Ninety years after Lewin: The role of familism and attachment style in social networks characteristics across 21 nations/areas

Abstract

Drawing on the literature on person-culture fit, we investigated how culture (assessed as national-level familism), personality (tapped by attachment styles) and their interactions predicted social network characteristics in 21 nations/areas (N = 2977). Multilevel mixed modeling showed that familism predicted smaller network size but greater density, tie strength, and multiplexity. Attachment avoidance predicted smaller network size, and lower density, tie strength, and multiplexity. Attachment anxiety was related to lower density and tie strength. Familism enhanced avoidance’s association with network size and reduced its association with density, tie strength, and multiplexity. Familism also enhanced anxiety’s association with network size, tie strength, and multiplexity. These findings contribute to theory building on attachment and culture, highlight the significance of culture by personality interaction for the understanding of social networks, and call attention to the importance of sampling multiple countries.

Keywords

Attachment familism person-culture fit social network

Introduction

When comparing people’s networks and the formation and maintenance of social ties between Germany and the U.S., Kurt Lewin (1936) argued that Americans engage in more distal “peripheral” relationships—being more willing to form new ties but not allowing them to evolve into deeper relationships.¹ Conversely, Germans who value close “private” relationships—are less willing to form new ties. Once ties are formed, they are more likely to evolve into close meaningful relationships. Lewin (1936) theorized that the differences in people’s social networking are due, like many other behaviors, to the interaction between people’s personality and the environment they are embedded in. Despite almost 90 years passing since Lewin (1936), no research to our knowledge has systematically tested Lewin’s proposition. Guided by recent cultural perspectives, to fill this gap, we conducted a large-scale international collaborative investigation across 21 different nations/areas. Specifically, we examined how familism—a cultural construct related to relationships (Campos et al., 2016) and attachment style— a personality trait central to close relationships (Bowlby, 1969) relate to one’s social networks.

Social networks

Social networks are social structures depicting people and their interpersonal connections. A main characteristic of social networks is the number of others or alters one has — termed network size (Walker et al., 2000). Alters can reside either in the periphery of the network (usually with more superficial and distal relationships) or in the private region of the network (with deeper and closer relationships). Using these regions, ties can be described across a dimension termed depth ranging from superficial to deep (less to more intimate). Depth can be directly assessed via network density , the structural closeness among members within a network, or tie strength , the intensity of bondedness between network members (Gillath et al., 2019). Both indexes represent the degree of relationship closeness and are based on people’s self-report of felt closeness or how frequently they interact with Alters (Marsden & Campbell, 1984).

Alters can also play different roles for people, share different situations with them, and satisfy different needs. The more roles alters play (e.g., classmate, roommate; Ferriani et al., 2013), the more situations they share, and the more needs/functions they fulfill (e.g., secure base, safe haven; Gillath et al., 2017) the higher the network’s multiplexity is.

Cultural differences in social networks

Previous studies have investigated the dynamics of culture and social networks. Americans have more friends than Ghanaians (Adams & Plaut, 2003) and people in Hong Kong (Wheeler et al., 1989). Potentially, because Americans’ friendships are less strongly tied (Li et al., 2015), Americans invest less in each friendship and thus can have more. Work on relational mobility—the ease of establishing new relationships (Yuki & Schug, 2012) demonstrates that Americans tend to be higher on relational mobility than Japanese. Potentially because ties in the U.S. are often weaker or more superficial, relationships are easier to establish and maintain than in Japan (Schug et al., 2010) and are easier to dissolve in the U.S. (Gillath & Keefer, 2016).

Related to multiplexity, when people fulfill fewer functions and roles for each other, there is a smaller chance or opportunity for friction to happen. Indeed, Americans tend to report less relational animosity than Ghanaians (Adams, 2005), and fewer concerns about negative relational consequences when asking for social support than Asians (Kim et al., 2008).

Most existing studies on the associations between culture and relationships, suffer from a major limitation: they compare only two nations or cultures, and thus cannot discern which of all possible differences between the two cultures contributes to their findings (Van de Vijver & Leung, 2000). Furthermore, often the U.S. serves as a comparison target, rather than the hypotheses being directly and systematically tested across multiple cultures. As a result, people may overgeneralize findings based on American samples, and the U.S. patterns of relationships might be treated as the standard (Hegarty & Pratto, 2001). In the present study, we overcame these limitations by assessing relationship patterns in multiple cultures. Specifically, we delved into the impact of familism within diverse cultural contexts.

Familism and social networks

Familism is the extent to which family is prioritized among one’s social relationships. People who endorse familism are more likely to prioritize family members and their welfare over other relationships and interests (Valenzuela & Dornbusch, 1994). This preference may manifest as strong identifications with family members, and strong interdependence, reciprocity, obligation, loyalty, and solidarity among family members (Triandis et al., 1982). Familism (Villarreal et al., 2005) is viewed as a subtype of collectivism (Realo et al., 1997). Global inequality in technology development and differences in sociodemographic factors determine cultural variations in familism across nations/areas (Greenfield, 2016). Nations that are more technologically developed, with higher income, and more urbanization, tend to be less family-oriented or low on familism. Researchers have shown that familism is associated with greater psychological health among European Americans, Asian Americans, and Hispanic Americans (Campos et al., 2014; Keeler et al., 2014), and greater self-esteem and life satisfaction among Hispanic Americans (Piña-Watson et al., 2013). Although familism is often assessed as an individual-difference measure, nations and cultures tend to differ on familism (e.g., Mair, 2013), and these nation-level differences are the focus of the present manuscript.

