Prerequisites for a beneficial knowledge transfer between manufacturing plants

Abstract

The paper aims at exploring the prerequisites for a beneficial knowledge transfer between manufacturing plants of multinational companies (MNCs), by taking the characteristics of the knowledge sending and knowledge receiving plant into consideration. This research seeks to understand how efforts undertaken by manufacturing plants, and how collaborative tools and coordination mechanisms influence a successful knowledge transfer. The study includes thirteen case studies conducted in manufacturing plants from four different European countries (i.e., Switzerland, Romania, Albania, and Macedonia). Given the exploratory nature of this study, the authors used a qualitative research approach. The main method of data collection involved multiple semi-structured interviews at manufacturing plants, uniformly applied in each country in order to observe general patterns across different cases. Their results show that the personal interaction between knowledge sending and receiving plants is more important for a successful knowledge transfer than information systems or prior related knowledge.

Full text

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VEZET ÉSTUDOMÁ NY / BUDAPEST M ANAGEMEN T REVIEW
LI. ÉVF. 2020. 11. SZÁM / ISSN 0133-0179 DOI: 10.14267/VE ZTUD.2020.11.01
STUDIES AND ARTICLES
PREREQUISITES FOR A BENEFICIAL KNOWLEDGE TRANSFER
BETWEEN MANUFACTURING PLANTS
A TERMELŐÜZEMEK KÖZÖTTI
SIKERES TUDÁSTRANSZFER ELŐFELTÉTELEI
MAIKE SCHERRER – PATRICIA DEFLORIN
– LEVENTE SZÁSZ – BÉLA-GERGELY RÁCZ
– ILDIKÓ-RÉKA CARDOŞ – ISTVÁN FÁBIÁN
The paper aims at exploring the prerequisites for a beneficial knowledge transfer between manufacturing plants of
multinational companies (MNCs), by taking the characteristics of the knowledge sending and knowledge receiving
plant into consideration. This research seeks to understand how efforts undertaken by manufacturing plants, and how
collaborative tools and coordination mechanisms influence a successful knowledge transfer. The study includes thirteen
case studies conducted in manufacturing plants from four different European countries (i.e., Switzerland, Romania,
Albania, and Macedonia). Given the exploratory nature of this study, the authors used a qualitative research approach. The
main method of data collection involved multiple semi-structured interviews at manufacturing plants, uniformly applied
in each country in order to observe general patterns across different cases. Their results show that the personal interaction
between knowledge sending and receiving plants is more important for a successful knowledge transfer than information
systems or prior related knowledge.
Keywords: knowledge transfer, manufacturing network, knowledge sending, knowledge receiving, multinational
companies
A jelen tanulmány célja megvizsgálni a sikeres és hatékony tudástranszfer előfeltételeit a multinacionális termelővállalatok
különböző telephelyei között, figyelembe véve a tudásküldő és tudásfogadó telephelyek tulajdonságait. A szerzők
kutatásukban azt vizsgálják, hogy a telephelyek által megtett erőfeszítések, az általuk alkalmazott kollaborációs
eszközök és koordinációs mechanizmusok miként járulnak hozzá a sikeres tudástranszferhez. A tanulmány tizenhárom
esettanulmányt tartalmaz, amelyeket négy európai országban készítettek (Svájc, Románia, Albánia és Macedónia).
Tekintettel a kutatás feltáró jellegére kvalitatív kutatási módszert alkalmaztak a szerzők. Az adatgyűjtés fő módja a
félig-strukturált interjúk módszertanára épült, amelyeket az említett négy országban egységesen hajtottak végre annak
érdekében, hogy párhuzamot tudjanak vonni a különböző esetek között. Eredményeik azt mutatják, hogy a személyes
interakció a tudásküldő és tudásfogadó telephelyek között sokkal fontosabb egy sikeres tudástranszfer tekintetében, mint
az alkalmazott információs rendszerek vagy az előzetes tárgyi tudás.
Kulcsszavak: tudástranszfer, termelési hálózatok, tudásküldés, tudásfogadás, multinacionális vállalatok
Funding/Finanszírozás:
The authors would like to thank the Swiss National Science Foundation (SCOPES Joint Research Project IZ73Z0_152505)
and UEFISCDI Romania (Executive Agency for Higher Education, Research, Development and Innovation Funding, PN-III-
P1-1.1-TE-2016-0502 project) for the financial support provided for the research project discussed in this paper.
Authors/Szerzők:
Maike Scherrer, professor, Zurich University of Applied Sciences, (maike.scherrer@zhaw.ch)
Patricia Deflorin, professor, University of Applied Sciences of the Grisons, (Patricia.Deflorin@fhgr.ch)
Levente Szász, professor, Babeş-Bolyai University, (levente.szasz@econ.ubbcluj.ro)
Béla-Gergely Rácz, assistant professor, Babeş-Bolyai University, (bela.racz@econ.ubbcluj.ro)
Ildikó-Réka Cardoş, assistant professor, Babeş-Bolyai University, (ildiko.cardos@econ.ubbcluj.ro)
István Fábián, PhD student, Babeş-Bolyai University, (istvan.fabian@econ.ubbcluj.ro)
This article was received: 23. 09. 2020, revised: 21. 10. 2020, accepted: 22. 10. 2020.
A cikk beérkezett: 2020. 09. 23-án, javítva: 2020. 10. 21-én, elfogadva: 2020. 10. 22-én.

