more so today than ever before [ 92 , 93 ]. People include their political party, positions on particular issues (e.g., environmentalism), and membership in political, religious, social and academic organizations, among other things, as key aspects of their identities. Our experiment helps us better understand how individuals behave when part of that identity is challenged.

That being said, no design is perfect, and this experiment only unpacks part of the causal mechanism. Like the early work on social conformity, it serves as a foundation for future studies to extend upon and further explicate the causal mechanism. For example, an extension on this design, such as controlling variation in the type and number of confederates [ 44 , 94 ], could help us better understand the nature and amount of pressure necessary to induce conformity across a variety of individual characteristics. For example, a potentially fruitful avenue of extension would be to provide the participant with one supportive confederate who verbally changes their opinion during the discussion. Having support reduces conformity pressure, but deviation by that support should increase it. Additionally, while we identify individuals whose behavior was prompted by either social pressure or information, the largest group responded to a combination of the two. Further parsing out the interaction between information, persuasion, pressure and the complexity of human dynamics will require an even more complex research design on a larger scale. The numerous extensions of Asch’s original experiment demonstrate the wealth of potential extensions of this design that can help unpack this black box. Doing so requires an incremental approach that will be time and resource intensive. This study provides the foundation for those next steps.

Supporting information

S1 file. information sheet provided to both treatment and control groups..

https://doi.org/10.1371/journal.pone.0196600.s001

S2 File. Confederate talking points.

https://doi.org/10.1371/journal.pone.0196600.s002

S3 File. Randomization.

https://doi.org/10.1371/journal.pone.0196600.s003

S4 File. Use of deception in the study design.

https://doi.org/10.1371/journal.pone.0196600.s004

S5 File. Breakdown of opinion change in the treatment and control groups.

https://doi.org/10.1371/journal.pone.0196600.s005

Acknowledgments

An earlier version of this paper was presented at the 2013 American Political Science Association Annual Meeting in Chicago, Illinois, and the April 2014 Center for American Political Responsiveness Brown Bag in State College, Pennsylvania. We would like to thank the editor, the anonymous reviewer, Ralf Kurvers, Rose McDermott, and conference attendees for their helpful comments and suggestions on this manuscript. We are also grateful to Ralf Kurvers for providing Fig 1 . We would like to thank our research assistants, Ronald Festa, Emilly Flynn, Christina Grier, Christopher Ojeda, Kimberly Seufer, and Matthew Wilson, that helped make this experimental protocol a success.

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Informational Social Influence (Definition + Examples)

How do you know what to do or what decisions to make? This is a big question, but it’s one worth considering. We often make decisions without even thinking about the people, information, or other factors that play into those decisions. Most of the time, this is okay, but following the crowd or relying on the influence of others doesn’t always help us make the best decisions. Informational social influence, or social proof, can lead us astray.

On this page, you will learn more about informational social influence and how it contributes to our everyday decisions and behavior. 

What Is Informational Social Influence? 

Informational social influence occurs when people look to others for information on how to behave. This is also known as social proof. We also use social proof to affirm our decisions. Although we may be influenced differently by different people, informational social influence often aligns with our “gut.”

Who Discovered Informational Social Influence? 

Although studies on informational social influence go back to Sherif’s work in the 1930s, “social proof” was first introduced as a term by Robert Cialdini in 1984. His book, Influence: Science and Practice, is significant in modern psychology.

Cialdini is most known for his work on persuasion. Social proof, or consensus, is considered one of his six principles of persuasion and influence. 

How Does Informational Social Influence Work?

Three factors play into the effectiveness of informational social influence: confusion, chaos, and self-categorization. 

Some situations are more confusing than others. Let’s say you’re looking to eat in your hometown. You probably know what restaurants are in the area. If you are deciding between chain restaurants, you may even know what foods are on the menu and the quality of your meal. You don’t need to go online and look up reviews for the local Applebee’s or IHOP. But what if you’re in a foreign country? Every restaurant is completely new to you. The cuisine is unfamiliar. How do you know a good restaurant from a not-so-good restaurant?

This is where information social influence comes in. Maybe you look up reviews or walk down the street and see what is busy. 

In a moment of chaos, you need to make a split decision. There is no time to look up reviews or do proper research. This is when informational social influence comes into play. You’re at a concert when you hear a large explosion. You see people running away from the stage, so you follow. Maybe this isn’t a conscious decision, but it’s made using social proof.

Importance of Self-Categorization

In a moment of chaos like the one mentioned, who do you look to? If the only people you can see are concertgoers, you may rely on their judgment. But what if you see a firefighter telling you to go in a certain direction? People are likely to turn to “experts” or those who have more authority than them. When you decide to buy a house, you are likely to take the advice of a realtor who knows the area. At a concert, you follow the instructions of the staff or even the person performing. On the other hand, you may not take the advice or be influenced by someone you believe has less authority than you. 

How we categorize ourselves and others is a central idea within many social psychology theories. 

More Examples of Informational Social Influence 

• You’re in a new city and unsure where to go to dinner. When you look for dinner places on your phone, you find an option that is rated 4.5 stars by 1,000 people and an option that is rated 2.5 stars by 1,000 people. This information tells you that the first option is probably pretty good.
• It’s your first time at a farmer’s market, and you’re unsure whether you can bring your dog. As you look around, you see a few people walking their dogs. You decide that it’s probably okay to bring your dog, too. 
• At school, the power goes out. Immediately, the professor tells you to wait out the situation because this happens often. You listen and stay calm. 

Informational vs. Normative Social Influence 

What happens if the information you are given doesn’t align with your judgment? Maybe one restaurant looks very delicious, but everyone around you is raving about a different restaurant. You think “C” is the right answer to the test, but everyone else is saying “B” is right. Do you change your mind? 

This is what Solomon Asch wanted to find out when he put together one of the most influential experiments in psychology: the Asch Line Study . The study asked participants to conduct a simple exercise. They were shown one line and three lines of different lengths. Researchers then asked a series of people, including the participant, to identify which lines were the same length. One answer was obvious, but the other people in the room, all actors, chose the wrong answer. 

What did the participants do? Did they answer what they thought was right, despite everyone else saying something different? About two out of three participants did. However, over a third of participants chose the wrong answer to fit in with the crowd. 

Sometimes, we make decisions just to fit in or be accepted by other people. This is called normative social influence. It’s slightly different from informational social influence. Normative social influence doesn’t rely on what is logical or right - just what everyone else thinks. 

Can’t We Combine These? (Referent Social Influence)

What about the decisions we make when we combine the influence of others with the desire to be correct and logical? Psychologists have identified this type of influence and called it referent social informational influence .

No one form of social influence is “better” than the other. We may make decisions due to any of these influences depending on the stakes at hand, the people we are around, or the information accessible to us. Think about some recent decisions that you made. Did you make them so that you could fit in with the crowd or because that’s simply what the crowd was doing then? Did a combination of both influence you? When we step back and think about how we make decisions, we might surprise ourselves!

How to Use Informational Social Influence On Others

Knowing what you know about being influenced, you can adjust your speeches, language, and messages to influence others. It’s not recommended that you create chaos or put people in an emergency so they listen to you, but these quick tips could help you get your intended message across to others and influence their decisions. 

Establish yourself as an authority figure. People are more likely to listen to you if they believe you have some expertise in your field or if you’re an authority figure. You don’t need to get a degree to give off this impression. Be confident when speaking. Dress sharply and professionally. Share the experiences that make you an expert, or at least knowledgeable in the subject that you’re sharing. 

Create confusion. It could be fun to create confusion or ambiguity while giving a presentation. This can grab a listener’s attention and intrigue them. Just be aware of whether the confusion you’re creating will misinform listeners. You only want to use confusion as a hook briefly.

Back up your message with more social proof. Are there reviews or testimonials that back up what you have to say? Share them! Maybe you want to tell people you’re a great plumber. Reading or sharing reviews from community members who enjoyed your services will further convince people that you are who you say you are. Let the social proof of others do all the work!

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Explanation Of Conformity NSI And ISI

March 4, 2021 - paper 1 introductory topics in psychology | social psychology.

• Back to Paper 1 - Social Psychology

Explanation Of Conformity The Dual Processing Dependency Model, Gerrard And Deutsch:

AO1, Description:  Any explanation of conformity has to make reference to the two types of conformity Internalisation and Compliance. For your exam, not only do you need to be able to define these two types of conformity, you also need to be able to describe why these types of conformity occur. Thankfully, one explanation/theory can be used to explain both these types of conformity. This theory is call  The Dual-Processing Dependency Model.

The Dual-Processing Dependency Model, Gerrard and Deutsch (1955)

Deutsch and Gerard (1955)  developed the Dual-Processing Dependency model which suggested that  people conform for one of 2 reasons:

• Normative social influence (NSI) (explanation of  compliance )
• Informational social influence (ISI) (explanation of  internalisation )

AO1, Description Normative Social Influence (NSI) Explanation Of Compliance:

This relates to an individual  adapting to a group position in order to be accepted  and gain approval and not be perceived as deviant by the other members of the group.   It is based on the  desire to be liked .  This type of influence also occurs as it is rewarding to be accepted and be a part of a group.  This usually involves  public compliance  in a group we may go along with the behaviour and the attitudes of others without truly believing or accepting it.  In this instance, we do not privately accept what we are saying or doing publicly.

Research to illustrate NSI:

Asch’s study can be seen to confirm this explanation of compliance. In Asch’s study, the participants were aware that they were giving an obvious incorrect answer, in interviews after the experiment the participants confirmed that the reason why they did this was because they wanted to be liked and accepted into the group/majority.

AO3: Explanation Of Conformity Evaluation Of The NSI Explanation Of Compliance:

(1)  Point:   Research has supported the normative social influence explanation as to why people conform.  Evidence:  For example,  Asch’s  (1951)  research demonstrates how individuals will conform with the majority on an unambiguous line comparison test (even when they know their response is incorrect) in order to be liked or in an attempt to avoid standing out from the group.  Evaluation:  This is a strength because  it shows that the normative social influence explanation is a valid assumption as to why people conform with the majority (i.e. for group approval).

(2) Point:  Furthermore, the practical value of this explanation has been highlighted in recent research emphasising the role of normative social influence in bullying.  Evidence:  For example, Garandeau and Cillessen (2006)  have shown how groups with a low quality of interpersonal friendships may be manipulated by a skilful bully so that the victimisation of another child provides the group with a common goal creating pressure on all group members to comply.  Evaluation:  This is a strength because  the research illustrates that sometimes the desire for acceptance is so strong that it outweighs an individuals moral code, showing the NSI’s assumption that people conform for group approval is valid.

Weaknesses:

Point:  The normative Social Influence explanation can be criticised for not acknowledging the importance of belonging to a group.  Evidence:  For example,  many studies (Sherif and Rohrer) have shown how conformity to group norms can persist long after the group no longer exists.  Evaluation:  This is a weakness because  participants in an experiment cannot fear group exclusion which implies that  factors other than dependency  on the group may be important as regards to whether or not an individual conforms.

AO1, Description Informational Social Influence (ISI) Explanation Of Internalisation

In this case, people are unsure what to do in a situation (e.g. they may not know what is the right or correct way to act) and so  they look to   others with seemingly more information in order to identify correct behaviour .  Thus if a situation is ambiguous (no obvious right or wrong answer), we look to others as a source of information to help us perceive the situation accurately and reduce ambiguity.  We tend to seek guidance from people who we see as being better informed than ourselves.  It is based on the  desire to be right .  This usually involves  private  acceptance  (internalisation) in this case people conform to the norms of others because they genuinely believe that they are right. This can result in a change in private beliefs and attitudes.

Research to Illustrate ISI :

Jenness (1932) gave participants a task with no clear answer; estimating how many jellybeans were in a jar. He found that individual estimates moved towards the estimates of others, showing that they genuinely (privately) believed the estimates of others to be correct. Demonstrating internalisation true conformity. Jenness participants answered in secret and so did no have to fear group disapproval, therefore the fact that the individual answers reflected the group indicates that they believed them to be true, they looked to others for information in an ambiguous situation.

AO3, Explanation Of Conformity Evaluation Of Informational Social Influence Explanation

(1) Point:  Further research has supported the assumptions of the informational social explanation as regards to why people conform. 

Evidence:  For example, Fein et al (2007)  showed how judgements of candidate performance in US presidential debates could be influenced by the knowledge of others reactions. Participants saw what were supposedly the reactions of their fellow participants on screen during the debate. This produced large shifts in participants judgements of the candidates performance.

Evaluation:  This is positive as  the research demonstrates support for the informational explanation assumption demonstrating the power of informational influence in shaping opinion.

(2) Point:  Research has supported the suggestions of the informational social influence explanation as regards to why people conform.

Evidence:  For example, Sherif’s (1936)  research demonstrates how the exposure to other people’s beliefs (i.e. their estimates as regards to how far and in which direction to light spot moved) has an important influence on other participant’s estimates especially when the participants are uncertain about what to believe themselves. 

Evaluation:  This is a strength because  the research supports the informational social influence explanation of conformity and the assumption that individuals will be influenced by members of majority who appear more informed than themselves.

(1) Point:  Sherif (1936) study can be criticised as to the extent in which it demonstrates conformity. 

Evidence:  For example, Cardwell et al (1996)  suggests that Sherif’s study demonstrates how groups norms emerge and not necessarily the process of conformity ( specifically internalisation ). He suggests that majority influence means a majority influencing a minority who then conform to the majority view. In Sherif’s study there was no majority or minority group, simply a number of people who had different views. 

Evaluation:  This is a weaknesses because  if Sherif’s study is not a true demonstration of conformity and internalisation then it cannot be used in support of  informational social influence  as an explanation of conformity.

Psychologists have found that there are many factors that can affect the level of conformity. Take a look at the types of variables including;  group size, unanimity and task difficulty,  that can affect whether or not an individual goes along with the majority.

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Resistance to Social Influence - Social Support

Last updated 22 Mar 2021

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Asch’s (1951) research demonstrates the power of social influence through conformity and his variations provide an insight into how group size, unanimity and task difficult can increase or decrease the influence of the majority. Milgram (1963) on the other hand, highlights our susceptibility to obeying orders and his variations reveal the different variables that can increase or decrease our willingness to follow orders.

