Love Thy Neighbour: The impact of social cohesion on community economic resilience

Abstract

This study examines the role of social cohesion in fostering communities’ economic resilience, defined as their ability to positively adjust and maintain economic functioning while facing adversity. Covid-19 caused worldwide disruptions, forcing firms to close overnight as a measure to stop the virus from spreading. However, considerable within-country regional heterogeneity manifested in the impact of the various applied measures. Focusing on organization exits across 379 Dutch municipalities and applying a mixed methods approach, we argue and find that social cohesion – conceptualized as involving kinship and residential stability – increases communities’ economic resilience and that family firms are a conduit through which social cohesion affects economic resilience. Kinship creates the willingness to help other members, even if this means suffering some losses. Residential stability fosters a social infrastructure that is necessary for the quick mobilization of a community’s resources. Family firms mediate the relationship between social cohesion and economic resilience, by leveraging their long-term orientation, strong, community-embedded relationships and internal resources.

Keywords

community economic resilience kinship mixed-methods residential stability social cohesion

Introduction

The Covid-19 pandemic brought economic life to an abrupt halt and exposed the limits of public health and other systems globally. Communities worldwide faced the same shock, with many countries, the Netherlands included, implementing so-called ‘lockdowns’ to gain control over the spread of the virus. Due to these lockdowns, numerous organizations were forced to close overnight or drastically limit their operations, cutting off revenue streams for thousands of local businesses (Bose et al., 2022; He & Harris, 2020; Hu & Zhang, 2021). Some industries, such as the hospitality and cultural industries, were impacted exceptionally hard, with neighbourhood cafes and restaurants seeing their income vanish overnight.

Yet, the economic consequences of this shared disruption varied strikingly across places. Some communities saw local organizations quickly mobilize, adapt and sustain economic activity despite prolonged restrictions, while others experienced far deeper and more persistent economic decline. These differences, however, cannot be explained by industry composition alone. Evidence from the United States (Chen et al., 2021), Austria (Bachtrögler et al., 2020) and Colombia (Bonet-Morón et al., 2020) show substantial within-country differences in the economic impact of Covid-19. Similarly, in the Netherlands not all communities, even those with similar industry composition, were impacted to the same extent.

This pronounced spatial heterogeneity raises a fundamental question about communities, which we define as ‘the populations, organizations, and markets located in a geographic territory and sharing elements of local culture, norms, identity, and laws’ (see Marquis & Battilana, 2009, p. 286). We argue that communities differ in their capacity to handle uncertainty and economic hardship, not merely because of structural characteristics, but because of differences in their social cohesion (Aldrich & Meyer, 2015). In other words, some communities are more economically resilient than others due to how social cohesion enables collective responses to disruption. As such, in this paper we ask: How does social cohesion enable communities to sustain economic functioning during major disruptive events?

Resilience in general, and particularly economic resilience, has been discussed and studied primarily in the context of natural and human-made disasters, which result in a community’s partial, substantive, or complete destruction, where recovery and rebuilding are paramount due to homes and physical infrastructure having been destroyed by earthquakes, hurricanes, tsunamis, wars, and so on (e.g. Aldrich, 2012b; Aldrich & Meyer, 2015; Allenby & Fink, 2005; Ganor & Ben-Lavy, 2003). In these studies resilience, although conceptualized and operationalized in different ways, pertains to recovering what was lost. In this paper, however, we consider economic resilience as the positive adjustment and maintenance of economic functioning while facing adversity (e.g. Schneiberg & Parmentier, 2022; Simmie & Martin, 2010; Williams et al., 2017). With this definition, we stress economic resilience as an adaptive ability (Simmie & Martin, 2010, p. 28) rooted in the understanding that the focal community’s inhabitants, specifically, individuals and organizations, ‘do not simply react passively or in a “business as usual manner”’ (Rose, 2004, p. 307) when facing a major disruptive event. We recognize, in particular, that there is variance in who acts and how which, we argue, accounts for the observed spatial heterogeneity in economic resilience among communities.

Prior research has indeed shown how social cohesion explains differences in resilience across communities (e.g. Aldrich & Meyer, 2015; Ganor & Ben-Lavy, 2003; O’Brien, 2017; Schneiberg, 2021; Townshend et al., 2015). Though defined differently across individual and group levels (see for instance Friedkin, 2004; Tackenberg & Lukas, 2019), social cohesion is consistently conceptualized emphasizing social harmony (e.g. Tolsma et al., 2009), a greater need for and sense of social belonging, more interaction within the community, and overall high levels of participation and cooperation (e.g. Forrest & Kearns, 2001; Kearns & Forrest, 2000; Schuby, 1975; Tackenberg & Lukas, 2019). These features of social cohesion may not only strengthen everyday community functioning and activity (e.g. Gradstein & Justman, 2002), but also shape how community members respond during times of crisis, particularly in support of the community’s long-term economic well-being.

While social cohesion has been linked to economic activity and development (Briguglio, 2016; Briguglio et al., 2009; Gradstein & Justman, 2002), its role in shaping communities’ economic resilience remains underexplored. This study addresses this lacuna by theorizing how, when encountering disruption, individuals and organizations mutually support one another, fostering economic resilience, through social cohesion’s two key dimensions, kinship and residential stability. We emphasize that kinship and residential stability act as community resources, that spur the adaptive ability of a community to respond constructively when confronted with major disruptions (see Rose, 2004; Simmie & Martin, 2010; Williams et al., 2017). We also theorize that family-run businesses, due to their unique characteristics, serve as important conduits through which social cohesion enhances resilience. By distinguishing and measuring the independent effects of kinship and residential stability, and the mediating effect of family firms, we offer a nuanced account of how social cohesion contributes to economic resilience. Accordingly, we propose that regional differences in social cohesion help explain differing levels of communities’ economic resilience during disruptive events.

This paper aims to develop and test theoretical arguments on how social cohesion impacts a community’s economic resilience. In the following section, we elaborate on what social cohesion is and develop our hypotheses. We then introduce our empirical context of Dutch communities in which we test the hypotheses and provide qualitative evidence for the argued mechanisms that drive the results. To this end, we collected and analysed newspaper articles about local organizations and initiatives, covering the first lockdown in the Netherlands. We conclude with a discussion of the findings, limitations and directions for future research. This study contributes to the literatures on social cohesion and community resilience, by showing how social cohesion fosters economic resilience through individual and organizational support mechanisms. Specifically, by arguing and showing that members – both residents and organizations – of socially cohesive communities mutually forego short-term benefits for the greater good, we uncover an important mechanism that connects the social characteristics of a community to its economic performance in the face of major shocks and adversity.

Social Cohesion

Although social cohesion is a complex concept to define because it includes both individual- and group-level attitudes, affect and behaviours (Friedkin, 2004; Kawachi & Berkman, 2000), two dimensions regularly transpire in discussions of social cohesion. First, social cohesion includes an affective and normative aspect that binds groups together, referred to as kinship. Second, social cohesion has a cognitive aspect that encompasses the development of a relational infrastructure in the community, labelled residential stability (Simons et al., 2016). High residential stability denotes a cognitive-continuance commitment to the community that stimulates continued participation due to personal investments in a social system (Kanter, 1968, 1972), while kinship thrives on affective and normative commitment (Kanter, 1968), emphasizing the willingness of community members to take care of the community and each other (Aldrich & Meyer, 2015; Ganor & Ben-Lavy, 2003; Simons et al., 2016).