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STUDIES AND ARTICLES
Knowledge sharing enables a rm to develop itself
further and to become a learning organisation (Shi
& Gregory, 1998). Since many rms do not consist of
one manufacturing plant only but are actually dispersed
networks of plants scattered around the globe, knowledge
sharing and its challenges have gained even more attention
over the last couple of years (Dunning, 2006).
In this process, critique arose that studies analysing
knowledge ow in manufacturing networks stay at the
aggregated network level (Foss et al., 2010). Some authors
argue that it is not possible to fully understand the ow of
knowledge based on organisational-level analysis alone
(Argote & Ingram, 2000; Foss et al., 2010; Gupta et al.,
2007). They claim that the plant level, and especially the
underlying processes and relationships, need to be analysed
as well. It is assumed that they mediate the variables at
network level (Abell et al., 2008; Foss et al., 2010).
The objective to analyse both the network and the plant
level is also supported by the mixed results concerning
knowledge transfer benets. Studies analysing the achieved
benets by internal knowledge transfer demonst rate
positive (Ding et al., 2013), negative (Ambos et al., 2006),
mixed (Szász et al., 2016) and curvilinear eects (Erden
et al., 2014). To explain these dierences, some authors
suggest that the benet of knowledge transfer depends on
context similarities, adaptation cost, the cost of knowledge
transfer, or the competences of the knowledge receiver. In
addition, mechanisms, prerequisites, and motivation to
engage in knowledge transfer also need to be considered
(Tran et al., 2010).
Given the lack of research in this eld, our research is
primarily exploratory in its nature, and aims at exploring
the prerequisites for a benecial knowledge transfer
between manufacturing plants within MNCs at network
and plant levels. On the plant level we aim to analyse
knowledge transfer activities by taking the characteristics
of the knowledge sending and the knowledge receiving
plant simultaneously into consideration (RQ1). On the
network level, we take into consideration the coordination
mechanisms that inuence knowledge transfer activities
between plants belonging to the same network (RQ2). Thus,
in order to gain deeper understanding in when knowledge
transfer is benecial within a manufacturing network, the
study at hand follows to answer two research questions:
RQ1: “What are prerequisites of a benecial knowledge
transfer at the sending and receiving plant?”
a) “How do eorts undertaken by the knowledge
sending and receiving plant inuence knowledge
transfer success?”
b) “How do collaborative tools inuence a successful
knowledge transfer?”
c) “How do similarities between the knowledge
sending and receiving plant inuence knowledge
transfer benets?”
RQ2: “How do coordination mechanisms inuence a
successful knowledge transfer?”
We argue that the research questions are relevant
from both theoretical and practical perspectives: it aims
to contribute to lling a theoretical gap by enhancing
our understanding of the conditions of benecial
knowledge transfer within MNCs, and from a practical
perspective it aims to oer useful guidance for plant
managers to facilitate successful knowledge transfer
projects. The paper therefore builds on an exploratory
research, and is structured as follows. We start with
the literature review, followed by the description of the
case research methodology used. In the last two parts
we present our results and ndings followed by the
conclusions and discussions in the light of our research
questions.
Literature review
Knowledge transfer, knowledge sharing and
knowledge flow
As our paper is built around the concepts of knowledge
transfer, knowledge ow and knowledge sharing, it is
important to clarify their meaning. According to Baksa and
Báder (2020) knowledge sharing is the eciency-oriented
behaviour of employees, that implies two individuals
sharing their experience and knowledge of their elds
of expertise (Hankonen & Ravaja, 2017). Through
knowledge sharing, individuals can share valuable skills
and craftsmanship, resulting new knowledge, which can
benet the learning capacity of the whole plant (Ergün &
Avcı, 2018).
On the other hand, knowledge transfer and knowledge
ow are rather dened as a process of communication
(Minbaeva, 2007) on a manufacturing network level
(Tsai, 2002). Knowledge sharing between plants can lead
to performance benets to the knowledge-receiving plant,
resulting a better overall performance of the manufacturing
network (Szász et al., 2019). Poor knowledge transfer can
lead to uncertainty in the manufacturing network, but
also be a major waste of corporate resources (Pauleen &
Holden, 2010).
Knowledge ow is mentioned as one of the two
types of the information ow by Vereecke and De Meyer
(2006). The administrative information ow consists of
information on inventory, production plans, forecasts,
purchasing requirements etc. Knowledge ow is mainly
tacit, which is rarely written and relies more on the
experience of the individuals involved in the process.
According to other researchers, knowledge ow is one of
the main reasons multinational companies can exist and
are able to dissolve cultural (Pauleen & Holden, 2010) and
national boundaries (Dunning, 1993).
Knowledge flows in manufacturing networks
Literature recognises the internal knowledge transfer
as a valuable source of competitive advantage in a
manufacturing network (Argote & Ingram, 2000).
Researchers claim the importance to analyse knowledge
ow within manufacturing networks on both the network
and plant level (e.g., Foss et al., 2010; Gupta et al., 2007).
Subsequently, the literature research is organised along
this line.