Since Asch and Milgram’s research, psychologists have examined explanations of resistance to social influence, our willingness to conform or obey, including social support and locus of control.

• Social Support

One reason that people can resist the pressure to conform or obey is if they have an ally, someone supporting their point of view. Having an ally can build confidence and allow individuals to remain independent.

Individuals who have support for their point of view no longer fear being ridiculed, allowing them to avoid normative social influence. Furthermore, individuals who have support for their point of view are more likely to disobey orders.

Evidence for this explanation comes from one of Asch’s (1951) variations. In one of the variations, one of the confederates was instructed to give the correct answer throughout. In this variation the rate of conformity dropped to 5%. This demonstrates that if the real participant has support for their belief (social support), then they are likely more likely to resist the pressure to conform.

Furthermore, evidence for this explanation comes from Milgram (1974). In one of Milgram’s variations, the real participant was paired with two additional confederates, who also played the role of teachers. In this variation, the two additional confederates refused to go on and withdrew from the experiment early. In this variation, percentage of real participants who proceeded to the full 450 volts, dropped from 65% (in the original) to 10%. This shows that if the real participant has support for their desire to disobey, then they are more likely to resist the pressure of an authority figure.

Variations from Asch and Milgram suggest that if an individual has social support then they are likely to resist the pressure to conform or obey.

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A Study Of Social Anxiety And Perceived Social Support

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.

Learn about our Editorial Process

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.

Social anxiety , a common mental health concern, can be greatly influenced by an individual’s perceived social support. Those with strong, supportive relationships often report lower levels of social anxiety, as they feel more secure and accepted in social situations. Conversely, individuals who lack a robust support system may experience heightened anxiety due to feelings of isolation and fear of negative evaluation. Developing social skills and building a reliable support network can be crucial for managing social anxiety . Improving communication abilities, assertiveness, and emotional expression can help individuals navigate social interactions more confidently and effectively. Moreover, having trusted friends, family members, or a therapist to provide encouragement, validation, and a safe space to practice social skills can be invaluable in reducing anxiety and fostering a sense of belonging.

• Social anxiety is associated with lower perceived social support among both men and women.
• Communication styles mediate the relationship between social anxiety and perceived social support, but the specific communication styles involved differ by gender.
• For both men and women, lower expressiveness explains some of the link between social anxiety and lower perceived social support.
• For men, lower preciseness also mediates the relationship between social anxiety and perceived social support. For women, lower verbal aggressiveness and higher emotionality play mediating roles.
• The study has limitations like using self-report measures and a predominantly female college student sample, but provides insights into how socially anxious individuals may be able to increase perceived social support through modifying communication styles.

Social anxiety is one of the most common anxiety disorders (Kessler et al., 2012) and has been linked to lower perceived social support (Calsyn et al., 2005; Davidson et al., 1994; Torgrud et al., 2004).

While the size of one’s social network influences perceived social support, other factors like satisfaction with available support matter more (Sarason et al., 1983).

Among women in relationships, self-disclosure helps explain the social anxiety-perceived support link (Cuming & Rapee, 2010), suggesting interpersonal communication plays a role.

However, more research is needed on how communication styles impact the relationship between social anxiety and perceived support, and potential gender differences.

Previous studies found socially anxious individuals are less emotionally expressive and assertive (Davila & Beck, 2002; Turk et al., 2005).

Gender differences also exist in social anxiety, communication, and social support. Women have higher rates of social anxiety (Asher et al., 2017) and are more expressive and polite, while men are more assertive and verbally aggressive (Basow & Rubenfeld, 2003).

Women tend to provide, receive (Neff & Karney, 2005), and perceive more social support than men (Kendler et al., 2005).

Building on this research, the current study examined six communication styles as mediators between social anxiety and perceived social support among men and women.

Understanding these relationships could inform interventions to help socially anxious individuals harness communication skills to increase perceived support.

This study explored relationships between social anxiety, perceived social support, and six communication styles (expressiveness, preciseness, verbal aggressiveness, questioningness, emotionality, and impression manipulativeness) among college students.

It also examined gender differences in these variables and whether communication styles mediated the link between social anxiety and perceived social support differently for men and women.

Participants completed an online survey with measures of social anxiety, communication styles, and perceived social support. The order of the measures was randomized.

813 psychology students (233 men, 580 women) at a large southern U.S. university participated. They ranged in age from 18-30 (M=20.56 years).

The sample was 52.8% White, 16.5% Black, 19.7% Hispanic, 8% Asian, and 3.1% other ethnicities.

• Social Interaction Anxiety Scale-6 (SIAS-6) & Social Phobia Scale-6 (SPS-6): 12 items total measuring social anxiety on a 5-point scale. The SIAS-6 assesses anxiety related to social interactions, while the SPS-6 measures fear of being scrutinized during routine activities.
• Communication Styles Inventory (CSI): 96 items assessing 6 communication domains (expressiveness, preciseness, verbal aggressiveness, questioningness, emotionality, and impression manipulativeness) on a 5-point scale. Each domain consists of 4 facets measuring specific aspects of that communication style.
• Multidimensional Scale of Perceived Social Support (MSPSS): 12 items measuring perceived support from family, friends, and significant others on a 7-point scale. The scale provides a subjective assessment of the adequacy of social support from these three sources.

Statistical Analysis

Descriptive statistics, bivariate correlations, and independent t-tests compared men and women.

Multiple mediation models using PROCESS tested communication styles as mediators between social anxiety and perceived support, separately for men and women.

As hypothesized, social anxiety was associated with lower perceived social support (H1).

For both genders, social anxiety was related to lower perceived support through lower expressiveness (H2).

Social anxiety was linked to lower support through higher emotionality for women only (H3).

The mediating communication styles differed by gender:

• For men, social anxiety was associated with lower support through lower preciseness.
• For women, social anxiety was linked to lower support through lower verbal aggressiveness and higher emotionality.

This study highlights that the way socially anxious individuals communicate influences their perceptions of available support.

While prior research found self-disclosure impacted the social anxiety-perceived support link just for women (Cuming & Rapee, 2010), the current study showed that for both genders, being less expressive and contributing less to conversations explained some of the relationship.

The gender-specific mediators align with research on gender norms in communication. For men, conveying ideas precisely and substantively seems important for feeling supported and fitting masculine norms around clear, outcome-focused communication (Mulac et al., 2001).

For women, lower verbal aggressiveness or a lack of assertiveness to marshal support when needed mediated the link, perhaps reflecting expectations for women to be less direct (Palomares, 2009).

Higher emotionality also played a role for women, suggesting that socially anxious women feel their sensitivity and emotional expressiveness could burden others and reduce support.

This study extends prior work by revealing communication styles as a mechanism linking social anxiety and perceived support. It suggests socially anxious individuals could benefit from interventions targeting communication skills to bolster confidence in their ability to garner support.

Future research could incorporate third-party observations of communication and longitudinal designs to clarify causal relationships.

This study had several strengths, including:

• Explored an understudied mechanism (communication styles) in the link between social anxiety and perceived social support
• Examined gender differences
• Used well-validated measures
• Tested multiple mediators simultaneously
• Large sample size (N=813)

Limitations

This study also had several limitations, including:

• Cross-sectional design prevents conclusions about directionality and causality
• Predominantly female sample limits generalizability
• Self-report measures introduce potential for bias; no behavioral observations of communication
• College student sample may not generalize to broader population

Implications

The findings suggest that psychotherapy for social anxiety could incorporate communication skills training to help individuals develop tools to increase their perception of social support.

Increasing expressiveness may benefit both men and women. However, clinicians may need to tailor interventions by gender, for example helping men communicate ideas clearly and succinctly, assisting women with healthy assertiveness, and normalizing emotionality.

With social anxiety one of the most prevalent disorders, better understanding its interpersonal effects and the role of communication could substantially improve sufferers’ relational experiences and mental health.

However, more research in representative samples using observational methods is needed to establish causal links and inform interventions.

Primary reference

Barnett, M. D., Maciel, I. V., Johnson, D. M., & Ciepluch, I. (2021). Social anxiety and perceived social support: Gender differences and the mediating role of communication styles.  Psychological Reports ,  124 (1), 70-87. https://doi.org/10.1177/0033294119900975

Other references

Asher, M., Asnaani, A., & Aderka, I. M. (2017). Gender differences in social anxiety disorder: A review.  Clinical psychology review ,  56 , 1-12. https://doi.org/10.1016/j.cpr.2017.05.004

Calsyn, R. J., Winter, J. P., & Burger, G. K. (2005). The relationship between social anxiety and social support in adolescents: A test of competing causal models.  Adolescence ,  40 (157), 103.

Cuming, S., & Rapee, R. M. (2010). Social anxiety and self-protective communication style in close relationships.  Behaviour Research and Therapy ,  48 (2), 87-96. https://doi.org/10.1016/j.brat.2009.09.010

Davila, J., & Beck, J. G. (2002). Is social anxiety associated with impairment in close relationships? A preliminary investigation.  Behavior Therapy ,  33 (3), 427-446. https://doi.org/10.1016/S0005-7894(02)80037-5

Kessler, R. C., Petukhova, M., Sampson, N. A., Zaslavsky, A. M., & Wittchen, H. U. (2012). Twelve‐month and lifetime prevalence and lifetime morbid risk of anxiety and mood disorders in the United States.  International journal of methods in psychiatric research ,  21 (3), 169-184. https://doi.org/10.1002/mpr.1359

Sarason, I. G., Levine, H. M., Basham, R. B., & Sarason, B. R. (1983). Assessing social support: the social support questionnaire.  Journal of personality and social psychology ,  44 (1), 127. https://doi.org/10.1037/0022-3514.44.1.127

Keep Learning

Here are some potential discussion questions for a college class on this paper:

• How might cultural norms and values impact the relationships between social anxiety, communication styles, and perceived social support? What cultural factors would be important to consider in future research?
• What are some specific ways psychotherapy could help socially anxious individuals modify their communication to increase perceived social support? What role could assertiveness training, expressive writing, or role-playing conversations play?
• If you were designing a longitudinal study to clarify the causal links between social anxiety, communication, and perceived support, what variables would you measure at each timepoint? What would be the ideal time lag between assessments?
• The current study found that emotionality was linked to lower perceived support for socially anxious women. However, could there be contexts where emotional expressiveness helps elicit support? What factors might moderate this relationship?
• How might the increasing prevalence of digital communication impact the relationships explored in this study? Would you expect communication styles to play a smaller or larger role in linking social anxiety to perceived support in online interactions compared to face-to-face?

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Characterization of the Core Determinants of Social Influence From a Computational and Cognitive Perspective

1 Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, South Korea

2 Department of Psychology, The University of Edinburgh, Edinburgh, United Kingdom

Dongil Chung

Most human decisions are made among social others, and in what social context the choices are made is known to influence individuals' decisions. Social influence has been noted as an important factor that may nudge individuals to take more risks (e.g., initiation of substance use), but ironically also help individuals to take safer actions (e.g., successful abstinence). Such bi-directional impacts of social influence hint at the complexity of social information processing. Here, we first review the recent computational approaches that shed light on neural and behavioral mechanisms underlying social influence following basic computations involved in decision-making: valuation, action selection, and learning. We next review the studies on social influence from various fields including neuroeconomics, developmental psychology, social psychology, and cognitive neuroscience, and highlight three dimensions of determinants—who are the recipients, how the social contexts are presented, and to what domains and processes of decisions the influence is applied—that modulate the extent to which individuals are influenced by others. Throughout the review, we also introduce the brain regions that were suggested as neural instantiations of social influence from a large body of functional neuroimaging studies. Finally, we outline the remaining questions to be addressed in the translational application of computational and cognitive theories of social influence to psychopathology and health.

Introduction

Most human decisions are made among social others. It is broadly observed that individuals' choice patterns sometimes vary and reflect the social information ( 1 , 2 ). These phenomena highlight the importance of the social context at which the decision-making is taking place. Individuals being exposed to such “social influence” may have positive consequences; the decision maker whose actions were swayed by observing others' choices may benefit from the influence (e.g., joining others in following daily athletic routine) or get oneself to participate in spreading the good deed (e.g., ALS Ice bucket Challenge). However, in many other occasions, social influence is considered as a crucial factor that affects individuals negatively. For example, negative peer influence is known as a major risk factor for early initiation of substance use and other risky behaviors ( 3 ), and in line with this, having close friends and family members who suffer from substance use disorder is one of the prominent predictors for individuals' substance use problem ( 4 ). These bi-directional impacts of social influence suggest that the mechanisms how social information affects individuals could be quite complex.

There has been abundant amount of research carried out to understand the breadth and levels of social influence in individuals' choices. In classic social psychology studies, researchers largely focused on the impact of social environment in adolescents, given that adolescence is a critical neurodevelopmental period ( 5 , 6 ). Due to the complex nature of the natural settings, analyzing questionnaire data based on self-reports was not sufficient to answer why the impacts of social environment on adolescents' delinquent behavior sometimes are positive ( 7 ) but some other times negative ( 8 , 9 ). Addressing this issue, over the recent two decades, various types of experimental paradigms have been suggested to examine the impact of overt (e.g., advice from an expert) ( 10 , 11 ) and covert (e.g., presence of peers) ( 12 ) social contexts. In parallel, computational modeling of behavioral data from laboratory settings has been found useful in disentangling potential factors and plausible neurobehavioral mechanisms underlying social influence. Yet, experimental designs in laboratory settings are typically restricted by the specific factors-of-interest (e.g., age group, delivery methods or contents of social information) in line with their hypotheses, and thus suggested computational models still have room for improvement.

In this review, we aim to review previous research on social influence from various fields of studies, and to suggest core factors that would play key roles determining how individuals process and respond to social contexts. In the next section, we overview the recent computational approaches suggested to explain why and how individuals are affected by social contexts. In the following three sections, we review three dimensions of determinants that are known (or expected) to modulate the extent to which individuals are influenced by others: characteristics of the individuals who are receiving the social influence, the forms that the influence is conveyed, and the domains and processes of decisions that the influence is modulating. In the last section, we discuss about future directions in understanding of social influence and its translational application to mental illness. Large proportion of the studies we include here also provided functional neuroimaging results, which further supported their suggested cognitive and computational models explaining how social information is involved in decision processes. Thus, whenever found necessary, throughout the current review, we also introduce the brain regions that were suggested as neural instantiation of social influence.

