Personalized networks? How the Big Five personality traits influence the structure of egocentric networks

Accepted Manuscript Personalized networks? How the Big Five personality traits influence the structure of egocentric networks Carolin Rapp, Karin Ingold, Markus Freitag PII:

S0049-089X(17)30495-7

DOI:

10.1016/j.ssresearch.2018.09.001

Reference:

YSSRE 2208

To appear in:

Social Science Research

Received Date: 9 June 2017 Revised Date:

5 September 2018

Accepted Date: 9 September 2018

Please cite this article as: Rapp, C., Ingold, K., Freitag, M., Personalized networks? How the Big Five personality traits influence the structure of egocentric networks, Social Science Research (2018), doi: https://doi.org/10.1016/j.ssresearch.2018.09.001. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Personalized Networks? How the Big Five Personality Traits Influence the Structure of Egocentric Networks

[email protected] Karin Ingold2 [email protected] Markus Freitag2

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[email protected]

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Carolin Rapp1

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University of Copenhagen, Department of Political Science, Øster Farimagsgade 5, DK-1353 Copenhagen K, Denmark. Phone: +45 276 276 33 2

University of Bern (Switzerland), Institute of Political Science, Fabrikstrasse 8, 3012 Bern.

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Corresponding Author: [email protected]

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Personalized Networks? How the Big Five Personality Traits Influence the

Abstract

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Structure of Egocentric Networks

In this paper, we expand previous research on the psychological foundations of social behavior by

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evaluating the role of the Big Five personality traits with regard to the formation of individual social networks. More precisely, we ask if personality traits significantly relate to individuals’ social integration

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and position in their ego-network. While studies on both social capital formation and the impact of personality traits on social and political behavior have been flourishing in recent years, little is known about the main effects of personality traits, namely openness, agreeableness, conscientiousness, extraversion, and emotional stability, on the characteristics of social ties as well as the agency of egos in their networks. To test our research question, we rely on data from a Swiss population survey carried out

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in 2005 that combines detailed information on ties in egocentric networks and personality traits for about 1,600 respondents. We show that neurotic persons have a tendency towards triad structures encompassing structural holes, whereas extroverted persons show a preference for networks with weaker ties. Moreover,

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our findings support the potential relationship of the three hitherto neglected personality traits –

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agreeableness, openness to experience, conscientiousness – with personal networks structures.

Keywords: egocentric networks; personality; structural holes; social networks; interpersonal relations

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Personalized Networks? How the Big Five Personality Traits Influence the Structure of Egocentric Networks

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Abstract In this paper, we expand previous research on the psychological foundations of social behavior by evaluating the role of the Big Five personality traits with regard to the formation of individual social

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networks. More precisely, we ask if personality traits significantly relate to individuals’ social integration and position in their ego-network. While studies on both social capital formation and the impact of

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personality traits on social and political behavior have been flourishing in recent years, little is known about the main effects of personality traits, namely openness, agreeableness, conscientiousness, extraversion, and emotional stability, on the characteristics of social ties as well as the agency of egos in their networks. To test our research question, we rely on data from a Swiss population survey carried out in 2005 that combines detailed information on ties in egocentric networks and personality traits for about

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1,600 respondents. We show that neurotic persons have a tendency towards triad structures encompassing structural holes, whereas extroverted persons show a preference for networks with weaker ties. Moreover, our findings support the potential relationship of the three hitherto neglected personality

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traits – agreeableness, openness to experience, conscientiousness – with personal networks structures.

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Keywords: egocentric networks; personality; structural holes; social networks; interpersonal relations

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Introduction What factors structure an individual’s personal – i.e. egocentric – network? Although the main focus of social network analyses lies in the explanation of the general importance of networks (Borgatti, Brass, & Halgin, 2014) the question of network formation has also been addressed frequently (Borgatti et al.,

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2014; Burt, Kilduff, & Tasselli, 2013; Holland & Leinhardt, 1976; Ishiguro, 2016; Lepri, Staiano, Shmueli, Pianesi, & Pentland, 2016; Roberts, Wilson, Fedurek, & Dunbar, 2008). While an increasing amount of social science research underscores the explanatory power of personality traits on individual attitudes and

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behavior (Freitag & Bauer, 2016; Gallego & Oberski, 2012; Gerber, Huber, Doherty, & Dowling, 2011; Mondak, 2010; Mondak, Hibbing, Canache, Seligson, & Anderson, 2010), social network researchers

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scarcely make use of these fruitful approaches (Burt et al., 2013; Fang et al., 2015). For instance, the question of how individuals connect with their alters is mainly answered using directly observable sociodemographic attributes, such as age, gender or education. We thus try to enhance existing research in this field by asking: How do the Big Five personality traits relate to characteristics of egocentric networks? Contrary to socio-demographic factors, personality traits are internal individual characteristics that, to

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a large extent, exist prior to adult socialization experiences. Moreover, traits have a genetic component and are thus likely to be antecedent to values, attitudes, and behavior (Freitag & Rapp, 2014; McCrae & Costa Jr., 2008) – although it has been shown that personality traits are also likely to change throughout

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one’s life course (Borghuis et al., 2017).1 In recent years, personality psychologists refined our understanding of personality traits and reached a working consensus that these can be comprehensively

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conceptualized and reliably measured in terms of the Big Five personality traits: openness (to experience), agreeableness, conscientiousness, extraversion and emotional stability (Mondak, 2010). In this study, we use the Big

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Even though Mondak (2010, 14) argues that “if the predictor represents an enduring psychological structure rather

than the sum of attitudes, then we are on much firmer ground when deriving inferences regarding possible causal relationships”, there is the possibility that certain live events or individual behavior change one’s personality traits. In the present case, it could be that the structure of an individual’s social network as well as the interactions with their peers have a recursive effect on personality. As we cannot solve this problem by analytical means, e.g. panel data, we try to tone down any causal claims.

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ACCEPTED MANUSCRIPT Five model as a broad framework for the depiction of individual-level personality attributes, and then provide evidence regarding the function and value of this framework for understanding the impact of personality on the composition of social ties in egocentric networks. To date, only a handful of researchers, particular in the area of organizational research, have made use

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of personality aspects in their studies (Burt et al., 2013; Fang et al., 2015). For example, Klein, Saltz, and Mayer (2004) detected that neuroticism influences centrality scores in friendship networks. In similar ways, Ishiguro (2016) examined the effect of extraversion and neuroticism on network size. Zhu et al. (2013) investigated all five personality traits and their impact on subjective wellbeing, treating social

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networks as intermediary variables. Interestingly, they conclude that for agreeableness and openness there is no significant direct path between personality and wellbeing. They can only detect a relevant link via

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network size and the share of new contacts in networks. Further, Burt, Jannotta, and Mahoney (1998) underscored that aspects of an organizational personality influence network constraints. However, these studies exclusively focus on the quantitative structure of networks, such as their size, constraint or density. Much less is known about how the quality of social ties is defined and, in particular, in how far an

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individual’s personality shapes the structure of personal networks (Doeven-Eggens, Fruyt, Jolijn Hendriks, Bosker, & Van der Werf, Margaretha P.C., 2008; Kadushin, 2002; Kalish & Robins, 2006; Roberts et al., 2008; Totterdell, Holman, & Hukin, 2008). Regarding the characteristics of ties in egocentric

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networks, the most important contribution comes from Kalish and Robins (2006), which highly encourages a more detailed analysis of personality traits in explaining qualitative egocentric network

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characteristics.

