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Accessing Diverse Knowledge for Problem Solving in the MNC: A Network Mobilization Perspective

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Accessing Diverse Knowledge for Problem Solving in the MNC: A Network Mobilization Perspective

Abstract

Research summary The fundamental challenge of problem solving is synthesizing diverse knowledge for solution development. This paper addresses the trade‐off between knowledge diversity, i.e., approaching the most relevant individual to maximize the likelihood that s/he possesses diverse knowledge and the ability to access, i.e., recognize and assimilate this knowledge. We examine this trade‐off in relation to managers in subsidiaries of multinational corporations (MNCs) and two types of diverse knowledge—novel knowledge and specialist expertise. We use a network mobilization perspective and arguments on network range within and across organizational boundaries, testing our hypotheses on a dataset of 838 ties from 120 managers leading problem solving projects. Our study offers implications for the knowledge‐based view of the MNC as well as the problem solving perspective in strategy. Managerial summary We examine where managers in subsidiaries of multinational corporations (MNCs) access diverse knowledge to solve non‐routine problems. This is underpinned by a trade‐off. To increase chances that the other person holds diverse knowledge, they may reach far in terms of approaching an individual in a different work context; but such far reach diminishes their ability to integrate this knowledge. We resolve this trade‐off. We find that approaching individuals from an MNC unit in another country location but from the same function increases the likelihood of accessing novel knowledge. Whereas, accessing specialist expertise is more likely achieved by approaching an MNC colleague from another function located in another country and someone working for an external organization. Overall, we provide insights for problem solvers in organizations.
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Accessing Diverse Knowledge for Problem Solving in the MNC: A Network
Mobilization Perspective
Andrew Parker
Associate Professor
University of Exeter Business School
University of Exeter
Rennes Drive, Exeter EX4 4PU, United Kingdom
a.parker3@exeter.ac.uk
+44 1392 723485
Esther Tippmann*
Associate Professor
College of Business
University College Dublin
Michael Smurfit Graduate Business School, Carysfort Avenue, Blackrock, Co. Dublin, Ireland
esther.tippmann@ucd.ie
+353 1 7168846
Renate Kratochvil
Vienna University of Applied Sciences for Management and Communications
Währinger Gürtel 97, 1180 Vienna, Austria
renate.kratochvil@fh-wien.ac.at
+43 676 9182302
Accepted for publication in the Global Strategy Journal.
Andrew Parker and Esther Tippmann share first authorship.
* Corresponding Author
Running head:
Accessing Knowledge for Problem Solving in the MNC
Keywords:
Knowledge, network mobilization, multinational corporations, problem solving, subsidiaries
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Accessing Diverse Knowledge for Problem Solving in the MNC: A Network
Mobilization Perspective
Research summary
The fundamental challenge of problem solving is synthesizing diverse knowledge for solution
development. This paper addresses the trade-off between knowledge diversity, i.e., approaching
the most relevant individual to maximize the likelihood that s/he possesses diverse knowledge
and the ability to access, i.e., recognize and assimilate this knowledge. We examine this trade-off
in relation to managers in subsidiaries of multinational corporations (MNCs) and two types of
diverse knowledgenovel knowledge and specialist expertise. We use a network mobilization
perspective and arguments on network range within and across organizational boundaries, testing
our hypotheses on a dataset of 838 ties from 120 managers leading problem solving projects. Our
study offers implications for the knowledge-based view of the MNC as well as the problem
solving perspective in strategy.
Managerial summary
We examine where managers in subsidiaries of multinational corporations (MNCs) access
diverse knowledge to solve non-routine problems. This is underpinned by a trade-off. To
increase chances that the other person holds diverse knowledge, they may reach far in terms of
approaching an individual in a different work context; but such far reach diminishes their ability
to integrate this knowledge. We resolve this trade-off. We find that approaching individuals from
an MNC unit in another country location but from the same function increases the likelihood of
accessing novel knowledge. Whereas, accessing specialist expertise is more likely achieved by
approaching an MNC colleague from another function located in another country and someone
working for an external organization. Overall, we provide insights for problem solvers in
organizations.
ACKNOWLEDGEMENTS
We would like to thank two anonymous referees, the editors of the Global Strategy Journal and
Phillip C. Nell for their constructive comments. Research for this article was supported by the
Irish Research Council with co-funding from the European Commission and Vienna University
of Economics and Business (WU).
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INTRODUCTION
From the problem solving perspective, firms such as multinational corporations (MNCs) gain
competitive advantage if they develop valuable solutions to complex organizational problems
(Heiman, Nickerson, & Zenger, 2009; Nickerson, Silverman, & Zenger, 2007; Nickerson, Yen,
& Mahoney, 2012). To create valuable solutions, diverse knowledge needs to be synthesized in
unique ways. Most centrally, this involves accessing relevant diverse knowledge, which may be
located inside or outside the firm, to serve as input for solution development (Felin & Zenger,
2014; Foss, Frederiksen, & Rullani, 2015; Nickerson & Zenger, 2004). Hence, for managers
undertaking problem solving projects, there is a “tremendous burden […] to centrally identify
relevant knowledge” (Felin & Zenger, 2014: 919) as they decide who to involve.
Deciding which individuals to approach for diverse knowledge is, however, difficult
regardless of whether it occurs within a firm or inter-organizationally. It requires making
assumptions about the chances of accessing knowledge, i.e., knowledge to be recognized and
assimilated into the individual's own meaning structures. In contrast to the information
processing approach, that adopts a sender-receiver perspective (e.g., Gupta & Govindarajan,
2000), the definition of accessing knowledge used in this study has a pronounced actor-oriented
aspect. It emphasizes the inherent choices and required efforts. Specifically, we suggest that
accessing knowledge is underpinned by the diversity-accessibility trade-off. On the one hand,
managers make assumptions about the likelihood that another individual possesses diverse
knowledge. In terms of diverse knowledge, this person is often located outside of one's own
work context (Tortoriello, Reagans, & McEvily, 2012), implying an expansion in range. On the
other hand, managers form expectations about the likelihood to access this knowledge; a
judgment that is premised on their perceived ability to actually recognize and assimilate it.
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However, as managers reach out to individuals outside their own work context for more diverse
knowledge, it becomes difficult to access this knowledge (Hansen, 1999), suggesting a
contraction in range. Thus, these two criteriaproviding diversity of knowledge and ensuring
the ability to access itpredict opposite effects; an expansion and contraction of range in
selecting who to approach. Yet both of these criteria need to be met simultaneously, causing a
fundamental trade-off.
Prior literature on problem solving in MNCs has investigated typical knowledge search
patterns (Tippmann, Mangematin, & Sharkey Scott, 2013; Tippmann, Sharkey Scott, &
Mangematin, 2014a), but has not been concerned with examining the details of accessing, i.e.,
recognizing and assimilating diverse knowledge. Another stream of research has adopted a social
network approach. Within this, studies on how MNC employees access knowledge through their
networks have explored knowledge sharing drivers (Dasi et al., 2017; Reinholt, Pedersen, &
Foss, 2011) and frequency outcomes (Haas and Cummings, 2015; Tortoriello, 2015; Tortoriello
et al., 2012). These, and indeed seminal studies on networks, highlight that an individual's
network is important because it implies particular opportunities and constraints for knowledge
acquisition (Burt, 1992; Granovetter, 1973; Haas, 2006), and knowledge sharing (Lomi et al.,
2014; Mors, 2010; Paruchuri & Awate, 2017). Although demonstrating the value of an
individual’s personal relationships in general terms, these studies have neither specifically
explored the intricacies of accessing diverse knowledge nor addressed the diversity-accessibility
trade-off. In addition, prior literature using a social network approach typically takes an
undifferentiated view on the resource, such as knowledge type, that flows between one
individual and another (Cross, Borgatti, & Parker, 2001 is an exception). While there is
considerable evidence that individuals with bridging ties between groups (Granovetter, 1973),
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and ties that bridge structural holes (Burt, 1992), are more likely to access differentiated
knowledge (Borgatti & Halgin, 2011), this research stream neglects the complexities that arise
with regard to the type of knowledge that flows through network relationships. Therefore, the
different ways in which managers resolve the diversity-accessibility trade-off depending on the
knowledge type remains a theoretical gap that we seek to address.
Given our interest in resolving the diversity-accessibility trade-off, we develop a model
of network mobilization, which refers to those individuals that a person reaches out to when in
need of a particular resource (Smith, Menon, & Thompson, 2012). We combine this network
mobilization perspective with arguments that the diversity-accessibility trade-off is manifested
differently depending on the type of diverse knowledge, in our case novel knowledge and
specialist expertise. Specifically, we argue that the highest propensity for accessing novel
knowledge is achieved when mobilizing individuals of ‘middle’ range; whereas the highest
propensity for accessing specialist expertise is for mobilizing individuals of ‘far’ range.
Additionally, we propose a multiplex effect for accessing both novel knowledge and specialist
expertise. We test our arguments on a dataset of 838 network ties, i.e., relationships between 120
managers in subsidiaries leading problem solving projects in MNCs and their collaborators.
This study makes a number of contributions. First, we resolve the diversity-accessibility
trade-off with regard to two types of diverse knowledgenovel knowledge and specialist
expertise. Second, our study demonstrates the utility of a network mobilization perspective in
examining the role of agency in MNC networks and its link to achieving the circulation of MNC
knowledge. Third, our study contributes to building the problem solving perspective in strategic
management, specifically within the MNC context.
