Networking Capabilities on Organizational Networking in Entrepreneurial SMEs
Leila Marcano, Universidad de Puerto Rico, Mayagüez Campus
Maribel Ortiz, Juan Carlos Sosa Varela and Francisco Montalvo Fiol,
Universidad Ana G. Méndez, Gurabo, Puerto Rico
Abstract
Purpose – This study explores the relationship between top management network capabilities,
organizational networking capabilities, and performance in the context of Small and Medium-sized
Enterprises (SMEs) in Puerto Rico. This study develops a typology of network capabilities and
analyzes their effects to provide a deeper understanding of the role of top management in a firm's
networking behavior.
Design/methodology/approach – This study tested the proposed model using data collected from
SMEs in Puerto Rico. The focal constructs of the proposed model were operationalized using
multi-item reflective indicators on a five-point Likert-type scale, and PLS-SEM was used to test
the model. The results showed that all the measures of the reflectively measured constructs were
reliable and valid, and the proposed model was supported.
Findings – The study finds that high-level management networking capabilities tend to be more
reactive than proactive, and that the reactive dimension of networking significantly influences
information acquisition, opportunity enabling, and weak-tie resource mobilization. This study also
highlights the importance of understanding the interplay between potential absorptive capacity
(PAC) and realized absorptive capacity (RAC) for SMEs to effectively leverage their unique
resources and competencies and gain competitive advantage.
Research limitations/implications: The theoretical implications of Dynamic Capabilities theory
suggest that networking capabilities are a critical aspect of organizational capability that
contributes to absorptive capacity development. This study emphasizes the need for SMEs to
engage in continuous learning and adapt their networking capabilities to remain competitive.
Moreover, the study validates the existence of two dimensions of networking within an
organization, proactive and reactive, emphasizing the importance of dynamic capabilities in
managing networking activities. Leaders must decide which dimension to implement to bring
competitive and strategic advantages to the organization.
Practical Implications: Managers must consider several key considerations to effectively manage
organizational networking, such as developing networking skills; communicating with
stakeholders; building and sustaining social networks; utilizing both reactive and proactive
networking approaches; and creating a clear strategy, plan, and architecture that aligns with the
goals of the organization.
Originality/value: This study validates the existence of two dimensions of networking within an
organization: proactive and reactive, emphasizing the importance of dynamic capabilities in
managing networking activities. Additionally, the study highlights the interplay between potential
absorptive capacity (PAC) and realized absorptive capacity (RAC) and emphasizes the need for
firms to focus on leveraging their unique resources and competencies effectively. Finally, the study
provides valuable insights into Dynamic Capabilities theory, emphasizing the importance of
networking capabilities, absorptive capacity, organizational environment, and social trust
mechanisms.
Keywords – Networking capabilities, organizational networking, absorption capabilities, SMEs,
innovation
Paper type – Research Paper
1.0 Introduction
In today's rapidly changing business environment, organizations must adopt innovative and
adaptable business models to secure their future. Collaboration in the form of joint ventures,
partnerships, and alliances within a network has become increasingly critical for competitiveness
and performance (Adomako, Danso, Boso, & Narteh, 2018). Organizations must adopt appropriate
organizational forms to achieve a beneficial network position (Dyer & Singh, 1998; Mitrega et al.,
2012; Shan et al., 1994; Gulati, 1999).
Networks of business relationships offer firms the opportunity to access diverse ideas, worldviews,
and information, leading to the exploration of new business opportunities and efficient resource
mobilization (Aldrich & Zimmer, 1986; Thornton et al., 2014). However, research on business-tobusiness (B2B) marketing relationships in small and medium enterprises (SMEs) is limited, with
most networking research focusing on larger companies (Sharafizad & Brown, 2020). Despite this,
SMEs often see collaborative networking and marketing as a means to drive innovation, and
therefore, must expand their resources, knowledge, and contacts to achieve this through external
interactions and support networks (Hinestroza et al., 2011; Garousi Mokhtarzadeh et al., 2020).
Understanding the processes and capabilities required for effective business relationships is critical
for success (Mitrega, Forkmann, Ramos, and Henneberg, 2012).
Enhancing SMEs' innovation capabilities through external resources such as knowledge,
information, and collaborations is a significant challenge. To benefit from these resources, SMEs
must possess strong absorptive capacity, which refers to a firm's ability to recognize and apply
new information to commercial ends (Cohen & Levinthal, 1990). Absorptive capacity is developed
through a culture that fosters experimentation and learning as well as processes and structures that
facilitate the acquisition and integration of new information (Zahra & George, 2002). Research
has shown that absorptive capacity positively affects performance and innovation capabilities
(Andersén, 2015; Fredrich et al., 2019).
Organizational networking capabilities are crucial to dynamic strategic capabilities (Arasti et al.,
2021). Coordination, organization, control, exchange, and steering capabilities are essential for
developing and managing networks in B2B relationships (Mokhtarzadeh et al., 2020). However,
networking capabilities remain an emerging research area (Mouzas, 2013; Thornton et al., 2013;
Arasti et al., 2021), and understanding the complex relationships between actors, such as individual
and organizational capabilities and resource bonds between companies, is critical for effective
decision-making (Håkansson & Ford, 2002; Ritter et al., 2004). Recent literature has emphasized
the importance of considering both micro and macro perspectives in evaluating the impact of
networking capabilities on performance (Fritsch & Kudic, 2021; Arasti et al., 2021; Harini &
Thomas, 2021), leading to new research lines focused on networking and absorptive capabilities.
The behavior of firms is shaped by their network structure, which enables dynamic capabilities
that allow firms to identify and seize opportunities (Rivera et al., 2010). SMEs face resource
limitations that make networking critical to their competitiveness (Liao and Welsch, 2002),
requiring them to develop effective support networks that align with recognized market
opportunities and address organizational limitations (Mitrega et al., 2017). Networking can
provide access to helpful information, support decision-making, and increase awareness of marketrelated issues (O'Donnell, Gilmore, Carson, & Cummins, 2002; Rocks, Gilmore, & Carson, 2005).
The relationship between social capital and dynamic capabilities has been a topic of interest to
researchers. Martin and Bachrach (2018) explore how the combination of key strategists'
managerial cognition with social capital influences the aggregation of micro- and macro-level
dynamic capabilities, leading to a set of activities and routines for seizing opportunities. Research
has highlighted the crucial role of managers in building dynamic capabilities, which are linked to
the quality of managerial decisions, strategic changes, and firm performance (Helfat and Martin,
2015; Martin and Bachrach, 2018). Furthermore, the organizational capacity for change and
maintaining superior performance has been identified as an important factor (Widianto et al.,
2021).