The scope of our data allows us to create an added value to the few existing studies and to make two important contributions to the understanding of how personality relates to the composition of social networks. First, while most studies refer to the Big Five approach, they fail to integrate all five traits. Prior studies are limited to the analysis of extraversion and neuroticism (Ishiguro, 2016; Kalish & Robins, 2006; Roberts et al., 2008) or the role of self-monitoring (Klein et al., 2004). Yet we know from personality literature that the Big Five personality traits are highly interrelated (McCrae & Costa Jr., 2008). Accordingly, we cannot truly determine the influence of one Big Five personality trait if we do not control for the other four. Against this backdrop, we focus on a comprehensive assessment of the impact 3

ACCEPTED MANUSCRIPT of personality, thus scrutinizing the relationship between all five Big Five personality traits and the triad census for 1651 egocentric networks. Moreover, we are in the lucky position of being able to take advantage of a representative Swiss survey. To date, most of the research on the relationship between personality and network agency as well as structure are situated within the boundaries of organizational or

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professional networks (Fang et al., 2015). But network positions can also play an important role in egocentric networks as both kin and friendship ties help in forming social as well as other forms of capital (Coleman, 1988; Granovetter, 1973).

Second, our overarching aim is to make a distinct contribution to the literature on questions of how

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social relations appear and establish. According to Borgatti et al. (2014) it is time to change the focus from asking what individual networks are good for to how network structures emerge and, in particular,

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what motivates individuals in structuring their friendship ties. With respect to this, we focus on the agency of individuals as well as the structure of networks instead of the consequences of social ties. In detail, we want to answer the question of why some individuals are more likely to establish close-knit rather than weak networks. Or why someone might be interested in keeping his friends apart through

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structural holes. To the best of our knowledge, these questions have been hardly addressed in the context of egocentric networks. More importantly, the relevance of the present paper derives from the increased individualization of European societies: individualism is on a constant rise in modern civil societies which

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questions the general social knit and, thus, support among individuals (i.e. collectivism). This study tries to shed more light on how individuals structure their networks and the role personality factors play in this

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process.2

Characteristics of Egocentric Networks – The Triad Census A social network is more than the sum of its individual components, i.e. nodes and ties: structural elements are said to create an added value and to constitute potential benefits to network participants

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It has to be noted that there is the possibility of a reversed causality, i.e. that network structures shape one’s personality. It is,

however, beyond the scope and possibilities of this study clarify the causal direction.

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ACCEPTED MANUSCRIPT (Wöhler & Hinz, 2007). Those structures can impact the control over social norms (Portes, 1998), the spread of information (Rogers & Kincaid, 1981), the diffusion of innovation (Valente, 1995) as well as the establishment of trust and social capital (Coleman, 1988). Contrary to larger network arrangements, egocentric networks focus on one single node or individual (ego), and follow the assumption that an ego

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knows his or her alters, that is, his or her network connections. The most common purpose why scholars engage in ego-network analyses is to detect variation across individuals based on how they are embedded in ‘local’ social structures (Coleman, Katz, & Menzel, 1957; Newman, 2003). Egocentric networks are particularly concerned with the features of ties between individuals in a network. In this line of reasoning,

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researchers speak about strong ties, weak ties, or simply no ties between individuals.3 This notion goes back to Granovetter (1973), who was among the first to distinguish the characteristics of relations.

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Granovetter (1973, p. 1363) claimed that to fully understand egocentric network structures, we have to take a deeper look at the structure of relations between no more than three actors, i.e. triads. In doing so, we may gain a better picture of the local structure of networks in particular, as networks consist of multiple triad combinations.

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In this study, we follow the idea and notation of the triad census by Kalish and Robins (2006). Traditionally, the triad census was established for complete networks, measuring the characteristics of networks based on four elements (Berger, Zelditch, & Anderson, 1967; Holland & Leinhardt, 1976): the

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number of complete dyads, the number of asymmetric dyads, the number of empty dyads in the triad as

According to Crossley et al. (2015, 35) “ego-net research is particularly suited to capturing weak ties”. Weak ties

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thereby signify lose connections, such as mere acquaintances. Yet, the egocentric network approach often makes use of the so-called “name-generator” method to measure an ego’s network in national or cross-national surveys. The numbers of potential names ego can mention is thereby limited, which makes it questionable if ‘true’ weak ties, in terms of acquaintances, can be measured. For example, if ego can name up to three persons, she will most likely name close relations. Whether these relations are of a strong or weak nature is often assumed by additional questions, e.g. a self-rated scale of how close ego is to each of his alters (Kalish and Robins, 2006). With respect to this, the egocentric approach with its limited number of potential alters may not capture true weak ties, but rather ‘not so important’ strong ties. Thus, we have to be careful when drawing conclusions on ego’s ‘weak ties’. Nevertheless, we will speak of weak ties in the remainder of this contribution.

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ACCEPTED MANUSCRIPT well as the configurations that are not covered by the first three elements (Lepri et al., 2016). Kalish and Robins (2006) translated this approach to the study of egocentric networks and introduced a notation for these triad relations as depicted in Figure 1. Ties between individuals may be of a strong or weak structure or may be absent. In egocentric networks, however, the ties between an ego and his alters are always

FIGURE 1 ABOUT HERE

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strong or weak – they cannot be absent.

Figure 1 depicts all possible triadic relations between ego (grey shaded circle) and his/her two

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potential alters in a triad (hollow circles). Following Kalish and Robins (2006: 61), the different triad combinations can be identified by three-letter combinations, where S stands for strong tie, W for weak

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tie, and N for no tie. According to this notation, the first and the third letter identify the relation between ego and both alters; the second letter stands for the tie between alter1 and alter2. The notation follows a clockwise reading: for example, the first triad in the second row in Figure 1 denotes that ego has a weak tie to alter1, alter1 and alter2 are connected via a strong tie, and alter2 and ego also have a weak connection (WSW) – the solid lines identify strong ties and weak ties are depicted by dotted lines. Overall,

WNS.4

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we have 9 possible triad combinations as alters are interchangeable, i.e. the triad SNW is the same as

Yet, not all the depicted nine triads are equally important to our analysis: insights from social network

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literature suggest that some triads are more likely to occur in networks than others. According to Granovetter’s (1973) idea of the strength of weak ties, triads will have a greater probability to be closed,

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i.e. alters will be connected with each other if ego has strong ties to both alters. In his reasoning, in a triad consisting of A, B and C, if A and B as well as A and C are connected by strong ties, there is an increased likelihood that B and C are very similar to each other – for both of them having a close connection to A – and, thus, there should be a higher friendship potential between them once they have met. This assumption goes back to the cognitive balance theory by Heider (1946) as well as Newcomb (1961), which states that individuals have a general tendency to maintain consistency in their liking and disliking of one another (Janicik & Larrick, 2005; Zajonc & Sherman, 1967). According to Ritzer (2007), balance 4

For a more detailed discussion of the triad census see Kalish and Robins, 2006, pg. 59-63.