Computational Models of Social Influence

How does an individual make decisions under social influence? To answer this question, we need a better approach than simply observing individuals' behavioral patterns, because there could be different paths of decision processes that underlie the same exact choice. To shed light on the question, various studies in social influence used computational modeling approaches in conjunction with functional neuroimaging ( 13 – 20 ). Given that social information contributes to change of individuals' initial decisions, the extent to which individuals use or respond to social information is often explained within the framework of learning. However, depending on the specific goal of the task and the way how the social information is framed, potential motives that individuals are expected to show differ (e.g., following the norm, or collecting more information) and moreover, different learning models are suggested to best explain individuals' choice patterns (e.g., Rescorla-Wagner type reinforcement learning model, or Bayesian learner model) [for review, see ( 21 , 22 )]. In this section, we review putative mechanisms of social influence suggested in these recent studies following basic levels of computations involved in decision-making ( 23 ): valuation, action selection, and learning. Of note, we focus on cognitive processes that occur within individuals who are on the receiving end of the social information, and the mechanisms how one may decide to exert influence over others [e.g., ( 24 )] or how social information diffuses over a large group of people [e.g., ( 25 )] are out of the scope of the current review.

Adjustment of Individuals' Preferences

Under social context, on average, people tend to follow others' choices [( 2 ); c.f., ( 26 )]. One of the simplest explanation why people follow others' choices is that individuals become similar to social others who they are with. Previous studies suggested that having chances to observe others' choices sways individuals to change their own preferences—behavioral tendency how they make choices ( action selection ) in a particular context—to match that of social others. Individuals showed shifts in the extent to which they discount delayed rewards after observing the choices of the majority of the social group ( 27 ). Such a “contagion” of preference was observed even in the case when individuals were presented with choices from anonymous few social others rather than from a representative group. Individuals changed their choice behaviors (e.g., delayed reward, uncertain gambles, moral choices) after participating in a task phase where they were asked to predict others' choices, and the changes were explained by computational models that assumed shifts in individuals preferences toward the observed social others ( 19 , 28 , 29 ). These modeling results were corroborated by model-based neuroimaging results. Specifically, event-related blood oxygen-level-dependent (BOLD) responses in the dorsomedial prefrontal cortex (dmPFC), a brain region known to be recruited for social information processing ( 30 – 32 ), tracked individuals' beliefs about others' choices ( 19 , 27 ). This set of results suggested that individuals adjust their preferences in the direction that matches with social others, and in turn, show conforming behaviors.

Social Valuation

As any other decisions individuals make in life, choices under social contexts can be attributed to individuals' subjective valuation ( 33 ). This view assumes that individuals place value on the information obtained from social others and this additional social value can explain why they tend to make the same choices as social others. In contrast to the studies that reported individuals' preference change under social context, task contexts where individuals had a brief chance to observe others' choices successfully showed evidence for a transient use of social information. In recent studies, Chung et al. ( 17 , 20 ) used a formal model comparison and showed that a brief observation of social others' choices may affect individuals in their valuation rather than changing their preferences; the impact of observing others' choices on valuation was defined as “other-conferred utility”. Consistent with their model-based results, it was observed that the ventromedial prefrontal cortex (vmPFC), a brain region known to encode subjective values of social and non-social choices ( 13 , 34 , 35 ), tracked trial-by-trial decision values combining the social values in individuals' decision processes ( 17 ). Such an impact of social valuation was also observed in a learning context where individuals made choices whether or not to follow social others' advice ( 15 ). Specifically, individuals' advice following behavior was explained by their adaptive learning process in which the value of obtained reward (or punishment) gets modulated for the choices advised by others. This value level premium, termed as “outcome-bonus”, was tracked in the septal area and the caudate, brain regions implicated in signaling rewards and reward prediction errors ( 36 – 38 ). Another recent study suggested that individuals may encode social value in the anterior cingulate cortex (ACC) through vicarious simulation conducted from observing others' choices, and that this distinct value signal is combined with experience-based value signal in the vmPFC for subsequent decision-making ( 39 ). These results suggest that individuals' motivations to conform emerge from their computations of the value of social information and/or the value of sharing membership with the social group.

Learning From Social Others

The two perspectives introduced above are not mutually exclusive, but rather intertwined one another ( 22 , 23 ). At a first look, the results would seem contradictory such that some studies suggest stable and non-changing individual preferences [e.g., ( 17 )] whereas others suggest changes in preferences under social context [e.g., ( 19 )]. However, social learning framework provided explanation why and how such subtle differences in the contexts may trigger differential responses from individuals. When individuals receive social information that is deviant from their own, BOLD responses in the dorsomedial prefrontal cortex (dmPFC) associated with social and cognitive conflicts were observed ( 14 , 40 , 41 ). Moreover, it was shown that this error signal is used as social prediction error, which individuals use to reduce the difference between self and others by learning from social others ( 14 , 40 , 42 , 43 ). When individuals do not have a full access to social others' choice preferences or intentions (as in most of the social interactions), but believe others' choices are informative, individuals have to infer what others would be thinking to optimize one's own actions. In these contexts, individuals make inference about reliability of others' choices ( 44 , 45 ), emulate others' intention ( 46 ), and combine the inferred social information with their own ( 44 – 47 ). This set of results suggests that individuals are influenced by social contexts because they use the information in learning how to adjust their choices at a specific context (e.g., interacting with the same social partner repetitively, observing choices of randomly assigned partner).

As briefly reviewed above, cognitive mechanisms of social influence may take different forms depending on the context in which its impact is examined. Depending on how the social information is provided, individuals may use the information as a transient nudge toward others' opinion or as a normative guide directing them to be changed. To date, computational modeling approach has been found useful in delineating such variant mechanisms ( 21 , 22 , 48 , 49 ). However, there are still many remaining questions regarding the mechanisms, such as why some individuals are more susceptible to social information, and how does the value of a certain type of social information determined. To address these, we suggested that further practices in quantifying potential modulatory effects of latent variables are crucial. In the following sections, we review studies on social influence from various fields of studies and highlight three dimensions of determinants that are known to modulate the extent to which individuals are influenced by others.

Cognitive, Psychological, and Contextual Determinants of the Impacts of Social Influence

Who is more susceptible to social influence.

Everyone is bound to live under social influence, but some are more affected by others. Over the decades, a considerable amount of literature in social psychology has been published on the association between individual-specific characteristics and the extent to which individuals are influenced by social influence [e.g., ( 50 , 51 )]. The individual-specific characteristics that have been investigated across various fields of studies include demographics (e.g., age, socioeconomic status) and individuals' psychological characteristics (e.g., anxiety level, self-esteem). In this section, we review major factors that may mediate or modulate the impact of social influence on individuals' decision processes.

Demographic Factors: Age and Socioeconomic Status

Age has been considered as one of the most salient determinants that modulate social influence. Early pioneering research focused on the negative impacts of peers on adolescents' behavior. A seminal work by Gardner and Steinberg ( 10 ) showed that adolescents, compared to adults and young adults, take more risks when in peer groups. Adolescents' increased risk-seeking behavior was accounted for by the imbalance between adolescents' reward and cognitive control circuits ( 52 , 53 ). In line with this neurodevelopmental model, their heightened social susceptibility was suggested to be associated with socio-emotional neural system ( 54 , 55 ). Supporting these neural sensitivity models for adolescents, adolescents who exhibited increased risk-taking choices under the presence of peers indeed showed increased BOLD responses in the reward circuit, including the ventral striatum and orbitofrontal cortex ( 12 ).

In contrast to classic studies on social influence in adolescents, recent studies gave more attention to positive impacts of social influence ( 56 ). Do et al. ( 57 ) specifically compared adolescents' conforming behaviors toward different types of social influence. In this study, adolescents tended to stick to their original attitudes toward various types of behaviors, but on the cases when they change their attitudes, adolescents conformed to constructive behaviors (e.g., working hard in school) more than unconstructive behaviors (e.g., smoking a cigarette). Another study used computational modeling approach and showed neural and behavioral evidence for positive peer influence in adolescents ( 20 ). Adolescents were making a series of gamble choices and presented with social others' choices before they made each choice. Consistent with the results observed in adults ( 17 ), adolescents followed others' choices on average, and such conformity was explained by added social value to the option others chose. In particular, adolescents who never used any types of substances were influenced by others' safe choices, whereas adolescents who have used were not. Although these studies did not directly compare adolescents' decision patterns from those of adults, the results suggested the mechanisms how individuals use social information in their adolescence, a sensitive period for sociocultural processing ( 6 ).

Considering the hormonal effects on biological development of the brain, one should consider pubertal stage as a determinant as important as age in developmental research. Indeed, across many adolescent studies, it has been reported that the extent to which individuals are susceptible to social influence is heightened during adolescence and usually diminished after pubertal growth ( 55 , 58 , 59 ). Moreover, recent functional neuroimaging studies suggested that puberty might play a more important role than chronical age in structural and functional development of the brain [( 60 ); for review, see ( 61 )]. This set of studies again highlights that individuals' age would explain considerable variability in their neural and behavioral patterns reflecting individual differences in social information processing.

Another noteworthy demographic factor is socioeconomic status (SES). There have been fairly consistent results suggesting that individuals' socioeconomic status has a significant effect on their behavior in social context. Psychological research suggested the association between individuals' social class and their perspectives over the social environmental ( 62 ). Specifically, individuals' high and low classes were considered to be shaped by abundance (or scarcity) of available resources, which in turn may underlie their behavioral tendencies either to focus on one's own internal states or to external factors ( 62 ). Consistent with this view, empirical research on social influence among marginalized groups also reported that they tend to conform to their peers more not to be excluded from their community and assert their identity in the group ( 63 ).

Recent neuroimaging research further supported the role of SES in individuals susceptibility to social influence. Casio et al. ( 64 ) examined whether individuals' SES moderates the relationship between brain responses to social exclusion and the extent to which they conform to peer influence. Specifically, individuals who had low SES showed positive association between neural sensitivity to social exclusion measured in the “social pain” network regions [including dorsal anterior cingulate cortex (dACC), anterior insula, and subgenual cingulate cortex (subACC)] and their conforming tendencies, whereas individuals who had high SES showed the opposite association. Comparable moderating effects of SES were observed for the brain regions implicated in mentalizing [e.g., medial prefrontal cortex (mPFC), temporoparietal junction (TPJ)] ( 64 , 65 ). These results together imply that SES is neurocognitively linked to the way people process social information.

Of note, the measurements of SES vary across studies and these results should be interpreted with caution. The most common indices include income and educational levels ( 64 , 66 ), and subjective assessments, such as perceived neighborhood quality ( 67 ) and the MacArthur ladder, which measures individual belief about one's location in a status order ( 65 ). Although these assessments are usually correlated, they should not be used interchangeably, because they might have enough differential effects on the brain development ( 68 ).

Psychological Characteristics: Anxiety and Self-Esteem

Among individuals who have the same demographic profiles, social influence still may have very different impacts, contingent upon individuals' psychological characteristics. Given the social characteristic of the information processing, social anxiety is one of the closest psychological factors that may modulate the effect of social influence. A recent study reported that individuals' social anxiety was positively associated with their conformity to bullying under social influence, such that individuals who show highest social anxiety level conforms to others the most ( 69 ). Even in learning directly from experience, highly anxious individuals showed a negative bias (i.e., learning better from bad news) when social others were observing ( 70 ). Another study examined social influence differences between healthy individuals and individuals with social anxiety disorder ( 71 ). Consistent with the results from the subclinical population, individuals with social anxiety disorder showed higher susceptibility to social influence particularly when social others rated presented face as more attractive than they originally reported. This result was interpreted as evidence for increased motivation to pursue social acceptance and avoid social rejecting in individuals with high social anxiety.

Self-esteem is another psychological characteristic that may be associated with the extent to which one is swayed by others' opinion. Indeed, various classic social psychology research have examined whether individuals' self-esteem is a major moderator of social influence ( 72 – 74 ). Despite the general results showcasing negative association with individuals' susceptibility to peer influence—individuals with low self-esteem are more susceptible to others' influence ( 72 , 74 )—, other studies suggested that the relationship is rather more complex. Nisbett and Gordon ( 73 ) suggested that modulating effect of self-esteem may differ depding on the type of social influence. Particularly, individuals' self-esteem was negatively associated with the extent to which they are influenced by others for the type of social influence that is relatively easy to comprehend but implausible, while the association was non-monotonic or even opposite for a difficult but plausible message.

Recent neuroimaging studies corroborated this suggested association between self-esteem and their susceptibility to social information. Somerville et al. ( 75 ) reported that individuals who had low self-esteem not only reported that they received positive feedbacks less from others, but also were more sensitive to positive feedbacks received by others compared with individuals who had high self-esteem. This result implied that social feedbacks might be exaggerated in low self-esteem individuals, and thus have increased susceptibility to social influence. Will and colleagues ( 76 ) used computational modeling approach and suggested that individuals' self-esteem is established through the way how they learn about social others. These results altogether hint a possibility that self-esteem is more than a modulator for individuals' social susceptibility, but rather a dynamically changing characteristic shaped by the history of social interactions.

We reviewed various individual characteristics that are associated with the extent to which individuals are influenced by social contexts. As introduced above, vast amount of studies showed that a large variance of individual differences exists in susceptibility to social influence. However, only few studies directly took these associated factors into account in constructing a cohesive computational model of social influence. Individual characteristics such as age and socioeconomic status may be closely tied to developmental changes or differential learning experiences, while other characteristics (e.g., anxiety and self-esteem) may be linked to baseline traits each individual has and to a specific state individuals reside at the moment. Better mechanistic understanding of social influence spanning across these individual characteristics may provide explanation why minorities who are most vulnerable (e.g., adolescents), or marginalized and stigmatized cohorts are more susceptible to their social environment ( 3 , 12 ) and even likely to experience mental health problems ( 77 , 78 ).