THEORY AND HYPOTHESES DEVELOPMENT
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Network mobilization to access knowledge
When solving everyday problems, because they typically exhibit low complexity and are
relatively structured (Baer, Dirks, & Nickerson, 2013; Simon, 1962), firms often have an
established response (Cyert & March, 1963; Nelson & Winter, 1982). In contrast, non-routine
problems, the focus of this study, show greater complexity and are usually ill-structured (Baer et
al., 2013; Simon, 1962). In these situations, individuals need to develop a new solution,
facilitated by synthesizing diverse knowledge that has been accessed. This leads to higher
chances for more innovative, and thus valuable, solutions to organizational problems (Rosenkopf
& Nerkar, 2001; Sorenson & Fleming, 2004; Tippmann, Sharkey Scott, & Parker, 2017).
Subsequently, to access diverse knowledge, managers draw on individuals in their network.
Social resource theory suggests that the ties in an individual’s network are potential
sources of valuable resources (Lin, 1999, 2001). While having a high level of social capital is
deemed to be beneficial (Coleman, 1988; Lin, 2001), it is inefficient for problem solving
managers to approach everyone in their network. To satisfice search (Cyert & March, 1963),
decisions are made with regard to who to approach given a specific resource need. In this
respect, Smith et al. (2012) refer to the mobilized network, which are those individuals a person
actually reaches out to when they have a need to access a particular resource. Our focus is on this
mobilized network, i.e., the individuals that are reached out to during problem solving.
Our network mobilization perspective is in contrast to most network studies that focus on
all the instrumental ties, i.e., ties within a predefined organizational boundary that help
individuals complete their work. A focus on all ties does not differentiate between mobilized and
latent ties (e.g., Lomi et al., 2014; Tortoriello et al., 2012). Instead, our perspective is consistent
with recent research in network theory that focuses on individual agency (Gulati and Srivastava,
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2014), the micro-foundations of networks (Tasselli, Kilduff, & Menges, 2015), and how
individuals shape their networks (Parker, Halgin, & Borgatti, 2016). Moreover, our focus on
mobilized networks aligns with the micro-foundations agenda on furthering the understanding of
MNC knowledge processes as it attends to the behaviors of individuals (e.g., Andersson et al.,
2016; Andersson et al., 2015; Erkelens et al., 2015; Foss, 2009; Foss & Pedersen, 2016).
Tendencies in network mobilization and network range
A general tendency in network mobilization, when seeking to access knowledge, is to reach out
to people that are close in terms of one’s own knowledge (Cyert & March, 1963). This includes a
bias towards ties from the same function (McPherson, Smith-Lovin, & Cook, 2001). This is
based upon a higher opportunity of knowing a fellow employee within the same function than
outside due to organizational structures such as workflow, co-location, meeting attendance and a
common knowledge base (Thompson, 1967). It also means a bias towards geographically
proximate ties. It has been found that there is a tendency for knowledge sharing to decrease
between MNC units as geographic distance rises (Hansen & Løvås, 2004; Monteiro, Arvidsson,
& Birkinshaw, 2008) and individual knowledge seeking intensity to reduce with increasing
geographic separation (Allen, 1977; Haas & Cummings, 2015; Morris, Hammond, & Snell,
2014). As such, subsidiary managers have a preference to access knowledge from colleagues
from the same unit (Tortoriello et al., 2012)to search within their own subsidiary (Tippmann,
Sharkey Scott, & Mangematin, 2012; Tippmann et al., 2014a). While such close search, in terms
of same function and same location tie mobilization, is associated with a high ability to access
knowledge, its downside is the low chance that the knowledge is diverse.
To increase chances of the approached other possessing the sought knowledge, and to
fulfill the knowledge diversity criteria of the trade-off, it becomes necessary to stretch beyond
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closeness. This can be achieved through increasing the range of the individual's network, i.e.,
through mobilizing relationships that connect individuals to pools of knowledge that are different
to their own (Reagans & McEvily, 2003; Tortoriello et al., 2012). While Reagans and McEvily
(2003) defined range with regards to expertise areas, and Tortoriello et al. (2012) use it to
capture cross-unit networks, we suggest that network range can be conceptualized based upon a
more fine-grained view on organizational boundaries. This leads to the following dimensions of
range: (1) a different function (functional range), (2) a different country location (geographic
range), and (3) another organization (external range).
Individuals who mobilize networks across these organizational boundaries, we define as
having network range. It is important to differentiate network range from other network terms.
Ties that cut across organizational boundaries are boundary spanning ties. However, these ties
are not necessarily brokerage ties, if defined by the structure of the network in terms of being
intermediaries between unconnected others (Stovel & Shaw, 2012). As such, brokerage ties may
not span organizational boundaries. In addition, network range should not be confused with tie
strength. While ties within an organizational boundary are more likely to be strong ties as there is
more opportunity for frequent interaction, which is likely to lead to friendship (Granovetter,
1973) as well as normative pressures leading to trust (Coleman, 1988), ties across network range
can have varying degrees of strength. The strength of a tie across network range will depend
upon the interpersonal relationship of the two individuals (Tortoriello et al., 2012).
An underlying trade-off and effects of different types of knowledge
While expanding network mobilization along functional, geographic and external dimensions of
range likely connects an individual to others that hold different knowledge, thus increasing the
propensity to approach a person who possesses relevant knowledge, accessing it is not trivial.
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Indeed, increasing network range goes hand in hand with a diminishing ability to assimilate it
(Hansen, 1999; Lane & Lubatkin, 1998; Zahra & George, 2002).
For example, the individual's ability to engage in deep dialogue is reduced for diverse
knowledge compared to similar knowledge. Insufficient shared knowledge makes it more
challenging to achieve effective communication to articulate and understand diverse knowledge
(Black, Carlile, & Repenning, 2004; Carlile, 2004; Dougherty, 1992). In this case, insufficient
absorptive capacity, i.e., the ability to recognize, obtain and utilize new information (Cohen &
Levinthal, 1990; Lane & Lubatkin, 1998; Lane, Salk, & Lyles, 2001, Zahra & George, 2002),
prevents individuals from benefiting from the incoming knowledge. It is much easier to expand
knowledge along an existing specialization as it offers a high number of docking points, or
shared knowledge, for offered knowledge to be connected (Postrel, 2002). In the context of an
MNC, such docking points include similar local values, norms, institutional environments, as
well as similar competencies and skills learned to fulfill particular tasks (Bartlett & Ghoshal,
1989; Buckley & Carter, 2004). However, due to a lack of shared knowledge, individuals
seeking to access diverse knowledge find it challenging to recognize and assimilate it.
Combining these arguments, we suggest that the decision points when seeking to access
knowledge revolve around the diversity-accessibility trade-off. We further suggest that the way
in which the diversity-accessibility trade-off is manifested depends on the type of knowledge
(Dane, 2010). Knowledge comprises a range of components, that represent different types of
knowledge (e.g., Cross & Sproull, 2004; Grant, 1996; Pedersen, Petersen, & Sharma, 2003).
Each type has different attributes, which imply different levels of ease of flow or stickiness
(Szulanski, 1996). In this paper, we focus on two different types of diverse knowledgenovel
knowledge and specialist expertisethe two main types required for knowledge creation in
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organizations (Tsai, 2001). This allows us to hypothesize the optimal resolution of the diversity-
accessibility trade-off given the options a problem solving manager in a subsidiary has with
regard to network mobilization.
Hypotheses
Given the benefits of recombining new or innovative ideas or knowledge, our first hypothesis
concerns the accessing of novel knowledge, defined as knowledge that is new or innovative from
the perspective of the accessing individual. As knowledge is subjectively given, it is the obtainer,
based on his or her own experience, who decides if the knowledge is novel (Nonaka & Takeuchi,
1995). Fundamentally, novel knowledge is created by drawing on the tacit knowledge of others
(Grant, 1996; Nonaka, 1994). Repeated cycles of deep engagement between individuals are
required to achieve the conversions between tacit and explicit knowledge required for new and
innovative knowledge to emerge (Nonaka, 1994; Nonaka & Takeuchi, 1995). This may include,
for example, different perspectives on the problem and unique solution ideas that are only
possible if the problem solver and approached individual engage intensely. Hence, novel
knowledge is problem-specific, created through interaction.
When seeking to acquire novel knowledge, there are a couple of opportunities to break
out of same function and same location tie mobilization that only offers a low likelihood of novel
knowledge. The four options are to increase range either along geographic range only (i.e.,
approach individuals in the same function at another MNC unit), functional range only (i.e., to
target individuals working in a different function at the same subsidiary); opt for range along
both dimensions simultaneously (i.e., to reach out to individuals from another function located at
another MNC unit), and to use external range (i.e., to approach individuals from another firm).
However, the problem solving manager must consider the diversity-accessibility trade-off.
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In relation to increasing geographic range by seeking out individuals working in another
country location within the same MNC, it has been found that stimulating social interactions, as
required for novel knowledge to emerge, are more challenging to achieve via virtual
communication (Morris, Zhong, & Makhija, 2015). However, the positive characteristic of
mobilizing internal ties is their embeddedness in the same social community (Kogut & Zander,
1992). This offers a certain level of common language which facilitates engaged dialogue,
reducing some of these difficulties (Kogut & Zander, 1996; Nahapiet & Ghoshal, 1998). This
facilitates geographic range in ties mobilized. Overall, we expect that the social community
characteristic, combined with the problem solving manager’s desire to increase the likelihood of
the approached person having relevant knowledge, make it likely that novel knowledge emerges
in their interaction and gets assimilated. Consequently, this propels geographic range in tie
mobilizations within MNCs.
In relation to functional range, there are additional aspects that need to be taken into
consideration to contextualize the underlying trade-off. A shared knowledge base, in the sense of
common function-specific skills or practices between individuals, enables the required deep
interaction for the problem solving manager and approached individual to pursue the repeated
cycles of knowledge conversion to form problem-specific new or innovative ideas or knowledge.
Yet, cross-functional knowledge interactions are inhibited by difference in knowledge that can
lead to little, if any, shared knowledge. This causes major collaboration difficulties despite best
intentions to work together (Carlile, 2004; Dougherty, 1992; Postrel, 2002), diminishing the
ability to create, and hence access, novel knowledge.