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ACCEPTED MANUSCRIPT relations indicate that structures are stable. Consequently, structures are unstable in unbalanced situations, exerting pressure on individuals to change ties in the direction that makes them balanced. Transferred to our triads, this implies that balanced triads, where all nodes are connected equally, are more likely to occur than unbalanced ones. Likewise, in the words of Granovetter (1973, p. 1363), “the

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triad that is most unlikely to occur […] is that in which A and B are strongly linked, A has a strong tie to some friend C, but the tie between C and B is absent”. Such a forbidden triad is identified as SNS in Figure 1. In sum, triads comprising network closure should appear more frequently. Namely, these are SSS and, to some extent, SWS triads. Interestingly, Granovetter (1973) does not consider triads consisting

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of three weak ties (WWW) as closed networks as these ties do not “exhibit strong tendencies to closure”

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(Kalish & Robins, 2006, p. 60).

Burt (1992, 2000), in contrast, argues that these ‘forbidden triads’ are the most important ones. In his terms, such structural holes render vantage points for those bridging the gap. For instance, the exemplary ego in the third triad in the first row of Figure 1 (SNS) has, according to Burt (2000), an informational advantage compared to an ego in a closed network: While she is strongly tied to both alter1 and alter2,

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there is no connection between these alters; thus, there are no information channels between alters. Burt’s main argument is that in closed networks no one has an informational advantage – in particular in triads consisting of strong ties only – as every actor may rely on the same (symmetric) lines of communication.

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But, if structural holes are present, the person bridging the gap controls which information is distributed between nodes (Burt et al., 2013). Moreover, in such a triadic situation, an ego has access to two

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independent sources of information. For instance, in triad SNS, ego “can broker communication while displaying different beliefs and identities to each contact” (Burt, 2000, p. 354). Under these circumstances ego adds value to the triad by acting as entrepreneur – he is the “third who wins” (Simmel, 1955). In this line of reasoning, the triads comprising structural holes – SNS, WNW, and SNW – are of particular interest in the following analysis.5

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While we analyze the effects of the Big Five personality traits on all nine possible triads, we consider the following

triad structures as most important: SSS, SWS, SNS, WNW, and SNW.

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How Personality Traits Relate to Network Structures We assume that some individuals are more likely to have closed networks, whereas others have a tendency for structural holes, or prefer closed networks over structural holes. Different people want

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different things from their networks: some seek emotional support, whereas others look for new ideas and information. In contrast, some even might feel uncomfortable of having strong ties at all and try to avoid close social contacts. We argue that an individual’s personality can define what someone wants

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from their networks and, thus, may relate to their structure. In what follows, we present our argumentation of how the Big Five personality traits relate to differences in egos agency in networks and

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why they seek different benefits (Burt et al., 2013). While there are different ways to conceptualize an individual’s personality throughout social science and psychological literature, one of the most commonly used measures is the Big Five approach. The Big Five are composed of the traits of openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism , whereby each trait is based on multiple facets, giving us good insights into how individuals’ minds are made up (Goldberg, 1990; McCrae & Costa, 2003;

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McCrae & John, 1992). Although prior studies revealed that, in particular, extraversion and neuroticism relate to both the quantity and the characteristic of relations in egocentric networks, we know little about the role of the other three traits in this relationship with network composition (Burt et al., 1998; Ishiguro,

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2016; Kalish & Robins, 2006; Lepri et al., 2016; Roberts et al., 2008). Persons high on the openness to experience trait are creative, highly interested in new experiences, and

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eager to learn new things (McCrae & Costa, 2003; McCrae & Costa Jr., 2008; Mondak, 2010). The facets of the open personality, first and foremost, embrace artistic and cultural attributes. Open persons use their personal networks to gain new impressions, to satisfy their general curiosity, and to explore novelties in different areas of everyday life, such as new movies or dishes (Freitag & Bauer, 2016; McCrae & Costa, 2003). With respect to this, dense networks, i.e. without structural holes, might not offer the advantage open persons are looking for from their contacts: Open individuals want to be the first ones to know about the newest technologies and designs or new hot-spots in town. In this regard, they should be more likely to seek after highly diversified influences. Here, they can take the advantageous position of

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ACCEPTED MANUSCRIPT receiving information from different sources. Moreover, insights from organizational research (Fang et al., 2015, p. 1254) demonstrated that people high in openness engage in rather small and disconnected networks in which they can act as go-betweens, therewith interacting in multiple social roles and settings (Mehra, Kilduff, & Brass, 2001). Accordingly, we expect to see a higher proportion of triads including

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structural holes for persons high on openness to experience (e.g. SNS, WNW, SNW); the strength of these ties thereby should play a minor role, that is, open individuals will have both strong and weak ties.

Conscientious individuals like to keep control in their life – in particular, over their personal relationships as well as their careers (Dinesen, Nørgaard, & Klemmensen, 2014; McCrae & Costa Jr., 2008). Their

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behavior is driven by rationality and the pursuit of excellence. In general, conscientious persons “make plans in advance and [think] carefully before acting” (McCrae & Costa, 2003, p. 51). They are known to

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be dutiful, persistent, and hardworking (McCrae & John, 1992). For this reason, conscientious individuals need networks that offer them benefits such as stability, support, and norm-abiding behavior. They should favor networks on which they can rely, which results in the need for strong relationships, i.e. more strong than weak ties. Moreover, Klein et al. (2004) revealed that due to their problem-solving

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capabilities, conscientious individuals often take up brokerage positions in professional networks. Transferred to egocentric networks, we argue that regarding the yearning for control of more conscientious persons, we expect to see a higher proportion of structural holes in their networks (McCrae

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& Costa Jr., 2008). In sum, there should be a higher likelihood to observe SNS and SWS triads for individuals ranging high on conscientiousness.

