THE IMPACT OF DISTANCE ON KNOWLEDGE TRANSFER EFFECTIVENESS IN
MULTINATIONAL CORPORATIONS
Tina C. Ambos
Vienna University of Economics and Business Administration
Augasse 2-6, 1090 Vienna, Austria
Tel. +43 1 31336 4403
Email: [email protected]
Björn Ambos
Vienna University of Economics and Business Administration
Augasse 2-6, 1090 Vienna, Austria
Tel. +43 1 31336 5121
Email: [email protected]
Accepted for publication in Journal of International Management!
COPYRIGHTS: ELSEVIER JOURNAL OF INTERNATIONAL MANAGEMENT
We would like to thank Julian Birkinshaw, Michael Mayer, Lars Hakanson, Steve Tallman and
Nicolai Foss for their helpful advice, comments and suggestions. We also thank the seminar
participants at Copenhagen Business School, Hanken Swedish School of Economics and the
Second JIBS Conference on Emerging Research Frontiers for their input.
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THE IMPACT OF DISTANCE ON KNOWLEDGE TRANSFER EFFECTIVENESS IN
MULTINATIONAL CORPORATIONS
ABSTRACT
This paper aims to shed light on the interplay of knowledge transfer mechanisms and distance
within the MNC. While it is largely undisputed that cross-boarder knowledge flows contribute to
the firm’s success, our knowledge on the effects of specific transfer mechanisms is scarce. We
examine the impact of different dimensions of distance to test the applicability of personal
coordination mechanisms (PCM) and technology-based coordination mechanisms (TCM) in
situations of differentiation and dispersion. Data on 324 knowledge transfer relationships of MNC
units was used to test our hypotheses. While TCM function relatively context-free, we find that
PCM are moderated by distance. Our results support moderating effects of geographic, cultural and
linguistic distance, which are vital to our understanding of knowledge transfer effectiveness in
MNCs.
Keywords: Knowledge Transfer, Multinational Corporation, Geographic Distance, Cultural
Distance, Linguistic Distance
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1. INTRODUCTION
During the last years, academics and practitioners have realized that knowledge flows across
dispersed organizational units are vital for the companies’ success (Bartlett and Ghoshal, 1989;
Hedlund, 1994; Kogut and Zander, 1992). In fact, the ability to leverage its knowledge resources
globally has been proclaimed by many scholars as the true raison d’être of the MNC (Kogut and
Zander, 1992; 1993; Doz et al., 2001; Asakawa and Lehrer, 2003; Mahnke and Pedersen, 2004;
Andersson et al., 2007; Monteiro et al., 2008). In order to build on the organizations’ knowledge
stock, functional units of multinational corporations (MNCs) need to share knowledge across
organizational entities. They have to be able to transfer this knowledge within organizational
networks characterized by separation through time, space, culture and language. And while the
advent of modern information technology – which reduces or even eliminates some of the inherent
challenges posed by distance such as communication or coordination costs – has led some scholars
to declare the “death of distance” (Cairncross, 1997), others have recently reminded us about the
persistence of distance in international business (Nachum and Zaheer, 2005; Ghemawat, 2001). In
short, our understanding of how distance affects knowledge transfer between MNC units is still
scarce. If distance complicates international knowledge transfer: How and under which
circumstances does distance affect knowledge transfer effectiveness in the MNC?
Given the centrality of knowledge sharing in the MNC, much attention has been granted to
the question why and how knowledge transfers occur by focusing on the determinants and
obstacles of such flows (e.g. Szulanski, 1996; Simonin, 1999; Gupta and Govindarajan, 2000; Tsai,
2001; Minbaeva et al., 2003). To a much lesser extent, however, progress has been made with
respect to the organizational mechanisms or capabilities used by MNCs to transfer the knowledge
(Foss and Pedersen, 2002; Martin and Salomon, 2003a; Hansen and Lovas, 2004; Almeida et al.,
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2002). In an attempt to answer the question how different knowledge transfer mechanisms enhance
the effectiveness of knowledge transfer, the present paper takes a closer look at two distinct
mechanisms: personal coordination mechanisms (PCM) and technology-based coordination
mechanisms (TCM).
To shed light on this issue we employ a contingency perspective on the value of knowledge
transfers for the recipient unit. This perspective emphasizes that the value of obtaining and using
knowledge should be assessed by evaluating the benefit of the received knowledge to the recipient
unit, rather than by measuring the quantity of knowledge flows. In doing so, our paper follows
recent scholarly thinking that it is the benefit, relevance or performance rather than the mere
occurrence of knowledge flows that matters (Chini, 2004; Haas and Hansen, 2005; Schulz, 2003;
Mahnke et al., 2004). The contingency view implies that important contextual variables are likely
to influence to what extent the recipient unit is able to benefit from a knowledge transfer. Given
the paramount importance of distance in international business theory and for the
conceptualization of the MNC (e.g. Ghoshal and Westney, 1993; Nachum, 2003), we focus on the
question how various facets of distance (spatial, cultural and linguistic) influence the effectiveness
of transfers via PCM as well as TCM.
We utilize this perspective to empirically test a set of hypotheses on original data from 164
MNC units reporting on 324 knowledge flows. The subsidiaries in our sample applied the two sets
of transfer mechanisms to transfer knowledge from subunits separated by varying distances. We
examined the extent to which a focal subsidiary utilizes both, PCM and TCM to obtain and use
valuable knowledge from other subsidiaries in the MNC.
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The research contributions of this paper are threefold. First, this study adds to literature on
knowledge flows in multinational firms by accentuating the role of knowledge transfer
mechanisms. Second, it advances theory by grounding the knowledge flow debate in a more robust
contingency (context-fit-performance) framework, and demonstrates that the effectiveness of
transfer mechanisms and knowledge flows is moderated by important contextual variables. Third,
this study responds to calls for a more differentiated investigation of distance (Nachum and
Zaheer, 2005; Friedland and Boden, 1994) by investigating the separate effects of geographic,
cultural and linguistic distance. As such, it contributes to a much needed and more nuanced
understanding of distance.
The paper proceeds as follows: It starts with a review of relevant literature to outline the
value of knowledge and the role of knowledge transfer mechanisms in the MNC. We proceed by
introducing different dimensions of distance before we spell out some testable hypotheses. The
third section lays down the methodology and data gathering approach. Following the analysis and
presentation of our primary results we deepen our investigation into the interaction effects of
different dimensions of distance and knowledge transfer mechanisms in the MNC. Finally, we
conclude the paper by pointing out practical implications for managers in multinational firms.