Residential stability allows for the mobilization of resources that are needed to support oneself and others (e.g. Norris et al., 2008); it is the foundation of communication networks within a community that make possible the swift exchange of information and, hence, enable taking action (Ganor & Ben-Lavy, 2003). Kinship in turn provides the normative and affective ‘glue’ that binds community members. Both kinship and residential stability act as community resources, increasing the likelihood of positive adjustments in the face of adversity (Williams et al., 2017), and communities that are socially cohesive exhibit higher levels of both kinship and residential stability (Forrest & Kearns, 2001; Simons et al., 2016).¹

Moreover, we argue that family firms constitute a distinct type of organization through which social cohesion impacts a community’s economic resilience. The literature on family firms has long emphasized the deep embeddedness of family firms in their local environments (Lansberg, 1983; Lumpkin & Bacq, 2022), highlighting how their strategic decisions are often shaped by place-based ties and community interdependencies. Notably, family firms are more likely than their non-family counterparts to engage in proactive and enduring relationships with local stakeholders, not only enhancing their socio-emotional wealth (Cennamo et al., 2012), but also contributing to the generation of broader civic wealth (Lumpkin & Bacq, 2022). These patterns of engagement are not incidental; they reflect the firm’s desire to preserve affective endowments and reputational capital across generations. This locally embedded orientation becomes especially salient during periods of economic or societal adversity.

Kinship

Kinship refers to a form of emotional and social identification with a collective, grounded in shared values, goals and experiences (Simons et al., 2016). While kinship often refers to familial ties, in the literature, kinship extends beyond such ties to include a broader sense of belonging and mutual obligation among members of a community (Kanter, 1972). This sense of kinship fosters strong affective and normative bonds – two intertwined but distinct aspects that together contribute to community resilience, especially during times of adversity (Kearns & Forrest, 2000; O’Brien, 2017).

The affective aspect of kinship is rooted in emotional commitment, aided by an attachment process of communion in which community members have meaningful contact and ‘experience the fact of oneness’ with the broader community (Kanter, 1972, p. 73). This emotional mechanism cultivates fellowship and group consciousness, leading to more cohesive and emotionally satisfying communities (Kanter, 1972, p. 93). Community members who have developed a deep emotional commitment to the community will stay involved despite difficulties (Laryea, 2023). Moreover, community members not only feel connected; they also act out of a shared understanding that supporting each other is the right thing to do, predicated on a normative sense of mutual obligation and responsibility (Phan et al., 2009). These norms give rise to prosocial behaviour, as individuals and organizations perceive a duty to act on behalf of the collective (Graf et al., 2023).

We contend that communities with higher levels of kinship – expressed through both emotional connection and normative responsibility – are more likely to be economically resilient (Ganor & Ben-Lavy, 2003; Tse & Liew, 2004). Residents are more likely to support local organizations during times of hardship (Luomala, 2014), not only because they care about these organizations, but also because they feel a sense of duty to sustain the community’s well-being. For example, a group of residents from Zwolle (a mid-sized city in the Netherlands) organized a ‘lockdown café’ – an online event to raise money for local hospitality establishments. They did so to support the survival of local businesses, especially because they thought that these establishments are crucial for fostering the community members’ social fabric. Furthermore, in many countries, the willingness of residents to support local businesses is also reflected in increased spending at local shops.²

Similarly, organizations embedded in communities with strong kinship are also more likely to internalize the welfare of the community as part of their own well-being. This sense of shared fate (Boe-Lillegraven et al., 2023) fosters economic resilience by motivating behaviours such as supporting other local businesses, volunteering, or accepting temporary financial sacrifices to ensure communal continuity (Ortiz-de-Mandojana & Bansal, 2016; Schneiberg et al., 2023). Rather than shutting down the business or laying-off employees, organizations will actively search for ways to keep the business running, not only to survive, but to contribute to the broader community. Organizations that are well-embedded in the community evoke strong emotional ties, that reflect core values and norms experienced in their local context (Bruhn, 2011). However, not all organizations act for the good of their communities, especially those that are less involved in their local communities (see also Boe-Lillegraven et al., 2023). Indeed, organizations that are presumably not well-embedded in their community, are likely to consider their own interests first, and act less responsibly towards their community (see Boe-Lillegraven et al., 2023; Clark & Soulsby, 1998; Schneiberg et al., 2023). Such organizations can even exploit local communities and their resources in their search for financial and efficiency gains (Eweje, 2006; Pegg & Zabbey, 2013).

Hence, we argue that kinship – both affective and normative – enhances a community’s capacity to face and adapt to adversity. Affective bonds ensure ongoing engagement with the community, while norms motivate concrete support. Overall, this results in greater economic resilience:

Hypothesis 1: A high level of kinship in a community creates a strong sense of identification with and belonging to a collectivity, which positively affects a community’s economic resilience.

Residential stability

Residential stability is a second, important and distinct dimension of social cohesion (Altman & Low, 1992; Simons et al., 2016; Tartaglia, 2006). Residential stability develops due to cognitive-continuance commitment people have to their communities (Kanter, 1968, 1972). Cognitive-continuance commitment involves the ‘benefits’ of continued participation and ‘costs’ associated with leaving the community (Kanter, 1972, p. 72). Due to this commitment, members of the community are more likely to remain in it, resulting in residential stability. Residential stability fosters a relational infrastructure (Simons et al., 2016) – dense and enduring social relations among community members (Schneiberg et al., 2023, p. 67) – thus enabling quick communication networks that facilitate taking action. As such, residential stability fosters the collection and distribution of information, resources and organizational skills (Dutta, 2017).

For individual community members, the relational infrastructure must be viewed as rewarding in order for cognitive-continuance commitment to take hold (Kanter, 1968). By devoting time and energy to the development of relational infrastructures, future gains can be expected for those that have actively invested in the community. For instance, longstanding investments by local government employees resulted in profound and lasting connections with the community. By actively investing in the community for a long period of time, a deep-rooted sense of trust was built, which enabled these employees to successfully put forth various measures to enhance the well-being of their community (Trujillo & Beltran, 2023, pp. 8–9).

As such, investments in the community are also somewhat irreversible since reaping rewards can only happen after continued participation, further stimulating the desire to maintain a position or role in the relational infrastructure of the community (Kanter, 1972, p. 81). Relational infrastructures take much time and energy to build. Indeed, it sometimes requires extraordinary sacrifices from individual actors to construct these infrastructures (see Nepstad, 2004). Thus, there are also potential losses involved, since leaving the community would mean foregoing those investments and benefits (Kanter, 1968). When effective, relational infrastructures become increasingly valuable to community members and simultaneously it becomes more costly for individual members to leave.

Higher levels of residential stability thus result in better-developed latent relational infrastructures as, in places with little changes in member composition, chances are greater that community members know other community members, who are then more easily mobilized during times of need (Simons et al., 2016). That in turn, ‘enable access to and exchange of resources’ which are needed ‘in shaping immediate actions and ultimately enabling positive functioning in the face of adversity’ (Williams et al., 2017, p. 745). This relational infrastructure consists of strong ties built over time among members of communities and ought to provide a stabilizing sense of felt security (Altman & Low, 1992) that may facilitate resilience (see Masten, 2001). Specifically, residential stability fosters taking care of a place as residents ‘are motivated to seek, stay in, protect, and improve places that are meaningful to them’ (Manzo & Perkins, 2006, p. 347). In the Dutch context for instance, when the lockdown was implemented, neighbourhoods with high residential stability were able to set up support functions, such as the delivery of groceries to those neighbours that were unable to go to the supermarket, and to provide dog walking services,³ much quicker than communities with low residential stability who lack, or have an underdeveloped, relational infrastructure. Consequently, when facing adverse times, communities with high levels of residential stability are better equipped to identify the resources, and mobilize them quickly, making such communities more resilient compared to communities with lower levels of residential stability. As such, residential stability ‘greases the wheels’ that allow members of a community to cooperate more smoothly (see Aldrich, 2012a) and sustains the economic functioning of the community.