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Most of the research analyzing the relationship between personality and egocentric networks is dedicated to the trait of extraversion (Ashton, Lee, & Paunonen, 2002; Ishiguro, 2016; Kalish & Robins, 2006; Lepri et al., 2016; Roberts et al., 2008). Yet, there are competing assumptions concerning extraverts’ characteristics of triads (Kalish & Robins, 2006). Extroverted individuals, like agreeable ones, are considered to be more sociable and gregarious (McCrae & Costa, 2003). Moreover, they are talkative, active, and feel most comfortable surrounded by large groups. As a consequence of their outgoing personality, they are both more likely to create opportunities for interactions and to have large social support networks (Furukawa, Sarason, & Sarason, 1998; Kalish & Robins, 2006). Translated to the triad

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ACCEPTED MANUSCRIPT census, they should have proportionally more closely connected networks, such as SSS or SWS, and less networks including structural holes, like SNS. At the same time, however, another mechanism could be at play. According to Ashton et al. (2002) the fundamental element of extraversion is social attention. Thus, the benefit they are seeking for from their

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personal networks is attention, stimulation, and a variety of social activities, whereby this does not imply they are also seeking (or experiencing) popularity (Ashton et al., 2002). Klein et al (2004) even suggest that extroverts’ need for attention may translate into dislike from others. In addition, extravert’s preference for very large networks (Bolger & Eckenrode, 1991; Henderson, 1977) may make it difficult

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for them to introduce their alters to each other or the mere size of the network may only be sustainable by having weaker ties to alters. In accordance with Bossard (1945), we may also expect that while

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individuals high on extraversion attract many social relations, these relations should tend to be weak and disconnected (Bossard, 1945), such as WNW or WWW.

The most social personality can be found in persons high on agreeableness (Jensen-Campbell, Gleason, Adams, & Malcolm, 2003). Agreeableness gives the best impression about how people interact in social

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situations with others (Freitag & Bauer, 2016; Mondak, 2010). According to McCrae and Costa (2003, p. 50) they are selfless persons with an intense “desire to help others”. Further, they are likely to help in connecting people with each other and to overcome communication problems between groups (Fang et

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al., 2015; Jensen-Campbell et al., 2003). We, thus, assert that agreeable persons are more likely to establish strong relations with others. Their main goal is to provide general support and maintain good

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relationships with the persons in their network. Concerning the respective triad census, we assume that they have higher proportions of SSS and SWS triads, as their emphasis lies on strong connection to others as well as connecting others with each other. Furthermore, we expect agreeable individuals to have more balanced relationships in terms of less structural holes following their preference for relational harmony (Janicik & Larrick, 2005). In contrast, neurotic persons are considered as the least sociable individuals which are often seen as “hard to get along with” or high-cost interaction partners (Fang et al., 2015, p. 1245; Ishiguro, 2016; Kalish & Robins, 2006; Klein et al., 2004; McCrae & Costa, 2003, p. 48). Neuroticisms originate in the

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ACCEPTED MANUSCRIPT facets of fear and anxiety as well as a general feeling of inferiority to others. Above this, they are less trusting and carry the general fear of being betrayed by others (McCrae & Costa, 2003; McCrae, Costa, & Busch, 1986). Neurotic persons have more negative views of others (Henderson, 1977; McCrae & John, 1992), which may result in a very loose and rather small social support network (Furukawa et al., 1998;

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Kalish & Robins, 2006; Stokes, 1985). As a consequence, they should have more loosely connected networks due to their general fear and their desire to keep control over their networks (e.g. WWW). This makes them more likely to have higher proportions of triads that include structural holes (e.g. WNW). However, while neurotic persons may not be sought after as friends (Klein et al., 2004), they may seek

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social support, trust, and reliability from their networks. Following McCrae and Costa (2003, p. 48) neurotic persons are vulnerable and, thus, more dependent on strong support by others. Therefore, it

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could also be that individuals high on neuroticism have a higher tendency towards SWS or SSS triads in their networks. For example, Fang et al. (2015, p. 1249) find that neurotic persons are less likely to hold a central position in organizational networks.

It has to be noted, however, that even though the outlined theoretical assumptions support the idea

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that personality is antecedent to network formation, our analysis cannot rule out a potential reversed causality. In other words, we refrain from making any empirical causal claims. The structure of one’s network may influence certain aspects of one’s personality. In addition, we cannot address the potential

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influence of other personal and environmental factors, such as genetics or the occurrence of important life events. These factors could act as an important moderator of the relationship between personality

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traits and egocentric network structures. Unfortunately, our data does not render the possibility to control for these influences.

Data, Measures, and Method Data Our data stems from the 2005 MosaiCH-ISSP survey (“Measurement and Observation of Social Attitudes in Switzerland”) – a biennial cross-sectional survey that focuses on the Swiss population’s values and attitudes toward a wide range of social issues (Joye, Schoebi, & Kaenel, 2007). The 2005 version included 11

ACCEPTED MANUSCRIPT measures of personality traits as well as measures of egocentric networks. The respondents are drawn from a probabilistic sample representing the Swiss population aged 18 and above. Overall, we can identify 1651 respondents who named at least two alters. Those having only one relation were excluded from the analysis. The average age in the sample is 46.45 years; females are slightly overrepresented at 55.60

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percent (see Table 1).

Measures

Triad census. The MosaiCH 2005 captures egocentric networks via a name-generator: the respondents were

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asked to name up to four persons who played an important role in their life in the last six months.6 The average number of alters is 3.25 (SD=0.76) as shown in Table 1. The strength of the relationships

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between ego and their alters as well as between alters is accounted for by two basic premises: knowing each other and supporting each other. The latter thereby represents strong ties, whereas the former identifies weak ties.7 In our sample, we encounter, on average, 87 percent of strong ties between ego and her alters, compared with 13 percent of weak ties. No ties are simply measured by the absence of strong or weak ties.8 The triads depict characteristics of individuals’ social networks, our dependent measure; our

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main unit of analysis thus are individuals.

In the tradition of Kalish and Robins (2006) as well as Lepri et al. (2016), we operationalize the proportion of each triad type given the total number of possible triads dependent upon the number of

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alters in the network. In doing so, we are able to compare egocentric networks of different sizes. That means, each individual may have relations based on all nine triad structures, but they may also only have

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two different kinds of triads. For example, an individual naming three alters has the potential of having three different triads – total number of possible triads for ego. Hereof, all three triads could be marked by

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Some may argue that a network of up to four persons constitutes a rather close-knit network including only very

close and well-known persons. In other studies, the number of alters is unlimited. However, despite the small number of alters, we still observe different types of triads. Their proportions are thereby comparable with those from Kalish and Robins (2006). 7

As aforementioned, it is questionable whether these are real weaks ties or rather ‘not so strong’ strong ties.

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Unfortunately, the data set does not comprise any relevant information on the homophily of the ego’s networks.

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ACCEPTED MANUSCRIPT only strong relations, SSS, which would render a triad proportion of 100 per cent for SSS. If, however, we observe three different triad structures, then each of these triads would receive the proportion of 33.33 per cent. The average proportions per triad over all respondents are given in Table 1. As predicted, the most likely triad comprises only strong ties – on average, the proportion of this type lies at 39 percent in

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our sample. Likewise, the other closed network triad occurs with a proportion of 0.26 (Granovetter 1973), whereas structural holes occur at a proportion of 0.19 (SNS), 0.02 (WNW), and 0.02 (SNW). As a robustness check we further operationalize each triad in a binary way, that is, whether the respondent has the respective triad in his network (1) or not (0).