In industrial marketing, the capabilities necessary to create, develop, sustain, and end business
relationships have become a key research area (Forkmann et al., 2016; Mitrega et al., 2012, 2017;
Zaefarian et al., 2017). A model that outlines the relationship between organizational networking
capacity and company performance (Thornton et al., 2014) is crucial for small and medium-sized
enterprises (SMEs). Networks play a vital role in the development and diffusion of innovation as
they allow for the exchange of ideas in a co-creation process with other partners (LaPlaca, 2014).
However, the literature has not yet fully explored the influence of top management network
capabilities on organizational networking capabilities and performance.
This study contributes to the literature by examining the relationship between top management
network capabilities, organizational networking capabilities, and performance. By developing a
typology of network capabilities and analyzing their effects, this study provides a deeper
understanding of the role of top management in a firm's networking behavior. This research is
especially relevant to SMEs, which require effective support networks to address resource
constraints and capitalize on new market opportunities (Liao & Welsch, 2002; Mitrega et al.,
2017).
Firms can shape their networks through strong- or weak-tie relationships based on anticipated
business outcomes (Thornton et al., 2013), but the resulting interaction behaviors may not
necessarily lead to performance improvements. An organization must also be able to sense and
respond to network dynamics and absorb knowledge, transforming it into innovations that provide
a competitive advantage (Ford & Mouzas, 2013; Zahra & George, 2002). Effective networking
requires companies to leverage their relationships to support various business projects (Wang et
al. 2016). However, research on companies interacting within their networks is limited (Thornton
et al. 2014).
This study addresses the following questions: Do top managers’ networking capabilities affect
organizational networking capabilities? Do SMEs' organizational networking capabilities affect
their absorptive capabilities? To answer these questions, the networking capabilities of the top
managers of Puerto Rico SMEs and the organizational networking and absorptive capabilities of
their firms are studied.
2.0 Conceptual framework
2.1 Networking capabilities
Gemunden and Ritter (1997, 1999) introduced the concept of a capability-centered view of
networks and posited that the focal firm integrates the resources and capabilities of network
members to create and extract value based on its reputation, power, and centrality within the
network (Dhanaraj & Parkhe, 2006). There are a variety of definitions of networking capabilities
(NC) in the literature with varying meanings. For instance, Mitrega et al. (2012) define NC as a
complex organizational capability that manages business relationships and all key stages of the
relationship life cycle. Mu and Di Benedetto (2012) view NC as the competence a company must
acquire from its support network of colleagues or resources to establish, manage, and leverage
relationships that lead to value creation. Arasti et al. (2021) define NC as consisting of two
dimensions: network development capability (NDC) and network management capability (NMC).
In the context of SMEs, Mort and Weerawardena (2006) describe how small entrepreneurial firms
develop routines within their networks to configure and reconfigure their resources. They suggest
that owner-managers must develop such capabilities to enhance their businesses (Chell & Baines,
2000). Changes at the individual level can impact an organization's capacity and its
interorganizational network structure (Harini & Thomas, 2021). Networking is critical in enabling
companies to form partnerships for vertical and horizontal integration (Bengtsson and Kock, 2000;
Hadjikhani and Thilenius, 2005). The focus is on relational exchanges that prioritize cooperation
for mutual benefits (Anderson & Narus, 1991; Cardozo, Shipp, & Roering, 1992; Dunn & Thomas,
1994; Dyer & Singh, 1998; Morgan & Hunt, 1994). The concept of "value co-creation," involving
a collaborative and interactive process between suppliers and customers to create value, has gained
attention in recent years (Grönroos, 2012; Kowalkowski et al., 2012; Vargo Maglio & Akaka,
2008).
Relationship and network theorists have emphasized the significance of relationships in business
network contexts, arguing that both individual organizations and dyadic relationships contribute
to networks built on trust and commitment (Anderson, Håkansson, & Johanson, 1994; Becker,
2008; Håkansson & Snehota, 1995; Jain et al., 2014; Lindgreen, 2003; Morgan & Hunt, 1994;
Spekman et al., 1998). NC development is essential for shaping partner relationships and managing
relationship portfolios (Mitrega et al. 2012). Relational capabilities enhance and sustain
relationships through mutual trust, communication, and commitment (Ngugi, Johnsen, & Erdelyi,
2010). Networking ties provide information benefits, such as knowledge transfer and
communication efficiency (Walker, Kogut, & Shan, 1997; Rowley, 1997). Effective networking
requires companies to leverage relationships to support various business projects (Wang et al.,
2016).
Some firms adopt a proactive approach when interacting with their counterparts and actively seek
to establish interorganizational links (Chell & Baines, 2000). These firms reconstituted,
reconfigured, and extended their valuable networks, and developed new network ties to enhance
their dynamic networking capabilities (Eisenhardt & Martin, 2000; Mort & Weerawardena, 2006;
Teece, 2007). Prior studies have shown that a firm's market orientation (MO), comprising
proactive and responsive dimensions, affects its performance (Narver, Slater, & MacLachlan,
2004).
Chell and Baines (2000) identify four types of business owners: entrepreneurs, quasientrepreneurs, administrators, and caretakers. Entrepreneurs are proactive in their approach,
actively seeking opportunities and attempting to understand and satisfy customers' latent needs
(Narver, Slater, & MacLachlan, 2004). Quasi-entrepreneurs and administrators may have
entrepreneurial intentions, but their actions are inconsistent and weak. They may not persist in
pursuing opportunities, resulting in a reactive rather than proactive market orientation, attempting
to understand and satisfy customers’ expressed needs (Narver et al., 2004).
This passive approach may limit the potential growth of SME managers, who are classified as
quasi-entrepreneurs or administrators. Such managers may not actively seek new opportunities,
take the initiative to attend conferences, or coordinate meetings with potential partners, which can
hinder the development of strategic partnerships and opportunities for expansion. Hence, SME
managers need to be aware of their management style and strive to be proactive in achieving the
best business results.
Arasti et al. (2021) emphasize the significance of networking capability (NC) as one of the most
critical organizational capabilities that firms must understand and effectively manage. The authors
highlighted the importance of proactivity in shaping partner relationships and managing
relationship portfolios to enhance a firm's networking capabilities.
