Postdisaster Social Capital,Adaptive Resilience and Business Performance of Tourism Organizations in Christchurch,New Zealand

Abstract

The resilience of tourism organizations is an important issue for destinations. While studies examine the social capital of firms, researchers have yet to understand the relationship between social capital (structural, relational and cognitive) and organizational resilience as predictors of business performance. This study evaluates these relationships at the interfirm level among tourism organizations in the postdisaster context of Christchurch, New Zealand, where business performance for some tourism operations was severely impacted. Surveys of tourism organizations reveal that structural capital has a positive relationship with both cognitive and relational capital. Only relational capital has an influence on adaptive resilience. Adaptive resilience has a significant influence on business performance. By showing which elements of social capital contribute to adaptive resilience, these findings can be used by tourism organizations in their recovery phase to direct investments in building resilience and strengthening interfirm relationships.

Keywords

adaptive resilience social capital relational capital business performance PLS-SEM New Zealand

Introduction

In today’s turbulent and uncertain environment, organizations are susceptible to disruptive events (Knemeyer, Zinn, and Eroglu 2009) that can undermine stability and security (Bhamra, Dani, and Burnard 2011). Such disruptive events negatively impact the performance of organizations (Lengnick-Hall, Beck, and Lengnick-Hall 2011). In this regard, the notion of resilience (Holling 1973) has been applied in different fields to assess how systems cope under stress, adapt to change, and rebound after the disruption (Bhamra, Dani, and Burnard 2011). Resilience is the ability of a system to maintain its identity and adapt its essential structure and function in the face of disturbance (Holling 1973). Resilience has emerged as an important topic in tourism (Becken 2013; Biggs, Hall, and Stoeckl 2012; Dahles and Susilowati 2015; Strickland-Munro, Allison, and Moore 2010) to understand how the tourism system and its components can become more resilient to shocks. One significant omission in this literature is the study of organizational resilience in the tourism industry (Biggs, Hall, and Stoeckl 2012; Dahles and Susilowati 2015; Orchiston, Prayag, and Brown 2016), despite the topic being well researched in other fields (Lee, Vargo, and Seville 2013; McManus et al. 2008). Another significant omission is the lack of studies clarifying the relationship between organizational resilience and business performance. While several studies both within tourism (Biggs, Hall, and Stoeckl 2012; Prayag and Orchiston 2016) and outside (e.g., Bhamra, Dani, and Burnard 2011) allude to this relationship conceptually, they do not provide empirical support. Prior studies support the relationship between postdisaster recovery strategies and business performance (Corey and Deitch 2011; Runyan 2006) but do not examine whether organizational resilience allows firms to sustain or improve performance.

This study focuses on postdisaster adaptive resilience of tourism organizations for several reasons. First, amid the lack of studies on the resilience of tourism organizations (Dahles and Susilowati 2015; Orchiston, Prayag, and Brown 2016), little is known about how tourism organizations specifically adapt and recover from natural disasters (Orchiston 2013), except for the study by Orchiston, Prayag, and Brown (2016) in a postdisaster context. The limited existing studies on adaptive resilience are mainly from contexts other than tourism (e.g., Cowell, Gainsborough, and Lowe 2016; Pal, Torstensson, and Mattila 2014; Teller, Wood, and Floh 2016).

Second, there is a lack of consistency in identifying the internal and external factors that drive the development of adaptive resilience in organizations as opposed to adaptive resilience as an outcome (Cowell, Gainsborough, and Lowe 2016). Existing studies suggest that important internal drivers include strategic human resource management (Lengnick-Hall, Beck, and Lengnick-Hall 2011), leadership, organizational culture, decision making, and situation awareness (Vargo and Seville 2011). In the context of Swedish SMEs, Pal, Torstensson, and Mattila (2014) identified four enablers of adaptive resilience internally (investment finance and cash flow, material assets and networking, strategic and operational flexibility, and attentive leadership). These studies clearly demonstrate the importance of developing and managing internal factors for building adaptive resilience. However, a key research question remains as to whether external factors are necessary for building adaptive resilience postdisaster. To address this knowledge gap, this study examines the role and influence of supply chain partners in building adaptive resilience. Using social capital theory (SCT) (Aldrich 2012), we argue that networks and resources available to firms through their connections to others is a critical feature of resilience (Aldrich 2012; Seville, Van Opstal, and Vargo 2015). Existing studies in tourism have prioritized the social capital that emanates from within the firm (Casanueva, Gallego, and Sancho 2013; McGeheeet al. 2010; Sainaghi and Baggio 2014) and individual levels (M. J. Kim, Lee, and Bonn 2016; Mura and Tavakoli 2014; Zhao, Ritchie, and Echtner 2011) rather than that at the interfirm level (García-Villaverde et al. 2017) (see appendix). However, none of these studies have been conducted in a postdisaster context. In such contexts, Aldrich (2012) argued that social capital facilitates recovery of communities and survivors, with faster recoveries being linked to stronger social capital. While the literature focusing on social capital at the community level provides a strong theoretical underpinning for the importance and use of social capital as a recovery strategy postdisaster, limited research has examined its role at the firm level (Hall, Prayag, and Amore 2018). Accordingly, the main objective of this study is to assess whether business performance in a postdisaster context is influenced by adaptive resilience and the role of social capital in building adaptive resilience. These relationships are examined in the context of post-quake Christchurch, New Zealand.

The study contributes to the tourism literature in three main ways. First, social capital is conceptualized using the three dimensions of relational, structural, and cognitive capital (Nahapiet and Ghoshal 1998). To our knowledge, no previous study has tested the relationships among the three dimensions of social capital in the context of tourism firms’ relationships with their key suppliers postdisaster (see the appendix for a summary of current studies on social capital in the tourism literature). Our approach offers a more holistic understanding of interfirm social capital in comparison to existing studies (e.g., Dai et al. 2015; Martínez-Pérez, García-Villaverde, and Elche 2016; Nieves, Quintana, and Osorio 2014). Second, research on the resilience of the tourism industry has been conducted predominantly at the macro level, examining the resilience of tourism destinations (Calgaro, Lloyd, and Dominey-Howes 2014), communities (Lew 2014), and tourism subsystems (Becken 2013). The lack of studies on organizational resilience (micro-level) and its relationship with social capital has been noted in previous studies (Biggs, Hall, and Stoeckl 2012). Social capital as a determinant of the adaptive resilience of tourism organizations has not been studied. Third, investing in organizational resilience confers competitive advantage (Seville, Van Opstal, and Vargo 2015) and thus should positively impact business performance. By showing which elements of social capital at the interfirm level contribute the most to adaptive resilience post disaster and the subsequent impact on business performance, we offer practical knowledge on how to strengthen business relationships with supply chain partners to direct future investments in building organizational resilience and enable organizations to recover faster. Figure 1 illustrates the study’s conceptual model and hypotheses.

Figure 1.

Conceptual model and hypotheses.