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TABLE 1 ABOUT HERE

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Big Five personality traits. Our main explanatory variables, the Big Five personality traits of openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism, were measured based on the BFI-10 instrument. This is a shortened version of the standard Big Five Inventory encompassing 10 items, i.e. two per personality trait (Rammstedt & John, 2007). The questions and items capturing the different personality dimensions are displayed in Table 2. Answer categories range from ‘strongly agree’ to ‘strongly

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disagree’ (5-point scale).

TABLE 2 ABOUT HERE

The trait dimensions were then identified by, first, logarithmizing each item; then, secondly, items

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measuring each dimension were summed, and last, normalizing this additive index with values ranging from zero (lowest observed value) to one (highest observed value) (Mondak et al., 2010; Mondak, 2010).9

9

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The distributions in our sample for each of the five personality traits are depicted in Figure 2.10

According to Mondak (2010, p. 72) this procedure should largely eliminate skewed distributions – which should be

mainly due to social desirability effects – and make comparisons across trait measures easier. This method is particularly valid in shortened measures of the Big Five. 10

The correlations between the items range from 0.04 for agreeableness to 0.36 for both neuroticism and

extraversion. According to Gerber et al. (2010, p.119 footnote 11) correlations between items of short Big Fives measures, like the one implemented here, are negligible as these measures are not designed for reaching high correlations. They are designed for being brief, to reach high test-retest reliability and a sizeable correlation with

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ACCEPTED MANUSCRIPT Socio-demographic factors. Besides the personality measures, we add controls for basic socio-demographic attributes to our analyses. Namely, these are a respondent’s age, gender, and educational level. The average values and the distributions, respectively, are shown in Table 1. We further present the correlations between these measures and the Big Five in Table A1 in the appendix.

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Method & Software To test the influence of the Big Five personality traits on the triad census, we, first, implement linear regression analysis with clustered standard errors for individuals – as triads are nested within individuals –

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to test the relationship between the Big Five personality traits and the triad census in egocentric networks. In a second step, we make use of logistic regressions for each triad type, i.e. whether Ego has such a triad

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or not in his network.11 By combining these two approaches, we can test the relationship between personality traits and egocentric network structures from different angles.12 All estimated models control for all five personality traits as well as the socio-demographic measures presented above. The linear regression model further controls for the number of potential triads ego has to control for potential

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interdependencies between triads.13 Compared to the correlational analyses by Kalish and Robins (2006)

longer measures of the Big Five, such as the NEO-Personality Inventory. Thus, item correlation in short measures are less informative.

One may argue that the presented data has a hierarchical data structure as triads are nested within individuals. Yet

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multilevel modeling in egocentric networks follows an important assumption that is violated in our data: according to Crossley et al. (2015, 148), we can only run an unbiased model if the assumption that alter-alter ties are

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independent of ego or characteristics of ego is fulfilled. In the present case, our aim is to test that alter-alter ties are dependent on ego. By estimating clustered standard errors, we however control for the nested structure in the data. 12

Above this, we further estimated simple logistic regressions with the each triad type as dependent variable, i.e.

having that triad or not. The results (available on request) support our findings reported below. 13

There are numerous other potential factors confounding the relationship between personality traits and egocentric

network structures. Among others, environmental factors such as family structure, living in a rural or urban area as well as cultural values can play an important role in forming the relationship between personality and personal networks. Moreover, genetics may also have an influence, in particular, as they define one’s personality to a large degree. However, the data set we are using does not ask for these factors.

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ACCEPTED MANUSCRIPT this gives us the advantage that we can control for potential confounding influences as well as the interrelations of all five personality traits. All analyses as well as the data management were conducted using Stata MP Version 15.1.

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FIGURE 2 ABOUT HERE

Results

Before we turn to the triad measures, we first estimated a model testing the relationship between the Big Five traits and having strong ties, that is, the percentage of strong ties given ego’s overall number of

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ties. Figure 3 shows the results; here, the dots represent the estimated coefficient of each personality trait and the vertical lines constitute the 95% confidence intervals. If these confidence bands cross the vertical

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zero line, we may conclude that the estimated effect is not distinguishable from zero, i.e. statistically insignificant. We see that persons high on extraversion, openness or agreeableness have a stronger tendency towards strong ties in their networks. However, only the effect for agreeableness and openness reach statistical significance. With respect to our theoretical reasoning, we see that neurotic persons tend

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to have less strong ties. The estimate for conscientiousness also shows a slightly negative relationship, which fails to reach statistical significance.

FIGURE 3 ABOUT HERE

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Our main interest lies in the explanation of different triad census in egos’ network. Table 3 shows the estimates – based on linear regressions – for the dependent variable measuring the triad proportions (cf.

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Kalish and Robins 2006). We estimated nine models, one for each triad proportion. Starting with openness, Table 3 reveals that persons ranging high on openness to experience have a higher proportion of WSW-triads, i.e. triads in which they are less strongly connected to their alters, but their alters are strongly tied. Moreover, we do not find any significant or relevant relation supporting our assumption that open persons prefer structural holes, that is, being in an advantageous position for new information. More interestingly, we find the strongest effect of openness to experience on having a high proportion very close-knit ties, i.e. SSS triads. This is slightly supported by the negative relationship between openness and both the WWW and SWW triad, which indicate that individuals scoring high on

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ACCEPTED MANUSCRIPT openness have a tendency to avoid less supportive relationships. Yet, these effects are only significant at the 10 per cent level. With respect to this, the idea of balanced relationships in terms of Heider’s (1946) cognitive balance theory seems to be more supported by these findings than Burt’s (1992, 2000) assumptions about structural holes.

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Turning to the second trait, conscientiousness, we assumed, similarly to openness, that individuals high on this trait prefer structural holes. At the same time, they also seek supportive relations, i.e. strong ties. The results in Table 3 do not support this assumption. Likewise, none of our predictions seems to apply to individuals high on conscientiousness: there is no hint that they seek more strong relations to

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their alters (SWS, SNS, SNW) or that they would prefer balanced networks. Solely, they seem to have more balanced weakly tied networks as shown by the positive and significant relation with the WWW

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triad and a negative significant one with the WNW triad. This hints at the possibility that conscientious persons are less concerned with their personal network than with their other ambitions, e.g. career goals, and thus try to keep opportunity costs as low as possible by maintaining balanced and less strongly tied networks.