A cultural psychological approach positions familism in broader cultural ecologies of interdependence in which relationality is constructed in overlapping networks of thick connections. Adaptation to such cultural ecology in which relationships are closely intertwined and stable, includes maintaining more social bonding (Greenfield, 2016), maintaining sensitivity to obligations (Steidel & Contreras, 2003), and emphasizing caution in relationships to avoid making enemies and conflicts (Adams, 2005). Although familism emphasizes the nuclear family—kinship as the center of one’s social network, the relational mode that familism affords may extend to others beyond the boundaries of one’s family (e.g., friends, acquaintances, and colleagues; Restubog & Bordia, 2006), as familism is a culturally shared belief. Indeed, some friends can be called “family friends” due to their strong ties to the whole family.

Social network in familism culture may thus be constructed in a way that reflects embedded relationality—a network including a small number of friends who are very close to each other. The network can help to manage obligations towards close others in overlapping networks of embedded connection. Supporting this, outside the family, familism is associated with less interpersonal trust and civic engagement (Realo et al., 2008), which leads to forming fewer social connections and implies a smaller network size. Familism prioritizes ingroup needs over one’s own and emphasizes group harmony when facing conflicts in close friendships. Familism is positively associated with solution-oriented resolution rather than self-oriented resolution (Thayer et al., 2008), as a means to maintain closeness and bondedness between friends, indicating a deeper level of involvement among connections. Based on these findings, we predicted that national-level familism would be negatively associated with network size, but positively associated with network density, tie strength, and multiplexity.

Although there is little empirical evidence to support the associations between familism and these network characteristics, one may point out that cross-cultural studies assessing closeness and intimacy allude to the possibility that familism will be negatively correlated with relationship depth and multiplexity. For example, Marshall (2008) showed that Chinese Canadians who are likely to be higher on familism than European Canadians, reported lower intimacy in their dating relationships than European Canadians. The current study provides an opportunity to delve deeper into these proposed associations and explore both the individual influence of attachment style and the dynamic interaction between attachment style and familism.

Attachment theory and social networks

Attachment theory (Bowlby, 1969) is a leading theoretical framework often used to study close relationships and affect regulation processes. Attachment style delineates people’s cognitive, affective, and behavioral patterns, capturing the way individuals think, feel, and behave in their relationships. Attachment style has been found to be a reliable predictor of relational variables (Wilkinson, 2010), such as relationship satisfaction (Gillath et al., 2017) and network characteristics (Gillath et al., 2019). Attachment is assessed as two dimensions: anxiety and avoidance. Individuals high on attachment anxiety tend to worry about being abandoned and rejected by close others, whereas individuals high on avoidance are less likely to trust or depend on others or let others depend on them. Individuals low on both attachment anxiety and avoidance are thought to be secure—they find it easy to get close to, trust, and depend on others and let others depend on them.

Existing research shows a negative correlation between attachment avoidance and network size in Americans (Fiori et al., 2011), but there is a lack of evidence on the association between attachment anxiety and network size. Considering the apprehension of losing connections, it is reasonable to predict that individuals high on attachment anxiety are more likely to form larger networks to ease the anxiety associated with losing ties. We predicted that attachment avoidance would be negatively associated, whereas attachment anxiety would be positively associated with network size.

Both attachment styles are negatively associated with the tendency to maintain social ties (Gillath et al., 2011) and with density (Gillath et al., 2017). Attachment avoidance and anxiety also predict a stronger tendency to use exchange norms rather than communal norms (Clark et al., 2010). These findings suggest that attachment avoidance and anxiety would predict lower levels of density and tie strength. With regard to multiplexity, only attachment avoidance was found to be negatively associated with multiplexity online (Karantzas et al., 2012) and offline (Gillath et al., 2017). Based on this literature, we predicted that attachment insecurity, and especially avoidance, would predict lower multiplexity.

Social networks characteristics as a function of the interaction between familism and attachment style

Environment-level and individual-level factors are seen as mutually contingent and jointly affecting behaviors (Anderson et al., 2008; Erez & Gati, 2004). As Strand (2020) proposed, cultures are group-level reflections of individuals’ security-seeking and autonomy-seeking tendencies. Therefore, individuals may reside in a cultural environment characterized by varying degrees of familism that is aligned or not aligned with their personality. Our hypotheses were formulated based on the notion of cultural fit and misfit between familism and attachment style. According to the cultural fit hypothesis, culture constructs, such as familism, can amplify or suppress personality’s effects on behaviors depending on the fit or misfit between cultural norms and personality (Leung & Cohen, 2011; Yoo & Miyamoto, 2018).

Individuals high on attachment avoidance tend to hold more negative working models of others (Pietromonaco & Barrett, 2000). They are less likely to resort to social network members to fulfill their need for social connectedness. Thus, they are less likely to seek help from others (Vogel & Wei, 2005) and rely less on social bonds to regulate distress (Wildschut et al., 2010). Their tendency to develop a small network size is congruent with the familistic environments’ relational mode of being embedded with a few close others (cultural fit). However, the tendency of individuals high on attachment avoidance to maintain a shallow network connection is incongruent with the deeply intertwined relational model of familistic environments (cultural misfit): they may feel pressured living in such an environment, as it may be difficult to form and maintain the kind of relationships with which they feel comfortable. Therefore, familism may promote avoidance’s effect on network size but suppress avoidance’s effect on density, tie strength, and multiplexity.

