Network resource mobilisation limitations and the alliance portfolio network

Depósito de investigación de la Universidad de Sevilla https://idus.us.es/ “This is an Accepted Manuscript of an article published in Baltic Journal of Management on 2021, available at: https://doi.org/10.1108/BJM-06-2020- 0206.” 1 Network Resource Mobilization Limitations and the Alliance Portfolio Network Abstract Design/methodology/approach This study employs an interorganizational network approach to describe firms’ alliance portfolios; subsequently, it examines how alliance portfolio network parameters and resource mobilization determine financial performance. For this, sequential multiple regression models were applied to a sample from the Top International Airlines database, covering 135 portfolios that correspond to 1117 codeshare partnerships. Purpose A firms’ alliance portfolio network parameters influences the access to network resources, however, resource access as value creation differs from value capture in terms of the resources that are finally mobilized. Our paper contributes to the understanding of alliance portfolio performance by examining the extent to which a firm’s level of Network Resource Mobilization (NRM) plays a role in achieving financial performance and how this strategy conditions the benefits obtained from firms’ alliance portfolio. Findings Our analyses show that NRM level has an inverted U-shape relation with revenue performance, hence revealing the limitations and considerations in the strategic alliance strategy. In addition, we show how the resource mobilization decision moderates the faculty of alliance portfolio parameters to influence firms’ financial performance; thus exposing the nuanced relationship between alliance portfolio size, diversity, and redundancy. As a result our findings convey strategic and practical implications for managers in how to capture value from their alliance portfolios. Practical implications Our findings suggest the need for network resource mobilization to form part of forms’ alliance portfolio management capability, so that by acquiring superior strategic knowledge in network resource mobilization, the firm will be able to extract value from its alliance portfolio through the optimal exploitation of complementary assets. Originality/value Previous research had pointed to the multidimensional nature of alliance portfolios at the theoretical level; however, a simultaneous empirical analysis of various alliance portfolio parameters had been neglected so far. Our research has empirically analyzed alliance portfolio network and how its parameters affect financial performance in the presence of a resource mobilization strategy. In addition, we contribute by introducing the analysis of the curvilinear relation between the level of NRM and firm performance, as well as its role in advancing alliance portfolio literature. 2 Keywords Alliance Portfolio Configuration, Network Resources Mobilization, Airlines Performance, Strategic Alliances, Moderation Analysis. 3 1 Introduction The study of alliance portfolios (APs) continues to capture the interest in management research (Castiglioni and Galán González, 2020; Van Wijk and Nadolska, 2020). Previous studies on APs have centred on alliance portfolio configuration; i.e. who the partners of the focal firm are and how they relate with it and between each other (Hoffmann, 2007; Wassmer, 2010). In turn, further contributions have explored the portfolio management, as well as the process of seeking opportunities for alliances, designing relational governance or globally coordinating the alliance portfolio (Lavie, 2007; Sarkar et al., 2008); likewise, a research stream has flourished on how the alliance portfolio affects performance (Wassmer, 2010; Zaheer et al., 2010). In particular, the confluence of the resource-based view and the study of interorganizational networks has made it clear that the choice of partners by the firm and the structure of their interorganizational relations are relevant strategic decisions; and, as a result, the alliance portfolio acquires a multidimensional nature (Wassmer, 2010; Wassmer and Dussauge, 2011). Moreover, for the sake of understanding its multidimensionality, the study of APs is linked to the study of strategic networks. Thus, portfolio size, the diversity of the firm’s partners, the existence of structural holes in the network, the intensity of the links with partners and their characteristics may all be considered among its dimensions or parameters (Hoffmann, 2007). The way a firm´s AP is organized is linked to the choice of partners and, therefore, it is a widely covered theme from different points of view in the literature on strategic alliances. The AP network characteristics affects the access to network resources; hence, it also affects performance. Hoffmann (2007: 834) affirmed that the configurations of APs “determine the quality, quantity, and diversity of information and resources to which the focal company has access”. Accordingly, there is evidence that access to partner resources has positive effects on performance (Castro et al., 2014; Lavie, 2007; Todeva and Knoke, 2005); hence, the importance of the network resources that a firm can access through its ties (Gulati et al., 2000), which make up the network resources of the focal firm (Lavie, 2007). However, not all access to partner resources is ultimately mobilized by the firm. The extant literature on AP and resource access usually neglects that the actual level of network resource mobilization (NRM) will also affect performance. Therefore, the relation between a prominent AP and performance will be conditioned by the level of resource mobilization that the firm really achieves (Casanueva et al., 2014). In spite of this, previous literature on APs has not covered the separation between access to and mobilization of network resources (Batjargal, 2003); however, this separation is justified, provided that resource mobilization is indeed a strategic choice of the organization enabled by its operational capabilities to grasp the opportunities granted by the partners´ resources (Wang and Rajagopalan, 2015). A question therefore arises over