2. THEORETICAL BACKGROUND
In line with an increasing body of literature, we conceptualize the MNC as consisting of semiautonomous entities (Bartlett and Ghoshal, 1989; Gupta and Govindarajan, 1991; Hedlund, 1994;
Nohria and Ghoshal, 1997, Doz et al., 2001), in which units in dispersed locations take on various
missions and control heterogeneous stocks of knowledge (Foss and Pedersen, 2002). We define
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knowledge as “accumulated practical skill or expertise that allows one to do something smoothly
and efficiently” (Kogut and Zander, 1992, p. 386). Organizational knowledge, which is in the focus
of this study, embraces different functions of the firm and the whole range of managerial and
operational processes with the implication that know-how is generated in all productive activities
(Almeida et al., 2002). Applied and researched in a diverse set of disciplines, the notion of
knowledge has recently found an increasing interest among strategic and international management
scholars. It has been argued that knowledge, like other and more tangible assets, constitutes a
prime organizational resource (Grant, 1996). As such, knowledge seems to contribute to, or even
determine, the competitive position of the firm. However, the possession of knowledge-based
assets does not by itself guarantee that a firm will be able to exploit these assets in foreign
operations (Martin and Salomon, 2003b; Hansen and Lovas, 2004).
Existing studies on knowledge transfer in MNCs have tended to emphasize the
impediments or barriers of knowledge flows without spending much thought on the mechanisms
used to facilitate the transfer (for notable exceptions see Martin and Salomon, 2003a; Hansen and
Lovas, 2004). As a consequence, the prime concern has been to identify the contingencies under
which knowledge flows occur, but not whether the knowledge transfer is effective. Thus, while
previous research has enlightened our understanding of the determinants of inter-unit knowledge
flows, most scholars implicitly followed a relatively naïve conceptualization of knowledge as an
economic good (that has value by mere exchange) as opposed to an information good (that has
value in use) (Haas and Hansen, 2005). Recently, a few studies have investigated the effectiveness
of knowledge flows (e.g. Monteiro et al., 2008; Ambos et al., 2006; Szulanski and Jensen, 2006;
Mahnke et al., 2004; Haas and Hansen, 2005). Those studies suggest that not every transfer is
beneficial per se and that relevance, task-unit performance or application of knowledge in a new
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context will depend on important contingencies. In their study of international consulting firms
Hansen et al. (1999) identified two broad categories of transfer mechanisms and contend that
successful knowledge management depends on the “fit” or “coalignment” of different knowledge
transfer tools and mechanisms with the firm’s overall strategy. While they already introduce the
notion of alignment of knowledge transfer mechanisms with certain contingencies, these insights
are largely based on anecdotal evidence rather than on formal propositions.
Our study builds on this line of thinking and develops a more formal contingency view on
knowledge transfer within MNCs. The contingency perspective generally suggests that there is no
single best organizational orientation and no universalistic organizational choices which result in
optimal outcomes in every situation (Lawrence and Lorsch, 1967; Ginsberg and Venkatraman,
1985). Rather, organizational choices must be matched to the subunit’s external context and this
“fit” is the determinant of superior performance. During the last decades, the concept of fit has
played a key role for theory building in strategic management, organizational theory and
managerial orientation (Zajac et al., 2000; Venkatraman, 1989; Gupta and Govindarajan, 1984). In
this paper we adopt a similar position with regards to knowledge flows: the ‘coalignment’ or ‘fit’
between transfer strategy (operationalized as transfer mechanisms used) and the transfer context
will result in a higher transfer effectiveness. Hence, the main proposition of this study is that the
utilization of one or the other transfer strategy leads to higher benefit (performance) levels only to
the extent that there is a fit between the contextual imperatives and the mechanism being deployed.
Of prime importance from this perspective, then, is the identification of the critical
contingency relationships with respect to knowledge flows. In this study we focus on distance.
Understanding how distance affects knowledge transfer effectiveness is critical because transfers
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across distance come at a cost. MNCs do not only make significant investments in technology but
also maintain costly organizational structures and processes to initiate knowledge flows (Bartlett
and Ghoshal, 1989; Hedlund, 1994; Grant, 1996). Specifically, we propose that the development of
both PCM and TCM will positively impact the effectiveness of knowledge flows. Further, we
argue that when distance (cultural, geographic, linguistic) among the focal unit and the sending
unit increases, PCM become less effective in transferring knowledge, thus, eventually tipping the
scale in favor of TCM capabilities in contexts where large distances have to be bridged. The
relationships that we examine are shown in figure 1. In the next section, we develop hypotheses
about the nature of these relationships in the context of globally dispersed units of large European
MNCs.
************* insert Figure 1 about here *************
3. DEVELOPMENT OF HYPOTHESES
3.1. Knowledge Transfer Mechanisms
To mobilize knowledge firms rely on various transfer mechanisms. In line with recent research
(Schulz and Jobe, 2001; Hansen and Haas, 2004; Hansen et al., 1999), we broadly classify these
mechanisms in two categories: technology-based coordination mechanisms (TCM) and personal
coordination mechanisms (PCM). There has been some debate whether the two modes are
substitutes or complements, but there is a general consensus that both are instrumental in
transferring knowledge within the MNC. However, as we will argue later on, the two mechanisms
build on a contrasting logic, which has important implications for their applicability in the
international setting and merits a separate discussion.
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Technical infrastructure plays a central role in intra-organizational knowledge transfer as it
allows employees to codify, store and access knowledge. The main purpose of knowledge
management systems is to maximize the exploitation of resources that are embedded within a
network of units separated through time and space. Such infrastructure includes tools like business
intelligence, collaboration software, distributed learning, knowledge discovery and mapping.
Recent empirical evidence and the first pitfalls of knowledge management initiatives in practice
have shown that people often reject sophisticated knowledge management systems due to a lack of
user knowledge (Leonard-Barton, 1995) or a misalignment with the company’s overall strategy
(Hansen et al., 1999). But selected studies have provided empirical evidence that firms with an
ability to use technical infrastructure for knowledge transfer exhibit increased organizational
effectiveness (Becerra-Fernandez and Sabherwal, 2001; Gold et al., 2001). Accordingly, we
assume that utilization of TCM is positively related to effectiveness of knowledge transfers in the
MNC.