Individuals high on attachment anxiety have positive working models of others (and negative working models of the self; Pietromonaco & Barrett, 2000). They are hypervigilant to social cues (especially signs of rejection; Fraley et al., 2006), crave intimacy (Mikulincer & Shaver, 2009), and want to merge with close others while simultaneously feeling unloved and rejected. In societies characterized by a higher proportion of attachment anxiety, individuals prioritize security-seeking and engage in strong-tie networks (Yamagishi & Hashimoto, 2016). Therefore, for individuals with high levels of attachment anxiety, their tendency to be anxiously attached to others (although it may inadvertently result in a larger network size and less close relationships), may be more acceptable when they live in an environment that is high on familism that emphasizes closeness among key relationships (cultural fit). Indeed, there is a higher percentage of people with high attachment anxiety in familistic cultures (e.g., DiTommaso et al., 2005). Therefore, familism may promote anxiety’s effect on network size, density, tie strength, and multiplexity.

Current research

We obtained data at the country level for familism. Attachment style and social network were assessed at the individual level. We focused on two types of social network indexes: measures representing the depth of relationships and measures representing multiplexity. Depth was assessed using network size, network density, and tie strength. Multiplexity was assessed via the number and the degree of fulfilled attachment-related functions (proximity seeking, safe haven, and secure base, which are widely recognized in the literature; e.g., Hazan & Shaver, 1994).

We predicted that (1a) familism will be negatively associated with network size and (1b) positively associated with density, tie strength, and multiplexity. (2a) attachment avoidance will be negatively associated and (2b) attachment anxiety will be positively associated with network size. (3) Both attachment avoidance and anxiety will be negatively related to density, tie strength, and multiplexity (especially avoidance). (4a) Familism will enhance avoidance’s effect on network size but (4b) suppress its effect on density, tie strength, and multiplexity. Finally, (5) familism will enhance anxiety’s effect on network size, density, tie strength, and multiplexity.

Method

Participants

The final sample size was 2,977 participants from 21 nations/areas (sample sizes ranged from 77 to 273; additional demographic and country details in Table 1). These nations consisted of Australia, Canada, China, France, Greece, Guatemala, Hong Kong, India, Indonesia, Israel, Italy, Japan, Latvia, the Netherlands, New Zealand, Portugal, South Africa, Spain, Switzerland, the UK, and the US. No participants were excluded. This size allowed us to detect effect sizes as small as f² = 0.003 with 80% power. The selection of nations/areas was based on the availability of collaborators. The 21 nations/areas include about 52.2% of the world’s population and are spread geographically across six continents. The nations/areas also vary greatly in terms of economy, politics, and culture. Participants were college students except for the Australian sample in which 44% of participants were recruited online. The mean sample size for each nation/area was 141 (SD = 40.0).

Table 1.

Main demographic information by countries.

Nation/Area	N	Female%	Age mean	Language	Married (%)	Heterosexual	Years of education (since elementary school)	Social class (1 Upper-5 lower)
1 Australia	85	77.0	30.5	English	17.65	78.82%	13.02	3.11
2 Canada	273	78.3	22.4	French	2.88	84.4%	14.24	3.07
3 China	129	58.5	20.3	Chinese	0.78	94.5%	14.75	3.26
4 France	113	56.6	21.2	French	0.00	58.41%	11.13	2.81
5 Greece	175	79.7	24.7	Hellenic	4.57	96.00%	16.19	3.09
6 Guatemala	179	57.5	20.3	Spanish	2.23	78.77%	13.15	2.58
7 Hong Kong	128	69.6	20.1	English	1.56	94.53%	14.37	3.37
8 India	149	49.7	22.9	English	4.03	99.3%	16.47	2.77
9 Indonesia	77	50.6	20.6	Bahasa	0.00	92.21%	14.29	2.74
10 Israel	115	79.8	25.0	Hebrew	30.43	93.5%	14.12	3.12
11 Italy	134	54.5	22.5	Italian	1.49	97.76%	16.30	3.06
12 Japan	127	52.8	19.9	Japanese	0.00	NA	15.74	3.49
13 Latvia	120	58.3	19.6	Latvian	3.33	94.17%	12.58	2.97
14 Netherlands	149	75.7	19.8	Dutch	0.67	97.32%	13.99	3.00
15 New Zealand	110	74.8	21.0	English	2.73	87.0%	12.58	2.86
16 Portugal	159	56.7	23.6	Portuguese	11.32	87.3%	13.28	3.14
17 South Africa	141	78.4	21.7	English	2.84	93.62%	15.43	2.66
18 Spain	137	56.9	20.8	Spanish	0.00	94.16%	17.00	3.08
19 Switzerland	148	82.6	22.8	English	5.41	95.27%	14.10	2.87
20 UK	165	77.8	24.5	English	5.45	81.5%	15.30	3.18
21 US	164	65.0	19.0	English	0.61	97.0%	13.50	2.56

Measures and procedure

Familism

The familism index was derived from the Gelfand et al. (2004) GLOBE project (House et al., 2004). Familism was assessed at the country level, and subsequently, participants were assigned a familism score based on their country. The original familism items, which formed the basis for our index, evaluate the degree of interdependence within families and the degree to which individuals express pride and loyalty to their families. Familism score includes practice scores, which measure how participants perceive the existing cultural practices in their society, and value scores, which assess societal values. The final familism index for each nation was the average of the two scores. Although Gelfand et al. (2004) labelled these scores as in-group collectivism, other researchers have suggested that these items measure familism rather than collectivism (e.g., Realo & Allik, 2009). Familism is thought to be related but different from collectivism—being an orientation toward one’s family as opposed to one’s larger community (Gaines et al., 1997). Supporting the idea that the scores reflect familism, this measure is highly correlated with the strength of family ties scale (r = .48; Gelfand et al., 2004), and levels of respect for family and friends (r = .76; Gelfand et al., 2004).¹