the interrelation between the following aspects: (1) access to partner resources derived from the composition of the alliance portfolio network parameters; (2) the level of mobilization of those resources that the focal firms achieves, and (3) its performance. Until now it has been argued that a higher level of resource mobilization has a positive impact in firm performance (Capaldo et al., 2015; Thornton et al., 2019); however, we question whether a threshold exists and to what 4 extent more intensity of resource mobilization implies increased profitability given different sets of alliance portfolios. The objective of this work is to gain insight into how the resource mobilization choices of the organizations affect companies' performance as compared to just owning the possibility to access such resources, and to what extent the level of NRM interacts with key parameters of the AP to influence firms’ performance. The significance of solving these issue for managerial practice lies in determining how partnerships provide resources; but more importantly how the mobilization of such resources may be seized to enhance revenue streams, and to what extent the interaction between the mobilization strategy and the AP parameters are expected to influence firms’ financial outcomes (Wang and Rajagopalan, 2015; Wassmer, 2010). In response to this objective, access and mobilization of an important partner resource in the airline business at an international level have been studied: the partners’ city-pair destination markets covered that the focal firm can exploit through code-sharing agreements. We study the indicators of a network formed by 1,117 code share agreements and tested their influence on financial outcomes by means of a sequential multiple regression model to capture main effects, curvilinearity, and interaction between AP characteristics and NRM. The results show how AP network parameters and NRM have heterogeneous effects on firm performance; hence, the main contribution of this work is that it clearly presents the threshold of NRM in aiding firm performance, as well as the moderation roles exerted with the AP effect. 2 Theory and hypotheses Traditionally, organizational behavior has been studied by looking at the company as an autonomous body that seeks to grow its bundle of resources and meet the expectations of its stakeholders (Barney, 1986; Casson et al., 2016; Chen, 1996; Porter, 2000). This resource-based view emphasizes that firms perform better to the extent that they build ownership advantages by means of controlling greater resources which are valuable, unique and hard to imitate (Barney, 1991). This perspective has been extended by the relational view, which stresses that firms not necessarily need to own such resources, but they may access them by means of relationships with a network of allies (Gulati et al., 2000; Lavie, 2006; Zaheer et al., 2010). These contributions have led to the construction of multiple types of alliance portfolios (Castiglioni and Galán González, 2020) as a result of firms’ endeavour in establishing relationships with a set of partners in pursuit for relational rents (Dyer and Singh, 1998). The ubiquitous implementation of this strategy has led to consider the network in itself as a valuable and inimitable resource, since each firm acquires a unique number of relationships, with distinctive degrees of trust, complementarity and interdependencies with its partners (Gulati, 1999; Lavie, 2006). Like any type of resource, an AP is acquired and grown; in this case, by means of alliance development capability, managerial networking, and nurturing relationships by deploying relational assets (Kale and Singh, 2009; Wassmer, 2010). Once developed and available for utilization, the alliance portfolio takes the shape of an interorganizational resource network 5 which influences firm performance through its multidimensional features (Castro and Roldán, 2015; Wassmer, 2010). Specifically, resource access as a theoretical mechanism is able to explain how firms’ egocentric network centrality and position provides information, learning and capabilities to influence their performance (Zaheer et al., 2010). In turn, four key parameters of the AP network, namely the size of the alliance portfolio and its internal structure, the diversity of partners, and the intensity of the relations, basically determine the access to network resources (Hoffmann, 2007). In this sense, Hoffman (2007) considered that the multidimensional elements of the focal firm’s AP determines the quality, quantity and diversity of the resources to which it has access, as well as the efficiency in accessing those resources and the prominence of the focal firm in the interorganizational network. As a result, he proposed that four parameters determine the mechanisms through which alliance portfolio influences performance: the number of alliances, dispersion, redundancy, and the intensity or strength of the links. Each one of these parameters is straightforwardly measured by looking at the focal firm’s egocentric network of alliances (Wassmer, 2010; Zaheer et al., 2010). The first parameter, the number of alliances or portfolio size, will condition the volume of information and the amount of resources which the focal firm can access (Koka and Prescott, 2002). Indeed, one of the main reasons firms choose to expand their number of alliances is precisely to gain access to assets, knowledge or skills for market-seeking objectives or efficiency-seeking purposes (Gulati, 1998; Pangarkar et al., 2017). The second parameter refers to the diversity of partners, defined as the degree of partners category variation in relation to their resources, capabilities, knowledge and technological foundations. Previous investigations have pointed to the importance of establishing alliances with non-similar partners, particularly in global markets (Changming and Ronghua, 2010; Goerzen and Beamish, 2005). However, Collins and Riley (2013) expose that there is conflicting evidence of the portfolio diversity effect on firm performance, which is moderated by different types of business environments. Thus, they hypothesize that there is an inverted U-shape