Hypothesis 2: A high level of residential stability creates a relational infrastructure that facilitates the mobilization of a broad range of resources, which positively affects a community’s economic resilience.

Family firms

The community inhabitants we focus on are individuals (residents) and organizations. Organizations in a community, however, vary and thus their role, especially during times of adversity, possibly varies as well. Due to several interrelated features, we argue that family firms are of particular interest. First, family firms possess a unique mix of economic and noneconomic incentives that foster a long-term orientation and intergenerational continuity (Chrisman et al., 2011; Lumpkin & Brigham, 2011). Unlike non-family firms, which may prioritize short-term returns or shareholder interests, family firms are often motivated by the desire to preserve a lasting legacy (Calabrò et al., 2021). Consequently, they have a longer-term perspective and are more inclined to prioritize community stability and organizational continuity during crises, even at the expense of short-term profitability. This long-term orientation strengthens the community’s ability to absorb shocks and adapt over time (Lumpkin & Bacq, 2022).

Second, family firms benefit from access to the network of both familial and non-familial relationships rooted in trust, shared norms and reciprocity (Mzid et al., 2019). In times of crisis, this network becomes particularly valuable, enabling access to informal financial resources, emotional support and strategic knowledge (Mzid et al., 2019). While family firms often face limitations in accessing financial and human capital through formal markets, their embeddedness in dense social networks becomes an advantage that enhances resilience (Chrisman et al., 2011; Gedajlovic & Carney, 2010; Pearson et al., 2008).

Third, family firms possess what has been termed ‘survivability capital’, the willingness and ability of family members to make personal, financial and labour contributions to keep the firm viable in times of hardship (Calabrò et al., 2021; Sirmon & Hitt, 2003). These contributions, combined with the typically less formalized and more flexible governance structures of family firms, allow for quicker responses to adverse events. Such agility supports firm survival while stabilizing the local economic fabric by preserving jobs and supporting auxiliary businesses.

Importantly, family firms are often deeply rooted in local contexts, fostering reputational interdependence between the family and the community (Cennamo et al., 2012; Lumpkin & Bacq, 2022; Reay et al., 2015). As a result, family firms are more likely to take proactive roles in sustaining local economic and civic life, especially during times of collective need, thereby reinforcing both social cohesion and economic resilience. Taken together, these arguments suggest that family firms are not just recipients of the benefits of social cohesion, but active mediators that convert social embeddedness into economic resilience. Their ability to leverage their network, mobilize internal resources, and act swiftly in times of crisis positions them as organizational actors that strengthen the resilience of cohesive communities.

Hypothesis 3: The relationship between social cohesion and economic resilience is partially mediated by the share of family firms in a community.

Methods

Our study is set in the Netherlands during the first year of the Covid-19 pandemic. The Netherlands is a geographically small (41,526 km²) and densely populated country (17.44 million inhabitants in 2020). It provides an excellent empirical setting to test our hypotheses as the country is centrally governed, meaning that Covid-19 regulations and governmental financial support were identical for all regions (contrary to countries such as the US or Germany). To test our hypotheses, we gathered quantitative and qualitative data from various sources. Before detailing our data sources and the computations used to construct our measures, we address the operationalization of our level of analysis, the community. Given that we defined communities as the populations, organizations and markets located in a geographic territory and sharing elements of local culture, norms, identity and laws (see Marquis & Battilana, 2009), municipalities are the logical geographical entities to use in the Dutch context. Dutch municipalities (379 municipalities in 2018) are relatively small geographical units (the smallest being Westervoort, 7.03 km², the largest being Súdwest-Fryslân, 523.01 km²) that coincide with cities or towns and their surrounding area. Moreover, municipalities exhibit relatively high levels of economic (Tolsma et al., 2009) and social homogeneity (Van Tubergen et al., 2005). As such, they serve as an excellent operationalization of the concept of communities and were used as such in many prior policy (Plantinga et al., 2011), economic (Knoben et al., 2011) and sociology studies (Van Tubergen et al., 2005). Another requirement that Dutch municipalities fulfil is that they exhibit considerable variation on the community characteristics of interest, namely, social cohesion as well as regarding their economic resilience.

Data sources and measures

Dependent variable

Economic resilience is defined as the positive adjustment and maintenance of economic functioning while facing adversity (e.g. Schneiberg & Parmentier, 2022; Simmie & Martin, 2010; Williams et al., 2017). To adequately capture communities’ economic resilience to the adversity introduced by Covid-19 we used information on the number of organizations that exited the population, organizational exits. This is an appropriate measure of a lack of community resilience as it implies that an organization ‘gives up’ – ceases all economic activity, and all staff lose their employment, either voluntarily or when it is declared bankrupt. Data on the number of organizational exits per municipality for the period Q2 2020 to Q2 2021, the first year of Covid-19 in the Netherlands, were obtained from the Dutch Chambers of Commerce and converted to the 2018 municipal classification. The 2018 municipality classification was used as this was the year before the first observed cases of Covid-19, hence prior to the beginning of the pandemic. Most organizational exits are voluntary while bankruptcies in the Netherlands are quite rare (e.g. the number of voluntary organizational exits is typically more than 50 times greater than the number of bankruptcies). For our measure, the number of exits was divided by the total number of organizations in the municipality. The resulting variable is slightly skewed, but the fit with a normal distribution could be improved with a log-transformation (see Figure 1 in the online appendix). Our dependent variable therefore is the log-transformed percentage of organizations that exited. Figure 1 shows the proportion of organizational exits by community and exhibits substantial variation in economic resilience across Dutch communities.

Figure 1.

Organizational exits by community.

Independent variables

The first dimension of community cohesion, kinship, is measured using information from the National Residence Survey from 2015.⁴ In this survey a sample of 62,668 respondents, designed to be representative at the municipal level, are asked a myriad of questions regarding their living environment. Several of these questions are specifically designed to measure social cohesion (VROM, 2009). Specifically, we adopt the measure developed by Goudriaan et al. (2006) based on nine of the items from this survey.⁵ Each item is answered on a 5-point Likert scale and our measure of kinship is based on the average answer over the nine items. The reliability of this scale is very high (alpha = 0.84 in our data). To arrive at our community-level measure of kinship, we averaged the scores of all individuals in a given municipality.

Residential stability has mainly been operationalized as a structural component such as (average) length of residence (e.g. Manzo, 2003, 2005) and/or residential turnover (e.g. Brown et al., 2003; Buffel et al., 2014; Cruz et al., 2018; Kamalipour et al., 2012). In line with these practices, we measure our second dimension of community cohesion, residential stability, based on two elements: (1) the number of people entering and (2) the number of people leaving a community through residential moves. We summed both counts and divided by the number of inhabitants of a community to arrive at a relative measure of residential mobility. The resulting variable was standardized and finally inverted so that high scores of the variable reflect high levels of residential stability (Sampson, 1991, p. 47). Figures 2 and 3 visually depict the distribution of kinship and residential stability across the Netherlands’ communities.