Conceptual Framework and Hypotheses

Resilience of Organizations and Adaptive Resilience

McManus et al. (2008, p. 82) define organizational resilience as “a function of an organization’s overall situation awareness, management of vulnerabilities, and adaptive capacity in a complex, dynamic and interconnected environment.” Other terms such as business (Avery and Bergsteiner 2011) and enterprise resilience (Biggs, Hall, and Stoeckl 2012) have also been used to describe organizational resilience. Resilience of tourism firms is considered important within the broader discussion of organizational resilience because it differs from other industries (Hall, Prayag, and Amore 2018). For example, the tourism sector is highly sensitive and vulnerable to global trends, particularly during sudden onset disasters, and contains many micro-small-sized enterprises (Orchiston 2013). Such external factors, along with the heavy reliance on inbound visitors and destination marketing agencies, increases the exposure of tourism businesses to sudden shocks. In addition, recovery of tourism postdisaster can be protracted because of lingering concerns and negative perceptions of safety in the affected region (Orchiston and Higham 2016). For these reasons, however, it should be noted that organizational resilience can only partly explain business performance since there are exogenous factors that tourism operators have little control over. Yet, as mentioned before, there is no previous empirical evidence to show that adaptive resilience affects business performance postdisaster.

Adaptive capacity, as described by Smit and Wandel (2006, p. 300) in a climate change context, refers to the “ability of a system to adjust . . . and take advantage of opportunities.” From a psychology perspective, Paton et al. (2008) uses the term adaptive capacity almost interchangeably with resilience to describe personal and community capacity to adapt to, and thrive after, adverse events. In this study, we follow Paton et al.’s (2008) conceptualization of adaptive capacity, which is similar to adaptive resilience and applies the concept at the organization, rather than personal or community, level.

The existing literature on organizational resilience converges around two research strands (Lengnick-Hall, Beck, and Lengnick-Hall 2011). In the first strand, researchers view organizational resilience as simply an ability to rebound from unexpected, stressful, adverse situations and to pick up where they left off, with an emphasis on identifying coping strategies and resuming expected performance levels (Horne and Orr 1997; Sutcliffe and Vogus 2003). The second research strand views organizational resilience as more than simply bouncing back, rather as a chance to develop new capabilities, and new market opportunities in order to thrive after a disruptive event (Coutu 2002; Jamrog et al. 2006; Lengnick-Hall and Beck 2003, 2005; Weick 1988). In this view, resilient organizations are those that are capable of capitalizing on unexpected challenges and change by leveraging their resources and capabilities. As such, the organization is not only capable of resolving current dilemmas but also can exploit opportunities and build a successful future (Lengnick-Hall, Beck, and Lengnick-Hall 2011). In a disaster context, these adaptive and agile responses are seen as especially important as a business navigates through its recovery (Lee, Vargo, and Seville 2013). This epitomizes the transformational view of organizational resilience (Lengnick-Hall, Beck, and Lengnick-Hall 2011).

The transformational view of organizational resilience has similarities with the notion of adaptive organizational resilience or “adaptive resilience” (Lee, Vargo, and Seville 2013). Adaptive resilience is defined as the ability to respond effectively, recover quickly, and successfully renew in the face of adverse events (Nilakant et al. 2014), which is akin to Paton et al.’s (2008) notion of adaptive capacity. Adaptive resilience emerges during a crisis as a result of many factors, including strong leadership and culture, leveraging knowledge, and quick decision making. Menéndez and Montes (2016, p. 17) suggest managers need to focus on “developing internal capabilities aimed at strengthening resilience,” including resources, knowledge, and learning. Here, social capital is seen as critical to the resilience of businesses since managing relationships within and outside the organization are necessary activities for enhancing business performance. Being resilient enables organizations to dynamically respond to emergent situations (Lee, Vargo, and Seville 2013).

Resilience is not a static attribute that organizations do or do not possess (Seville, Van Opstal, and Vargo 2015). Rather, resilience results from processes that help organizations retain resources in a form sufficiently flexible, storable, convertible, and malleable to avert maladaptive tendencies and cope with unexpected circumstances (Gittell et al. 2006; Sutcliffe and Vogus 2003). Building resilient capability at the organizational level is not only essential for business survival but also for competitive advantage purposes (Vogus and Sutcliffe 2007). The strategic importance of the concept has been highlighted in many studies (McManus et al. 2008; Seville, Van Opstal, and Vargo 2015).

To develop adaptive resilience, organizations should have a number of attributes (Lee, Vargo, and Seville 2013). First, maintaining and managing sufficient internal resources ensures an organization is able to operate during business-as-usual but also provide the extra capacity required during crises (Lee, Vargo, and Seville 2013). Second, strong crisis leadership is needed to provide good management and decision making. Third, quick and delegated authority for making decisions enables employees to respond efficiently and effectively (Lee, Vargo, and Seville 2013). Fourth, critical information and knowledge, whereby the former is stored in a number of formats and locations and employees have access to expert opinions when needed. Lastly, roles are shared and employees are trained so that someone will always be able to fill key roles (Lee, Vargo, and Seville 2013).

Social Capital and Its Dimensions

Previous studies have emphasized the role of social capital in building the resilience of individuals and communities postdisaster (Aldrich 2012), but not much has been written on the relationship between social capital and organizational resilience (Hall, Prayag, and Amore 2018). Particularly, Doerfel, Lai, and Chewning (2010) mentioned that in the face of unprecedented crisis, firms might rely on established relationships to help with rebuilding. Both personal and professional networks can play an essential role (Granovetter 1985; Hall, Prayag, and Amore 2018; Leenders and Gabbay 1999). This demonstrates the importance of social capital postdisaster. According to social capital theory (SCT), social capital is a valuable asset that is derived from access to resources made available through relationships with other firms (Granovetter 1992; Krause, Handfield, and Tyler 2007). Social capital resides in relationships, and relationships are created through exchange (Nahapiet and Ghoshal 1998).

SCT has received a growing interest among tourism researchers. A brief review of past studies on social capital in the tourism field (see appendix) reveals that the concept has been applied at the individual level, such as the residents of a community or city, employees of a firm, and students (Campopiano et al. 2016; T. T. Kim et al. 2013; Liu et al. 2014; Park et al. 2012; Zhao, Ritchie, and Echtner 2011) but also at the firm level (Casanueva, Gallego, and Sancho 2013; Hsu, Liu, and Huang 2012; Sainaghi and Baggio 2014). Fewer studies have discussed social capital at the supply chain level (i.e., interfirm) (García-Villaverde et al. 2017; Martínez-Pérez, García-Villaverde, and Elche 2016; Nieves, Quintana, and Osorio 2014). One significant lacuna in the existing studies on social capital at the interfirm level is that they do not clarify which aspects of social capital in the supply chain they are focusing on—supplier or customer side. In this study we are focusing on the immediate or primary suppliers of the tourism firm, which include companies such as travel agents, food and beverage, and transportation.