Similarly, the ability to coordinate between different MNC units, which results in strong
structural ties, has been identified as a key mechanism for intra-MNC knowledge transfer (Hansen,
1999; 2002; Bartlett and Ghoshal, 1989; Gupta and Govindarajan, 2000). Intensive collaboration
between headquarters and subsidiaries as well as between peer subsidiaries provides a social basis
for knowledge exchange (Feinberg and Gupta, 2004; Galunic and Rodan, 1998) and supports
knowledge sharing processes within the organization (Hakanson and Nobel, 2001; Dyer and
Nobeoka, 2000; Szulanski, 1996; Almeida and Phene, 2004). The organizational development of
PCM is critical, as routines have to be established in order to transfer knowledge at a more efficient
level, leaving minimal space for causal ambiguity (Simonin, 1999). A strong relationship between
sender and recipient will not only ease understanding on a general basis, but will also make the
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identification of relevant knowledge more plausible. Thus, we expect that higher levels of PCM
will have a positive impact on the effectiveness of knowledge transfer.
3.2. The Moderating Effects of Distance
Distance affecting international business has commonly been treated as a multi-faceted construct,
including cultural, administrative, geographic, and economic dimensions (Ghemawat, 2001). To
conceptualize the relationships between organizational units, it is important to differentiate
between them as units are not equidistant on all dimensions. But to date only few researchers have
conceptually or empirically investigated the dimensions of distance that affect knowledge flows in
MNCs. A notable exception is Doz and Santos (1997), who distinguish between spatial
“dispersion” (i.e. distribution of knowledge senders and recipients in space) and contextual
“differentiation” (e.g. cultural, linguistic, professional differences of knowledge senders and
recipients) dimensions of distance.
To explore the challenges of managing knowledge under dispersion and differentiation, it is
best to start by looking at its anti-pole: The situation of collocation and co-setting. Collocation, the
sharing of same location, eases the work of knowledge transfer as it implies a high probability of
encounter and frequent action response (c.f. Allen, 1977). Co-setting, the sharing of context,
facilitates understanding and the integration of knowledge (Keida and Bhagat, 1998). Under the
condition of co-setting, people are part of a similar context, which involves culture, i.e. common
verbal, non-verbal language, the use of artifacts, and a common knowledge base. If units within the
MNC were not separated through distance, knowledge transfer would work with relative ease. But
such a setting hardly ever exists in the MNC. As Doz and Santos (1997) point out: Knowledge
transfer becomes ‘eventful’ as managers are confronted with perplexing and uncertain situations.
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Distance is potentially damaging to knowledge relationships, as “the decay and loss of
distance is precisely the decay and loss of knowledge, relationships, and trust, which in turn
undermines the ability to act at and over distance” (Goodall and Roberts, 2003, p. 1155). Following
this argument, PCM that rely on personal interactions between individuals are likely to be harmed
by distance. However, the relational aspect of transfer is expected to be less affected by TCM,
which are relatively insensitive to spatial distance as they work 24 hours a day and are accessible
from every network node. Information technology allows knowledge to be ‘disembedded’ and
‘decoupled’ from time and space (Giddens, 1990), so that only contextual distance is likely to
affect TCM. In particular, the quality of the contents may suffer from abstraction and displacement
of their context (Boisot, 1995), as knowledge has to be re-embedded in the new context in order to
be used at a distant location (Thompson, 1995). In analytical terms, we posit that dimensions of
distance will moderate the relationship between knowledge transfer mechanisms and knowledge
transfer effectiveness. Recognizing that distant locations often possess novel knowledge and
creative solutions that may be valuable to other units (Shane et al., 1995; Morosini et al., 1998),
our argument emphasizes that distance is not necessarily an inhibitor of knowledge flows, but that
it affects the effective work of certain transfer mechanisms (see also Tihany et al., 2005; Kirkman
et al., 2006). Departing from the relatively trivial situation of collocation and co-setting, we will
step-by-step relax these conditions and explore the moderating effects of each dimension of
distance.
3.2.1. Spatial Distance
Relaxing the condition of collocation, spatial distance is expected to limit knowledge transfer
effectiveness. In previous studies, spatial distance has been pointed out to prevent partnering
among employees (e.g. Allen, 1977), which constitutes the indispensable basis of knowledge
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exchange. Focal units may not only be less likely to interact if geographic distance between them is
high, but once an interaction is started, obstacles such as different time zones and long transmission
channels limit the effectiveness, as the cost and complexity of knowledge search and
communication increase with spatial distance (Daft and Lengel, 1986; Cyert and March, 1992).
Thus, local dispersion makes coordination difficult and may deter transfers of knowledge through
PCM (Zaheer, 1995; Hansen and Lovas, 2004).
H1a: The relationship of PCM and knowledge transfer effectiveness is expected to be
moderated by geographical distance between sender and recipient. The smaller the geographic
distance, the higher the effectiveness of knowledge transfers via PCM.
3.2.2. Contextual Distance
Relaxing the second condition of co-setting allows us to gain insights into the impact of contextual
distance on the effectiveness of knowledge transfer capabilities. In international business research,
national culture has served as the most prominent proxy to model contextual differences between
MNC units (e.g. Adler, 1991; Johanson and Vahlne, 1977). As national culture encompasses the
values, beliefs and assumptions of a group of people, it also shapes the interpretation of reality and
messages (e.g. Hofstede, 2001). Communication as such presupposes that knowledge can be
“translated” across cultures (Kim, 1998), but if the two cultural frameworks do not have sufficient
commonality, knowledge transfer may be less effective than when the symbolic cultural foundation
is consistent (Griffith and Harvey, 2001; Keida and Bhagat, 1998). The characteristics of the
resource knowledge require a deep and common ground of understanding between the parties
involved in order to extract knowledge that is useful for the recipient. Thus, cultural distance is
prone to affect both, TCM and PCM. For PCM, the people involved have to bridge diverse
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backgrounds, which may result in misunderstandings and prevent the joint solution of problem.
Similarly, knowledge transfer via TCM may be inhibited by cultural distance, as it is more difficult
to abstract from the context, codify and retrieve the key messages that are valuable for the recipient
(Ronen, 1986; Brannen, 2004).
H1b: The relationship of PCM and knowledge transfer effectiveness is expected to be
moderated by cultural distance between sender and recipient. The smaller the cultural
distance, the higher the effectiveness of knowledge transfers via PCM.
H2: The relationship of TCM and knowledge transfer effectiveness is expected to be moderated
by cultural distance between sender and recipient. The smaller the cultural distance, the higher
the effectiveness of knowledge transfers via TCM.
Managerially relevant differences between cultures have tended to focus on values. Recent
research, however, increasingly suggests extending this discussion to linguistic differences (e.g.