In relation to the option to approach individuals within the same subsidiary but working
in another function, this means the following. The problem solving manager and approached
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individual benefit from a shared host country context and local subsidiary organizational
structures (Bartlett & Ghoshal 1989; Galbraith 1973, 1977). This leads to a certain commonality
in knowledge. Nevertheless, across functions within a subsidiary, knowledge differs substantially
as distinct knowledge pools form around specific functional skills and unique sets of
competencies (Buckley & Carter, 2004). Subsequently, when problem solving managers increase
functional range even within their own subsidiary, there remains the challenge of lacking shared
domain-specific knowledge to facilitate deep interaction. Even if a problem solving manager has
the expectation that an individual in the same subsidiary but working in another function could
be useful in creating novel knowledge, thus fulfilling the knowledge diversity criteria of the
trade-off, there is a low likelihood in assimilating this knowledge. This undermines the
accessibility criteria of the trade-off.
Similar arguments apply to the remaining intra-organizational optionto reach out to
individuals where both geographic and functional range are present simultaneously. Here, our
arguments imply that there is an extremely high likelihood that the approached individuals
possess diverse knowledge useful for the creation of new or innovative ideas and knowledge
(fulfilling the relevance criteria of the trade-off), yet the underlying commonality in knowledge
is severely diminished. This exacerbates interaction challenges, making it unlikely that novel
knowledge can be assimilated, hence also undermining the accessibility criteria of the trade-off.
Another option for increasing network range to access specialist expertise is mobilizing
ties to external individuals.
1
Knowledge from externals has a high likelihood of being different to
1
For external range we do not detail functional and geographical range. We do not include functional range as
managers seek certain external specialist firms (rather than functions), such as a law firm or a consultancy firm to
fulfill particular tasks. We thus expect that problem solving managers premise their network mobilizations on the
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what individuals in the MNC currently know (Haas, 2010; Hocking, Brown, & Harzing, 2007;
Monteiro, 2015; Morris et al., 2014; Tortoriello, 2015). In particular, when solving non-routine
problems, the atypical nature of the challenge often means that there are knowledge gaps where
external sources can be valuable (Foss, Lyngsie, & Zahra, 2013; Liebeskind et al., 1996). As
such, using external knowledge can improve learning during problem solving (Soo, Devinney, &
Midgley, 2007).
Despite external knowledge having a conducive degree of difference, thus likely fulfilling
the knowledge diversity criteria of the trade-off, accessing knowledge externally faces particular
challenges. Exchange relationships governed through markets provide weaker incentives for
knowledge sharing compared to exchange relationships governed through hierarchy (Felin &
Zenger, 2014; Nickerson & Zenger, 2004). Relationships with externals may thus fail to offer
strong incentives that foster deep dialogue. Moreover, firm outsiders often possess knowledge
that has a different architecture to the knowledge of the MNC (Henderson & Clark, 1990; Morris
et al., 2014). We contend that these factors lead to difficulties to engage in iterative cycles of
knowledge conversion to create problem-specific new ideas and knowledge. We thus do not
hypothesize the accessing of this type of diverse knowledge through external tie mobilization.
Combining these ideas, we suggest that approaching individuals across geographic range
but within the same function optimizes the diversity-accessibility trade-off for novel knowledge.
This option of middle range likely offers access to sufficient diversity in knowledge to facilitate
the creation of novel knowledge, without unnecessarily increasing interaction difficulties as is
likelihood of the firm possessing relevant knowledge rather than functional affiliation. In terms of geographical
range, we do have data, but there are only a small number of cases in the different location category, and this limits
the convergence of our models.
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the case for cross-functional knowledge seeking. Both, the knowledge diversity and accessibility
criteria of the trade-off can be met simultaneously. Therefore, we hypothesize:
Hypothesis 1: Mobilizing same function but location heterogeneity ties is associated with
the accessing of novel knowledge.
As solution development during problem solving requires filling specific knowledge gaps
(Tippmann, Sharkey Scott, & Mangematin, 2014b), our next hypotheses concern the accessing of
specialist expertise, defined as expert skills or knowledge in a specialized field. A person is
perceived to have specialist expertise if s/he is a reliable source with extensive knowledge and
ability in a specific domain (e.g., Chi, Glaser, & Farr, 1988; Ericsson, 2006; Prietula & Simon,
1989). Such specialized expertise builds on prolonged experience and practice (Polanyi, 1966),
and offers the problem solving manager additional knowledge (Cross & Sproull, 2004; Hargadon
& Sutton, 1997). Importantly, it has been found that specialist expertise used during problem
solving often is externalized tacit knowledge (Tippmann et al., 2014b), such as engineering, legal
and financial advice. As such, specialist expertise represents pre-existing knowledge that
becomes explicit, or articulated, when the holder extracts it (Nonaka, 1994; Nonaka & Takeuchi,
1995). This contrasts novel knowledge, which is specifically created knowledge through
repeated cycles of knowledge conversion. As such, specialist expertise is more easily accessible
as externalizing knowledge is less demanding than repeated cycles of knowledge conversion.
In relation to intra-organizational tie mobilization within the MNC, akin to the accessing
of novel knowledge, we expect that the social community characteristics propel the pursuit of
geographic range and make it likely that specialist expertise can be accessed. However, given the
fundamental difference between these two knowledge types, we expect that subsidiary managers
make different assumptions about their ability to recognize and assimilate specialist expertise
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across functional range. In contrast to novel knowledge, specialist expertise is pre-existing, and
does not require repeated cycles of knowledge conversion. Rather, it can be extracted through
externalization when the knowledge holder articulates it (Nonaka, 1994). A large shared
knowledge based is, therefore, less of a requirement as pre-existing knowledge can be more
easily recognized and assimilated. In addition, the need to fill specific knowledge gaps
encourages individuals to reach out beyond their own work context. This propels an increase in
functional range. In terms of the diversity-accessibility trade-off, we therefore suggest that a
problem solving manager in a subsidiary can pursue far rangeto combine geographic and
functional range simultaneously to maximize chances that the approached person holds specialist
expertise without curbing their ability to recognize and assimilate this knowledge. This leads us
to propose:
Hypothesis 2: Mobilizing different function and location heterogeneity ties is associated
with the accessing of specialist expertise.
Concerning the option of external range to access specialist expertise, different arguments
apply to this type of diverse knowledge compared to the accessing of novel knowledge. It is
common that very specific knowledge gaps arise during problem solving (Foss et al., 2013). This
suggests that subsidiary managers have well-defined needs for specialist expertise, which is
either not held by the MNC or not accessible. Seeking this specialist expertise externally offers
great flexibility in terms of seeking out the most relevant experts (Liebeskind et al., 1996), and,
as one of multiple diverse knowledge components used for problem solving, can aid the
development of innovative solutions. As specialist expertise can be accessed through
externalization, there is less of a requirement for close collaboration (Nonaka, 1994). Therefore,
the weaker incentives to share knowledge in relationships governed through markets compared
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to relationships governed through hierarchy (Felin & Zenger, 2014; Nickerson & Zenger, 2004),
represent less of a hurdle. Indeed, for this type of knowledge, we expect that such weaker
incentives suffice to encourage knowledge externalization for its access. In this respect, the
problem solving managers may benefit from the external embeddedness of their subsidiaries,
which refers to the trustful and reciprocal relationships of subsidiaries, especially within their
local environment (Andersson, Forsgren, & Holm, 2002; Nell & Ambos, 2013). This gives
subsidiary managers options to build their external network (O’Brien et al., 2018), which they
may utilize to mobilize ties. The trust and reciprocal nature of these relationships encourages
individuals to externalize their expertise. This allows the accessibility criteria of the trade-off to
be met. We therefore propose the suitability of far range:
Hypothesis 3: Mobilizing external ties is associated with the accessing of specialist
expertise.
In the previous hypotheses we have theorized the types of ties problem solving managers
mobilize to access novel knowledge and specialist expertise independently. These two resources,
however, can potentially be accessed from the same tie. When considering the diversity-
accessibility trade-off, problem solving managers are in a different situation when accessing
novel knowledge and specialist expertise jointly. In this scenario, the accessing of one type of
knowledge can facilitate the accessing of the other type, leading to a multiplex effect (i.e., where
both types of knowledge are accessed from the same individual).
Research on different aspects of advice seeking suggests that advice can be broken into
different components with less complex types of advice, such as giving answers, and more
complex types of advice, such as validation and legitimation (Cross et al., 2001). Further, Cross
and colleagues (2001) found that when an individual received the complex type of advice, they
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also received the less complex one. Likewise, Cross & Sproull (2004) found that seeking one
type of knowledge from an individual increased the likelihood of seeking a second type of
knowledge.
Following this and the argument of satisficing search (Cyert & March, 1963), problem
solving managers will likely return to a tie where a knowledge flow has already been achieved.
In this case, the approached individual is familiar with the project, which facilitates further
engagement and hence chances for other types of knowledge to be accessed. Therefore, the ties
that problem solving managers have mobilized to access one type of knowledge should increase
the likelihood that they can recognize or create, and then assimilate, the other knowledge type.
This suggests that accessing both types of knowledge can be done through middle range (i.e.,
same function, but different location ties) and far range (i.e., different function and different
location ties). Likewise, accessing one type of knowledge externally to the organization will
facilitate access to the other one.
Hypothesis 4: Mobilizing (a) same function but location heterogeneity ties, (b) different
function and location heterogeneity ties, and (c) external ties is associated with the
accessing of both novel knowledge and specialist expertise.
