Elsevier

Computers in Human Behavior

Volume 98, September 2019, Pages 174-188
Computers in Human Behavior

Full length article
The relationship between personality traits and susceptibility to social influence

https://doi.org/10.1016/j.chb.2019.01.032Get rights and content

Highlights

  • Users who are high in Neuroticism are more likely to be susceptible to Social Learning, Social Proof and Social Comparison.

  • Users who are low in Openness are more likely to be susceptible to Social Proof and Social Comparison.

  • Users who are low in Conscientiousness are more likely to be susceptible to Social Learning and Social Poof.

  • The relationships between Neuroticism/Openness/Conscientiousness and the three social influence strategies cut across gender.

Abstract

Research has shown that social influence can be leveraged as a persuasive strategy to elicit beneficial behaviors, especially if it is tailored to the target audience. However, research on the impact of personality traits on users' susceptibility to social influence is scarce. To bridge this gap, using a sample of 350 participants, we investigate: (1) the personality-based determinants of users' susceptibility to three social influence strategies—Social Learning, Social Proof and Social Comparison—which are currently being employed in persuasive applications to motivate users to engage in beneficial behaviors; and (2) the moderating effect of gender. Our results reveal that Neuroticism is the most consistent determinant of users’ susceptibility to social influence, followed by Openness and Conscientiousness. Specifically, we found that: (1) individuals who are high in Neuroticism are more likely to be susceptible to all three social influence strategies than individuals who are low in Neuroticism; (2) individuals who are low in Openness are more likely to be susceptible to Social Proof and Social Comparison than individuals who are high in Openness; and (3) individuals who are low in Conscientiousness are more likely to be susceptible to Social Learning and Social Poof than individuals who are high in Conscientiousness. Finally, based on our findings, we provide a number of design guidelines for personalizing persuasive applications to users who are high in Neuroticism, low in Conscientiousness and/or low in Openness.

Introduction

Over the years, especially with the inception of the Internet and social media, in particular, social influence has played and continues to play a major role in our day-to-day lives—be it politically, economically and socio-culturally—in the dissemination of information and change of beliefs, attitudes and behaviors. More recently, applications intentionally designed to influence human behaviors in a certain way are becoming more and more pervasive in the persuasive technology landscape, including domains such as health (Orji & Moffatt, 2016; Oyibo et al., 2018; Toscos, Faber, An, & Gandhi, 2006), commerce (Adaji, Oyibo, & Vassileva, 2018; Kaptein, 2011; Muna & Stephen, 2014), energy conservation (Emeakaroha, Ang, Yan, & Hopthrow, 2014; Gustafsson, 2009; Midden, Mccalley, Ham, & Zaalberg, 2008), etc. For example, in the e-commerce domain, it is projected that the cost of digital advertising will grow from $135 billion in 2014 to $240 billion in 2019, resulting in a compound annual growth rate (CAGR) of 12.1% over the forecast period (Reiss-Davis, 2016). However, research (Association for Psychological Science, 2012; Hirsh, Kang, & Bodenhausen, 2012) has shown that, for persuasive strategies (e.g., adverts) to be more effective, there is a need to move beyond the traditional way of tailoring messages based on demographic characteristics towards leveraging more fundamental individual differences responsible for shaping the target user's behavior. Moreover, in the digital health domain, where persuasive applications are targeted at changing hardened unhealthy lifestyles, research has shown that most individuals lack personal motivation, even when they know the benefits of engaging in the target healthy behavior (Conroy, Elavsky, Hyde, & Doerksen, 2011; Orji, 2014). Often, they lack agency (e.g., self-efficacy and self-regulation), which, based on the Social Cognitive Theory (Bandura, 1986; Bandura, 1997; Oyibo et al., 2018), are key drivers of human behaviors. This makes employing alternative strategies outside of the individual necessary in order to motivate him/her to engage in the target behavior.