relationship between this parameter and portfolio performance. However, Jiang, Tao and Santoro (2010) have differentiated between types of diversity, suggesting that organizational and functional diversity results in positive outcomes, while governance diversity results in negative outcomes. In our research we study partners (organizational) diversity, all in the context of the same type of governance mechanism that is code-share agreements. In addition, research in this business setting suggest that there are benefits from resource complementarity; thus, providing evidence that a more diversified and balanced alliance portfolio generally produces a positive impact on firm performance (Collins and Riley, 2013; Duysters et al., 2012; Jiang et al., 2010) The third parameter refers to the structural holes presented by the ego net, which represent the redundancy of the connections and rarely have a uniform structure. For example, sparse networks with few redundancies, low constraint and many structural holes will imply asymmetry in access to information and other resources within the ego net (Burt, 2009; Rowley and Baum, 2004; Sapsed et al., 2007). Unlike the effect that redundancy has on innovation (Lahiri and Narayanan, 2013; Vanhaverbeke et al., 2009), an ego network that is rich in structural holes provides advantages of control, lower overlapping objectives 6 among allies, and better allocation of resources to favor better overall financial performance (Hoffmann, 2007). In the same line, firms that are less constrained by the partners density of connections will have higher non-redundant contacts and therefore obtain better market information (Koka and Prescott, 2002, 2008). Finally, the last parameter refers to the intensity of the links. In accordance with the concept of Granovetter on strong rather than weak links, these parameters of the alliance portfolio will condition characteristics and attributes such as trust and the quality of relations, which will to a large extent stem from the history of the company and its reputation (Granovetter, 1992). Accordingly, Das and Teng (2003, 2014) point out that interfirm trust constitutes a critical variable in partner satisfaction and alliance success. Therefore, a richer mutual history signals deeper connections and relations that facilitate trust and mitigate opportunistic behavior perceptions, which in turn allows firms alliance portfolio to succeed in its objectives (Collins and Riley, 2013; Gulati and Sytch, 2008). These four parameters make up the key mechanisms that drive firms’ alliances portfolio outcomes. So, the focal firm, through the establishment of cooperative agreements, will continue to develop a relational pattern that, if done with strategic intention, should benefit the ego by composing a high performance portfolio (Ozcan and Eisenhardt, 2009; Prashantham, 2011). Some researchers point out that the AP, with its multidimensional parameters, impacts in a positive way on the performance of firms (Castro et al., 2015). In addition, there is evidence that each of the parameters of the portfolio affects performance. Alliance portfolio size constitutes an important variable to explain performance differentials (Mouri et al., 2012; Wassmer and Dussauge, 2012). Having access to varied partners can be an important source of novel and diverse knowledge as well as other resources for the ego firm, which can increase its performance (Phelps, 2010; Vasudeva and Anand, 2011). A higher level of structural holes in the alliance portfolio, i.e. a less constrained egocentric network, allows the focal firm to enhance the performance of its alliance portfolio (Castro et al., 2015; Dyer et al., 2008). The strength or intensity of the nexus of contacts in the alliance portfolio will have a positive impact on the quality of the relations, which in turn contributes to the performance of the AP (Sarkar et al., 2008). Finally, in order to measure the performance of the focal firm to analyse how the alliance portfolio management influences it, we opted for global financial aspects (Koka and Prescott, 2008; Lavie, 2007; Wassmer and Dussauge, 2012). Therefore, H1: The alliance portfolio network parameters are directly related to its financial performance. H1a: The higher the alliance portfolio network size of the firm, the higher the financial performance. H1b: The higher the diversity of the alliance portfolio network, the higher the financial performance. H1c: The lower the constraint of the firm (more structural holes) in the alliance portfolio, the higher the financial performance. 7 H1d: The higher the intensity of links (strength of ties) in the alliance portfolio network, the higher the financial performance. Yet, not all the resources to which a firm has access through its relations are finally utilized or mobilized (Grant and Baden-Fuller, 2004). For instance, these resources may be dispersed through the firm’s different networks (Chou, 2016). Therefore, its level of access to resources may be considered a different construct to their level of mobilization; hence, it remains to be understood what the role of AP features is in the presence of measured NRM. For this reason, we explore NRM, understood as the organizational strategy to capitalize the opportunities granted by a company’s partners in the context of an alliance network (Casanueva et al., 2015; Gulati et al., 2011). It is worth noting that resource mobilization in the organizational context differs from the well-known and mature construct in the field of sociology, where it is understood as a way to trigger community movements and communitarian resource utilization to accomplish a social cause (Agostini et al., 2019; McCarthy and Zald, 2017). Whereas in the current stream of interorganizational studies the resource mobilization perspective is a rather neglected idea, with just a few studies addressing it beyond just network ties. The first studies to address resource mobilization started by identifying how the resources accessed and mobilized by small business owners’ social ties had an impact on performance (Zimmer and Aldrich, 1987). More recently, some interest has been shown in how mobilizing resources embedded in a