Social capital is a multidimensional concept (Casanueva, Gallego, and Sancho 2013; Martínez-Pérez, García-Villaverde, and Elche 2016; McGehee et al. 2010; Putnam 1995). Nahapiet and Ghoshal (1998) identified three distinct dimensions of social capital: structural, relational, and cognitive. Structural capital has been defined as the overall configuration between organizational actors or units and the means through which these actors or units are connected (Nahapiet and Ghoshal 1998; Preston et al. 2017). Cognitive capital is built when organizational actors share similar ambitions, visions, and cultural values (Nahapiet and Ghoshal 1998; Preston et al. 2017; Tsai and Ghoshal 1998). It facilitates the development of common understandings and collective ideologies, outlining appropriate ways for buyers and suppliers to coordinate their exchange (Roden and Lawson 2014). Relational social capital entails the strength of the relationship, in which trust, friendship, respect and reciprocity are embedded and developed through firms’ repeated transactions with their partners (Li, Zhang, and Zheng 2016; Nahapiet and Ghoshal 1998; Tsai and Ghoshal 1998). In the tourism field, the majority of studies examining all three dimensions have been conducted at the individual level (T. T. Kim et al. 2013; J. Liu et al. 2014; Zhao, Ritchie, and Echtner 2011), while firm-level studies use either one or two dimensions (Casanueva, Gallego, and Sancho 2013; Sainaghi and Baggio 2014). Except for the study of García-Villaverde et al. (2017), the three dimensions of social capital have not been examined in the tourism literature, and no existing studies have established the relationship between social capital, organizational resilience, and business performance.

Relationships between Dimensions of Social Capital

Structural and Relational Capital

In tourism studies, structural capital has been linked either directly (Sainaghi and Baggio 2014) or indirectly (T. T. Kim et al. 2013) to organizational performance via knowledge-sharing behaviors. Other studies establish positive relationships between structural capital and network resources (Casanueva, Gallego, and Sancho 2013) and radical innovation (García-Villaverde et al. 2017). Existing tourism studies fail to examine the interrelationships among the three dimensions of social capital. As suggested in other fields, structural capital acts as a conduit for information and resource flows providing the time, opportunity, and motivation to strengthen the relational aspects of the interfirm relationship (Yu, Liao, and Lin 2006; Zaheer, McEvily, and Perrone 1998). It allows both parties to establish norms for cooperation and teamwork (Preston et al. 2017). Interactions with supply chain members can be viewed as prerequisites to the creation of trust (Li, Ye, and Sheu 2014). The openness of this interaction encourages behavioral transparency, while simultaneously discouraging free-riding and information asymmetries (Carey, Lawson, and Krause 2011). Thus, we propose that the strength of structural capital is likely to increase the level of relational capital of tourism firms.

Hypothesis 1: Structural capital between the tourism firm and its key suppliers is positively associated with relational capital

Structural and Cognitive Capital

Tourism studies that evaluate the dimensions of cognitive and structural capital (García-Villaverde et al. 2017; T. T. Kim et al. 2013) have not tested a relationship between the two dimensions. SCT generally supports the importance of structural capital in the form of frequent social interactions in shaping a common set of goals and values among network members and sharing those goals and values among partner firms (Li, Ye, and Sheu 2014; Tsai and Ghoshal 1998). Through social interactions, business partners share their cultures, codes, values, and practices, which builds cognitive capital. Structural capital also provides a platform on which a firm and its suppliers can discuss, analyze, interpret, and share knowledge, which enables the development of compatible mental models and common language and codes between them (Carey, Lawson, and Krause 2011; Zhang, Lettice, and Zhao 2015). Based on the above argument, we articulate the following hypothesis:

Hypothesis 2: Structural capital between the tourism firm and its key suppliers is positively associated with cognitive capital

Cognitive Capital and Relational Capital

A trusting relationship between two parties implies that “common goals and values have brought and kept them together” (Barber 1983, p. 21). Since cognitive capital highlights shared values and beliefs, adherence to the associated norms of behavior is likely to develop trust as the parties identify and conform to the shared ideologies underpinning the relationship (Nahapiet and Ghoshal 1998). Relational capital is thus unlikely to accrue in a relationship (e.g., firms and its key suppliers) if neither party understands each other (Adler and Kwon 2002). When cognitions are shared between firms and their key suppliers, both parties become more inclined to trust one another, with the expectation of reciprocity, interaction and working toward collective goals (Tsai and Ghoshal 1998). This relationship between cognitive capital and relational capital has not been tested in previous tourism studies (García-Villaverde et al. 2017; T. T. Kim et al. 2013; Sainaghi and Baggio 2014). We therefore propose the following hypothesis:

Hypothesis 3: Cognitive capital between the tourism firm and its key suppliers is positively associated with relational capital

Relationship between Social Capital and Adaptive Resilience

Developing and maintaining relationships between different actors is one of the core elements of building resilience both before and during disruptive events (McManus et al. 2008). SCT captures the importance of such relationships in building resilience (Aldrich 2012) in the context of disaster research. Social capital is particularly useful for understanding the antecedents of a firm’s resilience capability (Prasad et al. 2015) since social capital can act as an information channel and provide access to resources, increase the efficiency of information diffusion, and minimize redundancies (Burt 1992). Therefore, social capital can facilitate access to broader resources of high-quality, timely information and practical business advice (Coleman 1988), which is extremely useful for coping with unexpected disruptions (Prasad et al. 2015). More recently, studies have stressed social networks and social capital as drivers of long-term postdisaster recovery (Aldrich 2011; Marín et al. 2015; Munasinghe 2007; Pelling and High 2005), suggesting that the quality of the social fabric in which individuals, groups, and firms are embedded is more important than other features (e.g., economic conditions) and external determinants (e.g., amount of damage) in explaining successful recovery (Marín et al. 2015). Recent evidence also suggests that social capital strengthens the resilience of micro organizations (Biggs 2011). It can lead to a quick disaster recovery and may even have a positive effect on the economy in the long run (Prasad et al. 2015) by improving business performance. Social capital can also facilitate a quick response from organizations facing disruptive and uncertain conditions (Lengnick-Hall, Beck, and Lengnick-Hall 2011).

Relationship between Structural Capital and Adaptive Resilience

The structural dimension of social capital mainly focuses on close, personal and informal relationships between firms and their key suppliers (Nahapiet and Ghoshal 1998; Tsai and Ghoshal 1998). Such interaction is an actor’s investment in building social resources (Lin 2002) which encourages developing a certain level of connectedness to each other through the network in which the interaction takes place (Lin 2002; Putnam 2001). Several beneficial outcomes can emerge when organizations use their established networks to link and collaborate with each other postdisaster, such as ability to colearn, access additional resources and enhance capacity to respond (Nilakant et al. 2014). Collaboration within the network serves as informal insurance in providing firms with required information, resources, and assistance after the disaster (Aldrich 2010), offering a boost for faster and better recovery (Aldrich 2012; Marín et al. 2015). Through social interaction and collaboration, firms can build adaptive resilience (Nilakant et al. 2014). Therefore, firms with better social connections and collaboration with their supply chain partners can adapt faster than less connected firms. Based on these arguments, we provide the following hypothesis:

Hypothesis 4: Structural capital between the tourism firm and its key suppliers is positively associated with the adaptive resilience of tourism firms

Relationship between Cognitive Capital and Adaptive Resilience

Given that the cognitive dimension of social capital refers to cognitive attributes such as shared language, shared values, and shared common perspectives among entities (Nahapiet and Ghoshal 1998), strong identification among supply chain members can stimulate a positive and constructive cognitive orientation that gives a sense of direction during disruptive events (Collins and Porras 1994). It encourages supply chain members to frame conditions in favor of preserving shared values and to take action to move forward despite disruption-related uncertainty (Dutton and Jackson 1987). Organizational resilience studies (e.g., Lee, Vargo, and Seville 2013; McManus et al. 2008; Nilakant et al. 2014) have emphasized the need for shared understandings in building resilience. It can therefore be argued that organizations that have such understandings are better capable of adapting to adverse conditions than those who do not. In addition, cognitive capital can serve as a substitute for financial capital in a time of need. When facing disruptive events that might endanger the survival of a particular organization, supply chain members who share the same values, vision, and purpose may be more willing to take appropriate steps to secure the organization (Prasad et al. 2015). As such, tourism firms that share the same values, vision, and goals with their suppliers will exhibit higher adaptive resilience after a disaster. Based on these arguments, we articulate the following hypothesis:

Hypothesis 5: Cognitive capital between the tourism firm and its key suppliers is positively associated with adaptive resilience of tourism firms

Relationship between Relational Capital and Adaptive Resilience

The relational dimension of social capital pertains to the strength of ties developed by a firm in relation to other members of the supply chain. It is composed of attributes such as trust, commitment, reciprocity, friendship, and respect (Nahapiet and Ghoshal 1998; Tsai and Ghoshal 1998). Firms with strong relational capital have the ability to access a network of committed and trustworthy supply chain partners. During unexpected disruptions, these suppliers are likely to offer resources and services to bounce back from the disruptive event (Prasad et al. 2015). In the context of the Canterbury earthquakes of 2010–2011, Seville et al. (2014) found that 94% of surveyed organizations believed their suppliers were somewhat or completely capable of meeting the organization’s needs after the earthquakes. This demonstrates the supportive role of suppliers in the recovery phase of a disaster. Specifically, if the postdisaster business environment is very different from the predisaster environment, adaptive resilience requires mobilizing both internal and external resources to capitalize on new opportunities (Nilakant et al. 2014). The relational aspects of organizations then become critical in responding to crises (Gittell et al. 2006). Relational aspects allow organizations to develop solutions for unanticipated changes in the business environment and help direct the common effort toward reaching mutually beneficial solutions (Ortiz-de-Mandojana and Bansal 2015), thus contributing to the adaptive capacity of the organization. Based on these arguments, the following hypothesis is suggested:

Hypothesis 6: Relational capital between the tourism firm and its key suppliers is positively related to the adaptive resilience of tourism firms

Relationship between Adaptive Resilience and Business Performance

Despite several tourism studies suggesting that social capital is related to business performance during business as usual (T. T. Kim et al. 2013; Sainaghi and Baggio 2014), including financial performance (Dai et al. 2015) and operational performance (Casanueva, Gallego, and Sancho 2013), studies have yet to establish the impacts of adaptive resilience on business performance in a postdisaster setting. Adaptive resilience is composed of both tangible (e.g., internal resources) and intangible resources (e.g., good leadership, quick decision making, and critical information and knowledge), suggesting that it is an internal resource that helps organizations adapt to external crises and opportunities, thus potentially improving firms’ business performance (C.-L. Liu et al. 2018). In a model of economic resilience postdisaster, Rose (2004) argues that adaptive resilience has a positive impact on the regional economic performance of a country. Drawing on the resource-based view of firms (Wernerfelt 1984), competitive advantage is built on the basis of distinctive internal competences (i.e., resources) of an organization (Barney 1991; Wernerfelt 1984). These resources can be tangible and intangible and are controlled by the firm to enhance its effectiveness and performance (Daft 2013). As such, resilience can be considered as a capability that offers a useful internal guidance system for organizational analysis and decision making in unexpected circumstances (Lengnick-Hall, Beck, and Lengnick-Hall 2011). Practices related to strong leadership—such as taking a long-term view of strategic planning and developing staff skills and competencies through ongoing training—contribute to building organizational resilience (Lee, Vargo, and Seville 2013). Such practices favorably impact performance outcomes such as financial performance and long-term shareholder value (Avery and Bergsteiner 2011). In the agriculture and farming industry, evidence suggests that drought-affected farms that remained resilient through prudent expenditure and enterprise diversity were capable of performing better than nonresilient farms (Lawes and Kingwell 2012). Based on the above arguments, we offer the following hypothesis:

Hypothesis 7: Tourism firms’ adaptive resilience is positively associated with its business performance.

The constructs and hypotheses are summarized in Figure 1. Next, the Canterbury earthquake sequence and its impact on tourism businesses is briefly discussed and the method of the study is described. A more detailed account of the impact of the Canterbury earthquakes on the tourism industry can be found in other studies (Orchiston and Higham 2016; Orchiston, Prayag, and Brown 2016; Orchiston, Seville, and Vargo 2014).

Method

Study Context

In September 2010, a damaging earthquake (magnitude 7.1, 0435 local time) struck 30 km west of Christchurch, the largest city in the South Island of New Zealand. It caused damage to rural communities and farming infrastructure and also resulted in some damage to vulnerable heritage buildings in Christchurch city. While there was no loss of life, the city was sufficiently affected to cause a central government response regarding the potentially negative tourism outcomes. A rapid communication effort was launched, using the Tourism New Zealand portal as the main distribution channel, to reassure international visitors that the situation remained “business as usual” for New Zealand tourism, and that Christchurch would be back to normal soon. The summer tourist season followed, with few negative consequences for the tourism sector (Orchiston and Higham 2016).

However, five months later on February 22, 2011, a destructive and deadly aftershock struck beneath Christchurch city (magnitude 6.3, 1247 local time, population 350,000), killing 185 people, including many foreign nationals, and injuring more than 7,100 people (Johnston et al. 2014). More than 15,000 residential homes were damaged by liquefaction and ground shaking and several hundred heritage buildings were irreparably damaged (Ingham and Griffith 2011). The CBD was cordoned off, remaining so for two years. It was quickly realized that Christchurch would be unfit to host visitors for many months to come (Orchiston and Higham 2016), a situation exacerbated by the significant reduction in hotel capacity (1100 hotel rooms, compared to 3750 before (Wood 2012). The earthquake (and the continuing aftershock sequence) caused an unprecedented downturn in visitor arrivals to Christchurch, the Canterbury region and the wider South Island (Orchiston and Higham 2016). Christchurch received 555,000 international visitors in 2010 compared to 398,000 in 2013 (CCT 2013), and inbound arrivals only rebounded to pre-2011 levels five years after the event (MBIE 2018). However, while some businesses were struggling to survive, others were able to capitalize on the opportunities presented by the earthquake and reported positive outcomes (Orchiston, Vargo, and Seville 2012). Details of the tourism management approaches that were developed by regional and national tourism authorities, particularly in recovery marketing, are described in Orchiston and Higham (2016).

Sampling and Data Collection

The database of tourism organizations developed for the Christchurch and Canterbury tourism impact survey in 2012 (Orchiston, Vargo, and Seville 2012) contained full contact details for 719 tourism operators across the region, with 498 in Christchurch city. After eliminating those that had closed or left Christchurch after the earthquakes, 251 organizations remained in operation in 2016. In June 2016, all 251 organizations were sent a postal survey with the survey instrument, cover letter, and a prepaid envelope along with a link to complete the survey online via the Qualtrics survey platform. The cover letter specified that the survey is focused on tourism recovery and resilience post disaster, particularly the resilience of their business and business networks. Of the postal surveys sent, 26 were returned as the listed addresses were no longer correct, shrinking the sampling frame to 225 organizations. Two weeks after the initial mail out, a first reminder was sent to organizations that had not yet responded, excluding those who had already completed the surveys (n=45) and the undelivered postal surveys. Two weeks after the first reminder was sent, an additional 11 responses had been received. Then phone calls were made to the remaining 169 organizations that had not yet responded. Of these, 65 agreed to fill in the survey but only a further 28 completed surveys were received. The total number of useable surveys was 84, which represents a response rate of 33.4% based on the initial 251 organizations in the database. This response rate is in line with expectations when surveying organizations (Baruch and Holtom 2008) and the sample size is comparable to previous studies on social capital in the tourism and hospitably industry (see McGehee et al. 2010; Sainaghi and Baggio 2014).