West and Graham, 2004; Triandis, 1994). In addition to merely semantic differences, scholars have
consistently pointed out that our thinking is affected by our language (Hofstede, 2001; Usunier,
1998; Adler, 1991), and thus may constitute a prime inhibitor in cross-national knowledge
reception. Common language facilitates the formation of identity and provides structures for
conceptualizing and reasoning (Sapir, 1921; Whorf, 1940). Marschan-Piekkari et al. (1999a;
1999b) found that collaboration across linguistic boundaries frequently involves misunderstandings
and, more interestingly, that language acts as a power structure in companies and creates ‘linguistic
hierarchies’. Linguistic distance between headquarters and subsidiaries reveals a hierarchy in the
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organization which does not necessarily coincide with the units’ formal position. Given this
evidence we assume that PCM used to transfer knowledge between headquarters and subsidiaries
will also be negatively affected by linguistic distance. However, the work of systems is unlikely to
be affected by linguistic distance as technical infrastructure is usually designed around one
language and people using these systems are likely to be proficient in the respective language.
H1c: The relationship of PCM and knowledge transfer effectiveness is expected to be
moderated by linguistic distance between sender and recipient. The smaller the linguistic
distance, the higher the effectiveness of knowledge transfers via PCM.
4. METHODOLOGY
4.1. Sample Design
The European Top 500 served as a sample frame for this study. The research plan involved data
collection at two levels, headquarters and subsidiaries. To ensure variety, both in terms of
subsidiaries and industries involved, we restricted our sampling efforts to those firms known to
operate at least six overseas subsidiaries (Vernon, 1966), whilst on the same hand using direct
proportional strata on ten industries to ensure industry variety. An initial target sample of 60 MNCs
was set. Data collection started in May 2002. Firms within each strata were contacted in
descending order. Whenever a company declined to co-operate in the survey, the next largest
company in terms of turnover was approached. Starting with the largest corporations, senior
managers from each headquarters were contacted and asked to co-operate. Upon agreement, each
was asked to nominate four subsidiaries, which could participate in the study. This sampling
procedure led to a final target sample of 300 units, i.e. 60 headquarters and a total of 240
subsidiaries. A standardized mail survey was sent out to all participants. Two follow up rounds and
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the promise to provide results aimed to ensure a high response. Despite these efforts, and the initial
agreement of headquarters, it was impossible to collect responses of all the firms in time. The final
sample consisted of 162 MNC units belonging to 48 companies. Thereof 38 headquarters and 124
subsidiaries participated and reported on multiple knowledge transfer relationships within their
organization. Each headquarters reported on its knowledge transfer practices with two subsidiaries,
and each subsidiary on their interactions with headquarters and a peer subsidiary. This led to a total
of 324 transfer relationships, thereof 76 “forward” transfers from headquarters to subsidiaries, 124
“reverse transfers” from subsidiaries to headquarters, and 124 “lateral transfers” from subsidiaries
to other subsidiaries.
The sample composition shows significant variance. The final sample represents leading
MNCs of diverse industries, such as manufacturing (56%), finance and insurance (21%), and other
services including consulting companies (11%). Surveyed units operate in 29 countries. The
smallest distance between units is 0, i.e. a case where headquarters and subsidiaries located in the
same city. The highest distance is 9,910 air miles between Norwegian headquarters in Oslo and a
subsidiary in Sydney, Australia. The average headquarters employs 1,019 employees whereas the
average number of subsidiary personnel is 638. Although 8.5% of the units have more than 2,500
employees 50% of the sample have a relatively small unit size, i.e. less than 250 employees. Nearly
half of the subsidiaries (44%) have been formed as a greenfield, the remaining originated from a
merger or acquisition.
The unit of analysis in this study was a unit’s knowledge transfers to either headquarters or
a subsidiary. To assess non-response bias we tested whether responding firms differed from non-
15
responding firms with respect to size and turnover. Both tests showed non-significant differences
between responding and non-responding firms.
4.2. Dependent and Independent Variables
The data used in this study came primarily from the questionnaire. As headquarters’ and
subsidiaries’ managers were addressed, two slightly different versions of the questionnaire were
created. The questionnaires were pre-tested in a series of cognitive interviews with researchers, and
managers involved in the research topic. Several amendments, mainly in wording, were conducted
before the final instrument was sent to managers. Additional insights were gained through field
interviews in different subsidiaries of nine multinational firms to cross-validate our empirical
results. A summary of the measures is presented in the Appendix. The following section briefly
introduces the operationalization of our variables.
4.2.1. Knowledge Transfer Effectiveness
We subscribe to the view that “the key element in knowledge transfer is not the underlying
(original) knowledge, but rather the extent to which the receiver acquires potentially useful
knowledge and uses this knowledge in own operations.” (Minbaeva et al. 2003, p. 587). The
implications are twofold: (1) Not every knowledge transfer results in value added. (2) It is not the
replication of a sender’s message by the recipient which is important, but the extent of benefits
generated for the recipient’s operations. By applying this definition, we intend to capture effective
knowledge transfer in its most holistic way: through the eyes of the beneficiary of this knowledge.
Our approach can be seen as an ex-post analysis of knowledge transfer assuming that effective
transfer of knowledge will lead to benefits to the recipient’s operations. Put the other way round,
high knowledge inflows, which are not translated into an improvement of operations, are
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considered to be inefficient. The value of knowledge transfer created at the recipient was measured
with survey instruments that captured the perceived benefit to the operations from a specific
counterpart’s (headquarters or a subsidiary) knowledge transfers across several knowledge
domains (1= not at all; 7= a very great deal) (see also Gupta and Govindarajan, 2000). To test the
internal consistency of our scale, we calculated Cronbach alpha. The received value of 0.927 is
well above the threshold level of 0.70 recommended by Nunally (1978), indicating good internal
consistency. We also used a factor analysis to establish the unidimensionality of this scale. The
factor score of the resulting one-factor solution was subsequently used in all regressions.
4.2.2. Personal Coordination Mechanisms (PCM)
The operationalization of this variable was adopted from Gupta and Govindarajan (2000). The use
of coordination instruments in lateral and hierarchical relationships was explored separately.
Respondents were asked to indicate how much they relied on liaison personnel, temporary task
forces, and permanent teams as means of knowledge sharing in different directions. The scale
ranges from 1 (very infrequently) to 7 (very frequently). Cronbach alpha is 0.756, indicating
acceptable internal consistency (Nunally, 1978). As above, we conducted a factor analysis to
confirm unidimensionality. The factor score was subsequently entered into the models.