2.2 Organizational Networking Capabilities
Organizational networking refers to a firm's behaviors and activities aimed at capitalizing on its
network of business relationships, both directly and indirectly (Thornton et al., 2014). Thornton et
al. (2014) proposed a measurement model of organizational networking consisting of four firstorder reflective constructs: information acquisition, opportunity enabling, strong-tie resource
mobilization, and weak-tie resource mobilization.
Establishing support networks is a strategic approach companies use to understand and leverage
their network environment (Mouzas & Naudé, 2007). Companies can gain insight into untapped
opportunities and improve their reputations through interactions with their counterparts (Thornton
et al., 2013). According to Ford and Mouzas (2013), creating networks requires deliberate efforts
by one or more business actors to alter or develop certain aspects of their relationships with other
actors. Interactions, information exchanges, and resource mobilization within support networks
occur at the organizational level and are initiated by actors such as managers, directors, and senior
executives (Thornton et al., 2013).
Organizational networking encompasses a firm's behaviors and routines, which allow it to make
sense of and benefit from a network of business relationships (Thornton et al., 2014). Hagedoorn
et al. (2006) defined it as a firm's specific capabilities for forming relationships that position the
company within a network of partners. The four dimensions Thornton et al. (2014) proposed are
as follows.

Information Acquisition refers to companies’ actions to obtain the information required
for decision making. Companies may establish formal or informal processes for



identifying, accessing, and collecting relevant data and for designing, planning, organizing,
and executing actions that benefit business operations.
Opportunity Enabling refers to a company's actions to identify opportunities and enhance
its reputation through conscious interactions with relevant parties in its business network.
For SMEs, staying connected to the business ecosystem is crucial, enabling them to detect
attractive opportunities more efficiently and to become part of the processes that drive
growth.
Strong-tie Resource Mobilization refers to a company's actions to mobilize resources
linked to its direct or established relationships. Strong ties are characterized by emotional
intensity and intimacy, but they may limit the generation of new information and
perspectives essential for creating and exploiting business opportunities (Chell & Baines,
2000; Partanen et al., 2014). A results-oriented strategy should focus on identifying
resources related to the opportunity and establishing connections that effectively
collaborate in executing actions to address the opportunity.
Weak-tie Resource Mobilization refers to a company's actions to mobilize resources
linked to new, indirect, or weaker relationships (Kozan & Akdeniz, 2014). Weak ties may
be short-lived and infrequent, but they allow individuals to reach beyond their immediate
social circle and draw upon information, advice, and assistance from a large and diverse
pool (Chell & Baines, 2000). Identifying resources that are not necessarily strong or
relevant to serving or developing opportunities helps to avoid investing time and resources
in resources that do not align with the opportunity. Many entrepreneurship advocates argue
that weak-tie networking is a fundamental aspect of entrepreneurial behavior (Aldrich &
Zimmer, 1986; Birley et al., 1991; Dubini & Aldrich, 1991; Bryson et al., 1993; Partanen
et al., 2014).
These four dimensions encompass all actions that allow companies to make sense of and leverage
their direct and indirect relationships networks. Organizational networking goes beyond simple
contact or casual interaction and refers to a company's ability to turn a strategic meeting into a
valuable business relationship that generates benefits (Thornton et al., 2014). Organizational
networking can indicate social conditions that foster collective action, learning, and adaptive
capacity (Bodin & Crona, 2009). Small companies can enhance their organizational networking
by improving their competencies or moving from lower- to higher-level networks.
Based on these definitions and insights, the following hypotheses were formulated:
H1a. Proactive Networking capabilities positively impact the Organizational Networking
H1b. Reactive Networking capabilities positively impact the Organizational Networking
2.3 Absorptive capabilities
The concept of Absorptive Capacity (AC) has gained significant attention in business networks
because it refers to an organization's ability to recognize and utilize external information to achieve
its goals (Cohen & Levinthal, 1990). AC is a crucial aspect of organizational networking, as
companies can benefit from coordination, specialization, and collective learning through close and
long-term relationships between producers and users (Cooke & Morgan, 1993). However,
organizational networking can only achieve positive outcomes if the company has a high AC level.
Zahra and George (2002) defined AC as comprising four interrelated constructs: acquisition
capacity, assimilation capacity, transformation capacity, and exploitation capacity. Acquisition
capacity involves a firm’s ability to locate and acquire external knowledge that is critical to its
operations. Assimilation capacity refers to the processes and routines that allow for analyzing,
processing, and internalizing new information or knowledge. Transformation capacity involves
the development of internal routines that facilitate the transfer and combination of previous
knowledge with newly acquired or assimilated knowledge. Finally, exploitation capacity refers to
the organizational capacity to incorporate acquired, assimilated, and transformed knowledge into
operations.
Despite the significant attention given to AC, there are important gaps in the literature, including
ambiguous definitions of the construct and its antecedents and the lack of validation in most studies
(Camisón & Forés, 2010; Lane et al., 2006; Van den Bosch et al., 2003). Moreover, Volberda et
al. (2010) noted the need for empirical research to determine which inter-organizational
antecedents have the most significant impact on AC.
AC is a dynamic capability influenced by managerial decisions (Andersén & Kask, 2012). Zahra
and George (2002) provide a framework for understanding AC as comprising four dimensions:
acquisition capacity, assimilation capacity, transformation capacity, and exploitation capacity.
These dimensions can be divided into potential AC (acquisition and assimilation) and realized AC
(transformation and exploitation). Potential AC encompasses a firm's capability to acquire and
assimilate external knowledge and affects long-term performance through increased strategic
flexibility (Ben-Oz & Hill, 1998; Ben-Oz & Greve, 2015). Realized AC involves a firm's ability
to combine external knowledge with current knowledge to generate new products and processes,
affecting short-term performance through increased process and product innovation (Kogut &
Zander, 1992; Ben-Oz & Greve, 2015).
It is important to note that each of the four AC dimensions has unique features and antecedents
(Andersen, 2015). SMEs operating in dynamic industries such as retail can benefit from expanding
their absorptive capacity by acquiring and assimilating new knowledge from external partners.
However, these firms may struggle to transform and exploit newly acquired knowledge, affecting
their short-term performance and innovation capabilities (Gálvez and García, 2011). To overcome
this challenge, SMEs may need to invest in employee training and development to improve their
transformation and exploitation capabilities and fully leverage their potential absorptive capacity
(Andersén & Kask, 2012).