How Is the Social Influence Conveyed

Sometimes what matters is how you say it, rather than what you say. In the same vein, the exact same content can have a very different impact on people's behavioral changes depending on from whom or how it is delivered. Characteristics of the group (e.g., social distance, expertise) may shape the credibility of the social information, and thus individuals may be more (or less) influenced by a particular social group. Two distinctive ways of being exposed to social information includes directly observing others and in reverse, realizing that one is being observed by others. Depends on these specific circumstances, individuals may obtain different types of social information and in turn, be influenced differently. In this section, we review previous research that examined how the forms of social influence modulate the way how or the extent to which social influence affects individuals' choices.

Characteristics of Others: Social Closeness, Credibility, and Competence

When one has a chance to decide on the team members to work together, one would usually prefer others who he or she shares similar perspectives and relates one another easily. A biased behavioral tendency of being assorted based on individuals' preference is often observed in social context, such that individuals who are closer in their social network are more likely to have similar preference ( 79 ). Moreover, social closeness, a psychological construct that is well-described as a shared variance between oneself and others ( 80 ), was shown to have a significant effect on individuals' judgement about others ( 81 ). In other psychology studies where a dichotomous classification of social relationship is adopted (in- vs. out- group) showed consistent results, such that individuals showed a biased preference toward in-group members ( 82 ). Such biases toward socially intimate others might be accounted for by their motivation to keep their membership stable and to enhance self-esteem ( 2 ).

Recent neuroimaging studies presented further evidence explaining why and how such biases exist. Sip et al. ( 83 ) examined whether social feedbacks from a gender-matched close friend vs. from a confederate have differential impacts on individuals' decision pattern and on their neural responses. Individuals were responsive to social feedback and showed changes in choice patterns accordingly, but only when the feedback came from a close friend. This effect was reflected in BOLD responses in the vmPFC and posterior cingulate cortex (PCC), which they presented as supporting evidence for modulatory impact of social closeness on decision-making processes. A similar study that examined individuals' neural responses to social influence revealed differences when the influence originated from in- vs. out- group ( 84 ). Particularly, a set of brain regions including the medial prefrontal cortex (mPFC), amygdala, and ventral striatum (vStr) showed higher BOLD responses for the social influence from in-group than out-group members. Consistent with these findings, the default mode network ( 85 ), a set of brain regions including the medial PFC and PCC, and its interaction with subcortical regions are known to be closely associated with mental representation about self-other relationship ( 86 ). These studies together highlight that social closeness is an important determinant for social information processing.

Another very closely related factor is whether the achieved social information is perceived useful or not. When expertise of social others is explicitly informed, one can use this knowledge to judge whether social information from them is reliable or not. Supporting this view, various studies have shown that people tend to follow opinion and advice from people with expertise than from novice ( 87 , 88 ). Klucharev et al. ( 89 ) suggested that presenting an object paired with an expert enhances memory performance and moreover has a positive impact on the attitude toward the object. Such an impact of perceived expertise was associated with re-evaluation of an item ( 89 , 90 ), which may account for the reason why people are more likely to follow experts' opinion.

It is important to note that in most of the cases, it is not obvious whether the social information is useful or not. Thus, individuals should estimate how useful the social information is to maximize one's own benefit (or minimize the harm). As crude heuristics, opinion from larger group of people can be taken into account more heavily ( 45 ), and others' faster responses are considered more informative ( 91 ). Independent of the true usefulness of the information, individuals were more likely to be persuaded by others when presented with higher confidence ( 92 , 93 ). Evaluation of the confidence that is presented for (or estimated to be associated with) the social information was tracked in the vmPFC, an area dissociable from the region that encoded subjective value signal combining one's own and others' preference ( 44 , 94 ). These results support the view that by estimating who knows better or whether the social information is useful, individuals can choose their strategy to learn from social others ( 13 , 95 ).

The Way Social Information Is Given: Observing and Being Observed

When being around social others, there are different ways to acquire additional social information. The type of information one can achieve is yoked to the methods how social influence is acquired, and thus how one processes and uses the information naturally should be different accordingly. The most direct way to acquire social information is through a chance to observe others' choices which inform others' preferences and social norms. Chung et al. ( 17 ) showed that individuals tend to follow others' choices during risky decision-making. By conducting a formal model comparison, they suggested that such conformity is explained by a value-based decision process combining additional utility to the option chosen by others, rather than by changing individuals' original preferences. The mechanisms how individuals combine their own knowledge and preference with social information may vary. Individuals may project their own preference in predicting that of others ( 96 ), and also track whether others' intentions underlying the observed actions of others change over time ( 97 ). Other studies suggested that individuals use social information to adjust their own opinion and intend to match with that of others. Specifically, when individuals were asked to report attractiveness of a series of faces after viewing others' responses, their original attractiveness reports were adjusted toward the others ( 40 , 98 ). These results suggested that individuals are able to track the difference of the values (or preference) between their own and others ( 17 , 40 , 41 ), and change their choices (or ratings) accordingly to minimize the difference ( 40 ).

On other occasions, one can be mindful of being around others, but have no chance to directly observe others' choices. The impact of simple presence of others is largely investigated in adolescents, where presence of friends were found to increase adolescents' risk seeking behavior ( 10 , 12 , 99 ). Individuals tended to show higher sensitivity to rewards and more impulsive choices under presence of others even if the social others were not friends, but strangers ( 100 ). Such social influence was attributed to social reward, associated with approval from others ( 101 ). In a recent study, Powers et al. ( 102 ) also examined impacts of the contexts where friends were simply present at the same room or monitoring participants' choices. Particularly, options were more likely to be chosen when they were paired with friends' monetary gains compared with when they were paired with friends' losses. In adults, such adjustment of individuals' choice attitudes were more pronounced when friends were monitoring the choices than merely present, while adolescents showed comparable responses to the social contexts regardless of whether friends could witness the choices or not. These results suggested that individuals may take into account wellbeing of friends, particularly when others can immediately witness the choices.

Individuals may infer what others would expect from their choices and place social values toward meeting the inferred expectation ( 13 , 46 ). This perspective was closely examined in a recent study where participants were asked to predict others' choices ( 19 ). After successfully learning others' choices, individuals' preferences for risky choices changed toward that of others as if there was a “behavioral contagion”. The main goal of predicting others' choices might have motivated individuals to simulate others' preferences and mentalize ( 103 ), which may underlie why social context affects individuals differently.

We reviewed that how social influence is conveyed may shape the mechanism how a social context would affect individuals' choices. When individuals are under a social context, they may start extracting a set of information ranging from whether others share the same goal as them to whether others have more amount of information. In the inference process figuring out social others' goals, individuals may recalibrate their subgoals [e.g., to collaborate or compete with others, to mimic others' actions ( 104 ), to meet a consensus ( 105 )]. Given that real world is largely uncertain and volatile, we, as social agents, must be constantly solving such an inference problem to first evaluate the usefulness of social information and next alternate how to use the information ( 46 ).

What Decision Domains and Processes Is the Social Influence Applied to

Would a person who is susceptible to one type of social information always be sensitive to other types of social contexts? It is not uncommon in real life that the extent to which individuals respond to social information differs depending on the type of behavioral choices which are subject to the influence. For example, an adolescent who is not swayed by aberrant behaviors of peers may show tendencies to join her friend for volunteer opportunities, and an addict who easily gives in to craving around other substance users may not respond to intervention of social support groups. In this section, we review previous studies in social influence across different decision domains and processes. In addition, we discuss whether or not social influence is domain-general and if not, whether there are any latent variables that explain why individuals show domain-specific responses to social information.

Domain-Specific and Domain-General Mechanisms of Social Influence

Social influence can be readily observed in almost every kind of decision process in our life. Mirroring this, there were many empirical studies ranging from the simplest perceptual decision-making to complex moral decision-making where they used a variety of task paradigms to show the effects of social influence on human information processing. Perceptual decision-making tasks are based on the evaluation of sensory information, such as the length of lines ( 106 ), the dominant color of the presented patches ( 107 ), or the shape of three-dimensional objects ( 108 ). Personal preference tasks include variety of options, such as preference for t-shirts ( 41 ), faces ( 40 , 98 ), and works of art ( 108 , 109 ). In monetary reward tasks, there are explicit gains and losses of money associated with each of the choice option ( 17 , 19 , 110 ). Lastly, in social preference tasks, individuals encounter decision-making situation where they have to consider explicit losses and gains of social others and their own simultaneously ( 29 , 101 ). On average, behavioral changes indeed were observed under social influence across all of these studies that probe different levels of cognitive processing in humans. However, due to the variety of contexts each study adopted (e.g., cover stories) and the differences in the targeted cognitive processes (e.g., perception, valuation), there is no universal computational framework that explains the mechanism of social influence across domains.

There are a few computational frameworks that provided cross-domain accounts for social influence ( 21 , 22 ). First, individuals may be trying to learn others' preferences and values under social influence. Such “normative influence” of social contexts, where social others' choices are not necessarily based on a better set of information, were explained by reinforcement learning (RL) framework capturing individuals' change of behaviors toward others [( 40 , 98 ), c.f., ( 111 )]. Consistent with this perspective, individuals under social context were sensitive to the opinion differences between them and the others ( 41 , 112 ), and it was shown that a set of brain regions involved in social and monetary reward learning overlap ( 113 , 114 ). The RL framework successfully captured the extent to which individuals conform to others' preference-based choices over primary and social rewards ( 40 , 115 ). Second, individuals may be collecting more information from others' choices. Following such “informational influence” of social contexts, individuals seemed to be using others' responses and choices to appropriately adjust their original responses. To integrate information from two sources, individuals computed the importance and reliability of each piece of information ( 44 , 45 , 47 ). Such a Bayesian learner framework successfully explained individuals' conformity not only in perceptual, but also in value-based decision-making particularly when statistical inference was available.

It is worth noting that behavioral patterns which are well-explained by the same computational framework may in fact induced by different neural mechanisms; differential implementation level explanation as per Marr's three levels of analysis ( 116 ). For example, a recent study directly compared multivoxel neural patterns for social conformity with that for classic reward learning, and suggested that neural responses in the brain regions typically involved in non-social RL (e.g., striatum) do not explain whether or not individuals conform to social information ( 111 ). This emphasizes again the importance of interdisciplinary approaches in understanding human information processing. A careful consideration of specific contexts will shape individuals' motivation (“computation level”), but why and how individuals process social information in the context need thorough examination not only in algorithmic level (e.g., computational modeling) but also in implementational level (e.g., functional neuroimging) ( 117 ).

We briefly reviewed plausible mechanisms suggested to date of social influence over different decision domains. Although cognitive motivations defined over psychological constructs including value and information maximization accounted for neural and behavioral mechanisms under social influence ( 33 ), applying the same mechanism to different levels of cognitive processing has been challenging, because task settings (e.g., goal, order, amount of information) also varied across different studies. Future studies may tailor the study design to specifically examine individuals' cross-domain susceptibility to social information. By using the same task settings, but over different domain, we would get a direct chance to address whether individuals' domain-specific sensitivity and confidence, which will be manifested as preference for social information, affect the extent to which individuals use social information.

Concluding Remarks and Future Challenges

Research on social influence has been conducted across various fields of studies. Recent computational approach in conjunction with functional brain imaging technology provided new impetus for the study of social influence, and shed light on underlying mechanisms of individual cognitive processes under social context. Still, there are major challenges remaining given the sheer diversity of social contexts. In this review, we overviewed previous studies in social influence along the three axes of determinants (who are the receivers, how is the influence provided, and to what is the influence applied) that may modulate and mediate the impacts of social influence. These three dimensions are not mutually exclusive one another and thus, they would not completely compartmentalize the impacts of one axis from the other. Still, we hope that our review would highlight potential co-factors crucial to consider for expanding our mechanistic understanding of social influence to translational applications (e.g., intervention design) ( 118 , 119 ).

Given the complex nature of social contexts, simply adding up all the plausible factors into one experiment might not bring solutions. To address this issue, coherent and theory-driven computational modeling approaches should be proceeded ( 22 , 120 , 121 ). In parallel with this formal theory-driven approach, individual differences and extreme cases (e.g., cultural differences, race and gender discrimination, mental illness) cannot be overlooked as described herein. Thus, hypothesis testing in special population may provide further insights in examining the generalizability and transferability of the model ( 122 – 125 ). As an equally important research direction, data-driven understanding of behaviors in social contexts may provide complementary insights for latent variables. Nowadays, taking advantage of large-scale studies and open science practices, we now have better access to big data including personal habits and their social network ( 126 – 128 ). However, we still have to interpret the results with caution considering the sparsity and multi-dimensionality of the data ( 129 ).

Considering the importance of both theory-driven and data-driven approaches in mind, there are at least two issues to take into account when in designing future studies in social influence. First, dimensional measurements of potential determinants are preferrable than to have dichotomized classes. For example, most of the studies that investigated the impact of social closeness took contrast approach where the effects of a close friend vs. a stranger were examined ( 82 – 84 ). However, social closeness is not only associated with perception of social membership, but also trust and competence ( 130 , 131 ). That is, we cannot disentangle potential effects of social distance and other co-varying factors by having only two categories along the dimension. Second, volatility of the social context should be considered to better mimic real world interactions. Social environment and relationship between people constantly change and how we perceive the context gets adjusted accordingly. In perspective of formalizing its impacts in the model, changes in belief about others' advice ( 13 ) or active alterations between utilized strategies for social information ( 46 ) can be implemented. Alternatively, to explore naturally emerging dynamics in rich environment, new experimental designs may target for collecting neural and behavioral data from interactions between uncontrolled real dyads simultaneously ( 132 – 134 ), and even further, using naturalistic social stimuli such as real-time videos and virtual reality ( 135 ). Using naturalistic social environment would get us closer to directly simulate the impacts of social contexts simulating translational applications. However, as reviewed above, there are numerous factors that are already known to affect social processing, but we have close to no understanding how these factors interact and interfere each other. Thus, for broader generalizability and future individualized translational applications, it cannot be emphasized enough the importance of compartmentalized and computational understanding about the underlying determinants of social influence.

Author Contributions

HL and DC conceptualized the study, wrote the first draft of the manuscript, and contributed to manuscript revision, read, and approved the submitted version. All authors contributed to the article and approved the submitted version.