Name generators and interpreters

We collected egocentric network (networks that depict connections of specific respondents rather than all connections in a bounded network; Clifton & Webster, 2017) information from participants. Participants (egos) were asked to list up to 15 names of the most important people (alters) in their life (Marin & Hampton, 2007). We labelled the number of alters as n1. Participants were first asked to indicate how close they felt toward each person and how close they thought each alter felt towards them on a 7-point Likert scale, ranging from 1 (don’t feel close at all) to 7 (feel very close). Then, on a 15 × 15 matrix, participants were asked to list the same 15 alters names and report how close they thought each alter felt toward each of the other alters using the same response scale (21.1% of participants did not list the same number of alters). We, therefore, labelled the number of alters they listed in the second part as n2. “n1” and “n2” were significantly correlated, r = .87, p < .001, and both were used as dependent variables in the following analyses.

Two dependent variables were calculated from the name generator (and the two ns): network density and tie strength. Both variables represent the depth of one’s relationship that is computed based on the number of alters and the closeness between them (Hanneman & Riddle, 2005), but each one focuses on a different aspect. Network density focuses on the extent to which the network as a whole is dense (Granovetter, 1973). In the current study, it reflects the network’s structural characteristics. Tie strength focuses on the overall closeness between network members (Marsden & Campbell, 1984), and in the current study, it emphasizes the network’s reciprocal intimacies (see formulas below).

Density

The two density scores were computed based on two different common procedures in the literature. The first, which we denote as D1, was calculated based on the closeness and the number of alters (Zohar & Tenne-Gazit, 2008). In d1’s equation, r_ij represents the closeness between alter i and alter j, and n denotes the total number of alters. Note that only alter-alter relationships are included in the calculation of the density score. Conceptually r_ij is not equal to r_ji, because one represents the perception of closeness from one alter to another (e.g., from i to j) whereas the other represents the perception of the opposite direction (from j to i). When i = j, r_ij = 0.

d 1 = \frac{\sum_{i = 1}^{n} \sum_{j = 1}^{n} r_{i j}}{7 (n 2) (n 2 + 1)}

Density can alternatively be calculated. We denote the second density score as D₂, which reflects the number of connections that exist in the network when they meet a minimal closeness standard. It is calculated using the ratio between all possible connections an actor (ego) or network might have, and how many connections are actually present (Eckles & Stradley, 2012). D2 was based on the number of connections with at least a minimal closeness in the network. In d2’s equation, the function of K is counting the number of elements that are not equal to 1 (1 = “don’t feel close at all”) in the r_ij matrix. As we can see from the formula, D2’s calculation is focused less on closeness, and more on the existence of connections.

d 2 = \frac{K (r_{i j} \neq 1)}{(n 2) (n 2 + 1)}

Tie strength

The following formula shows how we computed perceived tie strength. W_i denotes each ego’s perceived closeness to each alter. X_i denotes each ego’s perceptions of each alter’s closeness to the ego.

t = \frac{\sum_{i = 1}^{n} (W i + X i)}{n 1}

Multiplexity

We used a modified version of the WHOTO scale (Fraley & Davis, 1997) to assess the level of attachment functions that each alter fulfils. The modified scale includes three attachment functions: proximity seeking (e.g., “I like to spend time with this person.”), safe heaven (e.g., “I turn to this person when I am feeling down.”), and secure base (e.g., “I want to share my successes with this person.”). Each function was measured using two items, and participants were asked to indicate the extent to which each alter (out of the list of 15) fulfilled each function on a 1 (strongly disagree) to 7 (strongly agree) Likert scale. Scores on each function are calculated by averaging the two items.²

We calculated the first multiplexity score (m1) by computing the average number of attachment functions fulfilled by each alter in a participant’s social network (Gerich & Lehner, 2006). Specifically, if the score for one function was above 4 (the middle point of the scale), we counted it as “1”, which meant that the alter fulfilled this function. The resulting possible multiplexity scores for each alter ranged from 0 (fulfilled no functions) to 3 (fulfilled all three functions); with higher scores indicating a greater number of functions fulfilled by the alter. This multiplexity index was computed by adding multiplexity scores for each alter and divided by the network size (see the formula below). In the equation, H_i represents the number of attachment functions fulfilled by each alter. This dichotomous index has been commonly computed in the literature (Felsher & Koku, 2018; Gillath et al., 2017).

m 1 = \frac{\sum_{i = 1}^{N} (H i)}{n 1} {, 0 \leq H}_{i} \leq 3

In addition to assessing the quantity of multiplexity, we also computed the mean of the WHOTO scale for each participant to capture the degree of multiplexity. We labelled this continuous variable as m2. Therefore, m2 serves as a complementary index to m1.

Attachment style

Adult attachment style was assessed using the short version of the Experiences in Close Relationship inventory (ECR-S; Wei et al., 2007). The measure included 12 items; six assessing attachment-related avoidance (e.g., “I want to get close to others, but I keep pulling back”), and six assessing attachment-related anxiety (e.g., “I worry that others won’t care about me as much as I care about them”). For the Indonesian participants, five items of the ECR were missing due to a clerical error. Hence, scores on attachment avoidance and anxiety for Indonesia were computed by using only seven items.³ Participants responded on a 7-point Likert scale, ranging from 1 (strongly disagree) to 7 (strongly agree). Cronbach’s αs for both dimensions without the responses from the Indonesian participants were adequate: α_avoidance = .71 and α_anxiety = .72.