Realized absorptive capacity refers to the actions taken by a firm to combine newly acquired
knowledge with its existing knowledge base to create new ideas and innovations (Andersén &
Kask, 2012). To improve realized absorptive capacity, organizations must establish internal and
external knowledge flows, recognize valuable knowledge, and transfer and exploit it effectively
(Flatten et al. 2011). Learning, knowledge transfer, and innovation processes enable SMEs to
acquire, assimilate, transform, and exploit knowledge and translate it into marketable goods and
services (Dalkir, 2011).
In line with the above discussion, we hypothesize the following.
Hypothesis 2a. Organizational capabilities positively impact potential absorptive capacity.
Hypothesis 2b. Organizational capabilities positively impact the Realized absorptive
capacity
Figure 1 presents our research model and hypotheses to be tested.
Networking Capacity
Organizational
Networking
Absortive Capacity
Information
Adquisition
Reactive
Networking
Proactive
Networking
Opportunity
Enabling
Potential
Absortive
Capacity
Strong Resource
Mobilization
Realized
Absortive
Capacity
Weak Tie
Resource
Mobilization
3.0 Methodology
3.1. Survey
To test the proposed model, we collected data from a sample of Puerto Rico’s SMEs. Small and
medium enterprises (SMEs) are crucial to market economies and play a vital role in maintaining
and creating a functioning market system (Karpak, 2010; Kessler, 2007). Despite the challenges
they face in the market, SMEs are significant to the economies of many countries, as they create
jobs and contribute to social cohesion and the diversification of economic activity (Lussier et al.,
2016). SMEs are also recognized as the backbone of developed and developing economies,
accounting for an average of 90% of businesses, jobs, and economic output (Zahoor et al., 2022).
According to the World Bank (2020a, 2020b), SMEs constitute approximately 50% of the
worldwide employment and 90% of businesses.
In Puerto Rico, SMEs are seen as a critical aspect of the economy as they represent approximately
95% of all private establishments and generate approximately 80% of employment (CCEPR, 2016;
Congressional Task Force, 2016). Therefore, establishing SMEs can significantly contribute to an
island's economic development.
Our research shows that most SMEs have been in the market for over four years (79.3 percent)
(see Table 1). Eighty-two percent of the respondents were in senior-level positions at their firms,
while 9.5% were middle-level managers.
While 65.4 percent of firms are microenterprises, 70.6 percent have less than $500,000 in sales
volume. Most firms surveyed were manufacturing and consulting firms (30.8 percent ).
Table 1. Overview of Sample
Characteristics
Firm characteristics
Number of employees
1-7
8-25
25-50
Share
65.4
18.6
16
Company age (in years)
less than 1 year
1-3 years
4-7 years
more than 8 years
4.8
26
29.9
49.4
Sales Volume
less than $500,000
$500,000 to $3,000,000
$3,000,001 to $10,000,000
70.6
14.3
15.2
Business Type
Manufacturing
Healthcare
Education
Communications
Banking
Personal care
Food
Auto
Consulting
16.5
8.2
9.5
5.6
1.7
3
12.1
2.2
14.3
Other
26.8
Position within the company
Owner / co-owner
63.2
President
13
Vice-president
General Manager
Director
Other
1.7
6.5
7.8
7.8
Gender
Male
Female
39
61
Table 2: Univariate Statistics
Dimension/Item
N
Mean
Std. Deviation
Reactive Networking
CN1
231
4.10
0.928
CN2
231
4.13
0.847
CN3
231
4.53
0.631
CN4
231
3.74
0.980
CN5
231
3.50
1.034
NOA1
231
4.24
0.819
NOA2
231
4.19
0.762
NOA3
229
4.39
0.683
NOA4
231
4.39
0.655
NOA5
231
4.10
0.872
NOH1
231
4.08
0.861
NOH2
231
4.51
0.691
NOH3
230
4.27
0.833
NOH4
231
4.32
0.799
NOH5
231
4.61
0.738
Strong Tie Resource
Mobilization
NOMF1
231
4.15
0.859
NOMF2
231
4.35
0.753
NOMF3
231
4.36
0.772
NOMF4
231
4.11
0.895
230
4.04
0.824
Proactive Networking
Information Acquisition
Opportunity Enabling
Weak Tie Resource Mobilization
NOMD1
NOMD2
231
3.36
1.122
NOMD3
231
3.96
0.948
NOMD4
229
4.51
0.705
NOMD5
231
4.14
0.865
CAAD1
230
4.00
0.765
CAAD2
231
4.39
0.749
CAAD3
231
4.27
0.767
CAAD4 .
230
4.08
0.890
CAAD5
231
3.96
0.876
CAAS1
231
3.73
0.969
CAAS2
231
3.48
1.042
CAAS3
231
3.42
0.938
CAAS4
231
3.66
0.880
CAT1
231
3.74
0.834
CAT2
231
3.55
0.893
CAT3
231
3.68
0.939
CAT4
230
3.94
0.859
CAT5
230
3.90
0.853
CAE1
230
3.60
0.979
CAE2
231
3.64
0.921
CAE3
231
3.53
1.062
CAE4
229
3.59
0.999
CAE5
229
3.49
1.024
Adquisition
Assimilation
Transformation
Explotation
Univariate analysis of items included in each dimension (Table 2) achieved low internal nonresponse bias, uniform means, and a reasonable range of standard deviations for the items
measured. The outcomes of the univariate statistics consistently indicate high-quality responses
from respondents.
3.2 Non-response and common method bias
To assess potential non-response bias within our sample, we follow Armstrong and Overton (1977)
and compare early versus late respondents across the various firm and respondent characteristics
and central constructs in our model. Therefore, the responses after the reminder emails were treated
as late responses, which were early responses. χ 2 and t-tests did not show significant differences
between the two groups, suggesting that late response bias is not an issue. Using our multipleinformant research design, we controlled for common method bias. In addition, various research
design procedures suggested by Podsakoff et al. (2003) were used to reduce the risk of common
method bias: neutral wording, assurance of respondents’ anonymity, and data confidentiality. In
addition to these preventive procedures, we followed several steps to assess ex-post whether
common method bias is problematic within our data. First, we used Harman’s single-factor test.
Common method variance is problematic if either a single factor emerges from exploratory factor
analysis (EFA) or if a single factor accounts for most of the explained variance. According to the
results of the unrotated EFA, the largest factor explained only 33.61 percent of the variance. In
comparison, all factors with eigenvalues above one account for 68.32 percent of the explained
variance.