In order to strengthen the results derived from the survey, nonresponse bias was examined by comparing the last 28 completed responses with the first 45 responses in terms of the respondents’ demographics and business characteristics (Martínez-Pérez, García-Villaverde, and Elche 2016). Chi-square tests on number of years in operation (χ²=9.69, p>.05) and number of full-time employees (χ²=6.32, p>.05) revealed no statistically significant differences on the type of businesses that responded after the initial mail-out and those that responded after the phone calls were made. A similar conclusion was also reached when examining differences on the basis of the respondents’ gender (χ²=.51, p>.05), age group (χ²=13.29, p>.05), and education levels (χ²=11.64, p>.05).

Survey Instrument

The constructs of this study were assessed using established scales from the literature. Social capital was measured using 17 items (α=0.97) adapted from several studies (Carey, Lawson, and Krause 2011; Tsai and Ghoshal 1998; Villena, Revilla, and Choi 2011) to identify the extent to which firms engaged in a set of behaviors postdisaster. Of these items, six measured structural capital (α=0.94) and cognitive capital (α=0.95) and five measured relational capital (α=0.95) on a five-point Likert-type scale (1 = strongly disagree and 5 = strongly agree). Adaptive resilience was measured using seven items (α=0.84) on a similar scale. These items were adapted from previous studies on organizational resilience (Lee, Vargo, and Seville 2013; Orchiston, Prayag, and Brown 2016). Business performance was measured using four questions (α=0.83) that assessed the organization’s overall performance, level of debt, overall profitability, and cash flow since the 2010–2011 earthquakes. These questions were adapted from previous studies (e.g., Kachali et al. 2012) and measured on a five-point Likert-type scale (1 = significantly worse off and 5 = significantly better off) for overall performance of the business. The organization’s level of debt (1 = very negative and 5 = very positive) and the overall profitability (1 = very poor and 5 = excellent) as well as cash flow (1 = very poor and 5 = excellent) were measured on five-point Likert-type scales. To assess the content validity of the survey instrument, three academics involved in Resilient Organizations (http://www.resorgs.org.nz/) in New Zealand were asked to assess the questionnaire. Qualitative feedback was also received from pretesting the questionnaire on businesses in Christchurch.

To assess for common method bias, Harman’s one-factor test was conducted on the 28 items used for testing the conceptual model (Podsakoff et al. 2003). The results showed that no single factor accounted for more than 35% of the variance observed. As such, common method bias is unlikely to be a significant concern in this study.

Data Analysis

Partial least squares structural equation modeling (PLS-SEM) has emerged as a viable alternative to the traditional covariance-based structural equation modeling (CB-SEM). While other disciplines have quickly adopted PLS-SEM, the application of the technique remains in its infancy in tourism studies (Valle and Assaker 2016). PLS-SEM offers several advantages over CB-SEM, including that the application of the technique does not require the criteria of multivariate normality to be fulfilled (Hair, Ringle, and Sarstedt 2011). Also, PLS-SEM is suitable for small sample sizes when the focus is on theory exploration rather than theory confirmation (Hair, Ringle, and Sarstedt 2011). Given the justifications for researchers to use PLS-SEM are pertinent to this study, we analyzed the data using SmartPLS 3.2.4 (Ringle, Wende, and Becker 2015). A path weighting scheme was used for the inner model estimation and Mode A for the outer model estimation (Valle and Assaker 2016).

Unlike CB-SEM that is based on the common factor model, PLS-SEM is based on the composite factor model. As a result, it can handle relatively small sample sizes (<100) compared with CB-SEM (Hair et al. 2017a). In general, CB-SEM requires larger sample size for a complex model, which is not the case for PLS-SEM (Hair et al. 2017b). In this study we used three different approaches to determine our sample size. First, we followed the sample size rule of “10 times the largest number of structural paths directed at a particular latent construct in the structural model” proposed by Hair, Ringle, and Sarstedt (2011, p. 144) for PLS SEM. According to this criterion we require a sample size of 50 to test our model and our current sample size (84) is larger. Second, we used power analysis software—G*Power (version 3.1.9.2) to determine the minimum sample size for our model (Hair et al. 2017b; Yadlapalli, Rahman, and Gunasekaran 2018). The alpha value of 0.05 and the power level of 0.80 were used for power analysis as the standard parameters levels recommended by Cohen (1990). In previous literature employing this method, the effect size was not reported except for a few recent studies (e.g., Siamionava, Slevitch, and Tomas 2018). Accordingly, for our model, we consider the standard medium effect size of 0.25 to be adequate as per suggestions in recent studies. For a medium effect size of 0.25 and statistical significance level (i.e., alpha value) of 0.05 with a power value of 0.80 for three predictors, the minimum sample size requirement is 27. Thus, our sample size is larger than this requirement. Next, post hoc analysis was conducted and the obtained power is 0.998 probabilities, suggesting the same results will likely reoccur in the same setting. This power analysis suggests the minimum sample size requirement is satisfied. Third, we reviewed papers that used PLS SEM on small sample size in other disciplines such as supply chain management (Filho, Ganga, and Gunasekaran 2016; Jabbour et al. 2016; C.-L. Liu and Lai 2016) and marketing (Claro, Vojnovskis, and Ramos 2018), finding that a sample size smaller than 84 is acceptable for the type of modeling undertaken in this study.

Findings

Sample Characteristics

The surveyed businesses had been in operation for more than 6 years but less than 10 years (14.5%) or were established businesses operating for more than 20 years (31.3%). Table 1 shows that half of the businesses were owner-operator and more than half (58.3%) were microenterprises with fewer than five employees. A comparable study by Orchiston, Seville, and Vargo (2014) showed that the tourism sector in Christchurch is predominantly owner-operated micro-enterprises. Tourism operations represented in the sample were from across subsectors, including accommodation (47.6%) such as hotels, bed and breakfast, backpackers, and farm stay. Other tourism operators represented in the sample included visitor transport (17.9%) and tourist attractions/activities (22.6%). These businesses reflect the tourism sectors represented in postquake Christchurch (see Orchiston, Prayag, and Brown 2016). The sociodemographic characteristics of the respondents revealed that a high percentage had either a professional qualification (24.1%) or a university degree (19.3%), with a good representation of both male (57.3%) and female (42.7%) respondents. The sample comprised mainly older respondents with a high percentage in the age groups of 45 to 54 years old (24.1%) and 55 to 64 years old (32.5%)

Table 1.

Business Characteristics and Sociodemographics.