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TABLE 3 ABOUT HERE

In contrast, we observe that our estimates support prior findings based on pure convenience samples

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concerning the relationship between extraversion and the triad census: persons ranging high on extraversion have a tendency towards networks with strong ties, SWS. At the same time, these persons

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tend to avoid structural holes if they maintain strong ties to their alters (SNS). Extraverted individuals, thus, fulfill the triad requirements made by Granovetter (1973), i.e. having closed networks and no “forbidden triads”. This is further supported by the finding that extraversion relates negatively to having weak connections in triads (WNW, WWW). Interestingly, we could not find a relevant or significant relationship between individuals high on extraversion and strongly tied networks, SSS, although Kalish and Robins (2006, 74) underscored in their study that this is the most important relation. Individuals ranging high on the agreeableness trait should be more likely to have more supportive ties to their alters. Although we see in Table 3 that an agreeable personality tends to have a negative

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ACCEPTED MANUSCRIPT relationship to triads including weak ties to their alters (WSW), the overall pattern of the results is quite diffuse. Individuals high on agreeableness reveal a negative tendency towards the very unbalanced triad of SNW and at the same time a positive relationship with the SWW triad. Given the fact that we might not truly capture weak ties in our data – rather they are less strong ties – the findings concerning agreeable

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individuals could be summarized as agreeable persons having a slight preference for balanced relations to their alters.

The least surprising results are found for the last personality trait, neuroticism. There is a decisively negative relationship between neuroticism and having triads with strong ties only. More importantly, this

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is the strongest relationship – in terms of effect size – we find over all estimated models. Neurotic persons seem to have a general urge to avoid relations connected to societal obligations, such as strong

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ties. This is supported by the positive and significant relationship between being more neurotic and the triads of WNW and WWW. Moreover, our estimations slightly support the idea that neurotic individuals tend to aim for control over their social relations and, thus, seem to be more likely to maintain structural holes in their triads (SNS and WNW). These results support prior findings in the field (Kalish & Robins,

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2006). Yet, we also see that individuals high on neuroticism do not completely avoid strong relations in their networks as can be seen by the positive relationship with SWS, SNS, and SWW triads. For example, Kalish and Robins (2006, 74) find a strong negative relationship between neuroticism and the SWS triad

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proportion. Again, this difference in findings could be due to the control of the other Big Five personality traits. Moreover, the observed results for neuroticisms could also be slightly underestimated as

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neuroticism is the trait that is most likely to be affected by social desirability effects in the survey as shown in Figure 2.

To test the robustness of our findings, we further estimated logistic models on the odds to have at least one triad of the respective type. The results are presented in Table 4, which shows the estimated coefficients. Overall, the findings in the logistic model are in line with the linear regression results in Table 3. There are only little deviations in the findings, in terms that we detect some relevant and significant relations that were not present in the linear estimates. In detail, we find two more important relations concerning openness to experience, that however support our theoretical assumptions. According to the results in Table 4, open individuals show a tendency to engage in SWS as well as SNW 17

ACCEPTED MANUSCRIPT relations. Moreover, we find that agreeableness has a rather sizeable influence on having a WNW relationship. This again supports the assumption that agreeable individuals prefer strong over weak ties and try to avoid structural holes.

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TABLE 4 AOUT HERE

Discussion and conclusion

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The primary goal of this study was to answer the general question of why egocentric network structures vary and what role personality traits play in defining the formation of these networks. We took the approach by Kalish and Robins (2006) a significant step further by integrating all Big Five personality traits into our models. To date, the bulk of studies in the field uses less broad personality measures (Burt et al., 1998; Mehra et al., 2001) or solely focus on the impact of extroversion and neuroticism (Ishiguro,

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2016, 2016; Kalish & Robins, 2006). Moreover, most research on the effects of personality on individual network positions is situated within the greater framework of organizational research (Burt et al., 2013; Fang et al., 2015; Kilduff & Tsai, 2003). We tried to transfer this approach to the framework of egocentric

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networks by asking why some people take what type of position in networks. Network position is often associated with certain advantages or disadvantages – such as access to new information – in particular in

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professional networks. This, however, may also apply for egocentric networks where the structure may define one’s access to different forms of capital, i.e. social capital (Coleman, 1988; Granovetter, 1973). Against this background, our results revealed that each of the Big Five personality traits relates to the explanation of strong and weak ties in egocentric networks. The explanatory power of our models thereby varies between 1 and 13 percent for the nine potential triads. Although this might not be the strongest variance explanation, personality offers both sizeable and relevant alternative explanations for differences in egocentric network ties. The explanatory power thereby also varies depending on the likelihood of the triads, that is, the best model explanation was observed for the triad SSS, which is also the triad that is

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ACCEPTED MANUSCRIPT most often encountered in egocentric networks. Most importantly, we could underscore that besides extraversion and neuroticism, openness to experience, agreeableness, and conscientiousness play a relevant role in explaining differences in egocentric network compositions. We derived our theoretical reasoning from two famous approaches in the network literature –

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Granovetter’s (1973) strength of weak ties and Burt’s (1992; 2000) theory of structural holes – as well as approaches from organizational research on individual agency (Fang et al, 2015). We found support for both theories and insights from prior research. Yet, our findings supported those from prior research on neuroticism, whereas they also revealed some contradicting results, especially concerning extraversion. In

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essence, we could show that more extroverted persons are those who are most likely to maintain rather strong relations and networks without structural holes, which supports the theory by Granovetter. This

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difference may be due to the inclusion of all five personality traits instead of focusing on only two. It is thinkable, that the effects of prior studies overestimated the importance of extraversion as they did not control for all Big Five personality traits. Our results for highly neurotic persons, however, support implications made by the structural holes theory (Burt, 1992): high neuroticism fosters a preference for

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triads with disconnected or loosely tied alters. These structural holes help them to keep control over information flows and potentially lowers their fears of being betrayed by others – a typical fear of emotionally unstable individuals (McCrae & Costa, 2003). Conscientious individuals, are more likely to

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engage in less strongly connected triads. In Granovetter’s view, this is a typical indicator for rational persons who want to take advantage of their position, for example, by creating a larger network. Last, our

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estimates revealed that agreeable persons are, indeed, the most sociable ones. They maintain a large diversity of connections that, on average, include rather strong ties. Although we had the great advantage of testing our research question with the help of a Swiss population sample, some questions remain unanswered. In particular, the general question arises of what we can learn from analyses on egocentric networks for the greater context of complete networks. Egocentric networks provide a limited and potentially biased view on networks. Social relations and ties are by definition not directional – as implied by egocentric networks. They are defined by interactions and interrelations of actors. An interesting avenue for future research may, thus, be to collect data on complete networks including personality characteristics of all actors. This could render deeper insights 19

ACCEPTED MANUSCRIPT into how different personalities interact and how they structure networks from different angles. Following the social capital literature, social relations symbolize an important social value. For this reason, our results can add to the understanding of how and why specific valuable relations are generated based on an individual’s deeply rooted personality traits. Another potential caveat is that our data only renders a

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snapshot of individual networks, personality, and behavior. We could not address the question of how distinct network structures influence an individual’s personality. Although Asendorpf and Wilpers (1998) found that personality influences relationship and not vice versa, new research in this area claims that social structures do have an impact on personality changes (Meeus, Duriez, Vanbeselaere, & Boen, 2010).