Figure 2.

Social cohesion: kinship.

Figure 3.

Social cohesion: residential stability.

Mediation variable

Family firms is measured as the percentage of family firms in a community in 2020 (first year these data are available). The data are obtained from Statistics Netherlands. In constructing the measure, Statistics Netherlands follows the definition proposed by the European Commission, that a family firm is a firm in which one family either directly or indirectly has majority vote, the family should be formally involved in the organization’s governance and excludes self-employed persons (CBS, 2024).

As Figures 2 and 3 show, not all areas with high levels of kinship also exhibit high levels of residential stability and vice versa, although there are areas that are characterized by high (low) levels on both dimensions of social cohesion. To illustrate, a typical case of a community with both high kinship and high residential stability is Edam-Volendam. This community is located 25 km north of Amsterdam, with fishery as its main industry and with many family-run businesses. Most of its inhabitants have lived there for generations and family and friendship ties are extremely strong. Inhabitants also identify strongly with the local folklore, such as traditional costumes and dances.

At the other end of the spectrum, Diemen is a prime example of a community with both low kinship and low residential stability. Diemen lies just 6 km south of Amsterdam and essentially functions as a suburb of Amsterdam. As a result, residents are quite transient and come and go in large numbers. Despite the long history of Diemen, it has no specific landmarks or cultural features that locals identify with.

Blaricum, situated about 30 km southeast of Amsterdam, is a typical case of a community with high levels of kinship, but low residential stability. The latter is mainly due to residents moving to, rather than from, the community. Blaricum has many amenities and typically attracts a high socio-economic population. The inhabitants primarily identify with each other based on their education, jobs and social status.

Finally, a good example of a community with low kinship but high residential stability is the community of Oss, situated in the south of the Netherlands. The inhabitants of Oss strongly identify with the industrial roots of the community centred around companies such as Unilever and Organon. However, there are no strong feelings of kinship among residents of Oss; the community has a reputation for being distrustful and the city has high crime rates.

To further substantiate the validity of our two measures of social cohesion we checked whether they correlate (differently) with various other community-level variables.⁶ Specifically we find that kinship has a significant association⁷ with social behaviour within the community such as performing volunteer work (β = 0.41, p < 0.01) and with providing informal (health)care for other community members (β = 0.19, p < 0.05). Residential stability is associated with those behaviours though they are quite a bit weaker (i.e. β = 0.09, p < 0.05 for doing volunteer work); it is, however, significantly associated with features of the place such as the number of official monuments in the community (β = 0.37, p < 0.01). Interestingly, kinship has a significant negative association with the number of monuments (β = −0.18, p < 0.05). These patterns strengthen both construct and discriminant validity of our measures of the two dimensions of social cohesion.

Control variables

In our analyses we control for multiple other characteristics of the municipalities that likely affect economic resilience and are highly related to social cohesion. First, as the lockdown affected the hospitality and the cultural industries and other business services (e.g. pet hotels, dog walkers, and so on) especially hard, and because the distribution of those industries is correlated with social cohesion,⁸ we control for the percentage of organizations from those industries in the community, respectively with percentages of organizations in the hospitality industry, cultural industry and in other business services.

Communities could also differ in the composition and characteristics of their firms. In some, firms are more risk-taking and growth oriented, whereas in others they are more continuity oriented, potentially affecting our results. We proxy firm strategic orientation using the percentage of innovative firms in the community. These data are provided by De Innovatiespotter, a data firm whose method has been adopted by Statistics Netherlands as well (CBS, n.d.).

We also control for the degree of urbanization, measured as the number of people in the municipality. Urbanized regions are, on average, less socially cohesive and their economic structure, and therefore resilience, is likely to differ from more rural regions due to the latter’s lower levels of competition and less dense markets. It is important to explicate that urbanization picks up the effects of various other community-level characteristics such as the availability of cultural amenities, crime rates, health care access, and so on. Instead of compiling a difficult-to-interpret composite index, we opt to use a single indicator that reflects the majority of these between-community differences. The information necessary to compute this variable was obtained from Statistics Netherlands.

Additionally, we control for the level of social-economic development in the municipality measured as the average household income in the community. We control for this since communities with higher levels of social-economic development have more resources at their disposal, which is likely to influence the level of resilience. At the same time, communities high in social-economic development are, on average, more residentially stable. As with urbanization, this variable picks up variation in several types of communities’ social-economic features such as education levels, unemployment rates, real estate prices, and so on, that affect the amount and availability of (financial) resources in a community. Again, we opt to use a single indicator that reflects the majority of these between-community differences. The information required to calculate this variable was obtained from Statistics Netherlands, which computes it as the total gross income in a region divided by the number of households in the region.

Finally, we control for the degree of religiosity, the level of political engagement and the percentage of resident workers. The degree of religiosity is measured as the percentage of voters voting for the SGP, the main conservative religious party in the Netherlands. We control for religiosity because religious communities are often found to be highly socially cohesive. Furthermore, religiosity could constitute an alternative basis for mutual help in a community, above and beyond social cohesion. The level of political engagement is measured as the turnout during the 2019 elections for the Provincial Council. Political engagement is considered as a control variable since this may also capture residential civic involvement beyond kinship or residential stability. The percentage of resident workers is measured by the share of residents in a municipality that also work within that municipality. This variable is important as a control variable, since it may explain why residents support local businesses, i.e. to remain employed, and why local businesses would go out of their way to support the local community, i.e. to satisfy employees. The information necessary to compute religiosity and political engagement was obtained from www.verkiezingsuitslagen.nl; the data necessary to compute the percentage of resident workers are from Statistics Netherlands.

Table 1 shows the descriptive statistics and the pairwise correlations of all variables. As the table shows, there are no surprising correlations between the variables. As expected, we observe a positive correlation between organizational exits and the percentage of organizations in the hospitality industry (0.37) and the percentage of organizations in other business services (0.25). This indicates that our measure of resilience is, as expected, strongly related to the industry structure of the communities.

Table 1.

Descriptive statistics.

Variable		Mean	SD	Min	Max	Correlations
1	Organizational exits	3.00	0.20	2.30	3.95	-
2	% Organizations in hospitality industry	0.04	0.03	1.48	24.11	−0.09	-
3	% Organizations in cultural industry	0.05	0.02	1.46	14.64	0.39	0.38	-
4	% Organizations in other business services	0.07	0.01	3.42	13.33	0.29	0.13	0.01	-
5	% Innovative firms	0.20	0.01	0.00	0.05	0.02	−0.19	−0.17	−0.06	-
6	Urbanization	45213.38	69721.97	932	854047	0.38	−0.02	0.47	−0.07	0.03	-
7	Social-economic development	45.85	6.27	33.80	95.80	−0.20	−0.28	−0.19	−0.49	−0.23	−0.22	-
8	Religiosity	3.17	6.68	0.00	40.23	−0.17	−0.14	−0.43	−0.22	0.22	−0.09	0.10	-
9	Political engagement	58.26	5.45	44.76	79.61	−0.32	−0.06	−0.10	−0.53	−0.01	−0.26	0.53	0.46	-
10	% Resident workers	29.83	12.55	6.1	80.00	0.00	0.41	0.19	−0.03	0.24	0.42	−0.39	0.09	−0.16	-
11	Kinship	4.63	0.14	4.21	5.29	−0.63	0.24	−0.25	−0.21	−0.15	−0.45	0.32	0.07	0.38	−0.11	-
12	Residential Stability	0.00	1.00	−8.55	2.93	−0.19	−0.14	−0.35	0.11	0.08	0.00	−0.08	0.18	−0.06	0.22	0.08	-
13	% Family firms	0.21	0.05	0.11	0.38	−0.68	0.28	−0.49	−0.03	0.16	−0.38	−0.16	0.33	0.10	0.26	0.57	0.29