No. of years business operating	%	Tourism sector	%
1–5	10.8	Accommodation	47.6
6–10	14.5	Visitor transport	17.9
11–20	43.4	Tourist attractions/activities	22.6
⩾20	31.3	Other	11.9
		Gender
Role in the business		Male	57.3
Manager	50	Female	42.7
Owner/operator	50	Age group, years
No. of employees		18-25	0
<5	58.3	26-34	9.6
5-49	33.3	35-44	20.5
50-99	4.8	45-54	24.1
100+	3.6	55-64	32.5
Education level		⩾65	13.3
High school or less	41
University graduate	19.3
University post-graduate	8.4
Professional qualification	24.1
Other	7.2

Outer Model Evaluation

The outer model was assessed by examining the reliability and validity of the measures (Chin 2010). The recommended threshold for item reliability is 0.7 (Hair, Ringle, and Sarstedt 2011). Two items for structural capital, one item for cognitive capital, and two items for adaptive resilience did not fulfil this criterion and were deleted. All of the remaining items had significant factor loadings (p < 0.01). Several measures such as Cronbach’s alpha (α), Rho_A, and composite reliabilities (CR) were used to assess the internal consistency of the items to represent the constructs. As shown in Table 2, all Cronbach’s α and CR were above the minimum level of 0.7 (Nunnally and Bernstein 1994). Unlike Cronbach’s α and CR, which measure sum scores, Rho_A measures construct scores and is regarded as the most important reliability measure for PLS (Dijkstra and Henseler 2015). All Rho_As were above the recommended 0.7 (Henseler, Hubona, and Ray 2016).

Table 2.

Factor Loadings, Reliability, and Validity Measures for Each Construct.

Constructs	Items	Standard Loadings	Cronbach’s α	CR	Rho_A	AVE
Structural Capital	StC1: We spend time together in social occasions with our key suppliers	0.903	0.930	0.950	0.933	0.827
	StC2: We maintain a close social relationship with our key suppliers	0.902
	StC3: We know the key suppliers at a personal level	0.922
	StC4: We communicate frequently with our key suppliers outside of our working relationship.	0.911
Cognitive Capital	CogC1: Our organization share the same ambitions and vision with our key suppliers	0.873	0.937	0.952	0.938	0.800
Q²=0.405	CogC2: People in our organization and those of our key suppliers are enthusiastic about pursuing a collective goal for the whole supply chain.	0.921
	CogC3: Our organization and our key suppliers share similar corporate culture and management style	0.884
	CogC4: Our organization and our key suppliers share the same business values	0.924
	CogC5: Our organization and our key suppliers agree on what is in the best interest of the relationship we have with them	0.869
Relational Capital	RelC1: Our relationship with key suppliers is characterized by close personal interaction	0.898	0.938	0.953	0.939	0.801
Q²=0.523	RelC2: Our relationship with key suppliers is characterized by mutual respect	0.908
	RelC3: Our relationship with key suppliers is characterized by mutual trust	0.920
	RelC4: Our relationship with key suppliers is characterized by personal friendship	0.889
	RelC5: Our relationship with key suppliers is characterized by high levels of reciprocity	0.859
Adaptive Resilience	ARes1: Our organization maintains sufficient resources to absorb some unexpected change	0.707	0.838	0.884	0.872	0.605
Q²=0.015	ARes2: If key people were unavailable, there are always others who could fill their role	0.708
	ARes3: There would be good leadership from within our organization if we were struck by a crisis	0.856
	ARes4: We are known for our ability to use knowledge in novel ways	0.801
	ARes5: We can make tough decisions quickly	0.805
Business Performance	BP1: Overall performance of the organization after the earthquakes of 2010–2011	0.876	0.863	0.902	0.968	0.700
Q²=0.041	BP2: Level of debt since the 2010–2011 earthquakes	0.738
	BP3: Organization’s cash flow since the 2010–2011 earthquakes	0.902
	BP4: Organization’s level of profitability since the 2010–2011 earthquakes	0.824

Fornell and Larcker’s (1981) criterion of average variance extracted (AVE) for each construct should be greater than 0.5 for establishing convergent validity. Table 2 shows that the AVE for the constructs ranged from 0.605 to 0.827, thus exceeding the stipulated threshold. Discriminant validity was established using two criteria. First, according to Fornell and Larcker (1981), the square root of AVE of each of the latent constructs should be higher than the construct’s highest correlation with any other constructs. As shown in Table 3, all correlations were less than the square root of AVE, thus establishing discriminant validity of the constructs. Second, the heterotrait–monotrait (HTMT) ratio of correlations (Henseler, Ringle, and Sarstedt 2015) was used to establish discriminant validity. The HTMT is an estimate for the factor correlation. In order to clearly discriminate between two factors, the HTMT ratio should be significantly smaller than the conservative level of 0.85 (Henseler, Hubona, and Ray 2016). Table 4 shows that all the correlation ratios are below the critical level.

Table 3.

Fornell and Larcker Criterion for Discriminant Validity.

Latent Constructs	Adaptive Resilience	Business Performance	Cognitive Capital	Relational Capital	Structural Capital
Adaptive resilience	0.778
Business performance	0.294	0.837
Cognitive capital	0.031	−0.080	0.894
Relational capital	0.164	0.017	0.793	0.895
Structural capital	−0.016	−0.090	0.730	0.757	0.910

Note: Square root of the average variance extracted is shown in bold in the diagonal.

Table 4.

Heterotrait–Monotrait Criterion for Establishing Discriminant Validity.

Latent Constructs	Adaptive Resilience	Business Performance	Cognitive Capital	Relational Capital	Structural Capital
Adaptive resilience
Business performance	0.319
Cognitive capital	0.100	0.115
Relational capital	0.175	0.090	0.842
Structural capital	0.076	0.125	0.780	0.807	–

Inner Model Evaluation and Hypothesis Testing

Following the outer model evaluation, the inner model was evaluated to assess the explanatory power and predictive relevance of the proposed model. Also, the size of the path coefficients and the significance of the hypothesized relationships were estimated. In PLS, the main criterion for evaluating the structural model is the variance explained (R²). The model explained 53.2% and 69.7% of the variance in cognitive capital and relational capital respectively. The model also explained 8.2% and 8.6% of the variance in adaptive resilience and business performance, respectively. To further assess the structural model, the Q² and f² effect sizes for the endogenous latent variables were calculated (Henseler, Ringle, and Sinkovics 2009). The Q² values were calculated using the blindfolding method with an omission distance of 8, lying within the criteria of between 5 and 10 as proposed in the literature (Hair et al. 2012). Table 2 shows that all Q² values were positive, demonstrating that the exogenous constructs have predictive relevance for the endogenous constructs in this study (Chin 2010). Table 5 shows that all f² values are also positive, indicative of the proposed latent variables being relevant for explaining the variance observed in business performance.

Table 5.

Estimates of Path Coefficients, Effect Sizes, and Hypothesis Testing.