Demographic questions

Participants reported their gender, age, and levels of social class. Social class was measured on a 1 (upper) to 5 (lower) scale.

Data analytic plan

Data were initially examined through descriptive and correlational analyses. Subsequently, we employed multilevel modeling for further analysis to test the familism × attachment interactions. As an exploratory analysis, response surface analyses were conducted, which assessed the mismatch between attachment style and familism in a three-dimensional space, and results are present in the supplementary materials (Table 3S, Figure 1S, and Figure 2S).

Results

Descriptive results

Table 2 shows the means and SDs for key variables across nations/areas. Table 3 shows the grand means, standard deviations, and correlations between variables for all participants. Overall consistent with our hypotheses, familism was negatively correlated with network sizes (n1, n2; supporting H1a) and positively correlated with network density (d1, but not d2), tie strength, and multiplexities (m1, m2; supporting H1b). Attachment avoidance was negatively correlated with all dependent variables (supporting H2a & H3), and attachment anxiety was negatively correlated with all dependent variables (supporting H3) except for network size (n1, n2; not supporting H2b) and one measure of multiplexity (m2, but not m1).⁴

Table 2.

Means (standard deviations) of the key variables in this study across nations/areas.

Nation/Areas	Familism	Avoidance	Anxiety	n1	n2	d1	d2	t	m1	m2
1 Australia	4.96	2.56 (1.18)	3.37 (1.10)	12.07 (3.58)	10.61 (4.76)	0.42 (0.10)	0.51 (0.23)	11.16 (1.36)	2.30 (0.79)	5.27 (0.72)
2 Canada	5.12	2.67 (0.94)	3.54 (1.11)	7.27 (3.30)	6.80 (3.51)	0.36 (0.11)	0.48 (0.21)	11.47 (1.31)	2.58 (0.66)	5.53 (0.75)
3 China	5.45	3.12 (0.96)	3.92 (1.02)	10.78 (3.68)	10.43 (3.92)	0.34 (0.09)	0.44 (0.17)	10.99 (1.32)	2.35 (0.65)	5.00 (0.86)
4 France	4.90	2.95 (0.96)	3.73 (1.34)	11.27 (3.15)	11.11 (3.33)	0.35 (0.09)	0.47 (0.17)	11.06 (1.18)	2.40 (0.50)	5.04 (0.76)
5 Greece	5.37	2.76 (0.99)	3.59 (1.13)	9.78 (3.54)	8.94 (4.24)	0.39 (0.12)	0.54 (0.42)	11.35 (1.45)	2.55 (0.54)	5.41 (0.73)
6 Guatemala	5.89	3.07 (0.92)	3.44 (1.09)	8.45 (3.68)	7.23 (3.94)	0.45 (0.12)	0.48 (0.23)	12.10 (1.27)	2.61 (0.56)	5.74 (0.79)
7 Hong Kong	5.22	3.05 (0.83)	3.97 (1.01)	11.11 (3.62)	10.92 (3.83)	0.31 (0.08)	0.42 (0.16)	10.61 (1.25)	2.60 (0.48)	5.27 (0.63)
8 India	5.62	3.15 (0.98)	3.83 (.66)	6.58 (3.40)	6.52 (3.27)	0.43 (0.13)	0.60 (0.49)	12.37 (1.51)	2.83 (0.33)	5.94 (0.77)
9 Indonesia	5.68	2.61 (1.00)	4.29 (1.02)	10.25 (4.13)	10.32 (4.33)	0.39 (0.13)	0.55 (0.33)	11.07 (1.55)	2.65 (0.54)	5.29 (0.94)
10 Israel	5.23	2.76 (0.94)	3.05 (1.13)	10.36 (3.47)	9.80 (3.88)	0.43 (0.12)	0.52 (0.21)	11.89 (1.21)	2.39 (0.59)	5.18 (0.83)
11 Italy	5.33	2.52 (1.00)	3.41 (1.09)	8.24 (3.44)	7.78 (3.68)	0.42 (0.10)	0.55 (0.18)	11.99 (1.34)	2.59 (0.61)	5.47 (0.92)
12 Japan	4.95	3.28 (0.96)	3.54 (1.09)	10.45 (3.85)	10.36 (4.03)	0.33 (0.10)	0.39 (0.18)	11.55 (1.59)	2.15 (0.78)	4.17 (0.78)
13 Latvia	5.28	2.94 (1.04)	3.81 (1.00)	8.17 (3.33)	8.09 (3.29)	0.39 (0.12)	0.55 (0.16)	11.16 (1.58)	2.52 (0.54)	5.21 (0.86)
14 Netherlands	4.44	2.38 (0.80)	3.61 (1.05)	10.40 (3.30)	10.35 (3.30)	0.42 (0.10)	0.59 (0.15)	11.55 (1.22)	2.47 (0.56)	5.18 (0.72)
15 New Zealand	4.96	2.78 (1.05)	3.34 (1.17)	11.19 (3.14)	11.09 (3.31)	0.40 (0.10)	0.54 (0.18)	11.22 (1.42)	2.52 (0.52)	5.30 (0.74)
16 Portugal	5.73	2.61 (0.91)	3.67 (1.16)	9.78 (3.76)	9.71 (3.75)	0.42 (0.12)	0.59 (0.17)	11.55 (1.39)	2.54 (0.49)	5.39 (0.80)
17 South Africa	5.21	2.75 (1.04)	3.45 (1.18)	10.26 (3.08)	10.26 (3.12)	0.41 (0.10)	0.56 (0.16)	11.34 (1.43)	2.53 (0.51)	5.33 (0.82)
18 Spain	5.62	3.09 (0.84)	3.94 (1.00)	9.12 (2.91)	9.11 (2.91)	0.44 (0.10)	0.60 (0.12)	11.77 (1.05)	2.68 (0.43)	5.62 (0.75)
19 Switzerland	4.60	2.35 (0.91)	3.33 (.97)	11.21 (3.29)	10.91 (3.52)	0.35 (0.09)	0.51 (0.22)	10.76 (1.45)	2.45 (0.54)	5.19 (0.62)
20 UK	4.82	2.92 (1.08)	3.71 (1.29)	9.96 (3.74)	9.95 (3.57)	0.38 (0.11)	0.56 (0.30)	11.29 (1.53)	2.29 (0.61)	4.88 (0.80)
21 US	5.01	2.63 (1.05)	3.18 (1.11)	11.15 (2.66)	10.89 (3.01)	0.43 (0.11)	0.55 (0.17)	11.37 (1.31)	2.53 (0.47)	5.40 (0.79)