DC was supported by UNIST internal funding (1.220032.01) and the National Research Foundation of Korea (NRF-2020S1A3A2A02097375). The funders had no role in study design, decision to publish, or preparation of the manuscript.

Conflict of Interest

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

Publisher's Note

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• About Adverse Childhood Experiences
• Risk and Protective Factors
• Program: Essentials for Childhood: Preventing Adverse Childhood Experiences through Data to Action
• Adverse childhood experiences can have long-term impacts on health, opportunity and well-being.
• Adverse childhood experiences are common and some groups experience them more than others.

What are adverse childhood experiences?

Adverse childhood experiences, or ACEs, are potentially traumatic events that occur in childhood (0-17 years). Examples include: 1

• Experiencing violence, abuse, or neglect.
• Witnessing violence in the home or community.
• Having a family member attempt or die by suicide.

Also included are aspects of the child’s environment that can undermine their sense of safety, stability, and bonding. Examples can include growing up in a household with: 1

• Substance use problems.
• Mental health problems.
• Instability due to parental separation.
• Instability due to household members being in jail or prison.

The examples above are not a complete list of adverse experiences. Many other traumatic experiences could impact health and well-being. This can include not having enough food to eat, experiencing homelessness or unstable housing, or experiencing discrimination. 2 3 4 5 6

Quick facts and stats

ACEs are common. About 64% of adults in the United States reported they had experienced at least one type of ACE before age 18. Nearly one in six (17.3%) adults reported they had experienced four or more types of ACEs. 7

Preventing ACEs could potentially reduce many health conditions. Estimates show up to 1.9 million heart disease cases and 21 million depression cases potentially could have been avoided by preventing ACEs. 1

Some people are at greater risk of experiencing one or more ACEs than others. While all children are at risk of ACEs, numerous studies show inequities in such experiences. These inequalities are linked to the historical, social, and economic environments in which some families live. 5 6 ACEs were highest among females, non-Hispanic American Indian or Alaska Native adults, and adults who are unemployed or unable to work. 7

ACEs are costly. ACEs-related health consequences cost an estimated economic burden of $748 billion annually in Bermuda, Canada, and the United States. 8

ACEs can have lasting effects on health and well-being in childhood and life opportunities well into adulthood. 9 Life opportunities include things like education and job potential. These experiences can increase the risks of injury, sexually transmitted infections, and involvement in sex trafficking. They can also increase risks for maternal and child health problems including teen pregnancy, pregnancy complications, and fetal death. Also included are a range of chronic diseases and leading causes of death, such as cancer, diabetes, heart disease, and suicide. 1 10 11 12 13 14 15 16 17

ACEs and associated social determinants of health, such as living in under-resourced or racially segregated neighborhoods, can cause toxic stress. Toxic stress, or extended or prolonged stress, from ACEs can negatively affect children’s brain development, immune systems, and stress-response systems. These changes can affect children’s attention, decision-making, and learning. 18

Children growing up with toxic stress may have difficulty forming healthy and stable relationships. They may also have unstable work histories as adults and struggle with finances, jobs, and depression throughout life. 18 These effects can also be passed on to their own children. 19 20 21 Some children may face further exposure to toxic stress from historical and ongoing traumas. These historical and ongoing traumas refer to experiences of racial discrimination or the impacts of poverty resulting from limited educational and economic opportunities. 1 6

Adverse childhood experiences can be prevented. Certain factors may increase or decrease the risk of experiencing adverse childhood experiences.

Preventing adverse childhood experiences requires understanding and addressing the factors that put people at risk for or protect them from violence.

Creating safe, stable, nurturing relationships and environments for all children can prevent ACEs and help all children reach their full potential. We all have a role to play.

• Merrick MT, Ford DC, Ports KA, et al. Vital Signs: Estimated Proportion of Adult Health Problems Attributable to Adverse Childhood Experiences and Implications for Prevention — 25 States, 2015–2017. MMWR Morb Mortal Wkly Rep 2019;68:999-1005. DOI: http://dx.doi.org/10.15585/mmwr.mm6844e1 .
• Cain KS, Meyer SC, Cummer E, Patel KK, Casacchia NJ, Montez K, Palakshappa D, Brown CL. Association of Food Insecurity with Mental Health Outcomes in Parents and Children. Science Direct. 2022; 22:7; 1105-1114. DOI: https://doi.org/10.1016/j.acap.2022.04.010 .
• Smith-Grant J, Kilmer G, Brener N, Robin L, Underwood M. Risk Behaviors and Experiences Among Youth Experiencing Homelessness—Youth Risk Behavior Survey, 23 U.S. States and 11 Local School Districts. Journal of Community Health. 2022; 47: 324-333.
• Experiencing discrimination: Early Childhood Adversity, Toxic Stress, and the Impacts of Racism on the Foundations of Health | Annual Review of Public Health https://doi.org/10.1146/annurev-publhealth-090419-101940 .
• Sedlak A, Mettenburg J, Basena M, et al. Fourth national incidence study of child abuse and neglect (NIS-4): Report to Congress. Executive Summary. Washington, DC: U.S. Department of Health an Human Services, Administration for Children and Families.; 2010.
• Font S, Maguire-Jack K. Pathways from childhood abuse and other adversities to adult health risks: The role of adult socioeconomic conditions. Child Abuse Negl. 2016;51:390-399.
• Swedo EA, Aslam MV, Dahlberg LL, et al. Prevalence of Adverse Childhood Experiences Among U.S. Adults — Behavioral Risk Factor Surveillance System, 2011–2020. MMWR Morb Mortal Wkly Rep 2023;72:707–715. DOI: http://dx.doi.org/10.15585/mmwr.mm7226a2 .
• Bellis, MA, et al. Life Course Health Consequences and Associated Annual Costs of Adverse Childhood Experiences Across Europe and North America: A Systematic Review and Meta-Analysis. Lancet Public Health 2019.
• Adverse Childhood Experiences During the COVID-19 Pandemic and Associations with Poor Mental Health and Suicidal Behaviors Among High School Students — Adolescent Behaviors and Experiences Survey, United States, January–June 2021 | MMWR
• Hillis SD, Anda RF, Dube SR, Felitti VJ, Marchbanks PA, Marks JS. The association between adverse childhood experiences and adolescent pregnancy, long-term psychosocial consequences, and fetal death. Pediatrics. 2004 Feb;113(2):320-7.
• Miller ES, Fleming O, Ekpe EE, Grobman WA, Heard-Garris N. Association Between Adverse Childhood Experiences and Adverse Pregnancy Outcomes. Obstetrics & Gynecology . 2021;138(5):770-776. https://doi.org/10.1097/AOG.0000000000004570 .
• Sulaiman S, Premji SS, Tavangar F, et al. Total Adverse Childhood Experiences and Preterm Birth: A Systematic Review. Matern Child Health J . 2021;25(10):1581-1594. https://doi.org/10.1007/s10995-021-03176-6 .
• Ciciolla L, Shreffler KM, Tiemeyer S. Maternal Childhood Adversity as a Risk for Perinatal Complications and NICU Hospitalization. Journal of Pediatric Psychology . 2021;46(7):801-813. https://doi.org/10.1093/jpepsy/jsab027 .
• Mersky JP, Lee CP. Adverse childhood experiences and poor birth outcomes in a diverse, low-income sample. BMC pregnancy and childbirth. 2019;19(1). https://doi.org/10.1186/s12884-019-2560-8 .
• Reid JA, Baglivio MT, Piquero AR, Greenwald MA, Epps N. No youth left behind to human trafficking: Exploring profiles of risk. American journal of orthopsychiatry. 2019;89(6):704.
• Diamond-Welch B, Kosloski AE. Adverse childhood experiences and propensity to participate in the commercialized sex market. Child Abuse & Neglect. 2020 Jun 1;104:104468.
• Shonkoff, J. P., Garner, A. S., Committee on Psychosocial Aspects of Child and Family Health, Committee on Early Childhood, Adoption, and Dependent Care, & Section on Developmental and Behavioral Pediatrics (2012). The lifelong effects of early childhood adversity and toxic stress. Pediatrics, 129(1), e232–e246. https://doi.org/10.1542/peds.2011-2663
• Narayan AJ, Kalstabakken AW, Labella MH, Nerenberg LS, Monn AR, Masten AS. Intergenerational continuity of adverse childhood experiences in homeless families: unpacking exposure to maltreatment versus family dysfunction. Am J Orthopsych. 2017;87(1):3. https://doi.org/10.1037/ort0000133 .
• Schofield TJ, Donnellan MB, Merrick MT, Ports KA, Klevens J, Leeb R. Intergenerational continuity in adverse childhood experiences and rural community environments. Am J Public Health. 2018;108(9):1148-1152. https://doi.org/10.2105/AJPH.2018.304598 .
• Schofield TJ, Lee RD, Merrick MT. Safe, stable, nurturing relationships as a moderator of intergenerational continuity of child maltreatment: a meta-analysis. J Adolesc Health. 2013;53(4 Suppl):S32-38. https://doi.org/10.1016/j.jadohealth.2013.05.004 .

Adverse Childhood Experiences (ACEs)

ACEs can have a tremendous impact on lifelong health and opportunity. CDC works to understand ACEs and prevent them.

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• Published: 24 May 2024

How does narcissistic leadership influence change-oriented organizational citizenship behavior? Empirical evidence from China

• Yangchun Fang 1 , 2 ,
• Yonghua Liu 1 ,
• Peiling Yu 1 &
• Nuo Chen 1  

Humanities and Social Sciences Communications volume  11 , Article number:  667 ( 2024 ) Cite this article

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• Business and management

Based on conservation of resources theory and the work–home resources model, this study examines how and when narcissistic leadership influences employees’ change-oriented organizational citizenship behavior. A total of 363 employees from 61 teams across numerous enterprises based in central China were surveyed using a questionnaire. The study hypotheses were tested using structural equation modeling and Monte Carlo simulation analysis. The findings revealed that narcissistic leadership results in the development of a negative team climate, termed “team chaxu climate,” which, in turn, hinders employees’ change-oriented organizational citizenship behavior. Furthermore, this study explored the moderating role of leaders’ family affective support in the relationship between narcissistic leadership and team chaxu climate. This study contributes to our understanding of the relationship between narcissistic leadership and employee organizational citizenship behavior and empirically validates the work–home resources model.

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Introduction.

Recent studies have reported that narcissistic leaders are becoming increasingly common worldwide; the media have characterized prominent business leaders—such as Steve Jobs, Elon Musk, and Jack Welch—as narcissists (Visser et al. 2017 ). Incumbent leaders have the authority to allocate organizational resources and a strong sense of superiority when dealing with subordinates (Morf and Rhodewalt 2001b ; Rosenthal and Pittinsky 2006 ). Meanwhile, narcissists—typically self-assured, expressive, and inclined to exaggerate their strengths—tend to receive higher leadership ratings and are more likely to distinguish themselves and become leaders than non-narcissists (Brunell et al. 2008 ; Gauglitz et al. 2023 ). However, narcissists are extremely egocentric and arrogant, frequently acting for personal benefit, insensitive to others’ needs and recommendations, and challenging to form a healthy connection with (Hogan et al. 1990 ; Paulhus 1998 ). Narcissistic leadership generally precipitates unfavorable organizational outcomes (Ouimet 2010 ; Rosenthal and Pittinsky 2006 ), particularly by weakening employees’ organizational citizenship behavior (OCB). For example, Li and Zhang ( 2018 ) found that narcissistic leadership behaviors elevate employee hindrance stress and negatively impact employees’ OCB. Ha et al. ( 2020 ) demonstrated that narcissistic leaders disrupt the principle of reciprocity between leaders and employees, thereby suppressing employees’ OCB. Similarly, Wang et al. ( 2021 ) reported that narcissistic leaders undermine employees’ psychological safety and affective organizational commitment, thus decreasing the latter’s OCB. By contrast, some studies have found that narcissistic leaders also promote employees’ OCB. Narcissistic leaders enhance employees’ OCB by providing them with greater autonomy (Zhang et al. 2017 ) and encouraging them to freely express their opinions regarding current organizational issues (Zhang et al. 2022 ). Additionally, Zhu et al. ( 2023 ) found that subordinates engage in OCB because they admire narcissistic leaders. These divergent findings indicate that the relationship between narcissistic leadership and employee OCB is complex and that our understanding of narcissistic leadership is limited and requires further in-depth research. Specifically, recent research has reported that narcissistic leaders engage in behaviors that deviate from their narcissistic traits—such as developing satisfactory relationships with subordinates and involving them in significant decision-making (Carnevale et al. 2018 ; Owens et al. 2015 ). This finding is inconsistent with the previous one that narcissistic leaders cannot develop satisfactory relationships with their subordinates (de Vries and Miller 1985 ; Rosenthal and Pittinsky 2006 ). Notably, narcissistic leaders—instead of developing satisfactory relationships with all team members—befriend only some members and alienate others (Huang et al. 2020 ). However, why narcissistic leaders engage in such behaviors remains unclear.

To ascertain why narcissistic leaders exhibit this deviant behavior and how and when narcissistic leadership influences employees’ OCB, we constructed a moderated mediation model based on conservation of resources (COR) theory and the Work–Home Resources (W–HR) model. We argue that narcissistic leadership hinders employees’ change-oriented organizational citizenship behavior (COCB) by increasing team chaxu climate—a phenomenon whereby team leaders maintain close connections with a minority of members and simultaneously distance themselves from the majority (Liu et al. 2009 ). This reflects the varying degrees of relationship closeness formed among team members with the controller of team resources (generally, the team leader; Liu et al. 2009 ; Shen et al. 2019 ). In Chinese enterprises, a clique culture frequently prevails, wherein leaders categorize employees into different circles—such as “inner circles” and “outer circles”—based on attitudes, relationship proximity, and capabilities (Chen and Dian 2018 ). Consequently, team chaxu climate is frequently observed within teams in Chinese enterprises. Typically, employees having a stronger—compared with those having a weaker—relationship with the resource controller receive more job-related resources (e.g., trust, support, and empowerment; Liu et al. 2009 ; Shen et al. 2019 ). According to COR theory, individuals are incentivized to conserve existing resources and acquire additional resources to prevent the risk of prospective resource loss (Hobfoll, 1989 ). We argue that narcissistic leaders exhibit behaviors deviating from their traits to access relational resources. Narcissistic leaders emphasize self-interest (Liu et al. 2017 ; Rosenthal and Pittinsky 2006 ). To conserve their resources (e.g., time and energy), they may purposefully opt to maximize their resources by forging strong ties with a small number of “trustworthy, loyal, and talented” subordinates, while maintaining a distance from the majority (i.e., maintaining resources). This differential treatment of team members by the leader increases team chaxu climate (Liu et al. 2009 ; Shen et al. 2019 ).