3.3 Construct operationalization
The focal constructs of the proposed model were operationalized using multi-item reflective
indicators on a five-point Likert-type scale (anchored at 1 = “strongly disagree” and 5 “strongly
agree”), as it has been suggested that these types of response categories provide the most reliable
and valid scores (Preston & Colman, 2000).
To measure AC, we adopted the AC items developed and tested by Zahra and George (2002) and
deemed them valid and reliable. Next, we applied hierarchical component models (HCMs) to
obtain the PAC and RAC. The link between lower-order components (LOCs) and higher-order
components (HOCs) was characterized by a reflective-reflective relationship, as suggested by
Camisón and Forés (2010) and Limaj and Bernroider (2019). Using higher-order constructs is
appropriate for modeling multidimensional latent variables and reducing structural model
complexity, resulting in a more parsimonious PLS model (Hair et al. 2013).
Potential absorptive capacity was measured as a reflective-reflective second-order construct with
knowledge acquisition and assimilation as its underlying first-order dimensions. Likewise, realized
absorptive capacity was modeled as a reflective-reflective second-order construct, with knowledge
transformation and exploitation as its underlying first-order dimensions. Items to operationalize
the four first-order constructs were adopted from Zahra and George (2002). Here, five items were
used to capture the ability to acquire new knowledge: four items to measure assimilation, that is,
the routines and processes to interpret external knowledge; five items to assess the mechanisms
for transforming and integrating new knowledge with existing knowledge; and five items to
measure the routines and processes to exploit knowledge.
A repeated-indicator approach was used to establish the HCM (Hair et al. 2017). We calculated
the latent variable scores for LOCs, which served as manifest variables in the HOC measurement
model.
4.0 Results
4.1 Assessment of the measurement models
We used partial least squares structural equation modeling (PLS-SEM) to test the proposed model.
PLS-SEM has become increasingly popular in business and management research ( Hair et al.,
2012; Hair et al., 2012). PLS-SEM is advantageous for relatively small sample sizes and complex
models (Hair et al., 2012; Hair et al., 2012; Henseler et al., 2014; Reinartz et al., 2009) and allows
the testing of models that simultaneously use formative and reflective measurements, as well as
hierarchical models (Becker et al., 2012; Hair et al., 2012), which makes this an instrumental
analysis technique for our hypothesized model.
The results of the structural model are shown in Figure 1. The first step in evaluating the PLSSEM model is to examine the outer model (Hair et al. 2017). Relationships between the constructs
and their indicators as well as reliability estimates were assessed (see Table 3). The results showed
that all measures of the reflectively measured constructs were reliable and valid.
Figure 1 – Structural Model
Table 3: AVEs and reliability coefficients
Information Acquisition
Opportunity Enabling
Potential Absorptive Capacity
Proactive Networking
Reactive Networking
Cronbach's
Alpha
0.888
0.843
0.903
0.742
0.70
Composite
Reliability
0.923
0.889
0.921
0.884
0.835
AVE
0.751
0.617
0.567
0.792
0.629
Realized Absorptive Capacity
Strong-tie Resource Mobilization
Weak-tie Resource Mobilization
0.895
0.788
0.759
0.914
0.861
0.835
0.517
0.607
0.505
Composite reliability ranged from 0.9234 to 0.835 for all eight constructs, thus exceeding the
minimum requirement of 0.70 (Hair et al., 2017). Cronbach’s alpha ranged from 0.700 to 0.903,
all below the recommended requirement of 0.90, as excessively high values indicate strong
redundancies between the items, which may have adverse consequences for the model estimates
(Diamantopoulos et al., 2012). All indicator loadings were above 0.700 (except for one indicator
each for weak-tie resource mobilization and one indicator for acquisition). However, the
average was above 0.7, confirming satisfactory individual indicator reliability.
The average variance extracted (AVE) for the research model ranged from 0.505 to 0.792 for
all constructs, as shown in Table 3, while the cut-off was 0.50 (Hair et al., 2012), thus indicating
convergent validity for all constructs.
The Fornell–Larcker criterion (Fornell & Larcker, 1981) demonstrated that all AVEs were
higher than the squared inter-construct correlations, indicating discriminant validity for all
stakeholder constructs. Table 4 shows the AVEs on the diagonal and the squared inter-construct
correlations off the diagonal.
Table 4: Squared inter-construct correlations
Acquisition
Information Acquisition
Opportunity Enabling
Proactive Networking
Reactive Networking
Strong-tie Resource Mobilization
Weak-tie Resource Mobilization
Adq
0.826
0.525
0.512
0.272
0.225
0.549
0.522
IA
0.867
0.668
0.291
0.324
0.604
0.612
OE
PN
RN
SRM
WRM
0.785
0.38 0.89
0.501 0.261 0.793
0.713 0.395 0.327 0.781
0.627 0.276 0.408 0.667 0.709
The heterotrait–monotrait (HTMT) ratio was used to further explore discriminant validity as an
alternative assessment approach for discriminant validity (Hair et al., 2017). The analysis of
bootstrap-based bias-corrected and accelerated confidence intervals (5,000 subsamples, no sign
change option) shows that all values of the HTMT ratio in the sample are significantly different
from one (HTMTInference), providing support for discriminant validity (Henseler et al., 2015).
An examination of the cross-loadings showed that all indicator loadings were higher than their
respective cross-loadings. This, along with a qualitative assessment of the questions measuring
the constructs, provides evidence and proof of the overall discriminant validity (Hair et al.,
2017). Next, the results of the structural model were examined (Figures 1 and 2). The R2 values
for the five endogenous constructs – information acquisition, opportunity enabling, strong-tie
resource mobilization, weak-tie resource mobilization, and acquisition – were examined.
Table 5 summarizes the path coefficient estimates, t-values, p-values, and confidence intervals.
Assuming a 5% significance level, we find that most of the relationships in the structural model
are significant, except PN→ SRM, PN→ WRM, RN→ SRM, and RAC→ Tran.