Note. n1 and n2 are network size indexes, d1 and d2 are density indexes, t represents tie strength, and m1 and m2 are multiplexity indexes.

Table 3.

Means, standard deviations, and simple correlations of variables.

	1	2	3	4	5	6	7	8	9	10
Mean (SD)	5.20 (0.38)	2.80 (1.00)	3.59 (1.12)	9.69 (3.71)	9.36 (3.93)	0.39 (0.11)	0.52 (0.25)	11.44 (1.43)	2.51 (0.58)	5.30 (0.85)
1 Familism	1
2 Avoidance	.12**	1
3 Anxiety	.08**	.26**	1
4 n1	−.18**	−.07**	−.02	1
5 n2	−.19**	−.08**	−.01	.89**	1
6 d1	.14**	−.15**	−.09**	−.004	.003	1
7 d2	.02	−.11**	−.04*	−.07**	.18**	.60**	1
8 t	.14**	−.21**	−.15**	−.26**	−.24*	.38**	.17**	1
9 m1	.14**	−.23**	−.01	−.10**	−.04*	.18**	.16**	.35**	1
10 m2	.22**	−.31**	−.06**	−.19**	−.19**	.29**	.15**	.51**	.84**	1

Note. *p < .05, **p < .01. Familism is a level-2 variable while the rest of them are level-1 variables. n1 and n2 are network size indexes, d1 and d2 are density indexes, t represents tie strength, and m1 and m2 are multiplexity indexes.

Results of multilevel analyses

Multilevel analyses based on linear mixed modeling were conducted to test the effects of familism, attachment style, and the interaction between the two factors on social networks outcomes. Familism, attachment avoidance, and attachment anxiety were centered using the grand mean. Nation/area was treated as the second level unit. We defined the intercept of the mean as a random effect. The effects of familism, attachment style, and their interactions were all modeled as fixed effects. As no three-way interaction was significant we excluded them, and rebuilt the models with only the three two-way interactions. Table 4 shows the results of the multilevel analyses.⁵

Table 4.

The effect of familism, attachment style, and their interaction on network outcomes.

	Intercept	Familism	Avoidance	Anxiety	Familism × avoidance	Familism × anxiety	Avoidance × anxiety	VIFs	Effect size of the model (R²)
n1	9.96*** [9.42, 10.50]	−0.68* [−1.22, −0.13]	−0.23*** [−0.37, −0.10]	0.05 [−0.09, 0.18]	−0.16* [−0.30, −0.01]	0.16* [0.02, 0.30]	0.03 [−0.10, 0.16]	≤1.08	0.16
n2	9.73*** [9.17, 10.29]	−0.77* [−1.33, −0.20]	−0.26*** [−0.41, −0.12]	0.05 [−0.09, 0.19]	−0.19* [−0.34, −0.04]	0.13 [−0.01, 0.28]	−0.01 [−0.14, 0.12]	≤1.08	0.16
d1	0.39*** [0.38, 0.41]	0.02* [0.002, 0.03]	−0.02*** [−0.02, −0.01]	−0.01* [−0.01, −0.001]	0.01* [0.01, 0.011]	−0.001 [−0.01, 0.004]	0.001 [−0.004, 0.01]	≤1.08	0.15
d2	0.53*** [0.50, 0.55]	0.01 [−0.02, 0.04]	−0.02*** [−0.03 −0.01]	−0.01 [−0.02, 0.004]	0.01* [0.0002, 0.02]	−0.01 [−0.02, 0.004]	−0.001 [−0.01, 0.01]	≤1.08	0.06
t	11.41*** [11.24, 11.59]	0.24* [0.06, 0.41]	−0.29*** [−0.34, −0.24]	−0.15*** [−0.20-, −0.09]	0.07* [0.01, 0.12]	−0.07** [−0.13, −0.02]	0.03 [−0.02, 0.08]	≤1.08	0.15
m1	2.51*** [2.46, 2.56]	0.10*** [0.05, 0.15]	−0.14*** [−0.16, −0.12]	0.02 [−0.003, 0.04]	0.02 [−0.01, 0.04]	−0.01 [−0.04, 0.01]	−0.01 [−0.03, 0.01]	≤1.08	0.12
m2	5.28*** [5.15, 5.40]	0.22** [0.09, 0.35]	−0.27*** [−0.30, −0.24]	0.01 [−0.02, 0.04]	−0.01 [−0.04, 0.02]	−0.02 [−0.05, 0.01]	0.02 [−0.01, 0.05]	≤1.08	0.27

Note. n1 and n2 are network size indexes, d1 and d2 are density indexes, t represents tie strength, and m1 and m2 are multiplexity indexes.