METHOD
Sample
Our interest in this paper is not on all the work-related ties an individual has but on those
specifically mobilized for a non-routine problem solving project. Therefore, we focus on how
ties are mobilized in the context of problem solving projects within the MNC. Joining with
arguments that project-level examinations are valuable to understand value creation in problem
solving (Foss et al., 2015), we collected data on 120 problem solving projects from 60
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subsidiaries located in 16 European countries. Subsidiaries located in the U.K. or Ireland
contributed 74 projects, and 46 projects came from subsidiaries located in Central and Eastern
Europe.
The subsidiaries belonged to 40 different MNCs. To ensure generalizability of our
findings, we selected subsidiaries from MNCs that varied by industry (21 ICT, 10
pharmaceutical, and 9 from various other industries) and by size (mean turnover = $30.6 billion
with SD = $33.7 billion, mean number of employees 76,500 with SD = 92,100). There was also
variability in our subsidiaries by age (mean = 26.7 years with SD = 12.8), size (mean = 658 full
time employees with SD = 882), and mode of establishment (48 greenfield and 12 acquisitions).
We selected our problem solving projects based upon two sampling criteria. Each project
needed to have been completed within 12 months of selection to ensure accuracy in event recall
(Huber and Power, 1985). In addition, the problem solving was subsidiary-initiated as opposed to
being undertaken as the result of a decision by the MNC’s headquarters. We liaised with the
senior manager of each subsidiary to identify the problem solving project(s) that fitted our two
sampling criteria. Across the 60 subsidiaries of our sample, there were on average 2.0 projects
per subsidiary (min = 1 and max = 8). The projects selected ranged across various functions of
the subsidiary, including marketing/sales (37 projects, 30.8%), manufacturing (26 projects,
21.7%), services/customer support (24 projects, 20.0%), back-office support (17 projects,
14.2%), supply chain and logistics (8 projects, 6.7%), as well as research and development (8
projects, 6.7%). The projects focused on issues such as replacing a legacy process, increasing the
efficiency or quality of existing management or business practices, and creating new business
processes.
Data collection
19
Overview. We collected the data between August 2012 and June 2015. We surveyed project
leaders and asked them to report on their problem solving networks. The project leaders for each
project in our sample were identified by the senior manager in the subsidiary. Before we
undertook our data collection, our survey items were evaluated for face validity by a panel
consisting of four subsidiary managers and five academics. As a result of comments from the
panel, we undertook necessary amendments to ensure the clarity of our survey questions.
We achieved a 95.2 percent response rate as a result of the endorsement by the senior
manager in each of the subsidiaries in our study. To encourage accurate responses from our
survey participants, we emphasized that all responses would be anonymized and only aggregated
results would be used. In the five instances where two individuals were appointed as joint project
leaders, we interviewed them simultaneously, and they jointly agreed on the answers to our
survey questions. In total, we surveyed 125 project leaders.
Problem solving network survey. We used an ego-network design for our problem
solving network questions. The network questionnaire we developed followed Burt’s design
(1992) and used Marsden’s (1990) approach for generating names of individuals that were
involved in the problem solving project. We chose the network generator approach as opposed to
the roster method as it was not practical to create a pre-determined roster of all possible alters
that our respondents could have sought out regarding the focal problem. Each respondent was
asked to:
“Please identify up to ten (10) people that were important to you when
addressing the <name> problem within your organization. These may be people
within your department/subsidiary, in other departments/subsidiaries as well as
outside the organization. The individuals that you list may or may not be people
20
you communicated with on a regular basis, but should be the people you consider
as having played an important role during the problem solving. These people may
have been formally or informally involved. Please also think about the various
stages of the problem solving: the initial problem formulation, solution finding,
and later solution implementation. You may need to glance back at your email
list, communications or updates to help you identify these important people.
Please write down the name, nickname or initials. (This information will not be
analyzed and is only to help you to differentiate the key people in later
questions.)”
We focused the name generator question on the problem solving process in order that the
respondent would react to instrumental relationships rather than those based on affect. It has
been shown that behavioral specificity has a recall advantage, so limiting respondent
forgetfulness (Bell, Belli-McQueen, & Haider, 2007). This also has the advantage of increasing
the likelihood of the respondent recalling names of weak ties, countering the potential bias of
only strong ties being included by the respondent (Marsden, 1990). The name generator question
was phrased so that it included several prompts to different ways of involvement in the problem
solving process. This also helps to mitigate the possibility of respondents forgetting to include
weaker ties (Bell et al., 2007).
We limited the name generator question to ten individuals, and this resulted in N = 838
ties across all the problem solving projects. However, we also asked each respondent to indicate
how many individuals were important in addressing the problem. In 21 of the 120 problem
solving projects more than ten individuals were indicated as being important. On average,
however, respondents indicated that 7.92 individuals were important (min = 1, max = 25, SD =
21
4.19). Given our concern to manage the length of the survey, which requires an upper limit to the
number of individuals that can be nominated in the name generator question, this suggests that
our data offers a comprehensive view of each of our respondent's problem solving network.
We asked name interpreter questions for all individuals a respondent listed. Name
interpreter questions were used to capture the properties of the relationships each individual had
with those that they listed in the name generator question (Wasserman & Faust, 1994). For
example, respondents were asked to indicate whether the individual was from the same or an
external organization, higher or lower in the hierarchy than themselves, what location and
function the individual belonged to, and the relationship with the individual prior to the
beginning of the problem solving project. Overall, the survey allowed us to gather data on the
mobilized network of the manager who led the problem solving effort.
Measures
Dependent variable. We are interested in the propensity for problem solving managers to access
specific knowledge resources from another individual within or outside the MNC (e.g., Ambos,
Nell, & Pedersen, 2013; Hansen & Løvås, 2004). To measure the acquisition of resources, we
asked respondents to "Please indicate the types of problem solving ‘resources’ you received from
each person listed below."
2
While we asked about several different types of resources as part of a
larger research project, such as staff/people, financial resources and legitimacy, our focus in this
paper is on two types of knowledge: novel knowledge and specialist expertise. For each
2
It is possible that a respondent might report receiving novel knowledge or specialist expertise from an individual
without actively seeking that resource from this particular person. However, given that problem solving requires
accessing diverse knowledge, we have assumed that the manager in the subsidiary sought to access each type of
knowledge.
22
individual a respondent listed in the name generator question, the respondent was asked if they
received 1) "new and innovative ideas/knowledge" (novel knowledge), and 2) “specialist
knowledge, expertise or skills” (specialist expertise). For each person listed, respondents could
enter 1 if they received the specific resource from that individual or 0 if they did not receive it.
As we are interested in ascertaining if a novel knowledge or specialist expertise tie exists or does
not exist between two individuals, i.e., a specific event as opposed to the extent to which an
event occurred, we followed prior research and used a binary measure of resource receipt (see for
example, Lomi et al., 2014; Mehra, Kilduff, & Brass, 1998, 2001; Singh, Hansen, & Podolny,
2010; Zhao & Anand, 2013). To account for ties where novel knowledge and specialist expertise
are both accessed, our dependent variable has four categories (neither resource accessed, only
novel knowledge accessed, only specialist expertise accessed, and both novel knowledge and
specialist expertise accessed). We use accessing neither resource as the reference category.
Independent variables. Using objective organization, function and location information provided
for each tie, we created a variable that reflected whether a mobilized tie was external (external)
or not, as well as four variables for internal ties: (1) whether a tie was same function and same
country location (same function same location), (2) same function and different country location
(same function different location), (3) different function and same country location (different
function same location), and (4) different function and different country location (different
function different location). We used the same function same location variable as the reference
category in our analysis. Changing the reference category does not change the overall findings in
our results.
Network tie control variables (level 1). In our models, the level 1 control variables
include the extent to which an individual had a trust tie with the respective individual in their
23
problem solving network because the presence of trust has been found to facilitate interaction for
knowledge sharing (Chua, Ingram, & Morris, 2008; Hansen, 1999; Levin & Cross, 2004). Our
measure of benevolence trust was adapted from Levin and Cross (2004). Each respondent was
asked, "Prior to involving this person into this problem solving process, I assumed that he or she
would always look out for my interests." Answer options ranged from 1 = strongly disagree to 7
= strongly agree.
We also control for whether the tie was a new tie at the beginning of the project or
whether the problem solving manager had previously interacted with the respective individual
they listed as part of their problem solving network. New ties have been shown to be made due
to individuals’ intentions to gain additional resources (Dahlander & McFarland, 2013), and may
therefore influence the accessing of novel knowledge or specialist expertise. Each respondent
was asked, "At the beginning of the problem solving, what describes best the relationship with
each of the people listed below?" Answer options ranged from 1 = I did not know this person, 2
= I did not know this person, but someone else within the problem solving network had worked
with this person previously, 3 = I knew this person before, but did not communicate for at least 3
years, 4 = I knew this person before and interacted rarely, 5 = I knew this person before and
interacted often. Answer options 1 and 2 were coded as 1 = new tie, all other options were coded
as 0 = existing tie.
In addition, we control for the comparative hierarchical level (hierarchy) of the
individual the problem solving manger had a tie with. Ties to colleagues higher in the hierarchy
have been shown to distort knowledge exchanges due to the negative effects caused by power
imbalances (Reitzig & Maciejovsky, 2015). Answer options ranged from 1= Two or more levels
lower, to 5 = Two or more levels higher.
24
Aggregated network control variables (level 2). To account for the possibility of cluster
confounding, where the within-person effects are conflated with the between-person effects, we
also include an aggregated measure for each of the tie variables. In our models, we include,
average new tie, average trust, average hierarchy, average same function different location,
average different function same location, average different function different location, and
average external. These aggregated variables account for the within-and between-cluster
variation in level 1 covariates which otherwise would be captured by a single variable and could
potentially result in biased estimates.