Furthermore, we postulate that a high level of team chaxu climate diminishes employees’ COCB. Most types of OCB do not carry the risk of offending leaders or colleagues, and hence, employees engage in these types of OCB without much consideration (Bettencourt 2004 ; Choi 2007 ). However, COCB may cause trouble for employees. Fundamentally, COCB is a proactive behavior aimed at improving organizational performance by challenging the organizational status quo, which may cause leaders to experience a sense of unhappiness and anger and deteriorate employees’ rapport with leaders (Bettencourt, 2004 ). We believe that people both inside and outside the circle are reluctant to engage in COCB because it is a high-risk behavior. In teams characterized by high levels of team chaxu climate, leaders exclusively provide key work resources to individuals within the “inner circle”—that is, the “insiders” (Liu et al. 2009 ). However, the relationship between “insiders” and “outsiders” is transient: Employees who cultivate positive relationships with leaders (for example, by affirming their views and following their instructions) may become “insiders” (Liu et al. 2009 ; Shen et al. 2019 ). Therefore, employees strive to be “insiders,” for whom, maintaining a positive relationship with the leader is a prerequisite for obtaining access to more job resources (Liu et al. 2009 ); thus, they are less likely to engage in COCB as doing so might upset the leader. On the contrary, for “outsiders,” provoking the leader might diminish their likelihood of entering the inner circle (Liu et al. 2009 ). They might refrain from COCB to obtain access to the inner circle for more resources. Therefore, we believe that “insiders” and “outsiders”—to preserve their resources and expand their access to resources—would not engage in COCB as doing so increases the risk of offending leaders.

Additionally, our hypotheses’ premise is that narcissistic leaders develop close relationships with team members only when they lack relational resources. Therefore, relational resources are key to narcissistic leaders developing close relationships with their team members. Further, related research has indicated that leaders’ attitudes toward subordinates are affected by their relationship with their supervisors as well as coworkers (Huang et al. 2020 ; Tafvelin et al. 2019 ). Unfortunately, the role of other factors (e.g., family) has rarely been considered. An individual’s behavior in the workplace is influenced by both the work environment and familial factors (Bozoğlu Batı and Armutlulu 2020 ; Staines 1980 ). Specifically, Ten Brummelhuis and Bakker ( 2012 ) proposed the W–HR model, which describes how individual resources (e.g., time, energy, and affect) link one area’s demands and resources to the other area’s outcomes. Moreover, empirical studies have indicated that individuals’ lack of relational resources in the family causes individuals to seek alternatives in the workplace (Liu et al. 2015 ; Tsang et al. 2023 ; Witt and Carlson 2006 ). Therefore, we believe that considering family factors’ impact on narcissistic leadership behavior can help obtain a more comprehensive understanding of their behavioral motivations. According to COR theory, individuals experiencing the stress of resource loss are motivated to gain resources elsewhere (Hobfoll 1989 ; Hobfoll et al. 2018 )—consistent with the W–HR perspective. Hence, we introduced family affective support as a moderating variable and posited that when narcissistic leaders cannot obtain adequate affective support at home, they become more likely to seek relational resources at work to compensate for this lack, such as by establishing fulfilling relationships with—and obtaining praise and admiration from—specific subordinates at work to alleviate the stress of resource loss at home. This behavior of narcissistic leaders precipitates team chaxu climate, which, in turn, affects employees’ COCB.

Our study makes several contributions. First, based on COR theory, we analyzed why narcissistic leaders develop deviant behaviors: Notably, narcissistic leaders develop high-quality relationships with some team members to acquire relational resources and maintain distant relationships with the majority to preserve these resources; this finding enhances our understanding of narcissistic leaders. Second, we build on COR theory to elucidate how narcissistic leadership affects employees’ COCB. Narcissistic leadership precipitates team chaxu climate owing to the discriminatory treatment of employees therein. This team chaxu climate, in turn, prevents employees from engaging in COCB, which carries a high risk of adversely impacting the leader–employee relationship. This possibly explains previous studies’ contradictory results regarding the relationship between narcissistic leadership and employees’ OCB. That is, narcissistic leadership may discourage employees from engaging in challenging and transformative OCBs. Third, we employ the W–HR perspective to introduce leaders’ family affective support as a boundary condition in our research model. We demonstrated that the family emotional support received by narcissistic leaders is critical to whether they develop a small number of cronies in the work team, thus extending the boundary conditions for narcissistic leadership behavior and empirically validating the W–HR model.

Theory and hypotheses

Narcissistic leadership and employees’ cocb.

The concept of “narcissism” originates from the ancient Greek myth that a young man named Narcissus fell in love with his reflection in the pool and finally perished because of his over self-preoccupation (Campbell et al. 2011 ). Ellis ( 1898 ) used the term “narcissism” to elucidate a pathological state of “twisted” self-affection. Subsequently, Freud ( 1939 ) proposed a distinct personality type characterized by an externally composed demeanor, assurance, and, at times, a sense of superiority. Horney ( 1939 ) expanded on this notion, suggesting that the traits exhibited by individuals with narcissistic tendencies—such as self-aggrandizement, excessively high self-regard, and the expectation of admiration from others—are rooted in qualities that they lack. Further, scholars have theorized that narcissism is a personality disorder. According to Kernberg ( 1967 ), narcissists are excessively preoccupied with the self, desperately need others’ admiration, are skilled at exploiting others, and lack empathy. By contrast, Kohut ( 1996 ) postulated that narcissism is not inherently pathological but follows a developmental trajectory. Healthy narcissism contributes to positive traits, such as humor and creativity, whereas pathological narcissism occurs when an individual cannot reconcile idealized self-beliefs with personal inadequacies, resulting in a lifelong search for validation from an idealized parental figure. Subsequent research has followed this line of reasoning. For example, Morf and Rhodewalt ( 2001b ) considered narcissism a personality trait characterized by an inflated ego, dysfunctional interpersonal relationships, and a willingness to exploit others to enhance the self. Further, Campbell et al. ( 2011 ) suggest that individuals with narcissistic traits exhibit excessive self-confidence, extraversion, high self-esteem, a strong desire for attention, and an initial charm in interpersonal interactions. However, they are resistant to criticism, possess a pronounced sense of superiority, lack empathy, are willing to exploit others, and exhibit arrogance and grandiosity (Alsawalqa 2020 ; Campbell et al. 2011 ).

Researchers investigating the relationship between narcissism and leadership behavior have found that narcissists—compared to non-narcissists—are more likely to self-promote and self-nominate (Hogan et al. 1990 ) and are more likely to be leaders in general but less likely to communicate effectively with subordinates (de Vries and Miller 1985 ; Rosenthal and Pittinsky 2006 ). de Vries and Miller ( 1985 ) highlighted narcissistic leadership’s duality, noting that some leaders evoke perceptions of strength, authority, and concern, whereas others evoke memories of intimidation, malevolence, and harm. Rosenthal and Pittinsky ( 2006 ) summarized past research pertaining to the relationship between narcissism and leadership and introduced the concept of narcissistic leadership. They held that narcissistic leaders are apathetic to others’ interests and act to suit their grandiose notions and arrogant desires. Generally, they cannot build long-term, deep relationships with others owing to their arrogance (Paulhus 1998 ; Rosenthal and Pittinsky 2006 ). They frequently disregard others’ advice, take credit for their successes, blame others for their failures (Hogan et al. 1990 ), and constantly seek validation and superiority (Morf and Rhodewalt 2001a , 2001b ; Rosenthal and Pittinsky 2006 ). However, narcissistic leaders also have a charming side as they can enhance short-term staff motivation by exuding confidence and presenting a grand vision (Ouimet 2010 ). Fatfouta ( 2019 ) summarized the advantageous and disadvantageous aspects of narcissistic leadership. On the one hand, narcissists’ positive attributes (e.g., charisma) are linked to favorable results across levels, ranging from subordinates to peers to the entire organization. On the other hand, narcissists’ negative traits (e.g., entitlement) are correlated with numerous counterproductive work behaviors, which disrupt organizational performance (Fatfouta 2019 ).

Smith et al. ( 1983 ) introduced the term “organizational citizenship behaviors.” They argue that citizenship behaviors include both altruism and generalized compliance. Altruism is a class of helping behaviors aimed directly at specific individuals, while generalized compliance refers to an objective responsibility to make the organization operate more smoothly. Notably, OCB is significant in organizations and not easily explainable by the incentives that conformity to contractual role prescriptions and high production (Bateman and Organ 1983 ; Smith et al. 1983 ). Subsequently, Organ ( 1988 ) defined OCB as voluntary individual behavior that is not immediately or expressly rewarded by the formal reward system and that contributes to the organization’s efficient functioning. Characteristic examples of organizational citizenship behaviors include demonstrating concern for the organization’s reputation and goals, offering positive and helpful feedback, and actively participating in advancing the organization (Bateman and Organ 1983 ; Organ 1988 ; Smith et al. 1983 ). Further, employees’ OCB has been receiving a great deal of attention because it can significantly benefit an organization (Bettencourt 2004 ; Mackenzie et al. 2011 ; Organ 1988 , 2018 ; Smith et al. 1983 ). Numerous previous studies on OCB have investigated affiliative behaviors, including interpersonal helping, courtesy, and compliance, which solely aim to maintain and strengthen the existing situation (Choi 2007 ). Owing to OCB research’s abundance, several behaviors aimed at changing the status quo have been classified as OCB. Subsequently, Bettencourt ( 2004 ) introduced the concept of COCB—an OCB that emphasizes challenging and transforming the existing situation. Choi ( 2007 ) elucidated this concept as the constructive effort invested by employees toward the processes and policy systems at work to optimize their performance—manifested by proposing recommendations for process improvement or implementing new working methods. Moreover, COCB has been proven to improve the organization’s efficiency and competitiveness (Mackenzie et al. 2011 ). Narcissistic leaders emphasize superiority, authority, and control (Maccoby, 2000 ); are keen to dominate others (Rosenthal and Pittinsky 2006 ); and frequently deny others’ ideas and suggestions (Hogan et al. 1990 ; Huang et al. 2020 ). This can result in employees being reluctant to propose their ideas (Liu et al. 2017 ). Additionally, narcissistic leaders frequently pursue their self-interest through manipulation and exploitation, and take credit for their subordinates’ ideas and contributions, precipitating a reduction in employees’ drive for change (Locke 2009 ) and, consequently, in subsequent OCBs (Wang et al. 2021 ). Therefore, the following hypothesis is proposed:

Hypothesis 1 : Narcissistic leadership significantly negatively impacts employees’ COCB.

Team chaxu climate’s mediating role

The concept of chaxu climate—a term originally introduced by Fei ( 1948 )—has been derived from the indigenous Chinese notion of a chaxu pattern in society. He posited that traditional Chinese society is marked by a chaxu pattern, whereby individuals are central and their interpersonal relationships radiate outward in concentric “ripple circles.” As these circles widen, interpersonal relationships’ intimacy gradually diminishes; consequently, individuals at the center engage differently with those around them (Fei 1948 ). Zheng ( 1995 ) introduced the concept of the chaxu pattern into organizational research, positing that organizational leaders classify subordinates into “insiders” and “outsiders” based on three criteria—namely, the nature of their “guanxi” (a Chinese term for personal connections or relationships), the employee’s degree of loyalty, and their competence level. Liu ( 2003 ) observed that the chaxu pattern prevalent in Chinese society may result in leaders treating employees disparately, potentially cultivating a chaxu climate within teams. Focusing on work teams, Liu et al. ( 2009 ) suggested that team-level chaxu climate is determined by the variance in close relationships between team members and team leaders, with the latter usually controlling the resources. Subordinates with closer ties to team leaders are provided more power and resources (Liu et al. 2009 ; Shen et al. 2019 ). Chinese leaders tend to segment employees into “insiders” and “outsiders,” treating them differently in terms of providing opportunities and distributing resources, thereby cultivating a prevalent chaxu climate in Chinese organizations (Peng and Zhao 2011 ; Chen and Dian 2018 ; He et al. 2022 ). These studies indicate that leaders’ differential treatment of subordinates is closely related to team chaxu climate’s emergence.

Prior research has indicated that narcissistic leaders fail to create quality relationships with subordinates (Hogan et al. 1990 ; Maccoby 2000 ; Rosenthal and Pittinsky 2006 ); however, recent research has demonstrated that narcissistic leaders may exhibit deviant behavior, such as demonstrating compassion for subordinates or involving subordinates in key decisions (Carnevale et al. 2018 ; Owens et al. 2015 ). However, they do not create solid social relationships with all employees; instead, they choose a select few to establish a high-quality relationship with and alienate the majority (Huang et al. 2020 ). This is consistent with COR theory, which holds that individuals strategically utilize their limited resources to optimize valued resources’ accumulation—notably, in forming and maintaining social relationships. Individuals are incentivized to obtain respect and affective support as important psychological resources (Hobfoll 1989 ; Hobfoll et al. 2018 ; Hobfoll and Shirom 2001 ). Narcissists rely heavily on developing psychological resources, have a strong need for self-esteem and superiority, and desire to establish a grandiose self-image (Hogan et al. 1990 ; Horney 1939 ; Kernberg 1967 ; Rosenthal and Pittinsky 2006 ). Hence, they tend to interact more frequently with those who praise and admire them than with those who do not (Brown 1997 ; Campbell et al. 2002 ; Gauglitz et al. 2023 ). Additionally, narcissistic leaders pursue superiority in a long-term, holistic manner (Morf and Rhodewalt 2001a , 2001b ) and need to alienate most of their subordinates to preserve their superiority (Bernerth 2022 ; Huang et al. 2020 ). Moreover, building relationships with others requires time, effort, and other resources (Halbesleben et al. 2014 ). Hence, being on amicable terms with all subordinates may not be worthwhile for narcissistic leaders. Therefore, narcissistic leaders cultivate subordinates who can provide them with psychological resources as “henchmen” and, concurrently, decrease engagement with subordinates who cannot provide resources to protect their limited resources. This differential treatment of subordinates by narcissistic leadership increases the team chaxu climate.