Qualitative data collection and coding

Considering the option of alternative explanations for our quantitative findings we argue, first, that the time invariant nature of community characteristics such as social cohesion, for example, makes reverse causality problems unlikely. Moreover, omitted variable concerns are greatly reduced by our spatial econometric approach and the use of spatial lags in particular as this captures the potentially biasing effects of unobserved spatial characteristics that might explain differences in community economic resilience. However, the nature of our data and the phenomenon that we study make further causal inference difficult. To enhance our understanding of the underlying mechanisms and to strengthen the interpretation of our quantitative findings, we complement the analysis with qualitative data in the form of (local) newspaper articles. We focused on articles about local organizations and communities that covered the period of the first Covid-19 measures in the Netherlands (February to April 2020). We were particularly interested in the various motivations for local organizations to close down, to adapt their practices, and residents’ motivations for supporting (or not) the local community and its organizations, and the role of family firms in these processes. We relied on the Lexis-Nexis database to collect the newspaper articles, using combinations and variations of the (Dutch equivalents of the) keywords ‘firm(s)’, ‘lockdown’, ‘corona’, ‘covid’, ‘measure(s)’, ‘adapt(ation)’, ‘quit’, ‘close’, ‘community’, ‘support’, ‘motivation’ and searched all national and regional newspapers. In total, we collected 392 articles. We coded 234 of these articles, as 158 were either duplicates (i.e. different newspapers from the same publisher running the same story) or covered stories on countries other than the Netherlands, or were explanations of the government’s financial aid schemes to support organizations.

We created a preliminary coding scheme based on our hypotheses, paying particular attention to any information and quotes in the newspaper articles pertaining to (a lack of) economic resilience (e.g. adapting the business activities to keep employees working), kinship (e.g. resident initiatives to support local businesses), residential stability (e.g. the quick mobilization of resources) and family firms (e.g. family members’ ad hoc support to the organization). The research team of four researchers elaborated on the coding scheme by coding the same set of 10 articles each, to uncover other potentially relevant codes. In the next step, we independently coded the same set of 25 articles, and we then discussed the differences in coding until we reached consensus. The coding scheme is available in the online appendix. Three of the researchers proceeded with coding the remaining articles. Particularly interesting and illustrative quotes and excerpts from the newspaper articles were stored in a separate file and categorized according to the mechanism we identified.

Results

Analyses

Given the continuous nature and normal distribution of our dependent variable, we estimate ordinary least squares (OLS) regressions; see Table 2 for the results. However, it is important to acknowledge that the assumption of the independence of observations is often violated in spatial data, as observations are potentially influenced by spatial spillovers and common unobserved contextual influences shared across locations. Ignoring such spatial dependence can lead to biased or inefficient parameter estimates (Anselin, 1988). We therefore explicitly accounted for the spatial structure of our data in our models.

Table 2.

Regression results spatial error models.

	Spatial error models
	Model 1: Organizational exits		Model 2: Family firms		Model 3: Organizational exits
	beta	p	beta	p	beta	p
% Organizations in hospitality industry	−0.015	0.471	0.209***	0.000	0.115*	0.021
% Organizations in cultural industry	0.298***	0.000	−0.405***	0.000	0.029	0.613
% Organizations in other business services	0.254***	0.000	−0.109***	0.001	0.180***	0.000
% Innovative firms	0.073	0.176	0.074	0.064	0.120*	0.012
Urbanization	0.115*	0.053	−0.139***	0.000	0.018	0.698
Socio-economic development	0.167**	0.004	−0.282***	0.000	−0.023	0.648
Religiosity	0.086	0.064	0.123***	0.001	0.168***	0.000
Political engagement	−0.094	0.109	−0.066	0.141	−0.140**	0.005
% Resident workers	−0.102	0.126	0.194***	0.000	0.033	0.510
Kinship	−0.458***	0.000	0.465***	0.000	−0.153**	0.002
Residential stability	−0.094*	0.015	0.056*	0.039	−0.053	0.089
% Family firms					−0.657***	0.000

Lamba	0.731**	0.004	0.942***	0.000	0.761**	0.001
N	380		380		380
R2	51.7%		73.4%		63.8%
Model significance	0.000		0.000		0.000

p < 0.05, **p < 0.01, ***p < 0.001.

Spatial dependence may arise through two main channels: spatial autocorrelation and spatial heterogeneity (Anselin, 1988; Plummer, 2010). Spatial autocorrelation captures substantive spillover effects between neighbouring areas. For example, firms in one municipality may imitate the lockdown responses of firms in nearby municipalities. In contrast, spatial heterogeneity refers to spatial clustering in unobserved characteristics, such as similar levels of economic development or local institutions or culture, that can produce correlation in regression residuals across space. Because these two mechanisms can co-occur and are empirically difficult to disentangle, best practice recommends estimating both spatial lag and spatial error models and using Lagrange Multiplier (LM) diagnostics to identify the most appropriate specification (Doh & Hahn, 2008).

For the spatial lag model, we included a spatially lagged version of the dependent variable (ρ), capturing the degree to which outcomes in surrounding communities influence the focal community. We defined spatial weights using a negative-exponential distance-decay function, because this functional-form is widely used in spatial econometrics to represent smoothly declining influence with distance (see Tan et al., 2025) and yields greater flexibility relative to rigid contiguity definitions which limits spatial influence to directly neighbouring regions. Our approach is particularly suitable when interactions are not limited to administrative borders but attenuate smoothly with distance, which is a plausible assumption in our context. Mathematically, our spatial lag takes the following form:

ρ_{i} = \sum_{j = 1}^{n} \frac{Y_{j}}{{d_{i j}}^{2}}

where ρ_i is the spatial lag of variable Y for municipality i, and d_ij is the geographical distance as the crow flies between the centroids of municipality i and j.

For our spatial error models we allowed the error term to follow a spatial autoregressive process, capturing unobserved spatial heterogeneity. The error term is modelled with a parameter ‘λ’ which incorporates the spatial dependence. Mathematically, the error in the OLS estimation (ε_i) is expressed as:

ε_{i} = λ \sum_{j = 1}^{n} \frac{ε_{j}}{{d_{i j}}^{2}} + μ_{i}

where λ captures the effect of the errors in neighbouring municipalities (ε_j), and d_ij is the geographical distance as the crow flies between the centroids of municipality i and j. Finally, μ_i is the uncorrelated and homoscedastic residual (Anselin & Bera, 1998).

The results of the two approaches to modelling the spatial structure are extremely consistent in terms of sign and significance of the results. However, the LM diagnostics clearly favoured the spatial error specification (LM-Error: p = 0.000; LM-Lag: p = 0.464), indicating that unobserved spatially correlated factors rather than substantive spillovers primarily drive spatial dependence. Accordingly, we interpret our results based on the spatial error models and report the spatial lag estimates in the online appendix for completeness. All coefficients reported in Table 2 are standardized to ease comparisons across models.