We also control for the extent to which individuals in each manager’s problem solving
network collaborated with each other. There is considerable research to indicate that closed
versus open networks can influence accessing novel knowledge or specialist expertise (Burt,
2004; Tortoriello et al., 2012), For our measure of the density of collaboration network we asked
the following question: “Please indicate your perception of how closely each pair of individuals
is collaborating (i.e., actively work together) on solving the problem (excluding mandatory
updates and communications).” Respondents were asked to choose from the following four
options: 1 = not intensely at all (i.e., no active collaboration), 2 = not intensely, 3 = intensely,
and 4 = very intensely. The question was adapted from a measure used by Mors (2010) as well
one used by Battilana and Casciaro (2012). We dichotomized the responses at three and above as
this represented intense collaboration. Our results are unchanged if we dichotomize the responses
at two and above. We calculated the density of the collaboration network measure by dividing
the perceived number of intense and very intense ties between the individuals in the respondent's
problem solving network by the possible number of ties. The possible number of pairs of
25
collaborating individuals if a respondent had nominated N individuals in their network is (N *
(N-1))/2.
3
In addition, we control for the total number of ties that each respondent nominated as
having more ties could influence the possibility of designating a tie as being a novel knowledge
tie or a specialist expertise tie.
Problem solving project and individual attribute control variables (level 2). There is
potentially variance across the problem solving projects. Therefore, we control for problem
complexity (α = .681), asking “to what extent did the problem...” The three items were measured
on a 7-point Likert scale and developed based on Baer et al. (2013) and included the following,
“Involve a large number of variables, many of which were not directly observable (i.e.,
symptoms are known, but the cause is unknown),” “Involve a high degree of connectivity among
the elements of the problem (i.e., change in any one variable will affect other variables),” and
“Involve a dynamic component (i.e., the pattern of interaction is changing over time).” To
account for the scope of the problem, we control for problem duration, measured in months.
Furthermore, we include controls for the characteristics, ability and willingness of the
focal problem solving manager. As a characteristic of the individual, we include gender (coded
as female = 1 and male = 0), which has been shown to influence the types of ties created (Ibarra,
1992), therefore likely impacting the ability to access novel knowledge or specialist expertise. To
account for further aspects of ability to access novel knowledge or specialist expertise, we
control for tenure (number of years in the organization) as those with higher tenure have more
3
Perceptions of collaboration are frequently used in network research that use a name generator methodology. For
empirical examples, see Burt (1992, 2004).
26
experience in the organization, which has been found to influence an individual’s knowledge
sharing ability (Reinholt et al., 2011), and hierarchical level (ranging from 1 = team member to 5
= director) as those in more senior positions may also have larger and more diverse networks
(Burt, 1992), allowing them to build greater capacity in accessing knowledge across the
organization and from external partners. In addition, we control for the experience of the
problem solving manager with a measure of cross-functional experience because it increases the
likelihood that the manager has personal relationships that cut across functions and has trans-
specialist experience that makes it easier to acquire novel knowledge or specialist expertise
(Postrel, 2002). Cross-functional experience is based upon the number of functional areas an
individual has worked in during his/her career. Another ability-related control variable we
include is international assignment experience because managers who worked in other country
units are more likely to have personal relationships that span geographies and have gained more
experience in engaging with knowledge from other MNC units, which enable them to access
more diverse knowledge (Minbaeva, Mäkelä, & Rabbiosi, 2012). International assignment
experience is measured by the number of months of international assignment the problem
solving manager has had with the current employer (minimum of three months per assignment).
We also control for MNC networking which represents the network that an individual has had the
opportunity to build as a result of having an international role. This three item scale was adapted
from Ghoshal, Korine, & Szulanski (1994) and measures the number of days spent working and
travelling internationally. Finally, we control for value-based global identification (α = .809)
because, as a measure of the psychological attachment to the MNC, it proxies willingness of the
manager to initiate the flow of tacit knowledge (Osterloh & Frey, 2000) and leads to more
knowledge flow across local boundaries (Lomi et al., 2014). The four items were measured on a
27
7-point Likert scale adapted from Reade (2001) and included: “My corporation represents values
that are important to me,” and “I see no difference between my values and the ones of my
corporation.
Subsidiary control variables (level 3). Each of the problem solving projects and the
manager who is trying to solve the problem are nested in subsidiaries. To account for the
possible influence of the subsidiary on the selection of knowledge ties, we also include some
subsidiary-level controls. These include subsidiary size (log of the number of individuals
employed in the subsidiary) to account for the amount of potential knowledge available within
the subsidiary. We also account for subsidiary value chain scope as the breadth of competences
that are located at the focal subsidiary. This functional scope was a count of the number of value
chain functions performed by the subsidiary. In addition, we account for subsidiary mode of
establishment, i.e., whether the subsidiary was a greenfield site compared to an acquisition (1 =
greenfield, 0 = acquisition) because problem solving managers in acquired subsidiaries, by
contributing unique or complementary resources and assets to the MNC, may be more likely to
possess diverse knowledge than those in greenfield subsidiaries (Cantwell & Mudambi, 2005).
Likewise, we account for host region effects of the subsidiary, including whether a subsidiary is
in a developed market or an emerging market (1 = developed market, 0 = emerging market).
Finally, we control for the type of industry with the inclusion of dummy variables for Pharma
and ICT with the reference category being all other industries such as building materials, energy,
financial services, fast-moving consumer goods and machinery.
Method of analysis
To test our hypotheses, we use a random-intercept Generalized Linear Latent and Mixed Model
(GLLAMM) as our data has a dependent variable with four categories: accessing novel
28
knowledge; accessing specialist expertise; accessing both novel knowledge and specialist
expertise; and accessing neither types of knowledge, the latter being the reference category (see
Rabe-Hesketh, Skrondal, & Pickles, 2004 for a discussion of GLLAMMs). This model is also
able to account for the hierarchical nature of our data with alter network ties nested within
individual problem solving managers. In addition, each manager’s problem solving project is
nested within a subsidiary (Raudenbush & Bryk, 2002). We utilized the GLLAMM analysis
routine in STATA. This allowed us to control for higher level effects, while focusing on our
level 1 analysis of the tie data (Rabe-Hesketh et al., 2004).
RESULTS
Table 1 includes the descriptive statistics and correlations of the data in our analysis which
embeds characteristics of the individual ties within higher-level controls at the
problem/individual and subsidiary levels.
<Insert Table 1 about here>
In Tables 2-4, we detail our Generalized Linear Latent and Mixed Models (GLLAMMs)
predicting the mobilization of novel knowledge ties, specialist expertise ties, and both novel
knowledge and specialist expertise ties. In Model 1 are our control variables and in Model 2 we
add our variables of interest. For ease of interpretation we have divided the results into three
separate tables. In Table 2 we present our results for only the mobilization of novel knowledge
ties, in Table 3 we detail our results for mobilizing only specialist expertise ties, and in Table 4
we present our results for mobilizing both novel knowledge and specialist expertise ties.
In Model 1 (Table 2-4), we find that the variance between problem solving projects is
positive and significant (B = 0.982, p < 0.001) indicating that there is a significant difference in
the selection of novel knowledge and specialist expertise ties between the problem solving
29
projects. Hence, there is a need to use a random-intercept model to control for level 2 variables
(Raudenbush & Bryk, 2002). Including a level 3 random intercept was not found to be
significant, and we do not include it in our models.
<Insert Table 2-4 about here>
Accessing novel knowledge
In Model 1 (Table 2), we include the various control variables. The international assignment
experience variable is negative and marginally significant (B = -0.034, p < 0.1), indicating that
individuals with more international assignment experience are less likely to access novel
knowledge. Our measure of the density of collaboration network is positive and marginally
significant (B = 1.544, p < 0.1). The more problem solving managers have networks where
individuals collaborate intensely with one another the more likely they are to access novel
knowledge. The measure for trust is positive and significant (B = 0.801, p < 0.001) and indicates
that ties with a higher level of trust are more likely to be novel knowledge ties. Finally, our
measure of hierarchy ties is positive and significant (B = 0.441, p < 0.01), suggesting that
problem solving managers are reaching up the hierarchy for novel knowledge.
In Model 2, we add in the network alter variables of interest and their aggregated
controls. The same function and different location variable is positive and significant (B = 1.499,
p < 0.05) in relation to the comparison category of same function and same location. This lends
support to Hypothesis 1 whereby same function but location heterogeneity is associated with a
greater likelihood of the activation of novel knowledge ties. Managers that require novel
knowledge to solve problems are reaching out beyond their own subsidiary in an attempt to
access innovative ideas that they likely cannot get from more local sources. They do, however,
30
choose to stay within their own function, minimizing the knowledge accessibility issues that
come with reaching to knowledge that resides in other functions.
Accessing specialist expertise
In Table 3, we detail our Generalized Linear Latent and Mixed Models predicting the
mobilization of specialist expertise ties. In Model 1, we include the various control variables.
The developed market variable is positive and significant (B = 1.104, p < 0.05), indicating that
managers in developed markets were more likely to access specialist expertise from their
problem solving ties. The measure for new ties is positive and significant (B = 0.856, p < 0.05)
and indicates that ties created since the beginning of the problem solving project are more likely
to be specialist expertise ties. In addition, our measure of hierarchy ties is positive and
significant (B = 0.832, p < 0.001), suggesting that problem solving managers are reaching up the
hierarchy for specialist expertise.
In Model 2, we add in the network alter variables of interest and their aggregated
controls. The different function and different location variable is positive and significant (B =
1.455, p < 0.01) in relation to the comparison category of same function and same location. This
lends support to Hypothesis 2 whereby function and geographic range is associated with a
greater likelihood of the activation of specialist expertise ties. Managers that require specialist
expertise to solve problems are reaching out beyond their own subsidiary in an attempt to access
specialist expertise that they likely cannot get from more local sources. In addition, they choose
to go outside their own function, maximizing knowledge range that occurs with reaching to
knowledge that resides in other functions. The external ties variable is also positive and
significant (B = 2.539, p < 0.01) in relation to the comparison category of same function and
31
same location. This lends support for Hypothesis 3 whereby mobilizing external ties is
associated with a greater likelihood of accessing specialist expertise.