Table 5: Significance testing results
PN -> IA
RN -> IA
PN -> OE
RN -> OE
PN -> SRM
RN -> SRM
PN -> WRM
RN -> WRM
IA -> PAC
OE -> PAC
SRM -> PAC
WRM -> PAC
IA -> RAC
OE -> RAC
SRM -> RAC
WRM -> RAC
PAC -> Adq
PAC -> As
RAC -> Exp
RAC -> Tra
Path
Coefficients
t value
p
Values
95% Confidence
intervals
Significance
(p<0.05)
VIF
R2
Q2
0.221
0.266
0.270
0.431
0.335
0.242
0.179
0.359
0.139
0.083
0.311
0.208
0.080
0.210
0.323
0.069
0.940
0.893
0.916
2.987
3.399
3.558
7.008
4.226
2.971
2.602
5.448
1.651
0.993
3.533
3.036
0.994
2.343
3.422
0.911
97.552
58.521
73.108
0.003
0.001
0.000
0.000
0.000
0.003
0.009
0.000
0.099
0.321
0.000
0.002
0.321
0.019
0.001
0.362
0.000
0.000
0.000
[0.083, 0.373]
[0.105, 0.413]
[0.125, 0.426]
[0.308, 0.547]
[0.189, 0.495]
[0.075, 0.392]
[0.054, 0.319]
[0.230, 0.486]
[-0.027, 0.301]
[-0.088, 0243]
[0.141, 0.484]
[0.074, 0.346]
[-0.08, 0.238]
[0.025, 0.384]
[0.125, 0.502]
[-0.077, 0.22]
[0.919, 0.956]
[0.861, 0.920]
[0.891, 0.94]
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Yes
Yes
No
Yes
Yes
No
Yes
Yes
Yes
1.073
1.073
1.073
1.073
1.073
1.073
1.073
1.073
2.055
2.569
2.450
2.103
2.055
2.569
2.450
2.103
1.000
1.000
1.000
0.150
0.126
0.319
0.185
0.213
0.116
0.413
0.216
0.365
0.165
0.883
0.797
0.845
0.589
0.515
0.507
0.919
63.992
0.000
[0.888, 0.944]
Yes
1.000
0.840
0.507
4.2 Assessment of the structural model
Collinearity. In line with the structural model assessment procedure outlined in Hair et al.
(2017), we first assess the structural model for collinearity issues by examining the variance
inflation factor (VIF) values of all predictor constructs in the model. As all VIF values are below
the more conservative threshold of 3.3 (Diamantopoulos and Siguaw, 2006), we conclude that
collinearity is not at a critical level (Table 5).
Significance and relevance of the path coefficients. The results of the bootstrapping procedure
with 5,000 samples using the no sign change option (Streukens and Leroi-Werelds, 2016)
revealed that most of the structural model relationships were significant (Table 5). Specifically,
we found that strong-tie resource mobilization had a significant effect on potential absorptive
capacity (SRM -> PAC:0.311, p<0.01) and realized absorptive capacity (SRM -> RAC:0.323,
p<0.01), whereas information acquisition, opportunities enabling, and weak-tie resource
mobilization’s impact on these two constructs is much less pronounced (IA -> PAC:0.139
p>0.05; IA -> RAC:0.080 0.051, p>0.10; OE -> PAC:0.083, p>0.10; OE -> RAC:0.210, p<0.05;
WRM -> PAC:0.208, p<0.05; WRM -> RAC:0.069, p>0.10 ). Analyzing the impact of Reactive
and Proactive networking as antecedent constructs, we found that RN has a significant effect
on IA (0.266, p<0.05), OE (0.308, p<0.01), SRM (0.242, p<0.05), and WRM (0.359,
p<0.01).PA also significantly affected IA (0.221, p<0.05), OE (0.270, p<0.01), SRM (0.335,
p<0.01), and WRM (0.79, p<0.05).
In-sample model fit. To assess the model’s in-sample fit, we first considered the R2 (Table 3).
All endogenous constructs had R2 values ranging from 0.130 to 0.413. This level is lower than
that in Amaro and Duarte’s (2015) study, which considered a more complex model with
additional antecedent constructs. While the in-sample model fit is relatively small according to
absolute standards (Hair et al., 2017), we consider it acceptable for this study in light of its
complexity.
Out-of-sample predictive power. We use PLSpredict with 10 folds and one repetition to mimic
how the PLS model will eventually be used to predict a new observation rather than using the
average across multiple models.
In the first step, we find that all endogenous constructs’ indicators outperform the most naïve
benchmark, as all indicators yield Q2 prediction values above 0 (Table 5).
Table 5. PLSpredict assessment of manifest variables
Item
CAAD4
CAAD1
CAAD5
CAAD3
CAAD2
CAAS2
CAAS1
CAAS4
CAAS3
CAE5
CAE1
CAE3
CAE2
CAE4
NOA4
NOA1
NOA2
NOA3
NOH1
NOH3
NOH4
NOH5
RMSE
0.865
0.749
0.853
0.746
0.732
0.963
0.915
0.85
0.916
0.999
0.969
0.999
0.893
0.953
0.641
0.78
0.73
0.637
0.761
0.746
0.7
0.7
PLS-SEM
Q²_predict
0.057
0.044
0.058
0.061
0.05
0.151
0.113
0.071
0.054
0.044
0.02
0.12
0.065
0.085
0.049
0.097
0.087
0.126
0.223
0.197
0.235
0.104
LM
RMSE
0.838
0.766
0.846
0.751
0.746
0.919
0.902
0.851
0.917
1.018
0.948
0.965
0.898
0.944
0.638
0.779
0.736
0.639
0.755
0.752
0.708
0.71
PLS-SEM - LM
RMSE
-1.703
-1.515
-1.699
-1.497
-1.478
-1.882
-1.817
-1.701
-1.833
-2.017
-1.917
-1.964
-1.791
-1.897
-1.279
-1.559
-1.466
-1.276
-1.516
-1.498
-1.408
-1.41
NOH2
CAAS1
CAAD5
CAAD2
CAAD3
CAAS3
CAAS2
CAAS4
CAAD1
CAAD4
CAT1
CAE3
CAE1
CAE2
CAT5
CAT4
CAT2
CAT3
CAE4
CAE5
NOMF1
NOMF4
NOMF2
NOMF3
CAT4
CAT5
CAT1
CAT2
CAT3
NOMD4
NOMD2
NOMD1
NOMD3
NOMD5
0.658
0.915
0.853
0.732
0.745
0.916
0.964
0.85
0.749
0.865
0.804
0.998
0.97
0.893
0.837
0.862
0.849
0.893
0.954
0.999
0.791
0.844
0.737
0.733
0.863
0.836
0.804
0.849
0.893
0.678
1.108
0.782
0.941
0.792
0.096
0.112
0.057
0.051
0.061
0.054
0.15
0.072
0.044
0.057
0.078
0.122
0.019
0.065
0.038
-0.005
0.101
0.101
0.084
0.044
0.155
0.115
0.048
0.104
-0.009
0.039
0.078
0.101
0.1
0.071
0.03
0.099
0.02
0.167
0.661
0.902
0.846
0.746
0.751
0.917
0.919
0.851
0.766
0.838
0.813
0.965
0.948
0.898
0.839
0.86
0.821
0.869
0.944
1.018
0.796
0.858
0.715
0.734
0.86
0.839
0.813
0.821
0.869
0.685
1.106
0.787
0.946
0.78
-1.319
-1.817
-1.699
-1.478
-1.496
-1.833
-1.883
-1.701
-1.515
-1.703
-1.617
-1.963
-1.918
-1.791
-1.676
-1.722
-1.67
-1.762
-1.898
-2.017
-1.587
-1.702
-1.452
-1.467
-1.723
-1.675
-1.617
-1.670
-1.762
-1.363
-2.214
-1.569
-1.887
-1.572
Compared to the LM, the PLS-SEM analysis yielded lower prediction errors in terms of the RMSE
for all indicators. This suggests that the model has high predictive power (Hair, Risher, Sarstedt &
Ringle, 2019; Shmueli, Sarstedt, Hair, Cheah, Ting, Vaithilingam, & Ringle, 2019).