Hypothesis	Paths	Path Coefficients (p Level)	BCa Confidence Intervals		Effect Sizes (f²)	Hypothesis Supported
Hypothesis	Paths	Path Coefficients (p Level)	2.5%	97.5%	Effect Sizes (f²)	Hypothesis Supported
Hypothesis 1	Structural capital → Cognitive capital	0.730 (p<0.001)	0.593	0.832	0.138	Yes
Hypothesis 2	Structural capital → Relational capital	0.381 (p=0.001)	0.168	0.612	0.224	Yes
Hypothesis 3	Cognitive capital → Relational capital	0.515 (p<0.001)	0.251	0.746	0.409	Yes
Hypothesis 4	Structural capital → Adaptive Res	−0.276 (p=0.284)	−0.685	0.347	0.032	No
Hypothesis 5	Cognitive capital → Adaptive Res	−0.170 (p=0.550)	−0.626	0.567	0.010	No
Hypothesis 6	Relational capital → Adaptive Res	0.507 (p=0.031)	0.021	0.936	0.085	Yes
Hypothesis 7	Adaptive Res → Business Perf	0.285 (p<0.001)	0.095	0.406	0.094	Yes

Using the bias corrected bootstrapping method (1,000 subsamples), the path coefficients were calculated. The results revealed that five of the seven hypotheses were supported. Structural capital has direct and positive relationships with both cognitive (β=0.730, t=11.86, p<0.001) and relational (β=0.381, t=3.29, p=0.001) capital. Cognitive capital has a direct and positive relationship with relational capital (β=0.515, t=3.97, p<0.001). The findings support hypotheses 1, 2, and 3, suggesting that relationships exist between different facets of the social capital of tourism organizations. No direct relationship could be established between structural capital and adaptive resilience (β = −0.276, t=1.07, p=0.284) as well as between cognitive capital and adaptive resilience (β= −0.170, t=0.598, p<0.001). As such, the findings do not support hypotheses 4 and 5. Consistent with the literature, relational capital has a direct positive effect on adaptive resilience (β=0.507, t=2.16, p=0.031), thus supporting hypothesis 6. The adaptive resilience of a firm has a positive impact on its business performance (β=0.285, t=3.78, p<0.001), thus supporting hypothesis 7. To examine mediating effects of adaptive resilience on the relationship between the three dimensions of social capital and business performance, the bootstrapped indirect effects were examined (Hair, Ringle, and Sarstedt 2011). The results showed no statistically significant indirect effects.

The effects of industry sector and the number of years a business was in operation (see Figure 2) were evaluated as control variables on business performance. As suggested in previous studies (Orchiston, Prayag, and Brown 2016), sectors within the tourism and hospitality industry can have different resilience and performance levels following a disaster. None of the sectors (accommodation, visitor transport, tourist attraction/activity, and other) had a significant effect on business performance. Likewise, the number of years a business was in operation had no significant effect on business performance.

Figure 2.

Full structural model.

Discussion and Implications

The objective of this study was to assess the relationship between business performance and adaptive resilience in a postdisaster context and the role of various facets of social capital in predicting adaptive resilience. The findings indicate that interrelationships exist within the three facets of social capital (structural, cognitive, and relational) but only relational capital can predict adaptive resilience. Adaptive resilience has a positive effect on business performance. The findings have several theoretical and managerial implications.

While several studies outside the field of tourism allude to the relationship between organizational resilience and business performance (Lampel, Bhalla, and Jha 2014; Lawes and Kingwell 2012), existing studies on organizational resilience of tourism firms (Biggs, Hall, and Stoeckl 2012; Dahles and Susilowati 2015; Orchiston 2013; Orchiston, Prayag, and Brown 2016) do not provide empirical support for this relationship. The main findings of this study suggest that building cognitive and structural capital contributes positively to building relational capital at the interfirm level postdisaster. In turn, relational capital positively impacts adaptive resilience, contributing to tourism firms improved business performance postdisaster. This study is the first to establish the positive effects of resilience building activities on improved business performance for tourism organizations in the recovery phase of a disaster.

Tourism studies have established the importance of building social capital at community, individual, and firm levels (Hwang and Stewart 2017; McGehee et al. 2010; Zhao, Ritchie, and Echtner 2011). While recognizing that different systems can build social capital in different forms and ways, this study focused on social capital at the interfirm level emanating after a disaster. The findings confirm that tourism firms benefit from all three dimensions of social capital when interacting their key suppliers. On one hand, the findings highlight the role and influence of external social capital on firm behaviors and thereby extend current studies that have examined mainly internal influencers on the social capital of the firm post disaster (Lee, Vargo, and Seville 2013). On the other, the findings highlight that scale of analysis (e.g., firm, individual, and interfirm) plays a role in identifying which dimensions of social capital are relevant in studying organizational behaviors. By applying the three dimensions of social capital at the interfirm level, this study demonstrates their predictive relevance in understanding business performance of tourism organizations. Other studies conducted at the firm level employ only a single dimension (e.g., Sainaghi and Baggio 2014) or two of the three dimensions (Casanueva, Gallego, and Sancho 2013). As such, the findings add to the limited literature on interfirm level social capital post disaster and provide a holistic understanding of firm behaviors with respect to their external stakeholders.

By showing that structural and cognitive capital are related to relational capital, the study extends findings from existing studies conducted on individuals (Lee, Vargo, and Seville 2013; Zhao, Ritchie, and Echtner 2011), communities (J. Liu et al. 2014), and firms (Casanueva, Gallego, and Sancho 2013) that establish the independence of the three dimensions. More specifically, tourism organizations operating in a postdisaster context need both structural and cognitive capital in building relational capital with their key suppliers. Maintaining close social relationships and knowing key suppliers at a personal level, for example, contributes to these suppliers looking after the best interests of the tourism organization postdisaster, thus furthering the development of mutual trust and respect. In contrast to existing studies conducted at the interfirm level (Dai et al. 2015; García-Villaverde et al. 2017; Martínez-Pérez, García-Villaverde, and Elche 2016), the results demonstrate a relationship between structural and cognitive capital, suggesting that communication with key suppliers outside of working relationships, for instance, facilitates the pursuit of collective goals within the supply chain.

There is little consistency in the tourism literature in identifying which dimensions and levels (individual vs. firm) of social capital allow predictions of firm behaviors and performance. For example, previous studies have shown that at the firm level, relational capital has a relationship with firm behaviors such as knowledge sharing (T. T. Kim et al. 2013). Others (e.g., García-Villaverde et al. 2017) could not establish a relationship between relational capital and other firm behaviors such as innovation capability at the interfirm level. We extend these studies by showing that after an external shock, relational capital built with key suppliers influences adaptive resilience of an organization—an important firm behavior in the recovery phase of a disaster (Lee, Vargo, and Seville 2013; Nilakant et al. 2014). We also extend the interfirm social capital literature by showing that an important outcome of external social capital is the adaptive resilience of tourism organizations. Previous studies have mainly established this link at the community level (Aldrich 2011). By empirically confirming the relationship between relational capital and adaptive resilience, the study suggests that tourism organizations that build social capital with their primary suppliers can use these relationships as buffers in the face of crises (Biggs 2011; Biggs, Hall, and Stoeckl 2012).

Moreover, this is the first study to establish an empirical link between adaptive resilience and business performance. While several previous studies established relationships between social capital and either financial (Avery and Bergsteiner 2011; Dai et al. 2015) or operational performance (Casanueva, Gallego, and Sancho 2013), the findings of this study suggest that having social relationships with supply chain partners cannot improve organizational performance by itself but also requires adaptive resilience postdisaster in order to thrive. As such, the findings provide evidence from the tourism and hospitality industry that factors including good leadership, the ability to make tough decisions quickly, and having sufficient resources to absorb change in the case of unexpected events can improve the bottom line of a business.