Effects of familism

The effects of familism on network size, density (d1 but not d2), tie strength, and multiplexity were all significant (supporting H1a & H1b). A higher level of familism predicted a smaller sized, denser, and more tied network. A higher level of familism also predicted higher multiplexity. This indicates that on average, network members fulfilled more attachment roles for participants when they were embedded in a culture higher on familism.

Effects of attachment style

Attachment avoidance significantly predicted all dependent variables (supporting H2a and 3). The higher one’s attachment avoidance was, the smaller, less dense, and less mutually tied network people had. Higher avoidance also predicted lower multiplexity. That is, the higher one’s scores on avoidance, the fewer attachment roles that network members fulfilled. Attachment anxiety significantly predicted density (d1 but not d2) and tie strength, but not multiplexity (partially supporting H3). Participants with higher attachment anxiety tended to perceive their networks as less dense, and the people in their networks as tied less strongly to each other. Although we did not witness a main effect of anxiety on network size (not supporting H2a), anxiety’s predicting effect was moderated by familism as presented below.

The interaction between familism and attachment style

The analysis revealed five significant two-way interactions between familism and attachment avoidance—two for network size, two for density, and one for tie strength. Simple slope tests (Table 5; Figure 1) revealed that attachment avoidance was negatively associated with network size, which was more pronounced when familism levels were high (one SD above the mean; supporting H4a). Attachment avoidance was also negatively associated with both density and tie strength, with this negative association being more pronounced when familism was lower (one SD below the mean; supporting H4b).

Table 5.

Coefficients of simple slope tests for the interactions between familism and attachment.

	Levels of familism
	−1	0	+1
The effect of attachment avoidance
n1	−0.08	−0.23***	−0.39***
n2	−0.07	−0.26***	−0.45***
d1	−0.02***	−0.02***	−0.01***
d2	−0.03***	−0.02***	−0.01
t	−0.36***	−0.29***	−0.22***
The effect of attachment anxiety
n1	−0.12	0.04	0.21*
t	−0.07	−0.15***	−0.22***

Note. n1 and n2 are network size indexes, d1 and d2 are density indexes, t represents tie strength, and m1 and m2 are multiplexity indexes.

Figure 1.

Simple slope tests for the interactions between attachment styles (avoidance in group A and anxiety in group B) and familism on social network outcomes.

The analysis also revealed two significant two-way interactions between familism and attachment anxiety. Attachment anxiety was positively associated with network size but only when familism was high (one SD above the mean; supporting H5). Attachment anxiety was also negatively associated with tie strength when familism was at high and intermediate levels (one SD above and at the mean; supporting H5). These results suggest that familism enhanced the role of anxiety on network size and tie strength.

Discussion

In this study, we examined the influence of familism at the national level, attachment anxiety and avoidance at the individual level, and their interactions, on various network outcomes, to understand how culture and personality impact friendship processes.

Familism and social network

As predicted, individuals from cultures higher on familism reported a smaller network size and higher levels of tie strength. We found limited evidence that familism is associated with density and multiplexity. This research ruled out the competing hypothesis that familism negatively predicts intimacy/closeness (Marshall, 2008). We had the ability to test these associations by adopting a broader approach and using 21 countries/regions simultaneously. When all 21 nations were included, familism was found to positively correlate with intimacy. When focusing on only two nations to draw conclusions regarding a cultural pattern, the conclusions were largely dependent on- and limited by-selection choices (Van de Vijver & Leung, 2000). For example, a brief review of the means in Table 2 shows that Dutch participants exhibited more intimacy than Japanese participants on all four dependent measures and similarly American participants displayed more intimacy than Japanese participants on density, tie strength, and multiplexity. These patterns suggest that lower levels of familism may be associated with greater intimacy. However, these results were different when all 21 nations/areas were considered. The multi-site sampling of countries differing in a continuum of familism has helped overcome that limitation. The discrepancy between our findings and previous research also highlights the need to avoid essentializing culture into two presumably extreme poles on cultural dimensions.

Attachment styles and social networks

Regarding personality pertaining to relational propensities, our hypotheses were supported. Attachment avoidance was associated with smaller network size, lower levels of density, tie strength, and multiplexity (seven significant associations; Table 4). Attachment anxiety was related to lower levels of density and tie strength (two significant associations). These results are consistent with the literature showing attachment avoidance is more likely to predict social network-related outcomes than attachment anxiety (e.g., Gillath et al., 2011).

The interaction between familism and attachment style

The personality-environment fit perspective helped us shed light on the way in which culture (e.g., familism) interacted with personality (e.g., attachment style) in predicting network characteristics. We found that culture (familism) modifies the association between personality traits (attachment style) and social network outcomes.

We further found that the effects of avoidance on network size were more salient when familism was high (cultural fit). The effects of avoidance on density and tie strength were more salient when familism was low (cultural misfit). For anxiety, familism promoted its effects on network size and tie strength (cultural fit). These findings indicate that the influence of familism on the connections between attachment style and social network outcomes are contingent upon the particular index of social network under consideration. This insight deepens our comprehension of the complex interplay between personality and environment, highlighting how interaction patterns can vary based on specific outcome nuances.