Relevant studies have indicated that when the team chaxu climate is high, the work resources available to team members are unequal, and some members—those having a desirable relationship with the leader—receive more resources. By contrast, if the team chaxu climate is low, no significant difference exists in the resources available to all members (Liu et al. 2008 ; Liu et al. 2009 ; Shen et al. 2019 ). In Chinese organizations, employees’ relationships with leaders affect the allocation of work resources (Chen and Dian, 2018 ; Liu et al. 2008 ). Leaders tend to allocate more work resources to employees with whom they have a desirable relationship (Liu et al. 2008 ; Liu et al. 2009 ). This behavior often elevates the team chaxu climate (Liu et al. 2009 ). Related research has demonstrated that employees who are disadvantaged in organizational resource allocation may be less likely to exhibit motivation for change behaviors and neglect areas of improvement at work (Abdullah and Wider 2022 ). This is consistent with COR theory’s postulation that employees only act if they obtain resources in return for their invested effort (Hobfoll 1989 ; Hobfoll et al. 2018 ; Hobfoll and Shirom 2001 ). Therefore, we believe that when team chaxu climate is high, both “insiders” and “outsiders” abandon COCB after evaluating the return on their investment of resources. Specifically, “insiders”—who have a high-quality relationship with their narcissistic leader—receive more resources than other employees do because of this relationship (Liu et al. 2008 ; Liu et al. 2009 ). Their optimal strategy involves maintaining a satisfactory relationship with their leader (Liu et al. 2009 ). However, COCB deviates from established protocols and may also be deemed unmanageable by the leader, causing the leader to lose trust in the employee and thereby deteriorating the leader–employee relationship (Bettencourt 2004 ). Moreover, the identity of the “insider” is unstable, and at any time, insiders and outsiders may interchange identities (Liu et al. 2009 ). Employees must maintain positive relationships with leaders to preserve their “insider” identity (Shen et al. 2019 ). Therefore, COCB is an unwise choice for them. “Outsiders”—who are estranged from their leaders—receive relatively few resources (especially core resources) and are at a relative disadvantage in the team, which reduces their motivation, psychological security, organizational identity, and emotional commitment (Liu et al. 2009 ; Opoku et al. 2020 ; Stinglhamber et al. 2021 ; Zhao et al. 2019 ). However, job resources, motivation, psychological safety, organizational identification, and emotional commitment are significant for employees to engage in COCB (Choi 2007 ; De Clercq 2022 ; Hu et al. 2023 ; Koopman et al. 2016 ; Wang et al. 2021 ). On the contrary, narcissistic leaders enjoy others’ praise and admiration (Morf and Rhodewalt 2001a , 2001b ; Rosenthal and Pittinsky 2006 ); for employees, obeying leaders’ instructions and agreeing with their ideas is frequently a safer “to-be-inside” technique than COCB, which carries a high risk of adversely impacting the leader–employee relationship.

In summary, we suggest that narcissistic leadership precipitates team chaxu climate owing to differentiated relational strategies for different employees, which hinders employee COCB. Accordingly, we hypothesize as follows:

Hypothesis 2 . Narcissistic leadership impacts employees’ COCB through team chaxu climate.

Family affective support’s moderating role

As postulated earlier, when narcissistic leaders lack relational resources, they develop desirable relationships with some team members. Moreover, relevant studies have confirmed that the adequacy of leaders’ relational resources affects their behavior. For example, Tafvelin et al. ( 2019 ) reported that coworker support affects leaders’ behavior at work. Huang et al. ( 2020 ) revealed that the quality of the exchange relationship between narcissistic leaders and their superiors influences their attitude toward their subordinates. This aligns with COR theory’s perspective that when individuals fail to receive the resources that they value through some social connections, they choose to seek these resources in other relationships to compensate for the risk of resource loss (Hobfoll et al. 2018 ; Wilson et al. 2010 ). These studies provide valuable insights into our understanding of narcissistic leadership behavior. However, prior research has focused solely on organizational factors and neglected the potential impact of leaders’ family-related factors. The work–family relationship model asserts that individuals’ familial and professional statuses impact each other, and they seek the experiences that they cannot obtain in one system from the other (Staines 1980 ). Particularly, Ten Brummelhuis and Bakker ( 2012 ) introduced COR theory into the work–family relationship model to construct a W–HR model to illustrate how individual resources (e.g., time, energy, and affect) connect the demands and resources of one domain to outcomes in the other domain. The W–HR model suggests that individuals’ resource adequacy status in the family domain affects their attitudes and behaviors in the work domain, and vice versa (Ten Brummelhuis and Bakker 2012 ). Moreover, subsequent empirical research has supported this idea. For example, Huffman et al.’s ( 2014 ) four-year longitudinal study revealed that employees with high—than those with low—spouse career support exhibit higher job satisfaction and lower turnover rates. Cheng et al.’s ( 2019 ) COR-theory-based study demonstrated that family stress can cause disruptive behaviors at work. Furthermore, Bozoğlu Batı and Armutlulu’s ( 2020 ) study underscored that family–work conflict affects entrepreneurs’ investment decisions.

Narcissistic leaders tend to be highly egotistical and are more sensitive to the risk of resource loss (Maccoby 2000 ). If they do not receive adequate affective support from their family, they might exhibit anxiety and depression (Bushman and Baumeister 1998 ; Miller et al. 2007 ) and become likely to seek the fulfillment of their psychological resources through other means; meanwhile, subordinates’ respect, admiration, and loyalty is an effective countermeasure against the risk of resource loss (Huang et al. 2020 ). Considering COR theory and the W–HR model, we propose that the relationship between narcissistic leadership and team chaxu climate may be influenced by family affective support.

When narcissistic leaders’ family emotional support is low, the lack of affective resources may cause them to experience resource deprivation stress (Bushman and Baumeister 1998 ; Miller et al. 2007 ); to compensate for this lack, they may try creating “cronies” in their work team (Bernerth 2022 ; Huang et al. 2020 ). Further, the risk of resource loss created by a lack of family affective support makes narcissistic leaders particularly sensitive to resource investment (Hobfoll et al. 2018 ); hence, narcissistic leaders—to maintain their inadequate resources—alienate subordinates who cannot contribute resources to them (Huang et al. 2020 ). On the contrary, when certain needs of an individual are satisfied, other needs are increasingly prioritized (Maslow, 1943 ). Thus, when narcissistic leaders receive higher family affective support, their relational resources are satisfied, and the psychological resources obtained by establishing high-quality relationships with subordinates become less effective. In this phase, their concern predominantly pertains to preserving a favorable self-image, gratifying their sense of superiority, and isolating themselves from all subordinates (Huang et al. 2020 ; Morf and Rhodewalt 2001a , 2001b ). That is, when narcissistic leaders receive high family affective support, they alienate all subordinates to maintain their sense of superiority; consequently, team chaxu climate is alleviated. Thus, we propose the following hypothesis:

Hypothesis 3 : Family affective support moderates leadership narcissism’s positive effect on the chaxu climate, with the positive effect being stronger when family affective support is low.

As discussed earlier, narcissistic leaders impact employees’ COCB via team chaxu climate, whose influence is moderated by leaders’ family affective support. Therefore, the following hypothesis is proposed:

Hypothesis 4 : Family affective support moderates the team chaxu climate’s mediating effect. The stronger the family affective support, the weaker the team chaxu climate’s mediating effect.

Accordingly, this study’s research model can be developed (Fig. 1 ).

Research model.

Sample and procedure

We administered a three-wave time-lagged questionnaire with the assistance of in-service MBA students (full-time employees pursuing weekend MBA courses) from two universities in central China. Specifically, we sent invitations to 267 MBA students with the assistance of the MBA teaching centers at both schools; the respondents were asked regarding their willingness to participate in the survey and whether their team fulfilled the criteria for participation in this study. The inclusion criteria for the respondents’ team were threefold: The respondents’ team must (1) be a formal team in the organization, (2) collaborate to complete work tasks, and (3) exhibit a need for innovation in the work tasks. Notably, 84 eligible working MBA students confirmed their consent to participate in this survey. We utilized SoJump (a professional survey agency) to administer the online survey; we entrusted the recruited students to distribute the online questionnaires to their respective work teams. Specifically, each student was requested to distribute three or more questionnaires to their respective work teams. Participation was voluntary, and respondents were assured that their responses would remain confidential and that they could withdraw at any time. At Time 1, we asked the 84 students to distribute questionnaires among their team leaders to collect data on their narcissistic leadership tendencies and level of family affective support; we received 74 valid questionnaires. At Time 2 (two weeks after Time 1), questionnaires were distributed among the 74 teams to collect data on team chaxu climate; we received 408 valid questionnaires from 68 teams. At Time 3 (two weeks after Time 2), questionnaires were distributed among the 68 teams to collect data on employees’ COCB; we received 363 valid questionnaires from 61 teams. The leaders’ sample included 43 men (70.5%)—primarily aged 26–35 ( N  = 35, 57.4%) and 36–45 ( N  = 21, 34.4%) years—and the majority had been in their positions for 6–10 years ( N  = 46, 75.2%). Of them, 60 (98.4%) held a bachelor’s degree or higher. The employees’ sample included 188 men (51.8%)—most of whom were aged 26–35 ( N  = 148, 40.8%) and 36–45 ( N  = 152, 41.9%) years; their average tenure was 3–5 years ( N  = 164, 45.2%), and 285 of them had a bachelor’s degree or higher (78.5%).

We performed independent samples t-tests for team chaxu climate as well as for the control variables. The results indicated no significant difference between respondents who answered the questionnaire only at T2 and those who did so at both T2 and T3. Team chaxu climate ( t  = −1.566, p  > 0.05), gender ( t  = −0.476, p  > 0.05), age ( t  = 0.142, p  > 0.05), educational level ( t  = −1.024, p  > 0.05), and tenure ( t  = 0.217, p  > 0.05). Additionally, the χ2 test’s results revealed no significant difference between the above two groups of respondents (gender: χ 2 [1, N  = 408] = 0.227, p  > 0.05; age: χ 2 [3, N  = 408] = 0.769, p  > 0.05; educational level: χ 2 [3, N  = 408] = 4.052, p  > 0.05; tenure: χ 2 [4, N  = 408] = 2.281, p  > 0.05).

In this study, items pertaining to narcissistic leadership and COCB were translated from English to Chinese using a translation/back translation technique; all questions were handled by one English-proficient professor and two doctoral students collaboratively and assessed on a Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree).

Narcissistic leadership

Jonason, Webster’s ( 2010 ) scale, which has been demonstrated to have high-quality reliability and validity in the Chinese context, was used to measure narcissistic leadership (Li and Zhang 2018 ; Zhang et al. 2017 ). This scale comprises four items. An example item is “I tend to seek prestige or status.” In this study, the Cronbach’s α for this scale was 0.866.

Team chaxu climate

Team chaxu climate was measured using Liu’s ( 2003 ) 10-item scale, which is scored by members of a team. This scale—developed in the Chinese context—is currently the most commonly utilized scale for measuring team chaxu climate (Liu et al. 2009 ; Shen et al. 2019 ). An example item is “My manager has a great deal of contact and interaction with specific subordinates on the team.” In this study, the Cronbach’s α for this scale was 0.838. We aggregate the team-level chaxu climate; the analysis results are consistent with applicable standards, whereby the ICC1 value is 0.415, surpassing the standard of 0.12. The ICC2 value is 0.808, which exceeds the standard of 0.70. The average Rwg is 0.966, exceeding the minimum threshold of 0.70 (Bliese 2000 ).

COCB was measured using Choi’s ( 2007 ) scale, which comprises four items; an example item is “I frequently come up with new ideas or work methods to perform my tasks.” Numerous related studies have demonstrated the validity and reliability of this scale, which has been widely accepted by scholars (Ha et al. 2020 ; Hu et al. 2023 ). In this study, the Cronbach’s α for this scale was 0.797.

Family affective support

Family affective support was measured using the scale edited by Li and Zhao ( 2009 ), which comprises six items; an example item is “When I am exhausted by work, my family is always encouraging.” This scale was developed based on the Chinese context and is highly compatible with Chinese customs. It has exhibited high reliability and validity in previous studies (Li and Zhao 2009 ; Shi and Wang 2016 ). In this study, the Cronbach’s α for this scale was 0.919.

Control Variables

To exclude potential factors’ influence on the outcome variables, following prior research, we used the leader’s gender, age, educational level, and organizational tenure as control variables (Huang et al. 2020 ; Zhang et al. 2022 ).

Data analysis

First, we performed Harman’s one-factor test and the Unmeasured Latent Method Construct (ULMC) to test common method biases (CMBs). Second, we incorporated variables across multiple levels. To establish discriminant validity, we conducted multilevel confirmatory factor analysis (MCFA) using Mplus 7.4. Third, we constructed a multi-level structural equation model to assess the theoretical hypotheses. Finally, we conducted a Monte Carlo simulation with 100,000 replications using R version 1.3.1 to further examine the mediation and moderated mediation effects.

Confirmatory factor analyses and common method bias test

We used Mplus7.4 to run an MCFA to detect the discriminant validity between the constructs. As Table 1 indicates, the four-factor model (χ2 = 330.015, df = 248, χ2/df = 1.331; CFI = 0.929; TLI = 0.920, RMSEA = 0.030, SRMR [within] = 0.015; SRMR [between] = 0.084) exhibits a significantly better fitting index than other alternative models, suggesting that our study’s key variables are distinguishable.