4.2.3. Technology-Based Coordiantion Mechanisms (TCM)
To assess the units’ ability to use technical infrastructure for inter-unit knowledge transfer, a
multiple item construct was created based on Gold et al. (2001) and Becerra-Fernandez and
Sabherwal (2001). The items refer to a number of different tools, which are typically part of
knowledge management systems. These include tools to map knowledge, team collaboration tools,
pointers to expertise, repositories of best practices and lessons learnt. Possible answers range from
17
1 (used very infrequently) to 7 (used very frequently). Cronbach alpha is 0.771, thereby meeting
Nunally's (1978) 0.70 level for acceptability. As above, we conducted a factor analysis to confirm
unidimensionality. The factor score was subsequently entered into the models.
4.2.4. Geographic Distance
We measured geographic distance as the absolute number of airmiles between the two unit
locations, the sender and the recipient of knowledge. We logged this measure, as individuals most
likely do not perceive that the burdens of travel increase linearly with air miles (see also Hansen
and Lovas, 2004).
4.2.5. Cultural Distance
Based on the reasoning that actual (dis)similarities in values will hinder or facilitate understanding
and the ability to perform a task jointly, cultural distance was measured as the objective distance
among the two actors. We used the cultural distance measure introduced by Kogut and Singh
(1988) which builds on actual value differences among country pairs through assigning Hofstede’s
(2001) cultural value scores to all units. Index values were calculated by summing mean
differences over four cultural dimensions after dividing the difference of i-th home and host
country scores, by the variance of the i-th dimension.
4.2.6. Linguistic Distance
Linguistic distance was measured using a genealogical classification of languages (Grimes, 1992),
which captures relatedness of languages in the most comprehensive and conservative way.
Applying the methodology of Chen et al. (1995) and West and Graham (2004), we created an
18
index for the distance between languages by counting the number of branches on the language tree
necessary to connect the languages of two focal units.
4.2.7. Control Variables
As our sample firms operate in diverse industries, we controlled for the industry using a dummy
variable coded as “1” for manufacturing and “0” for all service industries. To account for the
specificities of each unit, we entered the unit’s size as well as its formal position in the model. Unit
size was measured as the (logged) number of employees at the focal unit. A dummy variable for
headquarters (1) and subsidiaries (0) served as a proxi for the unit’s formal position. Moreover, we
added the number of expatriates in the unit’s top management team. A substantial stream of
research has shown that the ability of recipients to connect to the contents of knowledge is crucial
(Hansen and Lovas, 2004; Hansen, 2002; Farjoun, 1998; Markides and Williamson, 1994).
Anticipating that effective knowledge transfer is facilitated through related prior knowledge, we
controlled for the relative competence of the recipient in the respective areas. The recipient unit’s
knowledge stock relative to the senders’ (i.e. headquarters or a subsidiary) in different knowledge
domains was used as a measure. The knowledge domains mirror those used for the effectiveness
measure. Cronbach alpha is 0.797 indicating acceptable internal consistency (Nunally, 1978). In
addition, the (dis)similarity of administrative regimes and practices between different units may
affect knowledge transfer effectiveness. Asakawa (1995) suggested that institutional isomorphism
has a strong impact on a local unit’s approach to structure knowledge and is thus likely to limit the
recipient’s motivation and ability to accept and utilize transferred knowledge (Kostova, 1999;
Jensen and Szulanski, 2004; Simonin, 1999). A two item scale, capturing the degree of similarity
between the transfer partners’ business practices, institutional heritage, and organizational culture
was used to measure this construct. Items were adopted from Simonin (1999) and answered on a
19
seven-point scale ranging from “I strongly disagree” to “I strongly agree”. Cronbach alpha for this
construct is 0.787, indicating acceptable internal consistency Nunally's (1978).
5. ANALYSIS AND RESULTS
5.1. Statistical Method
Moderated multiple regression was used to test our hypotheses. Data was carefully examined with
respect to linearity, equality of variance and normality. No serious deviations were detected. We
performed a Cook-Weisberg test for heteroscedasticity using Stata 9.0. This test suggested that
heteroscedasticity does not appear to be a concern in our data. Similarly, the results of a Harman
one-factor test (Podsakoff and Organ, 1986) indicate that common method bias is not a serious
problem. As the observations in our sample stem from different MNC units (i.e. headquarters and
subsidiaries), we specified fixed-effect models using the robust clustering procedure with Whitecorrected standard errors as implemented in Stata 9.0 (Moulton, 1986; Rogers, 1993, Stata, 2005).
The descriptive statistics are reported in Table 1.
************* insert Table 1 about here *************
5.2. The Moderating Effects of Distance
Table 2 presents our findings. Variables were entered sequentially in five blocks. Model 1 only
includes the control variables. Our central independent variables, PCM and TCM as well as the
three dimensions of distance, were entered in model 2. We constructed six interaction terms by
multiplying the (factor score) value for PCM or TCM with each distance dimension, i.e. the logged
airmiles for geographic distance, the Kogut and Singh index (1988) for cultural distance, and the
linguistic distance index for linguistic distance (Chen et al., 1995). The distance interaction terms
were introduced separately in model 3-5. To avoid potential problems due to multicollinearity, we
20
abstained from introducing all distance interaction terms jointly. An overview of all hypotheses
and results is given in table 3.
************* insert Table 2 about here *************
************* insert Table 3 about here *************
The coefficient indicating the unit’s formal position was marginally significant in model 1,
but across all other models only the similarity of organizational practices had a significant impact
on knowledge transfer effectiveness. None of the other control variables showed significant
regression coefficients. In model 2, as expected, both knowledge transfer mechanisms were
positively and significantly associated with knowledge transfer effectiveness. The direct effects of
geographic, cultural and linguistic distance failed to reach significance throughout.
Finally, we present the moderating effects of PCM and TCM in models 3-5. We
hypothesized that spatial, cultural and linguistic distance would mitigate the positive effect of PCM
on knowledge transfer effectiveness and that TCM would be moderated by cultural distance. To
test these moderating effects we introduced interaction effects. In all cases (geographic distance,
cultural distance and linguistic distance) we expected that the interaction with knowledge transfer
mechanisms reduces knowledge transfer effectiveness. Hence, support for our hypotheses would
assume a negative and significant coefficient for the interaction terms. An examination of models
3-5 provides overall support for such a negative effect. The moderating effects of geographic,
cultural, and linguistic distance on PCM were supported indicating a significant increase in Rsquare vis-à-vis the base model (model 2) and a negative and significant interaction term. These
results imply that any dimension of distance causes problems for the use of PCM. We will return to
21
this point in the discussion section (6.1.). Although the coefficient for the moderating effect of
cultural distance and TCM (hypothesis 2) shows the expected sign, it fails to reach significance.
