Full length articleThe relationship between personality traits and susceptibility to social influence
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.
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