We employed two methods to test for CMB. First, the results of Harman’s one-factor test revealed that the first factor before rotation explained only 24.133% of the total variation, which did not exceed the 40% threshold. Second, the unmeasured latent method construct was used to assess the CMB. After adding the common method factor, the five-factor model’s fit (χ 2  = 299.927, df = 232, χ 2 /df = 1.293, CFI = 0.941, TLI = 0.929, RMSEA = 0.028, SRMR [within] = 0.015, SRMR [between] = 0.088) did not significantly improve, compared to the four-factor model. Collectively, these two methods indicate that the CMB in this study has been effectively controlled to a certain extent.

Descriptive statistics and correlations

Table 2 presents the correlations, means, and standard deviations.

Hypotheses testing

Figure 2 presents the results of the multilevel structural equation modeling analysis. Narcissistic leadership significantly positively impacted team chaxu climate (β = 0.317, p  < 0.001), and team chaxu climate significantly negatively impacted employees’ COCB (β = −0.328, p  < 0.001); thus, H2 is supported. The interaction term of narcissistic leadership and family affective support significantly impacted team chaxu climate (β = −0.645, p  < 0.001); thus, H3 is supported.

Path analysis results.

The interaction in Fig. 3 illustrates that when leaders’ family affective support is strong, narcissistic leadership’s impact on the team chaxu climate weakens.

Family affective support’s moderating effect on the relationship between narcissistic leadership and team chaxu climate.

Table 3 presents the results of the Monte Carlo simulation with 100,000 replications. Evidently, narcissistic leadership’s indirect effect on employees’ COCB through team chaxu climate was significant (−0.104, 95%CI [−0.153, −0.061]). Therefore, H2 is further supported. Under high family affective support (mean +1 SD), narcissistic leadership’s indirect mediating effect on employees’ COCB through team chaxu climate was not significant (0.019, 95%CI [−0.0656, 0.001]). Under low family affective support (mean −1 SD), the indirect effect was significant (−0.228, 95%CI [−0.145, −0.063]). The effect size of the difference between high and low family affective support was significant (0.247, 95%CI [0.057, 0.084]), indicating that narcissistic leadership’s indirect effect on employees’ COCB through team chaxu climate was moderated by family affective support, thus supporting H4. Narcissistic leadership’s total effect on employees’ COCB was significant (−0.306, 95%CI [−0.375, −0.239]); hence, H1 is supported.

Based on conservation of resources theory and the W–FR model, we constructed a multilevel moderated mediation model to explore how and when narcissistic leadership impacts employees’ COCB. We used team chaxu climate as a mediating variable in the relationship between narcissistic leadership and COCB to reveal the underlying mechanisms. Additionally, we explored family emotional support’s moderating role therein.

Utilizing a three-stage time-lag questionnaire, we found that narcissistic leadership negatively influences employee COCB—consistent with previous research on the relationship between narcissistic leadership and employee OCB (Ha et al. 2020 ; Li and Zhang 2018 ; Wang et al. 2021 ). On the one hand, COCB carries the risk of offending leaders (Bettencourt 2004 ; Choi 2007 ); on the other hand, narcissistic leaders may take credit for their subordinates’ contributions (Hogan et al. 1990 ). Consequently, employees are reluctant to engage in COCB. Moreover, team chaxu climate’s mediating role was supported. From a COR perspective, narcissistic leaders tend to maintain positive relationships with a select few valuable employees within their teams to gain access to relational resources, thus precipitating team chaxu climate, which, in turn, discourages team members from engaging in COCB. This explains not only why narcissistic leaders exhibit deviant behavior but also how narcissistic leadership affects employee COCB. Notably, family affective support moderates the relationship between narcissistic leadership and team chaxu climate: The stronger the family affective support received by leaders, the weaker the negative relationship between narcissistic leadership and team chaxu climate. That is, when narcissistic leaders receive sufficient affective support from their families, their need for a grandiose image outweighs their relational needs, at which point narcissistic leaders no longer need to develop relationships with a select few employees in their team to access relational resources. This corroborates the idea that a leader’s relationship with others affects their attitudes and behaviors toward subordinates (Huang et al. 2020 ; Tafvelin et al. 2019 ). Our study not only expands the boundary conditions for narcissistic leadership’s effects on employee behavior but also contributes to the W–HR model’s development.

Theoretical contributions

Our study contributes to the literature on OCB, team chaxu climate, and narcissistic leadership. First, our finding that narcissistic leaders develop relationships with their employees to acquire relational resources deepens the understanding of narcissistic leadership behavior. Earlier research argued that narcissistic leaders are selfish, arrogant, and lacking empathy, and that they fail to develop satisfactory relationships with their employees (Hogan et al. 1990 ; Maccoby 2000 ; Rosenthal and Pittinsky 2006 ). However, recent research has reported that narcissistic leaders proactively develop positive relationships with their employees and involve them in significant decisions (Carnevale et al. 2018 ; Owens et al. 2015 ). This is contrary to our previous knowledge pertaining to narcissistic leaders. However, why narcissistic leaders engage in such behavior, which deviates from their personality, has not been elucidated. Our study—based on COR theory—explains why narcissistic leaders develop relationships with their employees; specifically, it highlights that narcissistic leaders need relational resources through praise and admiration from their subordinates. This elucidates the reasons driving narcissistic leaders’ deviant behaviors (Carnevale et al. 2018 ), thus deepening our understanding of narcissistic leadership.

Second, our study revealed team chaxu climate’s mediating role in the relationship between narcissistic leadership and employees’ COCB, thereby helping us better understand the relationship between narcissistic leadership and employees’ OCB. Previous research on the relationship between narcissistic leadership and employees’ OCB has yielded inconsistent results (Carnevale et al. 2018 ; Ha et al. 2020 ; Wang 2021 ; Zhang et al. 2022 ; Zhang et al. 2017 ; Zhu et al. 2023 ), thereby underscoring the limited understanding of the association between these two constructs. To address this gap, we elucidate how narcissistic leaders impact employees’ COCB based on COR theory. Narcissistic leaders acquire and safeguard resources by favoring a select few individuals within their team to become “insiders” and alienate the majority. Such differential treatment of employees by narcissistic leaders intensifies the team chaxu climate. At this time, both the “insiders” with high-quality—and “outsiders” with low-quality—exchange relationships with leaders refrain from engaging in high-risk COCB to obtain or preserve (existing) resources. Here, we particularly emphasize the high risk associated with COCB. Unlike most milder forms of OCB, employee COCB challenges the status quo, which may be distasteful for leaders (Bettencourt 2004 ; Choi 2007 ; De Clercq 2022 ), and offending their leaders may place them under tremendous pressure of losing resources (Hobfoll et al. 2018 ; Zaccaro et al. 2001 ). Thus, both “insiders” and “outsiders” may be discouraged from engaging in such behaviors. Our study provides a possible explanation for prior research’s contradictory results on the relationship between narcissistic leadership and employees’ OCB, deepens our understanding of this relationship, and also responds to the call for further in-depth research on team chaxu climate as a local variable (Chen and Dian 2018 ).

Third, we examine family affective support’s moderating effect, in response to Huang et al.’s ( 2020 ) call to explore the boundary conditions under which narcissistic leadership contributes to mitigating narcissistic leadership’s negative effects. Previous research has found that leaders’ stock of resources significantly impacts their behavior. If leaders do not have access to relational resources with their supervisors or coworkers, they look elsewhere to supplement their relational resources (Huang et al. 2020 ; Tafvelin et al. 2019 ). Nevertheless, research discussing how narcissistic leaders’ perceived level of family support impacts their work behaviors is scant. The W–HR model explains that individuals’ need for resources at either work or at home affects their attitudes and behaviors in the other domain (Ten Brummelhuis and Bakker 2012 ). We draw on W–HR to explore how narcissistic leaders’ behavior at work changes when they do not receive sufficient emotional support at home. Reportedly, narcissistic leaders are extremely sensitive to the loss of resources (Maccoby 2000 ). When they do not receive sufficient affective support from their family, they actively seek the recognition of a select few subordinates and establish high-quality relationships with them to compensate for this lack; meanwhile, leaders who receive adequate family affective support maintain their sense of superiority in the first place and reduce their interaction with subordinates. In sum, this study deepens our understanding of narcissistic leadership behavior’s boundary conditions and empirically validates the W–HR model.

Practical implications

This study’s findings have the following implications for organizational practice: First, companies must recognize that narcissistic leadership negatively impacts employees’ COCB. Narcissistic leaders habitually reject others’ opinions to maintain a sense of superiority (Hogan et al. 1990 ) and may also take credit for their subordinates’ innovations (Hogan et al. 1990 ; Rosenthal and Pittinsky 2006 ), precipitating employees’ reluctance to engage in COCB. This indicates that narcissists are not suited for leadership positions. Consequently, to avoid narcissistic leadership’s negative effects, organizations should screen for narcissistic personality traits when considering individuals for leadership positions and avoid promoting excessively narcissistic individuals to such positions. Moreover, the leader candidate’s peers or subordinates should be asked to assess their narcissism level, as narcissists tend not to exhibit their excessive narcissistic tendencies in front of their superiors but might present their true selves in front of their peers or subordinates (Campbell et al. 2011 ).

Second, organizations should adopt measures to reduce team chaxu climate. Per previous research, team chaxu climate forms an evident “circle phenomenon” within the team, thus decreasing employees’ performance and OCB (Shen et al. 2019 ). Furthermore, we found that neither “insiders” nor “outsiders” engage in COCB if the team chaxu climate is high. Therefore, the organization’s top managers should adopt measures to prevent the development of team chaxu climate. For example, the implementation of a teamwork resource-sharing model and a combination of team and individual performance appraisals can help avoid narcissistic leadership’s negative effects as well as provide psychological and work resource support for employees’ COCB.

Third, organizations must focus on their team leaders’ relational resource status. Previous research has demonstrated that among narcissistic leaders, the experience of relational resource stress intensifies their personality’s negative aspects (Huang et al. 2020 ). Moreover, we found that when narcissistic leaders do not receive sufficient emotional support in their families, they treat subordinates within their team differently, thus developing team chaxu climate. This, in turn, reduces the likelihood of employees’ COCB. However, when narcissistic leaders are provided with sufficient relational resources in the family (family emotional support), they do not treat team employees differently and the negative effects disappear. This indicates that senior managers should focus more on—and provide greater support to—team leaders. For example, communicating more frequently with team leaders and extending goodwill and support can effectively mitigate their psychological pressure (Li et al. 2023 ).

Conclusion, limitations, and future research

This study has some limitations. First, although we collected data utilizing a multi-source and multi-wave approach, this study does not employ a rigorous longitudinal study design; hence, it cannot definitively determine the causal relationship between narcissistic leadership and employees’ COCB. Arguably, employees’ COCB may cause leaders to reflect on their narcissistic leadership behaviors, thus reducing such behaviors. Our research model is consistent with the theoretical ordering of variables in the input–process–output (I–P–O) framework (Mathieu and Taylor 2006 ): Team inputs (narcissistic leadership) impact team processes (team chaxu climate), which, in turn, impact output (COCB). Nevertheless, we encourage future scholars to conduct rigorous longitudinal studies or field trials to assess the causal relationship between narcissistic leadership and employees’ COCB.

Second, our study focused specifically on the relationship between narcissistic leadership and COCB—a risky behavior; however, most other OCBs are less risky (Bettencourt 2004 ). Future research must examine the relationship between narcissistic leadership and other types of OCBs. For example, how narcissistic leadership affects service-oriented OCBs. Understanding the relationship between narcissistic leadership and different types of OCBs can help us better explain prior studies’ inconsistent results regarding the relationship between narcissistic leadership and employees’ OCB.

Third, although our study revealed the moderating role of leaders’ family affective support, most current research focuses on intra-organizational factors’ impact on narcissistic leadership behaviors (Huang et al. 2020 ; Tafvelin et al. 2019 ). Research on extra-organizational factors’ impact on narcissistic leaders’ behavior in the workplace is scant. We encourage future research to examine extra-organizational situational factors that can impact narcissistic leadership behavior. For example, future research can examine whether narcissistic leaders—in high-uncertainty environments—develop positive relationships with all team members to maintain their status and ensure team performance. This can be useful in furthering the current understanding of narcissistic leaders.

Fourth, we focused solely on a singular category of employee contributions in relation to leadership behavior—namely, employees’ COCB. Other employee contributions may also be affected by narcissistic leadership and team chaxu climate. For example, team chaxu climate attributable to narcissistic leadership can cause members to distrust each other, thus decreasing team performance. We suggest that future research should examine these other outcomes of leader narcissism and the resultant team chaxu climate.

Finally, most sample data in this study originate from organizations in China; hence, regional culture may have influenced the findings. Specifically, the mean for narcissistic leaders in our sample was 2.77 (5-point scale)—somewhat different from sample means in other national studies. For example, the narcissistic leadership mean in Aboramadan et al.’s ( 2020 ) study was 3.57 (5-point scale) and that in Ghislieri et al.’s ( 2019 ) study was 2.31 (5-point scale), suggesting that leaders from different cultures exhibit different narcissism levels. Therefore, future studies should employ samples from different cultures to cross-validate this study’s findings.

Data availability

The datasets generated during the current study are available in the Harvard Dataverse repository ( https://doi.org/10.7910/DVN/MUZS1X ).

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Yangchun Fang, Yonghua Liu, Peiling Yu & Nuo Chen

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Yangchun Fang contributed to conceptualization, writing—review, and editing. Yonghua Liu contributed to conceptualization, methodology, investigation, formal analysis, writing—original draft, review, and editing. Peiling Yu contributed to investigation, writing—review, and editing. Nuo Chen contributed to investigation, methodology, and writing—review. All authors have read and agreed to the manuscript’s published version.

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All procedures in this study were performed in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This study was approved by the School of Management, Zhejiang University of Technology, in September 2023 (No. 2023092101).

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Fang, Y., Liu, Y., Yu, P. et al. How does narcissistic leadership influence change-oriented organizational citizenship behavior? Empirical evidence from China. Humanit Soc Sci Commun 11 , 667 (2024). https://doi.org/10.1057/s41599-024-03159-2

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Received : 31 July 2023

Accepted : 13 May 2024

Published : 24 May 2024

DOI : https://doi.org/10.1057/s41599-024-03159-2

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