network of connections (NRM) allows for: star-ups innovation leveraging (Capaldo et al., 2015), multinationals taking advantage of international alliances (Casanueva et al., 2014), firms bridging technological discontinuities of industries (Chou, 2016), and knowledge acquisition for problem solving in multinational enterprises (Parker et al., 2019), among other possibilities. In practice, in the realm of organizations, the level of NRM is subject to a double condition: voluntarism and capability. Voluntarism is a two-edged process, in which there must be, on one side, a focal firm that is interested in using the resources that its partners possess and, on the other, a partner that is willing to concede the use of its own resources (Casanueva et al., 2014; Lin, 2003). Some authors have pointed to a series of reasons to understand this mutual willingness, for both economic and social motives (Huggins and Johnston, 2010), and for the power of negotiation (Kumar, 2010). Moreover, firms must deploy certain strategies to be able to mobilize the optimal level of network resources to which they potentially have access through their ties; that is, the composition of their alliance portfolio (Lavie, 2009; Wang and Rajagopalan, 2015). Accordingly, previous studies have provided evidence that it is not only the access to external resources what causes improved performance (Acquaah, 2007), but also their mobilization plays and important role (Batjargal, 2003); likewise, the skills that the firm owns to mobilize network resources are linked to its performance (Kauppila, 2015). The rationale is that the firm gets to capitalize the use of complementary resources without the significant financial, marketing and operational effort of acquiring them and putting them into function (Cobeña et al., 2016; Gulati, 1999). Furthermore, NRM would allow the firm to capture value by implementing economies of scale and scope at the network level, sharing risks with the partner and increasing operational flexibility (Lavie, 2009). Consequently, the relationship 8 between resource mobilization and performance have been usually hypothesized as positive and linear (Capaldo et al., 2015; Chou, 2016). However, this relationship is expected to have its limitations, in view that most managerial thought currents point to proprietary resources and control as a source of competitive advantage (Prahalad and Hamel, 1990; Zhao et al., 2004). For instance, the resource-based view is wary of collaborative orientation due to risks such as deferred control (Das and Teng, 2014), exposure to opportunistic behavior (Hill, 1990; Park and Ungson, 2001), or loss of customer focus (Luo et al., 2007), thus prioritizing firms’ independent action. In addition, each mobilization attempt requires further efforts in market research, promotional marketing, customer service, operations and relations with suppliers. In consequence, a strategic tension arises between internal value adding and external value capture; so that the firm must decide whether to limit itself to the exploitation of its current set of resources or to reach external means for its strategic objectives. We theorize that the positive impact of NRM on performance is expected to decline at a certain point because of two main reasons: deferred control and loss of market orientation. Firstly, deferred control refers to the fact that by offering services that are performed by third parties, the focal firm is exposed to constraining situations such as dependence on the quality of the partners’ services or limited availability of the resources (Czakon et al., 2019). Secondly, loss of market orientation refers to the difficulty of remotely coordinating the routine activities that the firm usually engages in serving its customer base, from market research and targeting, to operations and customer service. In this sense, when a high percentage of the firm’s resources comes from mobilized partners resources, the lower possibility to control and focus on the operation undercuts the capability to produce higher returns. In consequence, very high levels of NRM may signal that the firm is becoming dependent of its partners to achieve certain objectives; the firm may also create gaps between the market needs and its own capabilities by relying in a comfort zone (Dorn et al., 2016); hence, a balance is needed to allow for performance control (Lado et al., 1997; Luo, 2008). This balance is justified since organizations that isolate themselves may face constrained growth, ignore opportunities and waste complementarities by neglecting partnerships; in contrast, organizations that focus mostly on third party resource exploitation expose themselves to external decision-making processes and constraints that influence their performance (Kauppila, 2015; Rothaermel and Alexandre, 2009). H2: The level of network resource mobilization has an inverted U-shape relationship to financial performance. We have shown that access is a necessary, but not sufficient, condition for the mobilization of network resources (Kauppila, 2015), there being an inter-connection between the AP parameters and the NRM. Studies by Lavie (2006, 2009) distinguish between value creation strategies and value capture strategies; where value creation means that firms establish partnerships to pursue collective objectives to obtain the possibility to 15 (39.13) NRM x Constraint 3.036 (4.421) NRM^2 x Constraint - 214.7*** (98.03) Constant 1.189** 2.527*** 3.234*** 2.768*** (0.708) (0.772) (0.776) (0.781) R-squared 0.629 0.760 0.787 0.817 Standard errors in parentheses *** p<0.05, ** p<0.1, * p<0.15 6.2 NRM threshold We calculate the variability of NRM to build the graphs as presented in Figure 2. We use Models 3 and 4 in which this variable is contrasted with the pivotal AP characteristics in order to illustrate the relationship between NRM and performance. In model 3 the regression analysis shows that revenue has a significant and positive relationship to network resource mobilization (NRM: b = 4.068, pval = 0.005), but a negative and significant relationship with its quadratic term (NRM2: b = -5.523, pval = 0.014); thus, describing an inverted U-shape relationship. In this scenario. A high level of NRM allows a positive and stable revenue increase up to a threshold of 30% of partners’ resource mobilized; however, the relationship is negative to greater levels. After introducing the interactions in model 4, both NRM and NRM2 remain significative as well as the interaction terms with diversity and constraint. Thus, a high level of NRM allows for a positive and stable revenue increase up to a threshold of 16%. 