We will also elaborate on these non-findings in the discussion section (6.2.).
A graphical representation of the interaction effect relationships can be found in figure 2.1
The first of the three lines (dotted line) represents the relationship of knowledge transfer
effectiveness and PCM when the distance is low (i.e. mean value minus one standard deviation).
The remaining lines represent alterations, setting the distance at the mean values (broken line), and
at high levels (i.e. mean value plus one standard deviation) (solid line), which would for example
represent dyads of European and Asian units. The positive slope of low geographic and low
cultural distance illustrates that the use of PCM increases knowledge transfer effectiveness in these
situations. The broken lines in the same diagrams show that this effect is lower, but still positive, if
distance increases, i.e. is set to the mean value. In the case of geographic distance we observe a
further decrease of this effect for high distance as the line flattens out (no effect), whereas it fully
loses its positive effect for cultural distance. Linguistic distance exhibits a slightly different picture
as low levels of linguistic distance seem to have no effect and the use of PCM is negatively
associated with knowledge transfer effectiveness at medium and high levels of linguistic distance.
While these findings are intriguing and suggest that firms may rather use TCM instead of PCM, as
they are not affected by distance, one has to be cautious with the interpretation of the above results:
The graphs are generated holding all other variables constant at their mean value, which is unlikely
to be a realistic assumption. We will further elaborate on the trade-off between PCM and TCM in
the managerial implications section (6.3.).
1
To compute this graph we used the estimates from Model 3-5 in Table 2 whilst holding other variables constant at
their mean value.
22
************* insert Figure 2 about here *************
6. DISCUSSION AND CONCLUSION
Our study set out to investigate the impact of knowledge transfer mechanisms on knowledge
transfer effectiveness in MNCs. We advance the knowledge in the field of multinational
management in several ways. By focusing our investigation on the mechanisms of knowledge
transfer instead of characteristics of senders, recipients and knowledge we shed some light on a
phenomenon which has for long been regarded as a black box. We introduce a contingency
perspective to explain knowledge transfer effectiveness and provide a detailed analysis of the
impact of distance by disentangling various dimensions of this broad concept. Moreover, by
shifting the attention from the mere inflow of knowledge towards an assessment of the benefits
generated through knowledge flows, our study contributes to an emerging field of research, which
questions that knowledge flows create value through their mere occurrence (c.f. Haas and Hansen,
2005; Mahnke et al., 2004).
The empirical results produced in this paper warrant a discussion along several lines. To
begin with, our study indicates that, ceteris paribus, knowledge transfer mechanisms do indeed
impact the effectiveness of knowledge transfer. Most important however, our results also confirm
that PCM and TCM are differently affected by variations of spatial and contextual distance.
6.1. PCM and the Moderating Effects of Distance
The probably most interesting empirical finding is the moderating effect of spatial as well as
contextual distance on PCM. Research on knowledge transfer within MNCs has traditionally taken
23
into account spatial or contextual distance as an inhibitor of knowledge flows, but has neglected
the fact that distance affects certain knowledge transfer mechanisms more than others (Goodall and
Roberts, 2003). With few exceptions (Hansen and Lovas, 2004; Brouthers and Brouthers, 2001),
distance has usually been viewed as a direct effect on knowledge flows and other relational
variables in the MNC. Recent investigations on the notion of (cultural) distance came to the
conclusion, that moderator effects are often more appropriate than direct (negative) effects (Tihany
et al., 2005; Kirkman et al., 2006). Especially when distance is associated with knowledge,
creativity and innovation there is strong evidence that new knowledge and resources originating in
a distant environment are likely to contribute to enhanced MNC performance (e.g. Shane et al.,
1995; Morosini et al., 1998). Although studies have mostly focused on cultural distance, these
ideas can also be applied to other dimensions of distance. Our results showed that high levels of
geographic and linguistic distance are equally harmful to PCM as cultural distance and thus extend
our knowledge on the effects of differentiation and dispersion in the MNC (Doz and Santos, 1997).
PCM may be particularly sensitive to spatial distance due to the cost and complexity of knowledge
search and communication (Daft and Lengel, 1986; Cyert and March, 1992, Haas and Hansen,
2004), whereas contextual distance may complicate knowledge sharing processes due to the
different cognitive styles of individuals (Goodall and Roberts, 2003; Bhagat et al., 2002).
Our study was among the first in the field of international management as well as
knowledge management to introduce the construct of linguistic distance. While language barriers
have for long been highlighted in qualitative work (e.g. Marschan-Piekkari et al., 1999a; 1999b),
hardly any quantitative study had taken this factor into account. The operationalization we used
follows a genealogical or genetic classification, which identified dissimilarity of languages based
on the existence of inference of common linguistic ancestors (see also West and Graham, 2004).
24
This method generates a more comprehensive classification than measures focusing only on
vocabulary, syntax or morphology and is thus likely to reflect different approaches in cognition,
reasoning or conceptualizing which impede understanding. However, we acknowledge that the
available measure is very coarse-grained and the development of more sophisticated measures of
linguistic distance in the future is warranted.
As in many other studies, the measure of cultural distance in our analysis was empirically
constructed on the basis of Hofstede’s value differences using the methodology suggested by
Kogut and Singh (1988). Recently this measure has received increasing criticism (c.f. Shenkar,
2001). In the context of our study, which is concerned with the impact of different dimensions of
distance on knowledge transfer mechanisms, the most pertinent questions concerns the aggregation
of Hofstede’s value scores. I.e. Is the moderating effect on PCM driven by a single dimension of
value differences? To investigate this issue, we ran the models with the separate Hofstede
dimensions. As shown in Table 4, power distance does not support our hypothesized relationship
while all other dimensions show a negative and significant coefficient, confirming that the
aggregation of the Kogut and Singh index does not drive the results of our main regression (see
table 2, model 4).
************* insert Table 4 about here *************
6.2. The Insensitivity of TCM
As predicted, we found a direct and significant effect for TCM on the effectiveness of knowledge
transfer. In other words, organizations that invest in and build up capabilities around their technical
infrastructure will achieve higher benefits than firms that do not. But our findings show that these
25
capabilities are not moderated by variations in spatial and contextual distance. While our results
confirm a strong contextual dependence of PCM, TCM appear to be largely context-free. One
potential explanation is that due to the codified nature of the underlying knowledge ambiguity is
limited. Several streams of literature (e.g. Daft and Lengel, 1994; Nonaka and Takeuchi, 1995;
Hansen et al., 1999) come to the conclusion that codified knowledge is easier to store and transfer
in technical and less “rich” media. Thus, users of systems may be able to choose the right issues
and data formats for transfers that can be easily de-contextualized and re-contextualized in the new
environment (e.g. Giddens, 1990; Brannen, 2004; Bhagat et al., 2002; Schulz and Jobe, 2001).