Research (Anderson-Bill, Winett, & Wojcik, 2011; Cavallo et al., 2014; Foster, Linehan, Kirman, Lawson, & James, 2010; Oyibo, 2016) in the physical activity domain, has shown that social influence, which entails social support and interactions with family and friends (e.g., on social media), has the potential of increasing the chances of adopting and/or engaging in the target behavior. According to Cialdini and Trost (Cialdini & Trost, 1998), social influence “can be employed to foster growth and move people away from negative habits and in more positive directions, thereby creating the conditions for new change opportunities” (p. 51). In a systematic review on gamification for health promotion, for example, Edwards et al. (Edwards et al., 2016) found that 75% of the reviewed apps implemented social support as a behavior change technique. However, just as in e-commerce, marketing and advertising, employing social influence strategies alone may not be effective if fundamental individual differences are not taken into consideration in tailoring persuasive applications and messages to the target users (Halko & Kientz, 2010).

Recently, research (Halko & Kientz, 2010; Hirsh et al., 2012; Kaptein, Lacroix, & Saini, 2010; Kaptein, Markopoulos, De Ruyter, & Aarts, 2015) in persuasive technology has shown that adapting/personalizing persuasive strategies and messages to users' personality profiles will make them more effective in achieving behavior change than if they are not personalized. Though a number of prior studies (Caldwell & Burger, 1997; Oyibo, Orji, & Vassileva, 2017b) have found a link between personality traits and social influence, they did not specifically focus on closely related social influence strategies and how the resulting findings generalize across gender. Bridging this gap in the body of knowledge will help provide more empirical evidence on using personality traits to predict users’ susceptibility to social influence strategies in persuasive technology research. This will help in grounding existing findings on the relationship between personality traits and persuasive strategies based on social influence. In a broader context, Buss (Buss, 1992), as cited in Caldwell and Burger (Caldwell & Burger, 1997), noted that studies which establish the link between personality and social influence techniques “can contribute to the development of an interactional framework for linking personality and social psychology” (p. 1011).

Consequently, we conducted an empirical study among 350 participants by investigating the relationship between the Big Five personality traits (Gosling, Rentfrow, & Swann, 2003) and three commonly investigated social influence strategies (Social Learning, Social Proof and Social Comparison) employed in persuasive applications (Busch, Schrammel, & Tscheligi, 2013; Oinas-kukkonen & Harjumaa, 2009; Stibe, 2015; Stibe & Larson, 2016) and the moderating effect of gender. Our results, based on Partial Least Square Path Modeling (PLSPM) analysis, showed that Neuroticism is the most consistent predictor of the susceptibility of individuals to social influence, as it was involved in predicting users' susceptibility to all three persuasive strategies. Our model also revealed that Openness and Conscientiousness are the second most consistent determinants of the susceptibility of individuals to social influence as they were able to predict users' susceptibility to two of the persuasive strategies. However, in our model, Agreeableness and Extraversion turned out to be non-predictors of users’ susceptibility to any of the three social influence strategies. These findings hold potential for designers of persuasive applications with regard to leveraging Neuroticism, Openness and Conscientiousness as a basis for tailoring persuasive applications to users based on their personality traits. Moreover, we provide a number of design guidelines by which personality-based tailoring can be achieved in persuasive applications.

Section snippets

Background

In this section, we provide a brief overview of the three social influence strategies we investigated and the Big Five personality traits.

Related work

In this section, we review some of the relevant literature in social psychology, social network and persuasive technology domains.

Method

In this section, we present our research hypotheses, measurement instruments and the demographic information of study participants.

Results

In this section, we present the results of our analysis, including PLSPM (Sanchez, 2013), correlation analysis and analysis of variance (ANOVA).