Blindfolding was performed to evaluate the predictive relevance of the endogenous latent
construct indicators. The blindfolding procedure produces Q2, which applies a sample reuse
technique that omits part of the data matrix and uses model estimates to predict the omitted part.
A Q2 value greater than zero for PLS-SEM models in the cross-validated redundancy report
indicates predictive relevance. As a relative measure of predictive relevance, values of 0.02,
0.15, and 0.35 indicate that an exogenous construct has a small, medium, or large predictive
relevance for a selected endogenous construct, respectively (Hair et al., 2017).
Table 6: Predictive relevance of endogenous latent construct indicators
Predictive relevance Q2
Adquisition
Assimilation
Explotation
Information Acquisition
Opportunity Enabling
Potential Absortive Capacity
Proactive Networking
Reactive Networking
Realized Absortive Capacity
Strong-tie Resource Mobilization
Transformation
Weak-tie Resource Mobilization
Q²
0.589
0.515
0.507
0.106
0.185
0.216
0.165
0.116
0.507
0.087
Table 6 shows that Q2 exceeded zero for all ten endogenous constructs, indicating the predictive
relevance of the construct indicators. Values higher than 0, 0.25, and 0.50 depict the PLS path
model's small, medium, and large predictive accuracy (Hair, Risher, Sarstedt & Ringle, 2019).
The structural model examined the hypothesized relationship sizes and the significance of the
path coefficients. Bootstrapping was performed using 5,000 subsamples (Hair et al., 2017).
Figure 2 shows bootstrapping with t statistics.
Figure 2: Bootstrapping with t statistics
The hypotheses are also significant. An analysis of path coefficients and levels of significance
(Figure 1) shows that the six posited relationships were significant and meaningful. H1, H2,
H3, H4, H5, and H6 predicted a significant and positive relationship between the constructs.
However, these relationships have not been tested in previous studies. The sizes of the structural
coefficients for the accepted hypotheses are considered meaningful for interpretation (Hair et
al. 2017).
5.0 Discussion
This study examines the interplay between networking, organizational capabilities, and absorptive
capacity among 231 Small and Medium-sized Enterprises (SMEs) in Puerto Rico. The results
indicate that high-level management networking capabilities tend to be more reactive than
proactive, as shown in Table 2. This aligns with previous research indicating that SMEs exhibit
reactive behavior due to resource constraints and a lack of experience in a competitive environment
(Gloger & Carson, 1999).
The study found that the reactive dimension of networking significantly influences information
acquisition, opportunity enabling, and weak-tie resource mobilization. SMEs also exhibit strong
market orientation in a reactive manner, limiting their opportunity creation capabilities (Chell &
Baines, 2000). However, they tend to be responsive to consumer needs and adjust their product
lines accordingly (Baker and Sinkula, 2009). However, the proactive dimension of networking has
a greater impact on strong-tie resource mobilization. Proactive and reactive networking are two
complementary dimensions, with companies utilizing their reactive capabilities to search for
information, detect opportunities, and establish relationships, while utilizing their proactive
capabilities for strong-tie resource mobilization.
Networking in companies is often viewed as a complex adaptive system that is not fully
orchestrated by top management (Stacy, 1997). Companies engage in a self-organizing process in
which order emerges from micro-interactions upward to top management (Easton, Wilkinson, &
Georgieva, 1997; Wilkinson & Young, 2002). From this perspective, top management networks
can be reactive because of organizational microinteractions (Ritter et al., 2004). The challenge for
high-level managers of SMEs is to develop a networking capacity that enables them to connect
their resources with those of other actors. However, this can be hindered by the lack of
understanding of the construct (Ritter et al. 2004).
This study highlights the importance of understanding the interplay between potential absorptive
capacity (PAC) and realized absorptive capacity (RAC) for SMEs to effectively leverage their
unique resources and competencies and gain competitive advantage. Organizational environment
(OE) and social trust mechanisms (STM) are crucial for stimulating RAC (Thornton et al., 2014;
Selivanovskikh et al., 2020). High levels of interaction and social integration among company
members foster cooperation, communication, and trustworthy behavior, enabling the assimilation
and exploitation of knowledge (Levin & Cross, 2004; Smith et al., 2005; Fischer et al., 2004;
Schoorman et al., 2007; Mayer et al., 1995).
Additionally, recent research has validated the existence of two dimensions of networking within
an organization: proactive and reactive (Ritter et al., 2004; Glomore & Carson, 1999; Håkansson
& Ford, 2001). Leaders must decide which dimension to implement to bring competitive and
strategic advantages to the organization. Proactive networking is a strategic approach that
emphasizes cultivating relationships with customers and suppliers as essential elements in a
company's value chain. By actively coordinating meetings with clients to understand their needs
and organizing strategic meetings with suppliers to explore new offers, a company with a proactive
networking strategy can effectively manage "strong-tie mobilization" and maximize its absorptive
capacity (PAC and RAC).