To assess the mediation effect proposed in hypothesis 3 we utilize a variety of methods. Our basic approach follows Baron and Kenny (1986) and relies on the separate estimation of the effects of (1) social cohesion on economic resilience, (2) social cohesion on family firms and (3) family firms and social cohesion on economic resilience. Subsequently we assess the statistical significance of the mediation using both the Sobel test as well as the more sophisticated Preacher-Hayes bootstrap method (Preacher & Hayes, 2004; Zhao et al., 2010).

Table 3 presents the mechanisms, representative quotes and excerpts from the coded newspaper articles. Some local businesses, often younger firms lacking financial reserves and community ties, chose to close their business permanently. However, many organizations adapted their business activities to stay afloat or took action to benefit the local community.

Table 3.

Mechanism explaining social cohesion’s effect on economic resilience.

Concept	Type	Community/organization	Quote/excerpt
Resilience	Adjustment of organizational activities	Beer breweryTaxi companyGrocery bus	‘Beer breweries are having a difficult time, due to the closure of hospitality establishments. However, they do not want to do nothing. Hence, they produce alcohol for disinfectants. “I couldn’t bring myself to throw all that beer away”.As such a new and gigantic operation began. Drivers who normally fill the beer tanks at restaurants and bars, now collected the beer. In the brewery the beer is heated to distillate the alcohol. “Normally alcohol is a byproduct of the 0.0% beer, now it’s the main product”, according to the brew master. The distillate normally contains 20% alcohol, which we temporally increased to 80%; strong enough to make disinfectants. The beer that remains is now processed into animal feed, similar to other residuals from the brewing process.’ – Nederlands Dagblad, 25 April 2020 ‘I see great initiatives emerge. For instance, taxi companies have started to deliver meals, often out of goodwill and for free, because they are rooted in the local community. We also see taxi companies being asked to transport covid-patients who need kidney dialysis, for instance.’ – VNO-NCW, 3 April 2020 ‘It couldn’t be done in any other way, with the covid-measure in place. He couldn’t any longer drive around with the bus. Instead, he picks up shopping lists and then delivers the ordered groceries. That way he can continue to take care of regular customers that depend on him.’ – Rijssens Nieuwsblad, 5 May 2020
Lack of resilience	Voluntary organizational exit, failure to maintain the status quo	Restaurant/Inn	‘The new owner of A Gen Kirk couldn’t cope any longer. She sent an email at the beginning of April “I can’t make it any longer, I have to stop”.’ – Dagblad de Limburger, 17 May 2020
Kinship	Organization to community: fostering communionCommunity members to community members; maintaining communionEmployees supporting firm; feeling of kinship	Sport clubRestaurant ownersEmployment agencyResident initiativeSport clubFlower farm	‘Many of our older members are now missing their social activity, simply moving a bit, and having a nice chat over a cup of coffee. For them, but also for all the elderly people in Hengelo, we have made a special senior workout that they can do at home. We hope to help them in this way.’ – Hengelo’s Weekblad, 28 April 2020 ‘It’s [cancelling the annual dinner] a huge disappointment, but at the same time it’s an opportunity. The question is: How can we do something new that fits the current covid-measure, but is still special and creates a social bond?’ – Het Parool, 1 May 2020 ‘Of course, we wanted to help. It got even better as Yource bought the meals from a local restaurant, Tasman. The purpose of the Yource group is to support local businesses during these difficult times.’ – De Botlek, 1 April 2020 ‘We received many messages from people who want to do something nice for their neighbours during this time. They help those in a necessary profession that have to work exceptionally hard now, but also those neighbours that are extra vulnerable and at risk and therefore can’t leave the house, or people that are in quarantine.’ – Nederlands Dagblad, 1 April 2020 ‘Maybe the municipality has some money to support us, but as a club in a small village we prefer that support is offered to those that need it the most. For instance, small business owners. That they are given preference, fits with the social character and values of our club.’ – De Gelderlander, 27 March 2020 ‘We are a family business, even though we’re not all family it does feel like it.’ – Tubantia, 20 March 2025
Lack of kinship	No support network; lack of communion	Local entrepreneur	‘This is going to be a massacre. We were in good shape financially, but for this [covid-measures] nothing and no one is prepared.’ – Provinciale Zeeuwse Courant, 4 April 2020
Residential stability	Quick mobilization of resources; relational infrastructure	Sport clubLocal restaurants	‘Because we know many people as a club, there’s a lot of social monitoring.’‘We quickly organized debit cards with a limited day-limit for groceries. We also created a dedicated phone number and email address, so people can get in touch. We take turns operating the communication channels. Through a special support-app those in need and those offering assistance are connected.’ – Dagblad de Limburger, 1 April 2020 ‘Suppliers with whom we are in close contact donated products that couldn’t be sold, but were too good to throw out.’ – de Volkskrant, 15 April 2020
Family firms	Local embeddedness: family firm giving back to communityFamily firm mobilization of resources	Local restaurantBed manufacturerFlower farm	‘Restaurant owner created the Support Your Local Box, filled with local products. “I want to continue, also for my suppliers” and “Our parents started the restaurant. My sister and I took over. We mostly work with local products and keep close relationships with our suppliers”.’ – De Brug Nijmegen, 18 April 2020 ‘It’s not our purpose to make a profit [. . .] the idea came about as we were worried, we also read the news announcement about the shortages of protective face masks.’ – De Stentor, 16 April 2020 ‘“It’s about saving money and not investing at the moment. A new tractor isn’t an option, but we think we can survive as a firm.” Yet, the company is investing in the future as they are planting flower bulbs to be harvested in summer a job now assigned to the 77 year old dad of the owner.’ – Leidsch Dagblad, 25 March 2020

Hypotheses testing

The coefficient of kinship is negative and significant (see Table 2, model 1). The magnitude of the effect of kinship on organizational exits implies that a one standard deviation increase in kinship results in a reduction of organizational exits of −0.458 standard deviations. In an average municipality this would imply a reduction in organizational exits of roughly 9%. This shows that organizations in municipalities with higher levels of kinship will exert more effort to stay afloat and provides strong support for hypothesis 1.

The qualitative analysis reveals that kinship fosters not only a general willingness to help, but also a sense of moral duty among business owners, employees and community residents to adapt operations in ways that benefit the broader community. This helps to explain how kinship reduces organizational exits beyond what is captured in the statistical models. For instance, a local producer of food packaging quickly adapted practices and machinery to start the production of protective gear for healthcare employees, in addition to their regular production. As an explanation, a representative of this company stated, ‘as a Dutch family-owned firm we are closely connected with our clients and we believe that this is something we should be doing’ (Algemeen Dagblad, 9 April 2020). The quote highlights both the affective aspect (close connection to other community members) and normative aspect (what they should be doing) of kinship. Furthermore, it also points towards the role that family firms may play in this process, which we elaborate below.

Residents also expressed their willingness to support neighbours and local organizations. An example is the rise in popularity of an online platform ‘thuisgekookt.nl’ (cooked-at-home.nl) during the first period of Covid-19 measures in the Netherlands. This non-commercial platform offers local residents the opportunity to sell and buy locally prepared meals by private persons, pricing it at cost. The platform’s support staff noticed that ‘more residents want to do something to help their neighbours. Either those with crucial jobs who have an additional workload, but they also want to do something for those who are especially vulnerable or quarantined’ (Nederlands Dagblad, 31 March 2020).