6.3 AP network parameters relevance In general, save for intensity, the AP variables exhibited the expected signs. In the AP model 2, Portfolio size shows a positive and significative small effect on performance (b= 0.032, pval = 0.013), however this effect is less significant as we account for NRM (Model 3: b= 0.022, pval = 0.086); thus, increasing the number of alliances may not necessarily be accompanied by an increase in performance. As for portfolio intensity or strong ties, it has a small, negative, and non-significative effect on firm performance in the sample; these results hold across estimations. In relation to portfolio diversity, the effect of this parameter is positive and significant in the AP effects estimation (Model 2: pval = 0.047) but has a higher p-value (0.089) when estimating a model that accounts for NRM, still signaling the appropriateness of more diverse partnerships for performance objectives. The final focal variable, structural holes in terms of constraint has the expected negative sign, so higher constraint reduces the possibility of better revenue; the effect is significant in the AP estimation (Model 2: b = -0.626, pval = 0.019); yet, it loses significance when considering NRM in parallel (Model 3: b = -0.400, pval = 0.128). 16 6.4 AP parameters and NRM interaction The introduction of the interaction terms between the three significant AP network parameters in Model 2 with NRM and NRM2, improved the overall model fit. The sign of the interaction with the square terms, as expected, is negative for all AP parameters; however, only constraint x NRM2 is significant at the 5% level (pval =0.032), diversity x NRM2 is significant at the 10% level (pval = 0.100), and portfolio size x NRM2 is not significant (pval = 0.546). None of the AP parameter x NRM terms are significant; still the term is considered in the overall effect estimation. The sign of the coefficients is as expected for portfolio size and diversity, but it is not so for constraint. In the case of portfolio size and diversity, the interactions with NRM show that as NRM grows, the slope of these two AP parameter against revenue becomes steeper. This also means that at higher levels of each of the two parameters, the NRM has a steeper slope while at low and median levels, it has a flatter slope, as shown in Figure 2. On the other hand, the interaction between constraint and NRM shows the same patter as the portfolio size and diversity, which was not expected, since Model 2 and Model 3 hinted a negative relationship. This means that the relationship between constraint and revenue performance is more nuanced in the sense that its negative effect on performance at low NRM levels is mitigated when the firm has higher resource mobilization. Similarly, near the turning point of NRM it actually helps to have high constraint/few structural holes. This observed in Figure 2.c, where at higher NRM, the constraint-revenue curve changes slope. Figure 2. AP network parameters and NRM interaction a. Marginal effect at P. Size and NRM levels b. Marginal effects at Diversity and NRM levels 17 c. Marginal effects at constraint and NRM levels 7 Findings In Table 2 and Figure 2 we have reported the different analysis conducted. In relation to Hypothesis 1, our analysis suggests that some of the parameters of the firms’ alliance portfolio network play a role in reaching their performance goals; especially alliance portfolio diversity (H1b) and constraint (H1c) were found to have a significant effect on firm revenue. Still, while the effect of diversity of the alliance portfolio is highly relevant, i.e., on average, companies that had more diverse partners in their portfolio network were prone to obtain better results; its full effect is contingent on the NRM. This is true also for the structural holes variable. On the other hand, the data shows no evidence that portfolio size (H1a) by itself exerts an important effect on performance when NRM is accounted for. Similarly, there is no evidence to consider intensity of links (H1d) as a determinant of firm performance. In addition, Hypothesis 2 states that the level of network resource mobilization has an inverted U-shape relationship to company performance and our research found enough evidence to confirm the relationship between the NRM level and firm performance; thus disclosing its inverted U-shape nature. So, the effort of enhancing the mobilization to which the company has access from the median 11% pays increasing benefits in financial performance until reaching boundaries set at a mean level of 16% when all interactions are accounted for. In consequence, we found that it is more important for performance to have a considerable resource mobilization capability when aiming to have a better revenue performance than just having plenty of alliances; even though previous work declares the importance of having a high centrality in the overall alliances network (Casanueva et al., 2014); and that such centrality is sometimes achieved via expansion of the firm’s portfolio. Finally, Hypothesis 3 states that the relation between the AP parameters and financial performance is moderated by the firm’s level of network resource mobilization. Our findings suggest that indeed, a significant interaction exist between two AP network 18 features and NRM; which reveals the importance of being able to seize opportunities from the AP, instead of just building the alliance network. In particular, firms seize their portfolio diversity to the extent that NRM is optimized, while at low levels of NRM, portfolio diversity has a near null effect. On the other hand, NRM seems to revert the consequences of the lack of structural holes. This way, less constraint, meaning less redundancy, is detrimental for performance when the firm mobilize little network resources and acts positively when the firm is an optimum mobilizer. In essence, in order to obtain the optimal benefits from having a bigger, diversified and less constrained alliance portfolio, firms must develop an optimal mobilization of network resources. 