The management of strong- and weak-tie mobilization is closely linked to knowledge acquisition
and assimilation. Strong-ties mobilization involves encouraging collaboration and participation
among suppliers and business partners to address customer needs, which can foster positive
relationships with these stakeholders and enable the company to adapt to changes in the market.
Conversely, weak ties mobilization enables the organization to gain better visibility of external
market trends and identify growth opportunities and potential new suppliers, even if it is not
directly linked to the ecosystem. To achieve this, the company must assess competitors in the
business ecosystem and the customers that they currently serve. By acquiring and assimilating this
knowledge, a company can react and formulate new strategies to understand these new markets
and opportunities.
Moreover, the company's ability to transform and exploit the knowledge acquired and assimilated
is also linked to its handling of opportunity enabling and strong ties mobilization. Opportunity
enabling allows an organization to establish a reputation within its market through networking
strategies, maintaining current relationships with suppliers and customers, and connecting and
establishing relationships with new business partners, suppliers, and customers. Combined with
strong ties mobilization, this creates a dynamic and continuous process internally and externally
in the company with current suppliers and business partners. The organization can then work on
the adjustments and strategies that must be implemented to face the new opportunities these
initiatives can generate and improve their processes for their current options. By contributing
externally to the market, the organization can contribute to the ecosystem's growth and create new
opportunities for suppliers, business partners, and customers.
6.0 Theoretical Implications
Networking capacity, or a company's ability to establish and maintain relationships with external
The results of this study have several theoretical implications for Dynamic Capabilities theory.
First, it highlights the importance of networking capabilities as a critical aspect of organizational
capability that contributes to absorptive capacity development. The study found that high-level
management networking capabilities tend to be more reactive than proactive, suggesting that
SMEs need to develop their networking capacity and adopt a more proactive approach to connect
their resources with those of other actors. This finding emphasizes the need for SMEs to engage
in continuous learning and adapt their networking capabilities to remain competitive.
Second, the study emphasizes the interplay between potential absorptive capacity (PAC) and
realized absorptive capacity (RAC), which suggests that firms need to focus on both to leverage
their unique resources and competencies effectively. The organizational environment and social
trust mechanisms are crucial for stimulating RAC, which implies that firms need to establish a
culture of cooperation, communication, and trust to promote knowledge assimilation and
exploitation.
Third, this study validates the existence of two dimensions of networking within an organization,
proactive and reactive, emphasizing the importance of dynamic capabilities in managing
networking activities. Leaders must decide which dimension to implement to bring competitive
and strategic advantages to the organization. By adopting a proactive approach to networking,
firms can cultivate relationships with customers and suppliers and maximize their absorptive
capacity, whereas reactive networking focuses more on information acquisition, opportunity
enabling, and weak-tie resource mobilization. This finding emphasizes the need for firms to
develop dynamic capabilities to effectively manage both proactive and reactive networking
dimensions.
Overall, this study provides valuable insights into the Dynamic Capabilities theory by emphasizing
the importance of networking capabilities, absorptive capacity, organizational environment, and
social trust mechanisms. This study highlights the need for firms to develop dynamic capabilities
to manage their networking activities effectively and leverage their unique resources and
competencies to gain competitive advantage in the market.
7.0 Managerial implications
The managerial implications of Networking Capabilities on Organizational Networking in
Entrepreneurial SMEs are vital for ensuring the success of the organization. To effectively manage
organizational networking, managers must consider the following key considerations.
1) Development of Networking Skills: At micro level, managers must cultivate their
networking abilities to engage in productive interactions with others in both social and
business contexts. This includes social and business networking, which is fundamental for
establishing and preserving relationships with others.
2) Communication with Stakeholders: Efficient communication with staff, teams, or
departments is crucial for the success of organizational networking. This necessitates
managers to possess exceptional interpersonal skills and the capacity to communicate
effectively.
3) Building and Sustaining Social Networks: Building and maintaining social networks is
essential for effective organizational networking. Managers should focus on creating
strong relationships with individuals both inside and outside the organization.
4) Reactive and Proactive Networking: Reactive and proactive networking approaches
complement each other and are necessary for successful organizational networking.
Although reactive networking is an important social skill, it is not adequate to develop all
aspects of organizational networking in SMEs. However, proactive networking is crucial
for managers to manage organizational networking effectively, particularly in terms of
strong-tie resource mobilization.
5) Strong-tie Resource Mobilization: ties involve emotional intensity and intimacy, and
individuals trust each other. However, reliance on strong ties may constrain the generation
of new information and fresh perspectives that are critical for identifying and exploiting
business opportunities for growth and development. A results-oriented strategy recognizes
resources relevant to an opportunity and forms connections that collaborate with the
efficient execution of actions to address the opportunity.
6) Strategic Thinking, Planning, and Organizational Architecture: Strategic thinking,
planning, and organizational architecture play pivotal roles in effective networking.
Managers should focus on creating a clear strategy, plan, and architecture that aligns with
the goals of the organization and supports effective networking.
In conclusion, the managerial implications of Networking Capabilities on Organizational
Networking in Entrepreneurial SMEs are intricate and require careful consideration. By
developing their own networking skills, communicating effectively with staff and stakeholders,
and adopting a results-oriented strategy, managers can effectively manage organizational
networking and mobilize resources to attain growth and success.
8.0 Limitations and Future Research
Our research focuses on one setting (Puerto Rico’s SMEs). Therefore, although such a specific
research design allows us to control for many parameters, it limits the generalizability of the
findings. Further studies are needed to broaden our findings to other industries and countries,
specifically those that show a different cultural makeup from Puerto Rico.
Our research design had several advantages and disadvantages. Our sample of firms is not very
large but is adequate for the size of SMEs. We also proved that nonresponse bias was not a problem
in our study. Using single-informant survey data is problematic, but we attempted to mitigate this
risk by applying a multiple-informant research design. We also controlled for this problem by
testing the common method variance.
Further academic research in this area could focus on the specific mechanisms through which
networking capacity impacts organizational and absorptive capacities. This could include studying
the effects of different types of external partnerships (e.g., suppliers, customers, and competitors)
on a company's access to resources and capabilities, as well as investigating how a company's
internal structures and processes can facilitate or hinder the effective use of external networks.
This research could also explore the role of organizational factors, such as top management support
and organizational culture, in shaping a company's networking capacity. Another interesting area
of research is the investigation of the relationship between networking capacity and firm
performance. A company’s networking capacity is related to its performance and how it is affected
by networking capacity.
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