Notwithstanding the fact that not all knowledge within an organization can be transferred via
technological systems, these findings spur interesting speculations about the applicability of TCM
over PCM in situations of high dispersion and differentiation.
6.3. Managerial Implications and Concluding Remarks
Our findings have some interesting managerial implications as well. The negative relationship
between PCM used in situations of high distance and the effectiveness of knowledge transfers
raises concerns about the usability of PCM in the MNC context where transfers regularly occur
between organizational units located in distant countries. To shed more light on the potential tradeoffs firms face when deciding to develop PCM or TCM, we computed three graphs (Figure 3)
showing the effect of high levels PCM and high levels of TMC on knowledge transfer
effectiveness in different situations of distance.
************* insert Figure 3 about here *************
Keeping all other variables constant at their mean values, the first diagram illustrates the
effectiveness of high TCM as well as high PCM for increasing levels of geographic distance. In
26
line with our main findings, the graph shows that the use of PCM is favorable in situations of low
geographic distance, as personal mechanisms such as face-to-face meetings may allow smoother
transfer of knowledge. However, as geographic distance increases, we observe a steep negative
slope of PCM, indicating that its contribution to knowledge transfer effectiveness decreases
dramatically. The black line representing highly developed TCM also shows a slightly negative
slope but is not heavily affected by distance. For example, it does not make a big difference
whether you send an email to the office next door or from New York to Shanghai. In this graph,
which is based on our empirical database, we do not see an intersection of the two lines. However,
the question whether PCM are always better than TCM is somewhat more complicated than the
graph suggests as we do not take into account potential cost differences for developing those
knowledge transfer mechanisms.
The second graph shows a comparison of high PCM and high TCM across different levels of
cultural distance. Again assuming that an organization incurs the same costs to develop either
mechanism, the intercept of the two capabilities signifies the distance upon which TCM become a
superior mode of transfer. For our study this intercept is at 2.4. In other words, for transfers to
cultures below this point (e.g. most dyads located within the European Union) PCM are more
effective than TCM. Beyond this point managers of multinational firms might be better advised to
invest in TCM, whose effectiveness is not compromised by cultural distance.
A third graph was computed for linguistic distance, which shows a superiority of TCM
already at low levels of linguistic distance and a relatively steep decrease of PCM’s effect on
knowledge transfer effectiveness. Due to the limitations of our measurement and the novelty of the
concept of linguistic distance in international business literature, we can only speculate on the
27
meaning of these results. A logic which supports the underlying relationship in this graph is that
within a language family (e.g. Slavic languages) people may still be able to read and understand
knowledge transferred via TCM while it may be impossible for them to participate in discussions
or negotiations needed for PCM. But further interpretations of this issue probably require modeling
beyond linear representations.
The primary aim of this study was to uncover how two different knowledge transfer
mechanisms work to achieve effective knowledge transfer in situations of dispersion and
differentiation. Our results support the claims of recent studies that distance (still) matters in
international business (Ghemawat, 2001; Nachum and Zaheer, 2005). More specifically, we found
that firms should carefully adjust their transfer mechanisms to the distance between sender and
recipient in order to achieve the most effective knowledge transfer. On a more conceptual level, we
opened the floor for a contingency perspective on knowledge flows by focusing on the coalignment
of knowledge transfer mechanisms and different dimensions of distance and we are confident that
more insights in this area will follow in due course. In particular, studies investigating potential
reinforcing effects of the development of mechanisms and knowledge transfer effectiveness may
constitute fruitful avenues for further research
28
ACKNOWELDGEMENTS
We would like to thank Julian Birkinshaw, Michael Mayer, L. Felipe Monteiro, Lars Hakanson,
Steve Tallman and Nicolai Foss for their helpful advice, comments and suggestions. We also thank
the seminar participants at Copenhagen Business School, Hanken Swedish School of Economics
and the JIBS Conference on Emerging Research Frontiers for their input.
29
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Figure 1: Contingency Model of Knowledge Transfer
Cultural Distance
Technology-Based Coordination
Mechanisms (TCM)
Personal Coordination
Mechanisms (PCM)
+
Knowledge Transfer
Effectiveness
+
-
Geographic Distance
Cultural Distance
Linguistic Distance
35
Table 1: Descriptive Statistics
Variable
Mean
s.d.
Min
Max
1
.486
1.59
.00
1.10
1.00
8.85
1
2. Unit Size
.61
5.38
-.112
1
3. Formal Position
.76
.42
.00
1.00
-.016
-.216
1
4. Expatriates
.38
.73
.00
3.00
-.039
.000
-.009
1
5. Relative Competence
.00
1.00
-2.72
3.06
-.021
-.022
.512
-.017
1
6. Similarity of Practices
.00
1.00
-2.56
1.96
-.086
.070
-.031
.008
-.038
1
7. PCM
.00
1.00
-2.20
2.62-
-.011
.131
-.239
-.044
-.104
.085
1
8. TCM
.00
1.00
-2.43
2.53
-.189
.171
-.010
-.001
-.079
.248
.301
1
9. Geographic Dist
5.54
2.21
.00
9.20
-.170
-.028
-.054
.036
-.043
-.079
-.044
-.001
1
10. Cultural Dist
1.39
1.27
.00
6.94
-.012
.079
-.101
.016
-.045
-.010
.055
.098
.302
1
11. Linguistic Dist
1.61
1.44
.00
7.00
-.006
-.011
-.081
.129
-.054
-.148
-.094
-.081
.407
.365
1. Industry
2
3
4
5
6
7
8
9
10
Correlation Coefficient > .111 is significant at the 0.05 level (2-tailed).
36
Table 2: Regression Analysis of Effective Knowledge Transfer
Hypothesized
Relationships
Constant
Industry
Unit Size
Formal Position
Expatriates
Relative Competence
Similarity of Practices
TCM
PCM
Geographic Dist
Cultural Dist
Linguistic Dist
Geo Dist* PCM
Geo Dist * TCM
Cultural Dist*PCM
Cultural Dist * TCM
Linguistic Dist * PCM
Linguistic Dist * TCM
Model 1:
Controls
.238 (.356)
.059 (.115)
-.013 (.044)
-.320 (.184)*
.106 (.082)
.099 (.061)
.174 (.050)***
Model 2:
Base Model
.173 (.355)
.100 (.124)
-.032 (.040)
-.191 (.191)
.116 (.079)
.093 (.060)
.138 (.051)**
.115 (.065)*
.175 (.055)***
-.005 (.024)
.040 (.045)
.009 (.040)
H1a (-)
Model 3:
Geographic Dist
.188 (.380)
.118 (.122)
-.033 (.042)
-.176 (.194)
.118 (.077)
.086 (.060)
.133 (.051)**
.167 (.213)
.480 (.159)***
-.009 (.027)
.035 (.044)
.001(.040)
-.052 (.026)*
-006 (.034)
H1b (-)
H2 (-)
H1c (-)
R Square
F Value
* p< 0.1 ; ** p< 0.05 ; *** p< 0.01
Robust standard errors are displayed in brackets.