Accessing novel knowledge and specialist expertise
In Table 4, we detail our Generalized Linear Latent and Mixed Models predicting the
mobilization of novel knowledge and specialist expertise ties. In Model 1, we include the various
control variables. The developed market variable is positive and significant (B = 1.409, p <
0.01), indicating that managers in developed markets were more likely to access both novel
knowledge and specialist expertise from their problem solving ties. Our measure of the density of
collaboration network is also positive and significant (B = 2.478, p < 0.001). The more problem
solving managers have networks where individuals collaborate intensely with one another the
more likely they are to access novel knowledge and specialist expertise from the same tie. The
measure for new ties is positive and significant (B = 0.924, p < 0.01) and indicates that ties
created since the beginning of the problem solving project are more likely to be multiplex where
novel knowledge and specialist expertise are both accessed. The measure for trust is positive and
significant (B = 0.397, p < 0.001) and indicates that ties with a higher level of trust are more
likely to be multiplex and result in accessing novel knowledge and specialist expertise. In
addition, our measure of hierarchy ties is positive and significant (B = 0.873, p < 0.001),
suggesting that when problem solving managers reach up the hierarchy they are more likely to
access novel knowledge and specialist expertise.
In Model 2, we add in the network alter variables of interest and their aggregated
controls. The same function and different location variable is positive and significant (B = 1.052,
p < 0.05) in relation to the comparison category of same function and same location. This lends
support to Hypothesis 4a whereby same function but location heterogeneity is associated with a
32
greater likelihood of simultaneously accessing novel knowledge ties and specialist expertise.
Managers that require novel knowledge and specialist expertise to solve problems are reaching
out beyond their own subsidiary in an attempt to access innovative knowledge and expertise that
they likely cannot get from more local sources. The different function and different location
variable is positive and significant (B = 1.264, p < 0.01) in relation to the comparison category of
same function and same location. This lends support to Hypothesis 4b whereby function and
geographic range is associated with a greater likelihood of accessing multiplex novel knowledge
and specialist expertise ties. Managers that require both novel knowledge and specialist expertise
to solve problems are reaching out beyond their own subsidiary in an attempt to access
knowledge that they likely cannot get from more local sources. In addition, they choose to go
outside their own function, maximizing knowledge range that occurs with reaching to knowledge
that resides in other functions. The external ties variable is also positive and significant (B =
2.864, p < 0.001) in relation to the comparison category of same function and same location.
This lends support for Hypothesis 4c whereby mobilizing external ties is associated with a
greater likelihood of accessing novel knowledge and specialist expertise.
DISCUSSION
In summary, we find support for our hypotheses in our model of network mobilization. This
reveals insights into the way in which individuals achieve the accessing of novel knowledge and
specialist expertise. As predicted, mobilizing ties from an MNC unit in another country location
but same function increases the likelihood of accessing novel knowledge, meaning a preference
for middle range. Mobilizing ties from another function in an MNC unit located in another
country and from an external organization increases the chances of accessing specialist expertise,
representing a preference for far range. Also, there is a tendency of a multiplex effect in that
33
there is an increased likelihood for a second type of knowledge to be accessed from the same tie.
Next, we will discuss how these findings address shortcomings in prior research before outlining
implications for management and limitations.
First, we propose that the acquisition of knowledge is underpinned by the diversity-
accessibility trade-off, which is the simultaneous, yet conflicting, demands for increasing range
to ensure approaching an individual that possesses relevant diverse knowledge while ensuring
the ability to recognize and assimilate this knowledge through contracted range. Moreover, we
demonstrate the way in which this trade-off is manifested for different types of knowledge. We
thus provide an answer to the question of the optimal resolution of this trade-off to ensure the
criteria of offering diversity of knowledge and securing ability to access it are met
simultaneously, given particular attributes of certain knowledge types (such as evident in novel
knowledge and specialist expertise).
In resolving the diversity-accessibility trade-off for novel knowledge, our finding of
increasing geographic range while staying within the same function has interesting implications.
Prior literature finds that knowledge seeking by individuals in the MNC decreases as geographic
distance rises (e.g., Haas & Cummings, 2015; Lomi et al., 2014; Morris et al., 2015), suggesting
difficulties in interacting across geographic distance as a main impediment. Our findings,
however, imply that this impediment to accessing novel knowledge from individuals in other
country locations can be dealt with. Instead, it appears that functional diversity in knowledge is
more problematic and may be too cumbersome to overcome when novel knowledge is sought.
For this type of knowledge, our findings suggest that MNC managers are better able to navigate
difference in knowledge rooted in cultural values, norms and institutional environments, as
encapsulated in geographic range, compared to diversity in knowledge due to different domain-
34
related skills and competencies, as manifest in functional range. This reinforces the notion that
individuals in MNCswhen accessing knowledge—may suffer from “too much diversity”
(Mors, 2010) and substantiates arguments that within-function knowledge flows are an important
share of MNC knowledge exchanges (Ambos et al., 2013).
In relation to accessing specialist expertise, the optimal resolution to the diversity-
accessibility trade-off within the MNCto increase both geographic and functional rangealso
adds new insights. It suggests that MNC managers’ bias towards search close to their own
knowledge is unwarranted for certain knowledge types. In particular, when knowledge is pre-
existing (as is the case for specialist expertise), there is the highest propensity to acquire it when
reaching out the furthest as the ability to access this knowledge is not noticeably limitedan
individual’s ability to recognize and assimilate such knowledge despite the presence of
functional and geographic range is maintained.
In addition, we find that mobilizing employees from another function located at the same
subsidiary does not significantly increase the chances of accessing either novel knowledge nor
specialist expertise. Although accessing cross-functional knowledge contends with a lack of
shared domain-specific knowledge regardless of whether it occurs in a co-located or
geographically distant setting, these findings are nevertheless somewhat surprising. The literature
on problem solving in an innovation context consistently pinpoints the value of cross-functional
boundary spanning, typically within a co-located firm setting (e.g., Carlile, 2004; Dougherty,
1992). We believe that our findings do not deny the value of accessing knowledge across
functions from within the same subsidiary, for example, to boost the generation of creative and
innovative solutions. Rather our findings seem to imply that optimizing the chances of acquiring
35
valuable novel knowledge and specialist expertise in the MNC, in terms of knowledge diversity
and accessibility, involves spanning geographic boundaries.
With regard to external knowledge, our findings of an association between mobilizing
organization outsiders and accessing specialist expertise confirms the capacity of external
individuals to offer diverse knowledge, which is selectively activated to fill knowledge gaps
during problem solving (Foss et al., 2013; Soo et al., 2007). Moreover, our findings of accessing
specialist expertise from externals seems to suggest a constrained value of subsidiary
embeddedness in boosting access to diverse knowledge: It appears to be more valuable for
knowledge that is pre-existing and accessible through externalization, rather than knowledge that
requires creation through repeated cycles of knowledge conversion.
Additionally, finding support for a multiplex effect is interesting as it suggests that
network mobilization decisions are not made in isolation but with a view of optimizing chances
of accessing required resources, here by drawing on the same people for multiple knowledge
types. Previous research on multiplex ties has generally focused on the role that friendship ties
play in facilitating access to valuable information (Methot et al., 2016; Shah, Parker, &
Waldstrøm, 2017). In addition, Cross and colleagues (2001) have shown that accessing one type
of knowledge facilitates access to other types of knowledge. Our findings build upon this to
illustrate that problem solving managers facing the diversity-accessibility trade-off with regard to
knowledge can in some cases expand their range when accessing novel knowledge when it is
combined with accessing specialist expertise.
Combining these findings for the diversity-accessibility trade-off, our study reveals some
specific and previously unnoticed facets in the accessing of diverse knowledge in the MNC, thus
extending the knowledge-based view of the MNC. Moreover, by differentiating between
36
different knowledge types, this study extends the social network literature by departing from the
predominant view on treating resource and knowledge types in a rather generic and
undifferentiated manner.
Second, our study demonstrates the utility of a network mobilization perspective in
examining the role of agency in MNC networks and its link to MNC functioning and advantage.
The reintegration of diverse knowledge is the cornerstone of MNC advantage (Kogut & Zander,
1992, 1993), and interpersonal relationships are the underlying social fabric to facilitate any
knowledge process. Interpersonal relationships connect across the firm’s scope of knowledge
(Bartlett & Ghoshal, 1989; Doz, Santos, & Williamson, 2001; Ghoshal et al., 1994) and are the
constituent element of organizational pipelines that enable knowledge circulation across MNC
units and with external partners. However, how MNC employees, as individual agents, navigate
and selectively activate their interpersonal network for specific purposes remains largely
unanswered (Cano-Kollmann et al., 2016; Haas & Cummings, 2015). More generally, the
emphasis in prior research has typically been on examining an MNC employee’s network
structure, i.e., the relatively enduring and stable interpersonal network and its associated
opportunities and constraints (Kilduff & Brass, 2010). This neglects the aspect of individual
agency. We contribute towards filling this gap by showing that network mobilization by problem
solving managers is a mechanism for the MNC to achieve the circulation of its diverse
knowledge, much of which often remains tightly sealed in its location. More broadly, a network
mobilization perspective provides an important complement to the typical focus on social
structure as it offers an agency-inspired view to achieve a more complete understanding of the
role of networks in the MNC.