Interestingly, we also found several examples of employees willing to go the extra mile to support their employer or organizations in the employer’s supply chain. For instance, employees at a local flower farm were keen to temporarily move to and live at the flower farm, to ensure that necessary work could be carried out. Another example concerns employees of a restaurant who offered their labour at one of the restaurant’s main suppliers, an asparagus grower.

In support of hypothesis 2, residential stability has a statistically significant negative effect on organizational exits (see Table 2, model 1). The magnitude of the effect implies that a one standard deviation increase in residential stability is associated with a reduction of organizational exits of −0.094 standard deviations. For the average municipality, this corresponds to a roughly 2% reduction in organizational exits. While this effect is much smaller than the effect of kinship, it is nonetheless meaningful in shaping community-level resilience.

The qualitative data offer important but fewer insights compared to kinship, into the role of residential stability, particularly in enabling the mobilization of local social infrastructures during Covid-19. The relative scarcity of coverage may reflect a reporting bias, with newspapers more attuned to affective and emotionally compelling acts of solidarity than to the structural underpinnings that facilitate them. However, several newspaper articles illustrate how stable, longstanding community networks supported the swift mobilization of resources. A prominent example is that of a resident initiative in Doenrade, a small village, largely driven by the members and board of the local schutterij (a traditional marksmen guild). They quickly mobilized resources, such as debit cards with limited funds to buy groceries for those in need, they set up a phone line and email address for questions and concerns, and also for those residents that would just like to have a chat, and connected those needing help with those offering support (De Limburger, 1 April 2020). Interestingly, our qualitative data show that residential stability is not merely used to coordinate the mobilization and distribution of material and/or financial resources, but more so to create communication networks that allow members to stay connected, with a particular focus on those members who would otherwise be disconnected. Thus, the mechanism through which residential stability operates is by providing the embedded social infrastructure that enables rapid, collective action.

Hypothesis 3 is also supported; the effect of social cohesion is partially mediated by the share of family firms in a community. First, Table 2 model 2 shows that both kinship and residential stability are positively, and significantly, associated with the percent of family firms in a community. For both dimensions of social cohesion, it holds that more family firms are found in more cohesive communities. Subsequently, Table 2 model 3 shows that the percentage of family firms has a strong negative effect on organizational exits. The magnitude is such that for the average community, a one standard deviation increase in the percentage of family firms is associated with a reduction of organizational exits of 13%. This effect encompasses large parts of the effects of kinship and residential stability. The effect of kinship on firm exits is reduced by two-thirds and this mediation is highly statistically significant (Sobel p = 0.000, Preacher-Hayes p = 0.000). The effect of residential stability is roughly halved and again the mediation is highly statistically significant (Sobel p = 0.000, Preacher-Hayes p = 0.009). Because the main effect of residential stability was already weaker, it loses statistical significance in model 3. Combined, these results show that the percentage of family firms is a strong, partial mediator of the effect of social cohesion on community economic resilience.

Several examples from our qualitative data illustrate how family firms draw on familial resources, both financial and labour related, to sustain operations. For instance, the children of a restaurant owner assisted their father in the preparation and delivery of meals, as their school was closed, to keep the family business running (Tubantia, 21 March 2020). A potato farmer called on her sister and sister in-law to start a fries’ drive-through, as the potatoes would have otherwise gone to waste due to the decrease in sales to hospitality establishments (Brabants Dagblad, 11 May 2020). Interestingly, the role of family firms extends beyond internal family support and familial ties. These firms are often deeply embedded in local communities, which reinforces their long-term orientation and community commitment. This embeddedness fosters decisions that prioritize not just economic survival but also family and community well-being. As a customer of the fries’ drive-through commented, ‘I think it’s such a nice initiative, but I’m also doing it out of sympathy. My daughter practically grew up on the farm here’ (Brabants Dagblad, 11 May 2020). Another example is when a friend of a flower farmer’s neighbour suggested to make up for the loss of French buyers by selling directly to consumers, and helped setting it up (Gouwe Koerier, 8 April 2020). These examples suggest that family firms act as conduits through which social cohesion is translated into economic resilience. Their willingness to leverage family support and resources, while remaining attuned to broader community responsibilities, shows their mediating role as not only beneficiaries of social cohesion but also agents who reinforce and extend it in ways that enhance communities’ economic resilience.

Discussion and Conclusion

This study shows that social cohesion is a central organizing principle of communities’ economic resilience in the face of major disruptions. Communities characterized by strong kinship ties and high residential stability are better able to sustain economic functioning during crises such as the Covid-19 pandemic. These findings extend prior work showing that socially cohesive communities recover more effectively from natural disasters (Aldrich, 2011, 2012b; Aldrich & Meyer, 2015), reduce poverty (Béné et al., 2014) and collectively mobilize resources (Beninger & Francis, 2021). Our findings reveal not only that socially cohesive communities are more resilient, but how resilience is enacted through reciprocal, proactive efforts by residents and organizations who jointly safeguard their community’s economic and social fabric.

Specifically, we show that both kinship and residential stability significantly reduce organizational exists, albeit through distinct mechanisms. Kinship creates a willingness to support other community members and prioritizes collective well-being over short-term individual gains. Residential stability provides a cognitive commitment to a community, fostering a relational structure that enables the swift mobilization of resources during adversity. Moreover, we found that family firms play a pivotal role in enhancing the economic resilience of communities. Due to their deep embeddedness in local communities, family-run businesses are not merely passive beneficiaries of social cohesion, but active agents who invest in the survival of the community by maintaining employment relationships, preserving local supply chains and serving as moral anchors during periods of crisis.

By foregrounding these mechanisms, we contribute to the broader literature on resilience at different levels of analysis. For instance, Beninger and Francis (2021) found that disturbances in highly competitive markets transformed markets competitors’ behaviour to act more collaboratively, yielding greater market resilience. Sutcliffe and Vogus (2003) provided a model to explain organizational resilience and studies on natural disasters explained community resilience (Aldrich & Meyer, 2015; Béné et al., 2014; Berkes & Ross, 2013). We extend this work by theorizing and empirically testing a model that explains observed differences in communities’ economic resilience. Specifically, we have shown the substantive and significant effect of social cohesion on a community’s economic resilience during times of severe, unexpected hardship. Our results demonstrate that resilience is actively constructed, as individuals and organizations jointly mobilize resources, adapt behaviours and sustain institutions to maintain the social fabric of community life. An organization that exits the community changes that community’s social and economic structure and people’s everyday life. For instance, a local bakery is more than a place to buy bread; it provides jobs, supports local suppliers, serves as a social hub and anchors community routines and traditions. Its closure may not cause a bread shortage, but it would disrupt livelihoods and social ties. The community, thus, is likely to support the bakery, which in turn may accept short-term losses to ensure long-term survival. Resilience, therefore, emerges through mutual sacrifice and collective commitment.

Our qualitative evidence reinforces this interpretation. The newspaper articles reveal that communities, residents and organizations show keen willingness to take care of each other, aiming to benefit the community overall. Resilience, in this view, is not something communities have, but something community actors do together. Social cohesion itself is actively constructed and reinforced by community inhabitants. Communities, in that sense, are centred around relational anchors that stabilize people’s sense of attachment through ongoing daily practices that maintain dependencies in their communities (see Livne-Tarandach & Jazaieri, 2021). Sustained personal connections could be cultivated to enhance community resilience, even during lockdowns when direct contact is limited. Maintaining ties between community members nurtures a sense of belonging to a community (Lewicka, 2011), stimulates a sense of felt security and strengthens resilience through strong social bonds.