8 Discussion and Conclusions The objective of this work was to gain insight into how the resource mobilization capabilities of the organizations affects companies' performance, and to what extent it plays a moderating role in the relationship between alliance portfolio parameters and financial performance. We posit that a set of four key AP network parameters permit the focal firm to access valuable network resources, and along with the internal capability to mobilize these resources, it is set to increase the financial performance of the airlines. We used a multiple regression moderation analysis to achieve this objective. The results show that, indeed, network resource mobilization capability moderates the effect of two AP parameters on financial performance of the airline industry. 8.1 Theoretical implications The principal theoretical contributions of this work are twofold. First, the standard formulation of the relational view, borrowing from the resource-based view, is that firms must develop an alliance portfolio that generates value through the resources made available by a network of partners (Zaheer et al., 2010). Hence, the larger the pool of resources available, the easiest for the firm to achieve its objectives (Barney, 1991; Lavie, 2006). Furthermore, previous research at the theoretical level had pointed to a more nuanced AP with multidimensional characteristics that influence firm performance (Hoffman, 2007). However, a simultaneous empirical analysis of various alliance portfolio parameters that captures resource access has been neglected so far (Wassmer, 2010). Based on the extant conceptualization, our research has empirically analysed APs and how their different parameters affect financial performance in the presence of a resource mobilization strategy. In this sense, we contribute to divert the inflated focus existent on the number of alliances or portfolio size as determinant of firms performance; we show that, instead, it is mobilization level that plays a role in financial performance gains and not the number of alliances stablished alone. In addition, we show that for an AP to be effective, it needs to be accompanied by an optimum mobilization strategy. Particularly, the diversity of the alliance portfolio arose as one of the AP network parameters that most affect organizational performance (Goerzen and Beamish, 2005); but the biggest gains in performance are obtained when portfolio diversity is done by better mobilizers. This is also true for network constraint, which for below-average network mobilizers implies negative performance outcomes but improves performance for more strategic mobilizer firms. 19 A second theoretical contribution conveyed in this work resides in introducing the analysis of the curvilinear relations between the level of network resource mobilization (NRM) and firm performance, as well as its role in contributing to alliance portfolio literature. Wang and Rajagopalan (2015: 251) pointed that in the post-formation phase of alliances, partners have the opportunity “to create value by leveraging complementary assets and learning from each other”. In this sense, the extant literature has analyzed some features that may facilitate the value creation in this phase, such as the coordination capability (Schilke and Goerzen, 2010), communication (Schreiner et al., 2009), or interfirm learning (Simonin, 1997). However, few studies have focused on the mobilization of partners’ resources, which is an underlying strategic choice as depicted in our variable level of network resource mobilization (NRM). Thus, Wang and Rajagopalan (2015) asserts that the effects of this strategy may vary depending on the relative resource dependency over the partners’ resources; and subsequently, our research reveals that the NRM level has an inverted U-shape relationship to company performance. The rationale is that when the focal actor excessively increases its level of resource mobilization, this leads to diminishing marginal returns, by increasing in resource dependency with respect to external alliance partners, at the time that the focal firm defers control of its operations and dilutes its market focus. This same rationale is extended to the results obtained from analyzing NRM effects as a moderator of the set of AP parameters. 8.2 Managerial implications From a practical point of view, these findings suggest the need for network resource mobilization to form part of Alliance Portfolio Management Capability (Sarkar et al., 2008; Wang and Rajagopalan, 2015), in order to generate awareness of the nuances of a resource mobilization strategy (Casanueva et al., 2014). The foundations of such capability are prior experience in alliances, and the management of partner network relations within the portfolio (Schilke and Goerzen, 2010). On the other hand, it is necessary to clearly identify which partner resources are strategic and/or complementary for the firm, which partners possess them to a greater extent and which of them are willing to permit the focal firm to use them to its own benefit. The need is also proposed for an acceptable characterization of the resources of the focal firm. Doing so would mean that their mobilization may contribute to performance to a greater extent, as the characteristics of such disparate resources like those linked to knowledge as opposed to other more tangible or market-related ones will condition the decision-making by the firms that are involved. This means that acquiring superior strategic knowledge in network resource mobilization will allow the focal actor to extract value from its alliance portfolio through the optimal exploitation of complementary assets (Lavie, 2009; Wang and Rajagopalan, 2015). Furthermore, our research has made clear that managers in charge of alliance portfolio management within the firms must evaluate the extent to which network resource mobilization is affecting performance, provided that some operational and managerial efforts may be carried out in this process without obtaining the expected returns. 