N=324
Model 4
Cultural Dist
.175 (.348)
.110 (.116)
-.029 (.039)
-.231 (.196)
.101 (.077)
.100 (.060)
.126 (.050)**
.018 (.082)
.327 (.067)***
-.004 (.023)
.044 (.045)
.008(.040)
Model 5:
Linguistic Dist
.190 (.353)
.105 (.118)
-.030 (.040)
-.197 (.192)
.106 (.076)
.089 (.060)
.138 (.051)***
.088 (.090)
.296 (.074)***
-.009 (.024)
.038 (.046)
.007 (.040)
-.110 (.039)***
.082 (.046)
-.076 (.033)**
.027 (.037)
.051
2.67**
.104
4.78***
.115
5.11***
.121
5.24***
.114
6.23***
37
Table 3: Hypotheses and Results
Hypotheses
Predicted
Sign
Results
H1a: Personal coordination mechanisms (PCM) * geographic distance -> knowledge transfer
effectiveness
-
Supported
H1b: Personal coordination mechanisms (PCM) * cultural distance -> knowledge transfer
effectiveness
-
Supported
H1c: Personal coordination mechanisms (PCM) * linguistic distance -> knowledge transfer
effectiveness
-
Supported
H2: Technology-based coordination mechanisms (TCM)* cultural distance -> knowledge
transfer effectiveness
-
Rejected
Table 4: Robustness Check with Four Hofstede Dimensions
Constant
Industry
Unit Size
Formal Position
Expatriates
Relative Competence
Similarity of Practices
TCM
PCM
Geographic Dist
Linguistic Dist
Power Dist
Power Dist * PCM
Individualism
Individualism*PCM
Masculinity
Masculinity * PCM
Uncert. A.
Uncert. A. * PCM
Model 1:
Power
Distance
.175 (.350)
.100 (.121)
-.028 (.040)
-.192 (.191)
.106 (.081)
.092 (.060)
.136 (.052)**
.118 (.065)*
.234 (.085)***
-.004 (.025)
.019 (.039)
.000(.002)
-.001 (.001)
.
R Square
.104
F Value
4.39***
* p< 0.1 ; ** p< 0.05 ; *** p< 0.01
Robust standard errors are displayed in brackets.
N=324
Model 2:
Individualism
Model 3:
Masculinity
.109 (.340)
.118 (.117)
-.028 (.037)
-.136 (.194)
.115 (.076)
.084 (.060)
.138 (.049)***
.134 (.031)**
.334 (.082)***
.026 (.028)
.022 (.036)
.175 (.344)
.120 (.118)
-.028 (.040)
-.176 (.193)
.111 (.080)
.093 (.059)
.135 (.049)***
.127 (.063)**
.347 (.083)***
.000 (.026)
.024 (.037)
Model 4
Uncertainty
Avoidance
.138 (.343)
.120 (.118)
-.032 (.038)
-.142 (.192)
.119 (.077)
.087 (.059)
.141 (.050)**
.139 (.064)**
.316 (.072)***
.024 (.026)
.026 (.034)
-.007 (.003)**
-.006 (.002)**
-.001 (.002)
-.005 (.002)**
-.007 (.003)**
-.005 (.002)**
.138
5.80***
.124
5.60***
.139
5.38***
38
Knowledge Transfer Effectiveness
Figure 2: Plot of interaction effects of PCM and distance on knowledge transfer effectiveness
Low Geographic
Distance
Mean Geographic
Distance
High Geographic
Distance
Knowledge Transfer Effectiveness
Coordination Capabilities
Low Cultural
Distance
Mean Cultural
Distance
High Cultural
Distance
Knowledge Transfer Effectiveness
Coordination Capabilties
Low Linguistic
Distance
Mean Linguistic
Distance
High Linguistic
Distance
Coordination Capabilities
39
Knowledge Transfer Effectiveness
Figure 3: Plot of high levels of TCM and PCM across geographic, cultural and linguistic distance
High TCM
High PCM
Knowledge Transfer Effectiveness
Geographic Distance
High TCM
High PCM
Knowledge Transfer Effectiveness
Cultural Distance
High TCM
High PCM
Linguistic Distance
40
APPENDIX: Survey Questions for Dependent and Independent Variables
Respondents answered the questions below on a 7-point scale, ranging from 1 to 7.
Knowledge Transfer
Effectiveness
My units’ operations have benefited greatly from the transfer of: (1= I
strongly agree; 7= I strongly disagree)
Market data on customers
Market data on competitors
Marketing know-how
Distribution know-how
Technology know-how
Purchasing know-how
Personal Coordination
Mechanisms (PCM)
Indicate the extent to which your unit uses the following mechanisms to
coordinate with others: (1= very infrequently; 7= very frequently)
Liaison personnel
Temporary task forces
Permanent teams
Technology-Based
Coordination Mechanisms
(TCM)
Please indicate how frequently each of the following knowledge
management processes and tools are used. If one of these does not exist in
your company, please choose ‘Not applicable’: (1= very infrequently; 7=
very frequently)
Group learning facilities( from multiple sources or at multiple points at
time)
Mapping specific types of knowledge (i.e. an individual, specific system, or
database)
Chat groups/Web-based discussion group
Pointers to expertise (skills ”yellow pages” within the company)
Similarity of Practices
To what extent do you agree with the following statements? (1= strongly
agree; 7= strongly disagree)
“Generally, business practices and operational mechanisms are very
similar.”
“Generally, corporate culture and management style are very similar.”
Relative Competence
For Headquarters:
For Subsidiaries:
Generally, compared to all your subsidiaries, the headquarters’ knowledge
stock in the following areas is... (1= much lower; 7= much higher)
Generally, compared to other units, your subsidiary’s knowledge stock in
the following areas is... (1= much lower; 7= much higher)
Market data on customers
Market data on competitors
Marketing know-how
Distribution know-how
Technology know-how
Purchasing know-how
41