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In this line, the main contribution of this study to the broader social network literature is that personality factors should be taken into consideration more often. While there is already plenty of

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knowledge about how socio-economic and socio-demographic factors shape one’s network, we know little about the role of one’s personality. The personality framework offers a new dimension to capture heterogeneity in times of increased individualization. The way in which individuals connect with others in their private and professional life will receive increased attention in societies that focus more and more on

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individuals. What happens if individuals maintain mainly loose ties or structural holes to gain advantage points over other individuals? The personality framework may offer important insights into this individual behavior in social networks. A fruitful exercise for studies in this field would be to capture larger

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egocentric networks in population-based samples. Moreover, the personality framework can easily be taken to analyze the occurrence of asymmetric relations in egocentric or complete networks; given that

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we have information on both egos and alters. The question of how personality traits relate to asymmetric relationships in networks did not receive a lot of attention in prior studies. Future research could take An and McConnell’s (An & McConnell, 2015) publication on self-perceived centrality as a starting point and enhance it with a more detailed discussion on the role of personality traits in choosing asymmetric relationships. For example, drawing from the literature on personality and network positions (e.g. Fang et al. 2015) we might expect that more agreeable persons are more often in symmetric relations as they are quite amicable. In a similar vein, Fang et. al (2015) argue that open individuals are perceived as interesting and good conversational partners and thus should be sought after for friendship, i.e. more symmetric relations. In contrast, more neurotic individuals may be perceived as ‘high-cost interaction partners’ and 20

ACCEPTED MANUSCRIPT accordingly encounter less symmetric relations. These are just some ideas which scholars in this field should try and tackle in the future.

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https://doi.org/10.1016/j.socnet.2013.04.005

Social

25

ACCEPTED MANUSCRIPT TABLES AND FIGURES TABLE 1: Descriptive statistics of dependent and control measures SD

0.87

0.25

0.13

0.25

proportion of triad of all potential triads 0.39 0.26 0.19 0.03 0.04 0.02 0.02 0.04 0.02

M AN U

SC

SSS SWS SNS WSW WWW WNW SSW SWW SNW

RI PT

proportion of strong ties to alter of all ties between ego and alters proportion of weak ties to alter of all ties between ego and alters

Mean

socio-demographic variables age number of alters gender

TE D

education

AC C

EP

Big Five openness to experience conscientiousness extraversion agreeableness neuroticism Notes: N=1651 individuals; rounded values.

26

0.40 0.34 0.30 0.13 0.16 0.10 0.10 0.14 0.09

46.45 3.25

16.21 0.76

Category female male low level education medium level education higher level education

Proportion 55.60% 44.40% 7.51% 68.93% 23.56%

mean 0.78 0.83 0.77 0.77 0.49

SD 0.16 0.16 0.18 0.15 0.22

ACCEPTED MANUSCRIPT TABLE 2: BFI-10 Measure

AC C

EP

TE D

M AN U

SC

RI PT

I see myself as someone who… a. is reserved [E, reverse-scored item, recoded] b. is generally trusting [A] c. does a thorough job [C] d. is relaxed, handles stress well [N, reverse-scored item, recoded] e. has an active imagination [O] f. is outgoing, sociable [E] g. tends to find fault with others [A. reverse-scored item, recoded] h. tends to be lazy [C, reverse-scored item] i. gets nervous easily [N] j. has few artistic interests [O. reverse-scored item, recoded] Notes: O Openness to experience, C Conscientiousness, E Extraversion, A Agreeableness, N Neuroticism.

27

ACCEPTED MANUSCRIPT

TABLE 3: Linear regression results – triad proportions as dependent variable

number of triads in network1 two triads three triads four triads five triads gender (1=female) age educational level intercept

0.06*** (0.02) 0.06*** (0.03) -0.13*** (0.06) -0.07 (0.12) 0.11*** (0.02) -0.00*** (0.00) -0.03* (0.02) 0.33*** (0.09) 0.05 1101.92 1172.24 1651

0.10*** (0.02) 0.10*** (0.02) 0.06 (0.06) 0.01 (0.11) -0.00 (0.02) -0.00*** (0.00) 0.07*** (0.01) 0.13* (0.08) 0.07 682.89 753.21 1651

WNW -0.03 (0.02) -0.03*** (0.02) -0.03*** (0.01) 0.01 (0.02) 0.04*** (0.01)

SSW 0.01 (0.02) -0.01 (0.02) -0.01 (0.01) -0.01 (0.02) -0.00 (0.01)

SWW -0.04* (0.02) 0.01 (0.02) -0.02 (0.02) 0.06*** (0.03) 0.03*** (0.02)

SNW 0.01 (0.01) -0.01 (0.02) 0.00 (0.01) -0.03* (0.02) 0.01 (0.01)

0.00 (0.01) -0.01 (0.01) -0.01 (0.03) 0.14*** (0.06) -0.02* (0.01) 0.00*** (0.00) -0.00 (0.01) 0.01 (0.04) 0.02 -1312.56 -1242.24 1651

-0.00 (0.01) 0.01 (0.01) 0.03* (0.02) 0.09*** (0.04) -0.00 (0.01) 0.00 (0.00) -0.00 (0.01) 0.07*** (0.03) 0.01 -2755.55 -2685.23 1651

-0.00 (0.01) 0.01 (0.01) 0.01 (0.02) 0.22*** (0.04) -0.01*** (0.01) 0.00 (0.00) -0.01*** (0.01) 0.05* (0.03) 0.02 -2770.95 -2700.63 1651

0.02*** (0.01) 0.04*** (0.01) 0.16*** (0.03) 0.07 (0.05) -0.02*** (0.01) 0.00*** (0.00) -0.01 (0.01) -0.01 (0.04) 0.04 -1764.14 -1693.82 1651

0.01 (0.01) 0.02*** (0.01) 0.22*** (0.02) 0.07*** (0.03) -0.00 (0.00) -0.00 (0.00) -0.01*** (0.00) 0.05*** (0.02) 0.09 -3099.42 -3029.10 1651

RI PT

-0.19*** (0.02) -0.24*** (0.03) -0.38*** (0.07) -0.56*** (0.13) -0.04*** (0.02) 0.00*** (0.00) 0.01 (0.02) 0.36*** (0.10) 0.13 1450.15 1520.47 1651

WWW -0.05* (0.02) 0.05*** (0.03) -0.06*** (0.02) 0.02 (0.03) 0.05*** (0.02)

SC

neuroticism

WSW 0.05*** (0.02) 0.03 (0.02) 0.02 (0.02) -0.05*** (0.02) 0.00 (0.02)

M AN U

agreeableness

SNS -0.04 (0.05) 0.05 (0.05) -0.08*** (0.04) -0.05 (0.05) 0.13*** (0.03)

0.00 (0.01) 0.01 (0.01) 0.03 (0.03) 0.03 (0.05) -0.01 (0.01) 0.00*** (0.00) -0.02*** (0.01) 0.01 (0.03) 0.01 -1963.14 -1892.82 1651