Our findings also help us integrate and bridge the cultural (mis)fit literature. Culture (mis)fit effect posits that the mismatch between personality and environment predicts negative outcomes (e.g., lower levels of performance or satisfaction) in organizations (e.g., withdrawal behaviors; Kristof-Brown et al., 2005), relationships (e.g., relationship problems; Friedman et al., 2010), and reactions to COVID-19 (e.g., death rate; Kafetsios, 2022). One distinction between the current study and previous studies examining culture fit is that here, we had no a-priori predictions about potential negative outcomes due to misfit. Different networks represent the different ways individuals manage their relationships—and no one way is better than others.

In future studies, social networks, which acted as dependent variables here, could serve as mediators in the prediction of other outcomes with pre-defined positivity. For example, the discrepancy between personality and environment may affect one’s satisfaction from their social network. The counterforces from the environment may lower one’s friendship satisfaction when the inner tendency to build one’s preferred type of network is blocked by the culture they are immersed in. If this is indeed the case, this may help explain the inconsistencies in the correlations between individualism and life satisfaction (a null correlation; e.g., Spector et al., 2001; a positive correlation; Yetim, 2003). The discrepancy between personality and culture could be a stronger predictor of life satisfaction than either personality or culture.

Limitations

Methodologically, familism and our other variables were not assessed at the same time or using the same samples. Although a limitation, this is also a benefit, as obtaining variables from different sources may help rule out the possibility that the results were inflated by common method variance (Podsakoff et al., 2003). For multiplexity, we only assessed attachment-related functions, but there are numerous other functions and roles that relationships can fulfill. The sample sizes of some nations (e.g., Australia and Indonesia) was relatively small and there are many nations (e.g., Germany) that are not included, both of these issues could be resolved in future studies. Random sampling was not used here, limiting the possibility of generalizing conclusions directly to the general population (but see Straus, 2009, for a defense of this sampling strategy). The study involved university students, who may function psychologically in a more analogous manner worldwide because of their higher exposition to the globalization effect (Fernandez et al., 2014). Nevertheless, it is important to consider that the age of our participants could potentially restrict the generalization of our findings, given that age is inversely related to network size, closeness, and the number of non-primary-group ties. (Cornwell et al., 2008).

Future directions

Although the current research illuminates the moderating role of familism in the relationship between attachment style and social network outcomes, the broader impact on individuals’ daily lives remains a subject of inquiry. For instance, in cases where individuals experience a cultural mismatch (high familism coupled with high attachment avoidance), questions arise regarding the extent to which this might lead to reduced happiness. Furthermore, what coping strategies might individuals employ to adapt to such environments? Could such a situation prompt people to reside in environments high on relational or residential mobility (Oishi, 2010; Yuki & Schug, 2012) where individuals actively seek more culturally compatible socioecology? Future research can delve deeper into the downstream consequences of the social network effects uncovered in the current study. In our research, we concentrated on familism at the national level to align with our focus on cultural fit. However, it is conceivable that an individual’s perception of familism embedded in surrounding or immediate environments could interact with their attachment style in a manner akin to our findings. Such interaction might be more pronounced at the individual level, capturing a broader range of personal variance compared to the national level. Further exploration into the potential three-way interactions among national-level familism (macro-level), individual-level familism (micro-level), and attachment style could also be valuable.

Conclusion

This paper investigated how culture and personality are jointly associated with social networks. Grounded in Lewin’s seminal observation and theories of person-culture fit, we broadened those ideas into a more systematic cross-sectional work, merged it with attachment theory, and tested it in different cultural contexts. The results reveal unique predicting effects of familism, attachment, and their interactions on social network characteristics and illuminate the importance and value of endorsing the approach of culture × personality to studying social processes.

Supplemental Material

Supplemental Material - Ninety years after Lewin: The role of familism and attachment style in social networks characteristics across 21 nations/areas

Supplemental Material for Ninety years after Lewin: The role of familism and attachment style in social networks characteristics across 21 nations/areas by Xian Zhao, Omri Gillath, Itziar Alonso-Arbiol, Amina Abubakar, Byron G. Adams, Frédérique Autin, Audrey Brassard, Rodrigo J. Carcedo10, Or Catz, Cecilia Cheng, Tamlin S. Conner, Tasuku Igarashi, Konstantinos Kafetsios, Shanmukh Kamble, Gery Karantzas, Rafael Emilio Mendía-Monterroso, João M. Moreira, Tobias Nolte, Willibald Ruch, Sandra Sebre, Angela Suryani, Semira Tagliabue, Qi Xu, and Fang Zhang in Journal of Social and Personal Relationships.

Footnotes

Authors’ Note

João M. Moreira holds a position at the Faculty of Psychology and Research Center for Psychological Science (CICPSI), University of Lisbon, Portugal.

Acknowledgements

The first author wants to thank Tianyi Li, Zhiying Irene Zhen, Xue Sunny Xiang, and Man Mandy Luo for their support while this study was conducted.

Authors’ Contribution

Except Xian Zhao, Omri Gillath, and Itziar Alonso-Arbiol, the names of the other authors are in an alphabetical order. We thank Fons J. R. van de Vijver (Tilburg University, Netherlands) for his contribution on an early version of the manuscript.

Funding

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: This project was funded by a grant from the Basque Government to Research Groups (IT1598-22).

Open Science Statement

As part of IARR’s encouragement of open research practices, the author(s) have provided the following information: This research was not pre-registered. The data and materials can be obtained at: .

ORCID iDs

Xian Zhao

Omri Gillath

Itziar Alonso-Arbiol

Audrey Brassard

Konstantinos Kafetsios

Gery Karantzas

Supplemental Material

Supplemental material for this article is available online.

Notes

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