An emphasis on firm–community relationships is predominant in studies of social capital in the tourism field. However, results of this study highlight the role of firm–supplier relationships in building the adaptive resilience of organizations. After a disaster, building this adaptive capability or resilience requires an understanding of all three forms of social capital. In particular, relational capital at the interfirm level seems to play an important role in helping organizations survive. Building relational capital requires firms create relationships that are nurtured through mutual respect, trust, reciprocity, and personal friendship. In the context of Christchurch, given the preponderance of tourism SMEs and micro-enterprises (Orchiston 2012), developing, managing, and sustaining close social relationships between firms and suppliers seems to make such organizations more resilient. Previous studies (e.g., Pal, Torstensson, and Mattila 2014) show that the resilience of SMEs not only depends on flexible operations but also on social resources that involve collaborative relationships. The role of networks in times of crises has been emphasized in both the management and supply chain literatures (Sheffi 2015). For organizations to thrive in the aftermath of crisis, a deep social fabric of goodwill and interpersonal relationships provides the foundation for building and maintaining resilience (Adler and Kwon 2002; Lengnick-Hall and Beck 2005).

Likewise, the results also suggest that relational capital is dependent on both structural and cognitive capital. Hence, for tourism organizations to thrive post disaster they should invest in building structural capital. This can be achieved through organizing and facilitating social events that allow the firm to get to know their suppliers personally and by improving the type, as well as the frequency, of communication between the firm and its suppliers. Cognitive capital can be built through business activities and practices that require tourism businesses to understand the vision, values, and organizational culture of their key suppliers that will allow them to move toward a shared understanding. This shared understanding can be achieved by setting collective goals and adopting a management style that values communication and teamwork both internally and externally. In this respect, leadership within the organization is a critical resource to build both social capital (Prasad et al. 2015) and resilience (Lee, Vargo, and Seville 2013). Strong leadership during and postdisaster ensures that organizations are able to adapt and respond effectively (Nilakant et al. 2014).

Employees that are capable of multitasking are another critical resource that organizations need in order to build adaptive resilience (Nilakant et al. 2014). This study shows that organizations that are capable of filling roles quickly when key people are unavailable are better at adaptive resilience. This suggests competencies that organizations must develop among its employees include multitasking, engagement, and effective delegation (McManus et al. 2008). These can be achieved by job sharing and job rotation within the organization.

Conclusions, Limitations, and Areas of Future Research

This is the first study to link social capital at the interfirm level with the resilience of organizations and assess the subsequent impact on business performance. However, this approach is not without limitations, which offer avenues for further research on both social capital and organizational resilience. First, despite the sample size conforming to requirements of PLS-SEM and corresponding to the sample size of previous studies on social capital (Sainaghi and Baggio 2014), the results cannot be generalized to the entire Canterbury region. However, the results help both researchers and practitioners understand the underlying dynamics of building adaptive resilience. In particular, future studies can examine organizational resilience by comparing the predisaster resilience (planned resilience) with postdisaster resilience (adaptive resilience). Second, similar to other studies (T. T. Kim et al. 2013) ‘soft’ measures of business performance were used in this study to investigate their relationships with social capital and organizational resilience. Future studies can replicate the proposed relationships in this study using objective measures of profits, debt levels, and sales as commonly used when investigating the relationship between social capital and business performance (Dai et al. 2015; Sainaghi and Baggio 2014). Third, the underlying relationships proposed in Figure 1 were derived from the resource-based view of organizations, which itself has its own limitations (see Priem and Butler 2001). Future studies can investigate organizational resilience from a disaster life-cycle perspective, where organizational resilience is assessed at each of the four stages of preparedness, immediate response, recovery and mitigation. Supply chain resilience studies (e.g., Scholten, Scott, and Fynes 2014) highlight the need to understand the complexities at both firm and interfirm levels in building resilience in postdisaster contexts. To this end, the link between interfirm social capital and supply chain resilience of tourism organizations is another valuable area of future research. Fourth, as is common practice in tourism studies investigating social capital (Dai et al. 2015; T. T. Kim et al. 2013; Nieves, Quintana, and Osorio 2014) and organizational resilience (Orchiston, Prayag, and Brown 2016), a single respondent from each organization was sought in the sampling strategy. This approach does not always give a holistic perspective of the resilience of an organization. Future studies should attempt to include multiple respondents (e.g., senior managers and front-line employees) from each firm to fully understand the extent to which the firm is perceived to be resilient. Finally, several studies (Dahles and Susilowati 2015; Orchiston, Prayag, and Brown 2016) suggest that innovation is critical for building the resilience of organizations postdisaster whereas others (Biggs, Hall, and Stoeckl 2012) emphasize the role of social capital. The role of social capital and resilience in fostering innovation within business models and products/services in a postdisaster is another significant gap in the tourism literature.

Supplemental Material

Appendix_1_JTR_15_July_2018_(1) – Supplemental material for Postdisaster Social Capital, Adaptive Resilience and Business Performance of Tourism Organizations in Christchurch, New Zealand

Supplemental material, Appendix_1_JTR_15_July_2018_(1) for Postdisaster Social Capital, Adaptive Resilience and Business Performance of Tourism Organizations in Christchurch, New Zealand by Mesbahuddin Chowdhury, Girish Prayag, Caroline Orchiston and Samuel Spector in Journal of Travel Research

Footnotes

Declaration of Conflicting Interests

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

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Girish Prayag

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Mesbahuddin Chowdhury, PhD, is a lecturer in operations management in the Department of Management, Marketing and Entrepreneurship at the University of Canterbury, New Zealand. His research interests are related to social capital and supply chain resilience. His work has appeared in leading operations management journals such as the International Journal of Production Economics and International Journal of Production Research.

Girish Prayag, PhD, lectures in marketing and tourism in the Department of Management, Marketing and Entrepreneurship at the University of Canterbury, New Zealand. His research is related to place-based marketing and tourism resilience. His work has appeared in leading international marketing and tourism journals such as Journal of Business Research, Journal of Travel Research, Tourism Management, and Annals of Tourism Research. Girish is currently editor of the Method and Practice section of Current Issues in Tourism. He is also a coauthor of the book Tourism Resilience: Individual, Organizational and Destination Perspectives.

Caroline Orchiston, PhD, is a Research Fellow at the Centre for Sustainability at the University of Otago. She has an interest in resilience from the perspectives of tourism and communities, particularly in the context of natural hazard events. Caroline’s recent research has investigated tourism recovery following the Canterbury earthquake sequence. She has QuakeCoRE funding to model the economic outcomes of postdisaster tourist visitation around New Zealand following the Canterbury earthquakes. Caroline is also involved in the Resilience to Nature’s Challenges research program investigating rural and cultural aspects of resilience in New Zealand. Her work has appeared in leading tourism journals such the Annals of Tourism Research and Current Issues in Tourism.

Sam Spector, PhD, is currently the Programme Leader for Queenstown Resort College’s Graduate Diploma in Tourism and Hospitality Management. In addition to supervising students’ research projects, he teaches Fundamentals of Tourism and Hospitality, Sales and Marketing, and Strategic Management. Sam’s research interests include resilience to natural hazards, sustainable transport futures, and tourism in outer space.

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