Discussion

We have presented the path model of the relationship between personality traits and users' susceptibility to social influence strategies and the analysis of variance of the personality trait and social influence measures between the MP-SP and LP-SP subgroups. The results of the path analysis, in the light of our first two RQs, show that Neuroticism, Openness and Conscientiousness (NOC) are the most consistent determinants of users' susceptibility to social influence strategies such as Social

Conclusion

We presented a path model of the relationship between the Big Five personality traits and users' susceptibility to social influence strategies in the persuasive technology domain. Our model reveals that Neuroticism, Openness and Conscientiousness are the strongest determinants of users' susceptibility to the three social influence strategies (Social Learning, Social Proof and Social Comparison) we investigated. Specifically, the gender-based models explain between 20% and 30% of the variance of

Conflicting interests

The Authors declare that there is no conflict of interest.

Ethical approval

The studies complied with the research ethics guidelines provided by the University of Saskatchewan.

Contributorship

The first author designed and conducted the study. Both authors contributed in preparing the manuscript.

Funding

We thank the Canadian Government and the University of Saskatchewan for funding this research. The second author received the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery grant (RGPIN-2016-05762), while the first author received the College of Graduate Studies and Research International Dean's Scholarship.

Acknowledgements

Not Applicable.

References (104)

  • V. Pornsakulvanich

    Personality, attitudes, social influences, and social networking site usage predicting online social support

    Computers in Human Behavior

    (2017)
  • B. Prue et al.

    Application of personality theories for the design and development of cross-cultural decision-making tools

    Procedia Manufacturing

    (2015)
  • C. Ross et al.

    Personality and motivations associated with Facebook use

    Computers in Human Behavior

    (2009)
  • K. VanderZee et al.

    The relationship between social comparison processes and personality

    Personality and Individual Differences

    (1996)
  • I. Adaji et al.

    The effect of gender and age on the factors that influence healthy shopping habits in E-commerce

  • Y. Amichai-Hamburger et al.

    “On the Internet no one knows I'm an introvert”: Extroversion, neuroticism, and Internet interaction

    CyberPsychology and Behavior

    (2002)
  • E. Anagnostopoulou et al.

    Exploring the links between persuasion, personality and mobility types in personalized mobility applications

    (2017)
  • E.S. Anderson-Bill et al.

    Social cognitive determinants of nutrition and physical activity among web-health users enrolling in an online intervention: The influence of social support, self-efficacy, outcome expectations, and self-regulation

    Journal of Medical Internet Research

    (2011)
  • A.C. Andrés del Valle et al.

    The persuasive mirror: Computerized persuasion for healthy living

    Human Computer Interaction International

    (2005)
  • J. Asendorpf et al.

    Personality effects on social relationships

    Journal of Personality and Social Psychology

    (1998)
  • Association for Psychological Science

    Marketing is more effective when targeted to personality profiles

    (2012)
  • A. Bandura

    Social learning theory

    (1971)
  • A. Bandura

    Social foundations of thought and action: A social cognitive theory

    (1986)
  • A. Bandura

    Self-efficacy: The exercise of control

    (1997)
  • M. Barrick et al.

    The big five personality dimensions and job performance

    Personnel Psychology

    (1991)
  • M.R. Barrick et al.

    Conscientiousness and performance of sales representatives: Test of the mediating effects of goal setting

    Journal of Applied Psychology

    (1993)
  • N. Bolger et al.

    Personality and the problems of everyday life: The role of neuroticism in exposure and reactivity to daily stressors

    Journal of Personality

    (1991)
  • J. Brailovskaia et al.

    Comparing Facebook users and Facebook non-users: Relationship between personality traits and mental health variables - an exploratory study

    PLoS One

    (2016)
  • M. Buhrmester et al.

    Amazon's {M}echanical {T}urk: {A} new source of inexpensive, yet high-quality, data?

    Perspectives on Psychological Science

    (2011)
  • M. Busch et al.

    Personalized persuasive technology - development and validation of scales for measuring persuadability

  • D.M. Buss

    Manipulation in close relationships: Five personality factors in interactional context

    Journal of Personality

    (1992)
  • D.F. Caldwell et al.