8.3 Limitations and future lines of enquiry This work presents certain limitations; on the one hand, the analysis of the interactions between access (because of the AP network), resource mobilization and performance could be subjected to a dynamic and process-related analysis that has only been covered here at a 20 particular point in time. On the other, the consideration of a single network attribute (airline destinations), despite its relevance to the industry, limits the analysis of the global effect of the AP network parameters in performance. Future lines of research will attempt to gain a deeper theoretical understanding of what network resource mobilization capability means and how it should be managed. The relations studied in this work will include a dynamic and process-related component, by extending this type of analysis to other industries and to a larger number of relevant resources (which will also include intangible knowledge-based resources). Although previous studies have identified that AP parameters such as portfolio size (Cobeña et al., 2016; Wassmer and Dussauge, 2012) or strong ties (Han, 2006; Wu, 2008) have a relevance to firms’ performance, this is not necessarily true for the Airline business when accounting for resource mobilization capabilities. Hence, we have enough evidence to assume that alliance portfolio research must not ignore the organizational attributes concerning mobilization when studying its impact on performance. 21 9 References Acquaah, M. (2007), “Managerial social capital, strategic orientation, and organizational performance in an emerging economy”, Strategic Management Journal, Vol. 28 No. 12, pp. 1235–1255. Agostini, L., Nosella, A. and Teshome, M.B. (2019), “Inter-organizational relationships: toward a reconceptualization of constructs”, Baltic Journal of Management, Vol. 14 No. 3, pp. 346–369. Aiken, L.S., West, S.G. and Reno, R.R. (1991), Multiple Regression: Testing and Interpreting Interactions, SAGE Publications Inc, Thousand Oaks. Barney, J.B. (1986), “Types of competition and the theory of strategy: Toward an integrative framework”, Academy of Management Review, Academy of Management, Vol. 11 No. 4, pp. 791–800. Barney, J.B. (1991), “Firm Resources and Sustained Competitive Advantage”, Journal of Management, Vol. 17 No. 1, pp. 99–120. Batjargal, B. (2003), “Social capital and entrepreneurial performance in Russia: A longitudinal study”, Organization Studies, Vol. 24 No. 4, pp. 535–556. Borgatti, S.P. (1997), “Structural holes: Unpacking Burt’s redundancy measures”, Connections. Burt, R.S. (2009), Structural Holes: The Social Structure of Competition, Harvard University Press, Cambridge, available at:https://doi.org/10.2307/3322456. Capaldo, G., Fontes, M., Cannavacciuolo, L., Rippa, P. and Sousa, C. (2015), “Networks Mobilized to Access Key Resources at Early Stages of Biotech Firms: A Comparative Analysis in Two Moderately Innovative Countries”, European Planning Studies, Taylor & Francis, Vol. 23 No. 7, pp. 1381–1400. Casanueva, C., Gallego, A., Castro, I. and Sancho, M. (2014), “Airline alliances: Mobilizing network resources”, Tourism Management, Vol. 44, pp. 88–98. Casanueva, C., Gallego, A. and Revilla, M.A. (2015), “Access and mobilization of network resources and competitive advantage in hotels: A Conceptual Framework”, International Journal of Contemporary Hospitality Management, Vol. 27 No. 6, pp. 1279– 1300. Casson, M., Porter, L. and Wadeson, N. (2016), “Internalization theory: An unfinished agenda”, International Business Review, Vol. 25 No. 6, pp. 1223–1234. Castiglioni, M. and Galán González, J.L. (2020), “Alliance portfolio classification. Which portfolio do you have?”, Baltic Journal of Management, Vol. 15 No. 5, pp. 757–774. Castro, I., Casanueva, C. and Galán, J.L. (2014), “Dynamic evolution of alliance portfolios”, European Management Journal, Vol. 32 No. 3, pp. 423–433. Castro, I. and Roldán, J.L. (2015), “Alliance portfolio management: Dimensions and performance”, European Management Review, Vol. 12 No. 2, pp. 63–81. Castro, I., Roldán, J.L. and Acedo, F.J. (2015), “The dimensions of alliance portfolio 22 configuration: A mediation model”, Journal of Management and Organization, Vol. 21 No. 2, pp. 176–202. Changming, Y. and Ronghua, J. (2010), “Formation Mechanism and Evolution Character of Industrial Cluster Based on Resources View”, Ninth Wuhan International Conference on E-Business, Vols I-Iii, pp. 541–549. Chen, M.-J. (1996), “Competitor analysis and interfirm rivalry: Toward a theoretical integration”, Academy of Management Review, Vol. 21 No. 1, pp. 100–134. Chou, H.H. (2016), “Mobilising resources to bridge technological discontinuities”, Journal of Business and Industrial Marketing, Vol. 31 No. 6, pp. 784–793. Cobeña, M., Gallego, Á. and Casanueva, C. (2016), “Heterogeneity, diversity and complementarity in alliance portfolios”, European Management Journal, Vol. 12 No. 5, pp. 1–13. Collins, J. and Riley, J. (2013), “Alliance Portfolio Diversity and Firm Performance: Examining moderators”, Journal of Business and Management, Vol. 19 No. 2, pp. 35–49. Czakon, W., Klimas, P. and Mariani, M. (2019), “Behavioral antecedents of coopetition: A synthesis and measurement scale”, Long Range Planning, Elsevier, No. March, pp. 0–1. Das, T.K. and Teng, B. (2003), “Partner analysis and alliance performance”, Scandinavian Journal of Management, Vol. 19 No. 3, pp. 279–308. Das, T.K. and Teng, B. (2014), “Between Trust And Control : Developing Confindence In Partner Cooperation in Alliances”, Academy of Management Review, Vol. 23 No. 3, pp. 491–512. Doganis, R. (2006), The Airline Business, edited by Doganis, R., 2nd ed., Routledge, London. Dorn, S., Schweiger, B. and Albers, S. (2016), “Levels, phases and themes of coopetition: A systematic literature review and research agenda”, European Management Journal, Vol. 34 No. 5, pp. 484–500. Duysters, G., Heimeriks, K.H., Lokshin, B., Meijer, E. and Sabidussi, A. (2012), “Do firms learn to manage alliance portfolio diversity? The