Our study thus offers a more nuanced understanding of the relationship between social cohesion and community economic resilience, in particular, due to our focus on how organizations support individual community members and vice versa. The theoretical mechanisms we point to do not focus on organizations’ (charitable) actions towards community members alone (e.g. Smulowitz et al., 2020), nor do they only focus on individuals within a community who mobilize to assist a few neighbours. Rather, we suggest a fine-grained portrayal of the tissue that binds communities together consisting of actors – mutually interdependent individuals and organizations, their obligations and emotions, rooted in caring for a community and its members, and portray its effect on their community’s resilience. As such, our study moves away from the notion of declining levels of social cohesion in communities (Putnam, 2001) and increased individualism (Bauman, 2001). Instead, the mutual care between individual community members and local organizations suggests a ‘situational reaction’ of an entire community when faced with adversity or hardship (see Portes & Sensenbrenner, 1993, p. 1325). Family firms in particular seem to help buffer communities from economic shocks by maintaining employment relationships, preserving local supply chains and serving as moral anchors during periods of crisis (Calabrò et al., 2021; Lumpkin & Bacq, 2022).

At the same time, our findings point to a potential dark side of social cohesion. While cohesion enhances economic resilience, it could also impose pressure on organizations to deviate from their own preferences in favour of those of the community (Simons et al., 2016). In our specific case, organizations could keep trying to remain in business to no avail, thereby amassing debt or tapping into their financial reserves to the detriment of the organization, and perhaps even the community, in the longer run. More generally, high levels of social cohesion in communities sometimes instigate isolation from other communities and entails conservative, hard-to-change values (Van Der Cruijsen & Knoben, 2021). Earlier research has shown that this may hinder market entries of new, innovative organizations and increase favouritism towards incumbents, thereby stifling economic dynamism (Cruz et al., 2018). Future research should consider this ‘dark side’ of social cohesion together with its positive effects on community economic resilience.

An important limitation of our study is that we do not know much about the main drivers that foster kinship and residential stability. The Netherlands can be viewed as an enabling, or even extreme, case for studying community economic resilience, given its high levels of institutional support and social trust. Thus, the theoretical importance of the cohesion-related mechanisms is highlighted given their relevance in even such a generally favourable context. In settings with less extensive welfare provisions or lower institutional trust, similar mechanisms may operate even more intensely or rely on different social anchors, such as religious institutions, extended family systems, or informal community groups. For example, religious institutions such as the church used to be an important driving force of social cohesion in communities (e.g. Pepper et al., 2019). However, church attendance in the Netherlands, and many other societies, has steeply declined in the past decades. Yet, social cohesion has not necessarily similarly decreased. Our qualitative analysis indicates that sport associations, hobby clubs and other local initiatives such as local community quizzes may play an important role in fostering social cohesion. It would be interesting in future research to explore what are the driving factors and institutions that currently create, nurture and help maintain social cohesion within communities.

Taken together, we invite future researchers to continue work on social cohesion and economic resilience. Cohesive communities exhibit higher levels of both kinship and residential stability, cultivate fellowship and group consciousness (Kanter, 1972) and exhibit a normative sense of mutual obligation and responsibility among their members (Graf et al., 2023; Phan et al., 2009). This can accelerate collective action when resources are scarce. Crisis contexts also highlight how cohesion can emerge or deepen through shared adversity. The ‘social cure’ perspective (Haslam et al., 2018) suggests that when people see themselves as part of a collective facing the same threat, they are more willing to exchange resources, offer emotional support and collaborate toward recovery. Yet, cohesion does not happen overnight and requires continuous efforts. Research that identifies the mechanisms for cultivating cohesion could be particularly valuable.

Future studies might also examine how different stages of a crisis may call upon distinct dimensions of social cohesion. While initial emergency responses may rely on rapid mobilization of resources, i.e. residential stability, longer-term recovery might depend on kinship, reinforcing and stimulating normative commitment to the community (see Kanter, 1972). Understanding these temporal dynamics could help community leaders, policymakers and grassroots organizers to foster resilience more deliberately. By distinguishing between kinship and residential stability, we provide tools to diagnose latent vulnerabilities in communities before crises occur. This could inform place-based interventions aimed at strengthening the social infrastructure, such as supporting long-term residency, facilitating local associations or investing in institutions that cultivate durable social ties. Our results further suggest that encouraging family business formation may be an effective resilience strategy, because family firms function as stabilizing anchors that absorb shocks and sustain local economic life. As societies increasingly confront pandemics, climate-related disruptions and protracted economic uncertainty, insight into how social cohesion can be cultivated (see Porath et al., 2025) and how it shapes collective economic survival becomes not only theoretically important, but practically urgent.

Supplemental Material

sj-docx-1-oss-10.1177_01708406261432829 – Supplemental material for Love Thy Neighbour: The impact of social cohesion on community economic resilience

Supplemental material, sj-docx-1-oss-10.1177_01708406261432829 for Love Thy Neighbour: The impact of social cohesion on community economic resilience by Stephanie Koornneef, Joris Knoben, Tal Simons and Patrick A.M. Vermeulen in Organization Studies

Footnotes

Acknowledgements

We are grateful for comments from Maria Minniti and Christof Brandtner, the members of the strategy group at the Nijmegen School of Management, the members of the department of management at Tilburg University, the seminar attendants at TU Eindhoven, the attendants of the subtheme on Cords and Discords in Civil Society at EGOS 2022, and the attendants at Academy of Management Annual meeting 2023, together with the Co-Editor-in-Chief – Tammar Zilber, the Senior Editor – Jo-Ellen Pozner, and two anonymous reviewers for their insightful and valuable feedback on previous versions of this paper.

ORCID iDs

Stephanie Koornneef

Joris Knoben

Tal Simons

Patrick A.M. Vermeulen

Ethical considerations

Ethical approval was not required.

Consent to Participate

Informed consent to participate was not required.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

The data is not publicly available.

Supplemental material

Supplemental material for this article is available online.

Notes

Author biographies

Stephanie Koornneef is assistant professor of strategy at the Nijmegen School of Management, Radboud University. She obtained her PhD in management from Tilburg University. Her research focuses on the role of the past and temporality in organizational life, with particular attention to temporal anchoring devices and on the ways organizations shape, support and affect communities.

Joris Knoben is professor of strategy and entrepreneurship and Vice Dean of Research at Tilburg School of Economics and Management (TiSEM), Tilburg University. He received his PhD from Tilburg’s School of Social and Behavioral Sciences. His research focuses on the dynamics of innovation and entrepreneurship, particularly why and how firms cluster in certain regions, and how their environments influence strategic performance and innovation capacity.

Tal Simons is professor of organization theory at the Rotterdam School of Management, Erasmus University. She received her PhD from Cornell University. Her research interests include organization persistence and change, organizational ideology, and creative industries as well as the role of history and time in organizations. In her research, she often utilizes a mixed methods approach.

Patrick A.M. Vermeulen is a professor at Radboud University, specializing in organizational design and new organizational forms. His research, published in top-tier journals, explores organizational design, community-based organizations and community interplay in changing institutional contexts.

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