37
Third, we advance the problem solving perspective in strategy (Foss et al., 2015; Heiman
et al., 2009; Nickerson et al., 2007, 2012), specifically within the MNC context (Tippmann et al.,
2012; Tippmann et al., 2017), with respect to some important individual actions. Thus far,
individual activities and behaviors, especially in relation to integrating diverse knowledge, have
been recognized as central to the creation of high-value solutions. But these micro-foundations
require further theoretical development and empirical investigation (Baer et al., 2013; Felin &
Zenger, 2014; Nickerson & Zenger, 2004). By drawing attention to the actual mobilization of
ties in specific problem solving projects, we find that the chances of accessing novel knowledge
and specialist expertise are higher from certain external and MNC internal individuals. By
revealing where managers in subsidiaries are most likely to access different types of knowledge
from, our study offers insights into the micro-foundations of integrating diverse knowledge
during problem solving in geographically distributed organizations.
Management implications
During non-routine and complex problem solving, managers need to be encouraged to reach for
collaborators offering diverse knowledge. As these are more likely to be found outside the
boundaries of the subsidiary, managers must be given the opportunity to create these
relationships. This can be done by rotating managers through different parts of the MNC or by
setting up cross-subsidiary problem solving task forces (Cross & Thomas, 2009). At the same
time, our findings emphasize the importance of navigating wisely one’s interpersonal network
and to make smart choices regarding who to mobilize in specific situations.
Specifically, we reveal that novel knowledge is more likely accessed from peers from the
same function (e.g., R&D, manufacturing or marketing), but working in another MNC unit. This
implies that the important factor for accessing novel knowledge is that the approached individual
38
has common domain-specific knowledge to facilitate dialogue for new and innovative ideas or
knowledge to be created. For specialist expertise, the most important factor breaks with the
tendency for local search and suggests the need for confidence in reaching out across country
locations and across functions. As specialist expertise is pre-existing, it remains possible to
recognize and assimilate it even if reaching out far across the organization.
Managers also need to be encouraged to approach individuals outside the organization.
This can be done through encouraging them to attend practitioner-orientated workshops,
conferences, and other events that they can develop trustful relationships with experts and
consultants that they can selectively draw upon during specific problem solving situations. At the
same time, it appears easier to access specialist expertise than novel knowledge from externals,
which offers some realistic expectations as to what knowledge access is most likely achieved
through involving externals. Our findings also imply that it is worthwhile for problem solving
managers to try to seek another knowledge type from a person where one type of knowledge has
been accessed. There is a considerable likelihood of accessing two types from the same person,
allowing the optimization of effort in accessing knowledge.
Limitations and future research
Our measure of novel knowledge and specialist expertise ties is dichotomous which does limit
our ability to differentiate whether a problem solving manager received a high or low volume of
each resource. Future research could incorporate a continuous measure that would allow for a
more nuanced understanding of the volume of each resource that was accessed across different
organizational boundaries. In addition, while our sample is not fully a random sample of problem
solving projects in subsidiaries, it does include 120 problem solving projects from 60
subsidiaries located in 16 European countries and included 40 MNCs, increasing the
39
generalizability of our findings. As our sample of problem solving projects is only from
subsidiaries located in Europe, our findings are not generalizable to other parts of the world.
Future research on subsidiaries outside of Europe would help broaden the generalizability of our
findings. In addition, participation in problem solving projects can either be a result of being
centrally selected or self-nominated. Although our data does not capture whether individuals
were centrally selected or self-nominated, central identification predominates over self-selection
in problem solving within firms (Felin & Zenger, 2014). This gives confidence that network
mobilization is an adequate and relevant perspective. In addition, our identification of problem
solving projects was based upon identification by subsidiaries, and it would be worth examining
problem solving projects identified by MNC headquarters to test if the same patterns of
accessing novel knowledge and specialist expertise apply. Collecting data from the individuals
mobilized would also permit the collection of information on their ability and willingness to
actively and openly participate in the problem solving and contribute their knowledge (e.g.,
Cross & Sproull, 2004; Reinholt et al., 2011). This would extend the examination of the project
leader's ability and willingness, as included in our model. We only have limited data on external
ties in different locations. With a larger sample it would be possible to differentiate between
external ties in the same and different locations. In addition, we only gather data on the ties that
an individual successfully activated. Future research could examine those ties that an individual
wanted to activate but ultimately were not able to. Another area for future research is to examine
problem solving networks at different stages of the process, such as differentiating between
problem formulation, solution development and implementation, to better understand when
accessing novel knowledge and specialist expertise is most beneficial. Additionally, as the
accessing of novel knowledge and specialist expertise is a time-intensive activity, research is
40
required to systematically test its influence on different outcomes, such as project and innovative
performance. Last but not least, it seems worthwhile to explore more global subsidiary
embeddedness with external organizations as a manifestation of the international connectivity of
knowledgea central question in the MNC literature (Cano-Kollmann et al., 2016).
Conclusion
We develop a model of network mobilization to resolve the diversity-accessibility trade-off for
accessing novel knowledge and specialist expertise. This allows us to offer differentiated insights
into the way in which knowledge types shape the optimal resolution of this trade-off in the MNC
context. Our findings also generate new insights for the role of agency in MNC networks as well
as the literature on the problem solving perspective in strategic management.
41
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Table 1. Mean, SD and Pearson Correlations
SD
1
2
3
4
5
6
7
8
9
10
11
1. Pharma
0.50
1.00
2. ICT
0.47
-0.70
1.00
3. Subsidiary size (ln)
1.35
-0.34
0.13
1.00
4. Functional scope
1.55
-0.20
-0.06
0.20
1.00
5. Greenfield
0.40
-0.30
0.10
0.26
0.36
1.00
6. Developed market
0.41
-0.41
0.33
0.41
0.14
0.20
1.00
7. Problem complexity
1.30
-0.31
0.25
0.24
0.10
0.12
0.28
1.00
8. Problem duration
12.24
-0.13
0.16
0.01
-0.10
0.05
0.13
0.01
1.00
9. Hierarchical level
0.97
-0.02
0.07
-0.18
-0.20
0.00
-0.09
0.06
0.03
1.00
10. Tenure
6.79
0.17
-0.06
-0.16
-0.16
-0.08
-0.15
-0.09
0.10
0.09
1.00
11. Gender
0.47
0.01
0.01
0.13
-0.03
0.20
0.11
0.19
0.05
0.04
-0.10
1.00
12. MNC networking
24.02
-0.39
0.36
0.08
0.10
0.19
0.16
0.27
-0.03
0.14
-0.13
-0.13
13. Global identification
0.89
0.19
-0.17
-0.18
0.05
-0.30
-0.30
-0.02
-0.11
-0.16
0.14
-0.36
14. Cross-functional experience
1.30
-0.20
0.21
0.10
0.05
0.10
0.13
0.16
0.10
0.07
-0.04
0.06
15. International assignment experience
18.13
0.01
-0.08
-0.11
0.13
0.17
-0.21
-0.07
-0.10
0.09
-0.02
0.03
16. Total number of ties
2.09
-0.02
-0.10
0.12
0.09
0.13
0.06
0.19
-0.09
0.07
-0.06
-0.04
17. Density of collaboration network
0.22
-0.02
0.04
-0.01
-0.08
-0.09
-0.09
0.17
0.02
0.11
-0.05
0.05
18. Average new ties
0.23
-0.13
0.04
0.20
0.18
0.14
0.19
-0.04
0.21
-0.04
-0.12
0.04
19. Average trust
1.09
0.10
-0.08
-0.06
0.02
-0.15
0.10
0.03
-0.02
0.02
-0.06
0.09
20. Average hierarchy
0.65
0.17
-0.11
-0.19
-0.12
0.00
0.03
0.10
-0.13
0.21
0.02
0.21
21. Average same function different location
0.20
-0.35
0.36
0.02
0.13
0.08
0.26
0.15
0.17
0.06
-0.03
-0.17
22. Average different function same location
0.31
0.20
-0.24
-0.21
-0.01
0.08
-0.35
-0.16
-0.18
0.05
0.06
0.06
23. Average different function different location