diversity-Performance relationship and the moderating effects of experience and capability”, European Management Review, Vol. 9 No. 3, pp. 139–152. Dyer, J.H. and Singh, H. (1998), “The Relational View: Cooperative Strategy and Sources of Interorganizational Competitive Advantage”, The Academy of Management Review, Vol. 23 No. 4, pp. 660–679. Dyer, J.H., Singh, H. and Kale, P. (2008), “Splitting the pie: Rent distribution in alliances and networks”, Managerial and Decision Economics, Vol. 29 No. 2–3, pp. 137–148. Gimeno, J. (2004), “Competition within and between networks: The contingent effect of competitive embeddness on alliance formation”, Academy of Management Journal, Vol. 47 No. 6, pp. 820–842. Goerzen, A. and Beamish, P.W. (2005), “The effect of alliance network diversity on multinational enterprise performance”, Strategic Management Journal, Vol. 26 No. 4, 23 pp. 333–354. Granovetter, M. (1992), “Problems of explanation in economic sociology”, Networks and Organizations: Structure, Form and Action, Harvard Business School Press, Boston, pp. 25–56. Grant, R.M. and Baden-Fuller, C. (2004), “A Knowledge Accessing Theory of Strategic Alliances”, Journal of Management Studies, Vol. 41 No. 1, pp. 61–84. Gulati, R. (1998), “Alliances and networks”, Strategic Management Journal, John Wiley & Sons, Ltd., Vol. 19 No. 4, pp. 293–317. Gulati, R. (1999), “Network Location and Learning: The Influence of Network Resources and Firm Capabilities on Alliance Formation”, Strategic Management Journal, Vol. 20 No. 5, pp. 397–420. Gulati, R., Lavie, D. and Madhavan, R. (2011), “How do networks matter? The performance effects of interorganizational networks”, Research in Organizational Behavior, Vol. 31, pp. 207–224. Gulati, R., Nohria, N. and Zaheer, A. (2000), “Strategic Networks”, Strategic Management Journal, Vol. 21 No. 3, p. 203. Gulati, R. and Sytch, M. (2008), “Does familiarity breed trust? Revisiting the antecedents of trust”, Managerial and Decision Economics, Vol. 29 No. 2–3, pp. 165–190. Han, M. (2006), “Developing social capital to achieve superior internationalization: A conceptual model”, Journal of International Entrepreneurship, Vol. 4 No. 2–3, pp. 99– 112. Hanneman, R.A. and Riddle, M. (2005), “Introduction to social network methods”, University of California Riverside. Hill, C.W.L. (1990), “Cooperation, Opportunism, and the Invisible Hand: Implications for Transaction Cost Theory.”, Academy of Management Review, Vol. 15 No. 3, pp. 500–513. Hoffmann, W.H. (2007), “Strategies for managing a portfolio of alliances”, Strategic Management Journal, available at:https://doi.org/10.1002/smj.607. Huggins, R. and Johnston, A. (2010), “Knowledge flow and inter-firm networks: The influence of network resources, spatial proximity and firm size”, Entrepreneurship and Regional Development, Vol. 22 No. 5, pp. 457–484. Jiang, R.J., Tao, Q.T. and Santoro, M.D. (2010), “Alliance portfolio diversity and firm performance”, Strategic Management Journal, Vol. 31 No. 10, pp. 1136–1144. Kale, P. and Singh, H. (2009), “Managing strategic alliances: What do we know now, and where do we go from here?”, The Academy of Management Perspectives, Vol. 23 No. 3, pp. 45–62. Kauppila, O.P. (2015), “Alliance Management Capability and Firm Performance: Using Resource-based Theory to Look Inside the Process Black Box”, Long Range Planning, Elsevier Ltd, Vol. 48 No. 3, pp. 151–167. Koka, B.R. and Prescott, J.E. (2002), “Strategic alliances as social capital: A multidimensional 24 view”, Strategic Management Journal, Vol. 23 No. 9, pp. 795–816. Koka, B.R. and Prescott, J.E. (2008), “Designing alliance networks: The influence of network position, environmental change, and strategy on firm performance”, Strategic Management Journal, Vol. 29 No. 6, pp. 639–661. Kumar, M. V. (2010), “Differential gains between partners in joint ventures: Role of resource appropriation and private benefits”, Organization Science, Vol. 21 No. 1, pp. 232–248. Lado, A.A., Boyd, N.G. and Hanlon, S.C. (1997), “Competition, Cooperation, and the Search for Economic Rents: A Syncretic Model”, Academy of Management Review, Academy of Management, Vol. 22 No. 1, pp. 110–141. Lahiri, N. and Narayanan, S. (2013), “Vertical integration, innovation, and alliance portfolio size: Implications for firm performance”, Strategic Management Journal, Vol. 34 No. 9, pp. 1042–1064. Lavie, D. (2006), “The competitive advantage of interconnected firms: An extension of the resource-based view”, Academy of Management Review, Vol. 31 No. 3, pp. 638–658. Lavie, D. (2007), “Alliance portfolios and firm performance: A study of value creation and appropriation in the U.S. software industry”, Strategic Management Journal, available at:https://doi.org/10.1002/smj.637. Lavie, D. (2009), “Capturing Value from Alliance Portfolios”, Organizational Dynamics, Vol. 38 No. 1, pp. 26–36. Lazzarini, S.G. (2007), “The impact of membership in competing alliance constellations: Evidence on the operational performance of global airlines”, Strategic Management Journal, Vol. 28 No. 4, pp. 345–367. Lin, N. (2003), Social Capital: A Theory of Social Structure and Action, edited by Granovetter, M., Cambridge university press, Cambridge. Luo, X., Rindfleisch, A. and Tse, D. (2007), “Working with Rivals: The Impact of Competitor Alliances on Financial Performance”, Journal of Marketing Research, Vol. 44 No. 1, pp. 73–83. Luo, Y. (2008), “Structuring interorganizational cooperation: the role of economic integration in strategic alliances”, Strategic Management Journal, Vol. 29 No. 6, pp. 617– 637. McCarthy, J.D. and Zald, M.N. (2017), “Resource mobilization and social movements: A partial theory”, Social Movements in an Organizational Society: Collected Essays, Vol. 82 No. 6, pp. 15–46. Min, J. and Mitsuhashi, H. (2012), “Dynamics of Unclosed Triangles in Alliance Networks: Disappearance of Brokerage Positions and Performance Consequences”, Journal of Management Studies, Vol. 49 No. 6, pp. 1078–1108. Mouri, N., Sarkar, M.B. and Frye, M. (2012), “Alliance portfolios and shareholder value in post-IPO firms: The moderating roles of portfolio structure and firm-level uncertainty”, Journal of Business Venturing, Vol. 27 No. 3, pp. 355–371.