TE D

extraversion

SWS -0.05 (0.05) -0.08 (0.05) 0.10*** (0.05) -0.01 (0.06) 0.09*** (0.04)

EP

conscientiousness

SSS 0.13*** (0.06) 0.00 (0.06) 0.09 (0.05) 0.05 (0.07) -0.34*** (0.04)

AC C

Triad census openness to experience

Adj. R2 BIC AIC N Notes: unstandardized coefficients; standard errors in parentheses; * p < .10, ** p < .05, *** p < .01

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ACCEPTED MANUSCRIPT

TABLE 4: Logistic regression results (odds to have at least one triad of the respective type)

number of triads in network1 two triads three triads four triads five triads gender (1=female) age educational level intercept N AIC BIC

29

WNW -1.79* (0.93) -2.51*** (0.90) -1.69** (0.72) -0.19 (0.84) 2.59*** (0.71) 0.00 0.69* (0.39) 1.65*** (0.44) 2.82*** (0.70) 5.76*** (0.71) 0.05 (0.33) 0.00 (0.01) 0.24 (0.38) -1.76 (1.42) 1651 397.83 468.15

RI PT

WWW -0.15 (0.71) 1.21* (0.70) -0.99** (0.46) -0.38 (0.80) 0.64 (0.46) 0.00 0.79*** (0.24) 0.97*** (0.31) 1.57** (0.64) 3.27*** (0.58) -0.31 (0.21) 0.03*** (0.01) 0.01 (0.25) -4.82*** (1.19) 1651 752.32 822.64

SC

neuroticism

WSW 1.13 (0.72) 0.40 (0.56) 0.77 (0.52) -2.83*** (0.58) 0.46 (0.43) 0.00 1.04*** (0.30) 1.91*** (0.31) 2.91*** (0.56) 3.24*** (0.63) 0.02 (0.23) 0.02*** (0.01) -0.75*** (0.23) -3.13*** (1.11) 1651 688.21 758.53

M AN U

agreeableness

SNS -0.72* (0.37) 0.60 (0.39) -0.85** (0.34) 0.12 (0.45) 0.91*** (0.30) 0.00 1.72*** (0.14) 2.93*** (0.19) 2.85*** (0.45) 2.54*** (0.76) 0.06 (0.13) -0.02*** (0.00) 0.45*** (0.11) -1.73*** (0.66) 1651 1718.84 1789.16

TE D

extraversion

SWS 0.02 (0.35) -0.28 (0.39) 0.58* (0.33) 0.08 (0.44) -0.02 (0.28) 0.00 1.50*** (0.12) 2.97*** (0.21) 0.89** (0.43) 1.55** (0.63) 0.54*** (0.12) -0.02*** (0.00) -0.19* (0.11) -0.58 (0.63) 1651 1874.61 1944.93

EP

conscientiousness

SSS 0.74** (0.33) -0.16 (0.37) 0.45 (0.29) 0.22 (0.39) -1.78*** (0.26) 0.00 0.66*** (0.12) 1.62*** (0.19) 0.85** (0.43) -0.69 (0.59) -0.18* (0.11) 0.01* (0.00) 0.07 (0.11) -0.38 (0.59) 1651 2058.25 2128.57

AC C

Triad census openness to experience

SSW 0.79 (0.78) -1.01 (1.05) 0.22 (0.99) 0.66 (1.11) 0.10 (0.47) 0.00 0.39 (0.41) 1.92*** (0.39) 2.42*** (0.65) 0.00 (.) -0.49 (0.31) 0.02* (0.01) -0.48 (0.31) -4.36** (2.08) 1643 425.93 490.78

SWW -1.38** (0.66) -0.41 (0.58) 0.08 (0.68) 2.43** (1.01) 1.58*** (0.45) 0.00 1.64*** (0.29) 2.35*** (0.30) 5.34*** (0.53) 4.39*** (0.70) -0.51** (0.22) 0.03*** (0.01) 0.08 (0.20) -6.85*** (1.25) 1651 766.38 836.70

SNW 1.38 (1.11) -0.63 (0.86) -0.59 (0.82) -0.63 (0.97) 0.60 (0.80) 0.00 1.48*** (0.47) 2.09*** (0.48) 5.82*** (0.60) 5.04*** (0.93) -0.14 (0.32) -0.01 (0.01) -0.61** (0.25) -2.91* (1.69) 1651 423.87 494.19

ACCEPTED MANUSCRIPT

M AN U

SC

RI PT

FIGURE 1: Triads in egocentric networks

AC C

EP

TE D

Notes: Graph based on Kalish and Robins (2006, p. 74).

30

ACCEPTED MANUSCRIPT

30 0

0

5

5

RI PT

10

10

Percent 15

Percent 15

20

20

25

25

FIGURE 2: Distribution of Big Five measures

.1

.2

.3

.4 .5 .6 .7 openness to experience

.8

.9

1

0

.1

.2

.3

.4 .5 .6 conscientiousness

30 Percent 20 25

20

15

Percent 15

.8

.9

1

0

0

5

5

10

M AN U

10

.7

SC

35

25

40

30

0

.1

.2

.3

.4

.5 .6 extraversion

.7

.8

.9

1

0

.1

.2

.3

.4

0

5

10

15

TE D

Percent 20

25

30

35

0

.1

.2

.3

.4

.5 .6 neuroticism

AC C

EP

0

31

.7

.8

.9

1

.5 .6 agreeableness

.7

.8

.9

1

ACCEPTED MANUSCRIPT

FIGURE 3: Effect of the Big Five personality traits on having strong ties – linear regression results

RI PT

Openness

M AN U

SC

Conscientiousness

Extraversion

TE D

Agreeableness

-0.1

0

Note: 95% confidence intervals; N=1651 individuals.

0.1

0.2

0.3

AC C

-0.2

EP

Neuroticism

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ACCEPTED MANUSCRIPT APPENDIX Table A1: Correlations between main variables openne ss

conscientiousn ess

extraversi on

agreeablene ss

neuroticis m

stron g ties

1.00 -0.02

1.00

0.07* -0.03 -0.01 0.01 -0.02

0.05* 0.19* -0.04 0.02 0.03

1.00 0.18* -0.10* 0.10* -0.02

1.00 -0.19* 0.07* 0.03*

1.00 -0.19* 0.11*

gender

0.06*

0.07*

0.03

0.03*

0.15*

age

-0.10*

0.16*

0.01

0.12

0.02

1.00 0.71* 0.07* 0.08*

education

0.16*

0.03

0.04

-0.02*

-0.11*

-0.00

M AN U TE D EP AC C 33

ag e

educatio n

1.00 0.09 * 0.04 0.09 *

SC

Notes: *at least significant on the 5 per cent level.

gend er

RI PT

openness conscientiousn ess extraversion agreeableness neuroticism strong ties weak ties

wea k ties

1.00

0.07*

-0.18*

1.0 0 0.0 0

1.00