0.23
-0.14
0.17
-0.07
0.09
0.01
-0.08
0.04
-0.07
0.01
-0.04
-0.13
24. Average external
0.13
-0.05
-0.07
-0.02
-0.06
0.02
-0.21
-0.08
0.04
0.00
0.07
0.06
25. New ties
0.39
-0.08
0.02
0.12
0.10
0.09
0.11
-0.02
0.13
-0.02
-0.07
0.02
26. Trust
1.46
0.08
-0.06
-0.04
0.01
-0.12
0.07
0.02
-0.02
0.01
-0.04
0.07
27. Hierarchy
1.15
0.10
-0.06
-0.11
-0.07
0.00
0.02
0.06
-0.07
0.12
0.01
0.12
28. Same function different location
0.33
-0.21
0.22
0.01
0.08
0.05
0.16
0.09
0.10
0.04
-0.02
-0.10
29. Different function same location
0.47
0.13
-0.16
-0.14
-0.01
0.05
-0.23
-0.11
-0.12
0.03
0.04
0.04
49
30. Different function different location
0.37
-0.09
0.11
-0.05
0.06
0.00
-0.05
0.02
-0.04
0.01
-0.03
-0.08
31. External
0.27
-0.02
-0.03
-0.01
-0.03
0.01
-0.10
-0.04
0.02
0.00
0.03
0.03
32. Novel knowledge ties
0.49
-0.09
0.03
0.05
0.04
-0.03
0.10
0.08
0.10
-0.04
-0.02
-0.03
33. Specialist expertise ties
0.49
-0.04
0.01
0.00
0.04
0.03
0.13
0.01
0.03
-0.03
-0.08
-0.04
34. Novel knowledge & specialist expertise ties
1.23
-0.09
0.03
0.04
0.05
-0.0
0.13
0.07
0.09
-0.05
-0.05
-0.04
Note: N = 838, >.068 is sig. (p < .05)
13
14
15
16
17
18
19
20
21
22
12. MNC networking
13. Global identification
1.00
14. Cross-functional experience
-0.04
1.00
15. International assignment experience
-0.06
0.08
1.00
16. Total number of ties
0.12
-0.01
0.04
1.00
17. Density of collaboration network
-0.02
0.15
0.09
-0.14
1.00
18. Average new ties
-0.21
-0.09
0.14
0.01
-0.09
1.00
19. Average trust
0.04
-0.09
0.13
-0.16
0.23
0.02
1.00
20. Average hierarchy
-0.10
0.09
0.09
0.11
0.13
-0.15
0.23
1.00
21. Average same function different location
-0.08
0.14
-0.03
0.03
-0.15
0.12
-0.05
-0.13
1.00
22. Average different function same location
0.14
-0.02
-0.12
-0.02
0.00
-0.20
-0.19
0.01
-0.44
1.00
23. Average different function different location
0.17
0.12
0.20
-0.01
0.11
0.10
-0.09
-0.11
0.00
-0.32
24. Average external
-0.10
0.02
0.00
-0.14
0.06
0.31
0.04
-0.06
-0.21
0.01
25. New ties
-0.12
-0.05
0.08
0.01
-0.06
0.59
0.01
-0.09
0.07
-0.12
26. Trust
0.03
-0.07
0.10
-0.12
0.17
0.02
0.75
0.17
-0.04
-0.14
27. Hierarchy
-0.06
0.05
0.05
0.06
0.07
-0.08
0.13
0.56
-0.07
0.01
28. Same function different location
-0.05
0.08
-0.02
0.02
-0.09
0.07
-0.03
-0.08
0.60
-0.26
29. Different function same location
0.09
-0.01
-0.08
-0.01
0.00
-0.13
-0.13
0.01
-0.29
0.65
30. Different function different location
0.11
0.08
0.12
-0.01
0.07
0.06
-0.06
-0.07
0.00
-0.20
31. External
-0.05
0.01
0.00
-0.07
0.03
0.15
0.02
-0.03
-0.10
0.00
32. Novel knowledge ties
0.04
0.09
0.02
0.03
0.18
0.03
0.08
0.01
0.00
-0.07
33. Specialist expertise ties
-0.04
0.07
0.05
-0.02
0.08
0.03
0.08
0.08
0.06
-0.01
34. Novel knowledge & specialist expertise ties
0.02
0.10
0.03
0.02
0.18
0.03
0.10
0.04
0.03
-0.06
50
24
25
26
27
28
29
30
31
32
33
23. Average different function different location
24. Average external
1.00
25. New ties
0.18
1.00
26. Trust
0.03
-0.18
1.00
27. Hierarchy
-0.03
-0.02
0.12
1.00
28. Same function different location
-0.13
0.11
-0.10
-0.16
1.00
29. Different function same location
0.00
-0.19
-0.08
0.02
-0.26
1.00
30. Different function different location
0.05
0.13
-0.13
-0.12
-0.17
-0.30
1.00
31. External
0.49
0.28
-0.08
0.06
-0.10
-0.17
-0.13
1.00
32. Novel knowledge ties
-0.01
0.01
0.16
0.12
0.03
-0.06
-0.06
0.04
1.00
33. Specialist expertise ties
0.00
0.10
0.06
0.27
-0.02
-0.05
0.04
0.15
0.32
1.00
34. Novel knowledge & specialist expertise ties
-0.01
0.05
0.15
0.20
0.02
-0.07
-0.03
0.10
0.93
0.65
51
Table 2. Random-intercept Generalized Linear Latent and Mixed Models Predicting
Accessing Novel Knowledge
Model 1
Model 2
B
SE
Sig.
B
SE
Sig.
Constant
-5.904
2.787
*
-5.765
3.080
MNC
-0.306
0.573
-0.472
0.627
Pharma
0.344
0.561
0.397
0.598
ICT
Subsidiary
Subsidiary size (ln)
0.218
0.168
0.191
0.188
Functional scope
0.027
0.138
0.078
0.140
Greenfield
-0.724
0.570
-0.721
0.607
Developed market
0.631
0.537
0.735
0.632
Problem
Problem complexity
-0.103
0.167
-0.068
0.173
Problem duration
0.005
0.015
0.007
0.017
Individual
Hierarchical level
-0.223
0.202
-0.149
0.211
Tenure
0.004
0.026
0.006
0.027
Gender
-0.306
0.430
-0.357
0.455
MNC networking
0.001
0.002
0.002
0.002
Global identification
0.395
0.258
0.450
0.276
Cross-functional experience
-0.196
0.158
-0.151
0.166
International assignment experience
-0.034
0.019
-0.027
0.019
Network
Total number of ties
-0.076
0.091
-0.064
0.092
Density of collaboration network
1.544
0.860
1.500
0.890
Average new ties
-1.057
1.006
-0.677
1.182
Average trust
-0.780
0.241
***
-0.922
0.257
***
Average hierarchy
0.549
0.357
0.346
0.373
Average same function different location
-3.084
1.717
Average different function same location
-0.276
1.025
Average different function different location
-2.388
1.302
Average external
-0.207
1.913
Ties
New ties
0.328
0.561
-0.063
0.595
Trust
0.801
0.177
***
0.916
0.189
***
Hierarchy
0.441
0.156
**
0.520
0.165
**
Same function different location
1.499
0.641
*
Different function same location
0.063
0.467
Different function different location
1.054
0.696
External
1.061
0.981
Variance between problem solving projects
0.982
1.023
Variance within problem solving projects
0.305
0.323
Log likelihood
-938.39
-899.68
Note: N = 838, † < .10, * < .05; ** < .01, *** < .001
52
Table 3. Random-intercept Generalized Linear Latent and Mixed Models Predicting
Accessing Specialist Expertise
Model 1
Std. Error
Sig.
Model 2
Std. Error
Sig.
B
Std.
Error
Sig.
B
Std.
Error
Sig.
Constant
-1.109
0.608
-3.857
2.386
MNC
Pharma
0.143
0.467
0.574
0.502
ICT
0.078
0.473
0.254
0.506
Subsidiary
Subsidiary size (ln)
0.000
0.129
0.159
0.144
Functional scope
-0.009
0.109
-0.017
0.115
Greenfield
-0.025
0.436
-0.419
0.466
Developed market
1.104
0.444
*
2.004
0.527
***
Problem
Problem complexity
-0.148
0.130
-0.137
0.134
Problem duration
-0.008
0.014
0.000
0.015
Individual
Hierarchical level
-0.202
0.162
-0.259
0.169
Tenure
-0.032
0.022
-0.040
0.023
Gender
-0.519
0.348
-0.680
0.362
MNC networking
0.002
0.001
0.000
0.002
Global identification
0.048
0.200
-0.032
0.216
Cross-functional experience
-0.055
0.121
-0.212
0.133
International assignment experience
0.003
0.009
0.014
0.010
Network
Total number of ties
-0.076
0.073
-0.044
0.076
Density of collaboration network
0.241
0.713
0.214
0.764
Average new ties
-1.158
0.784
-1.573
0.880
Average trust
-0.185
0.188
-0.247
0.204
Average hierarchy
-0.048
0.281
0.070
0.297
Average same function different location
2.431
1.256
Average different function same location
2.195
0.852
*
Average different function different location
0.500
1.016
Average external
1.695
1.591
Ties
New ties
0.856
0.351
*
0.369
0.376
Trust
0.174
0.114
0.292
0.125
*
Hierarchy
0.832
0.117
***
0.888
0.126
***
Same function different location
0.159
0.487
Different function same location
0.084
0.355
Different function different location
1.455
0.464
**
External
2.539
0.635
***
Variance between problem solving projects
0.982
1.023
Variance within problem solving projects
0.305
0.323
Log likelihood
-938.39
-899.68
Note: N = 838, † < .10, * < .05; ** < .01, *** < .001
53
Table 4. Random-intercept Generalized Linear Latent and Mixed Models Predicting
Accessing Novel Knowledge and Specialist Expertise
Model 1
Model 2
B
Std.
Error
Sig.
B
Std.
Error
Sig.
Constant
-2.662
2.138
-3.786
2.365
MNC
Pharma
-0.477
0.454
-0.348
0.490
ICT
-0.426
0.460
-0.384
0.492
Subsidiary
Subsidiary size (ln)
-0.048
0.127
-0.005
0.141
Functional scope
0.031
0.109
0.052
0.114
Greenfield
-0.304
0.440
-0.454
0.466
Developed market
1.409
0.447
**
1.786
0.521
**
Problem
Problem complexity
-0.031
0.128
-0.040
0.132
Problem duration
0.016
0.011
0.018
0.012
Individual
Hierarchical level
-0.193
0.160
-0.222
0.166
Tenure
-0.029
0.022
-0.032
0.022
Gender
-0.532
0.349
-0.583
0.360
MNC networking
-0.001
0.001
-0.002
0.002
Global identification
0.151
0.202
0.166
0.217
Cross-functional experience
0.129
0.116
0.073
0.126
International assignment experience
0.013
0.009
0.017
0.010
Network
Total number of ties
0.029
0.074
0.040
0.076
Density of collaboration network
2.478
0.701
***
2.737
0.737
***
Average new ties
-0.814
0.766
-0.626
0.859
Average trust
-0.275
0.190
-0.427
0.202
*
Average hierarchy
-0.419
0.278
-0.441
0.292
Average same function different location
0.259
1.242
Average different function same location
0.492
0.850
Average different function different location
-0.915
0.989
Average external
-0.800
1.544
Ties
New ties
0.924
0.348
**
0.413
0.370
Trust
0.397
0.116
***
0.553
0.125
***
Hierarchy
0.873
0.117
***
0.942
0.125
***
Same function different location
1.052
0.455
*
Different function same location
0.262
0.343
Different function different location
1.264
0.465
**
External
2.864
0.637
***
Variance between problem solving projects
0.982
1.023
Variance within problem solving projects
0.305
0.323
Log likelihood
-938.39
-899.68
Note: N = 838, † < .10, * < .05; ** < .01, *** < .001
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