    Personality and social influence strategies in the workplace

    Personality and Social Psychology Bulletin

    (1997)
  • D.N. Cavallo et al.

    Social support for physical activity—role of Facebook with and without structured intervention

    Translational Behavioral Medicine

    (2014)
  • R.B. Cialdini

    Influence: The psychology of persuasion

    (2006)
  • R. Cialdini et al.

    Social influence: Social norms, conformity and compliance

  • W.R. Clark et al.

    Using the six principles of influence to increase student involvement in professional organizations: A relationship marketing approach

    Journal for Advancement of Marketing Education

    (2008)
  • D.E. Conroy et al.

    The dynamic nature of physical activity intentions: A within-person perspective on intention-behavior coupling

    Journal of Sport & Exercise Psychology

    (2011)
  • M. Deutsch et al.

    A study of normative and informational social influences upon individual judgment

    Journal of Abnormal and Social Psychology

    (1955)
  • T.J. Dunn et al.

    From alpha to omega: A practical solution to the pervasive problem of internal consistency estimation

    British Journal of Psychology

    (2014)
  • E.A. Edwards et al.

    Gamification for health promotion: Systematic review of behaviour change techniques in smartphone apps

    BMJ Open

    (2016)
  • L. Festinger

    A theory of social comparison

    Human Relations

    (1954)
  • D. Foster et al.

    Motivating physical activity at work: Using persuasive social media for competitive step counting

    MindTrek

    (2010)
  • G. Fournier

    Normative social influence

    (2018)
  • A. Furnham

    Personality at work: The role of individual differences in the workplace

    (2002)
  • S.M. Garcia et al.

    The psychology of competition: A social comparison perspective

    Perspectives on Psychological Science

    (2013)
  • H. Grassegger et al.

    The data that turned the world upside down

    (2017)
  • A. Gustafsson

    Power explorer: A casual game style for encouraging long term behavior change among teenagers

  • M.J. Gutierrez et al.

    Verbal conditioning of neurotic and psychopathic delinquents using verbal and nonverbal reinforcers

    Psychological Reports

    (1971)
  • J.F. Hair et al.

    A primer on partial least squares structural equation modeling (PLS-SEM)

    (2014)
  • Z. Hajnik

    The Big five personality trait in marketing: A literature review

    (2014)
  • Cited by (39)

    • PersonalityGate: A general plug-and-play GNN gate to enhance cascade prediction with personality recognition task

      2022, Expert Systems with Applications
      Citation Excerpt :

      It is worth noticing that LIWC and PR are not applicable to DBLP, for the ground-truth personality is fetched from the web page of Personality Insights and is in a style of percentile while LIWC and PR predict raw scores. The assumptions of our proposed framework for joint cascade prediction and personality recognition is based on existing psychological and behavioral research which revealed the correlations of individuals’ personality traits with information cascade (Hudson & Khamfroush, 2020; Oyibo & Vassileva, 2019). Two main assumptions are introduced in our framework: (1) users’ personality traits are correlated with their neighbors in the social network which inspires us to propose the PersonalityGate to utilize graph neural networks to obtain the personality traits representation of users; (2) individuals’ personality traits are correlated with the user’s participations in information cascade which inspires us to propose the MeCAPE framework to couple two separate graph neural networks for joint cascade prediction and personality recognition.

    • Why do consumers buy impulsively during live streaming? A deep learning-based dual-stage SEM-ANN analysis

      2022, Journal of Business Research
      Citation Excerpt :

      Individuals may exhibit a varying level of SSI. Individuals with a high level of SSI tend to exhibit low conscientiousness but high neuroticism and openness and thus, are more reliant on others to form opinions and more vulnerable to manipulation (Oyibo and Vassileva, 2019). In sum, SSI augments the relationship of SC and AR (Luo, 2005; Wang et al., 2018).

    View all citing articles on Scopus
    View full text