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Knowledge ow within the manufacturing network
de s cr ibes the transfe r ring pro cess betwee n th e knowledge-
sending and knowledge-receiving plant (Tseng, 2015;
Gupta & Govindarajan, 2000; Minbaeva, 2007). Three
dierent ows of knowledge exist: (1) forward, from
headquarters to a plant, (2) reverse, from a plant to
headquarters, and (3) lateral, between peer plants (Ambos
et al., 2006).
Since we are interested in the knowledge transfer
between peer plants, we concentrate in this paper on the
lateral knowledge ow. In lateral knowledge ows the
knowledge-sending plant needs to be willing to transfer
knowledge and needs to have transferring capabilities in
order for the knowledge transfer to be successful (Wang
et al., 2004; Szulanski, 1996; Mahnke et al., 2005; Szász
et al., 2019). The knowledge-receiving plant, on the other
hand, needs to have absorptive capacities to be able to
internalise the provided knowledge (Tsai & Ghoshal,
1998; Foss & Pedersen, 2002). The knowledge-receiving
plant furthermore needs motivation to accept and use the
provided knowledge, otherwise, the recipient may reject
the implementation or feign acceptance of the provided
knowledge (Hayes & Clark, 1985). According to Demeter
and Losonci (2016) there are several reasons why such a
knowledge transfer can become less eective, such as:
• lack of motivation at both plants, especially at the
knowledge-receiving, but also at the knowledge-
sending plant,
• low level of similarity between the technological
and/or geographical attributes of the sending and
receiving plants (Rosenkopf & Almeida, 2003),
• competitive relationship between peer plants (Dyer &
Nobeoka, 2000),
• causal obscurity, when we do not know how the
transferred knowledge aected the performance of
the knowledge-receiving plant,
• low absorptive capacity of the knowledge-receiving
plant, due to lack of experience and previous
knowledge (Cohen & Levinthal, 1990).
Manufacturing network coordination, and especially
mechanisms to coordinate the ow of knowledge
within one network have been recognised as essential
to combine the dispersed knowledge in the network
(Ferdows, 2006; Rudberg & West, 2008; Vereecke et al.,
2006). Even though many coordination mechanisms have
been discussed in literature, the answer is still lacking
which of these mechanisms serve as prerequisites to
coordinate the knowledge ow within a network. The
main coordination mechanisms are: the degree of
standardisation (Maritan et al., 2004; Rudberg & West,
2008; Scherrer-Rathje & Deorin, 2017), centralisation
(Feldmann & Olhager, 2011; Netland & Aspelund,
2014; Scherrer-Rathje & Deorin, 2017) and autonomy,
split into strategic and operational decision making
autonomy (Golini et al., 2016; Kawai & Strange, 2014).
Standardisation is the degree of similarity of products,
processes, or systems throughout the network (Maritan
et al., 2004). Centralisation and autonomy are linked, as
decentralisation of decision making is the main indicator
of a site’s autonomy (Maritan et al., 2004).
Based on the presented literature, for the paper at hand,
we take the dimensions of standardisation, centralisation
and autonomy into consideration when analysing
prerequisites of a benecial knowledge transfer at network
level (RQ2).
Knowledge flow on plant level
Knowledge ow within the manufacturing network
describes the transfer process between the knowledge-
sending and knowledge-receiving plant (Gupta &
Govindarajan, 2000; Minbaeva, 2007; Tseng, 2015). To
analyse the knowledge transfer on plant level, we follow
the suggestion of Szulanski (2000) and take the basic
elements of knowledge transfer into consideration: the
source, the recipient and the channel.
(1) The source of knowledge, in our case the
knowledge-sending plant, needs to be willing and needs to
have transferring capabilities in order for the knowledge
transfer to be successful (Mahnke et al., 2005; Szulanski,
1996; Wang et al., 2004; Szász et al., 2019).
(2) The recipient, in our case the knowledge-
receiving plant, needs to have absorptive capacities to
be able to internalise the provided knowledge (Foss
& Pedersen, 2002; Tsai & Ghoshal, 1998). Absorptive
capacity depends on the pre-existing stock of knowledge.
Literature discusses causal ambiguity to be an essential
prerequisite to internalise provided knowledge (Gupta &
Govindarajan, 2000; Phelps et al., 2012; Szulanski, 2000).
(3) The channel links the knowledge sending and
receiving plant with each other. These channels can be
formal or informal. Formal channels are those that are
coordinated centrally, whereas informal channels are
self-established friendships or other social ties within the
manufacturing network (Adenfelt & Lagerström, 2008;
Bell & Zaheer, 2007; Foss et al., 2010; Song, 2014). Thus,
most of these informal channels are based on the personal
interactions between employees of the knowledge sending
and receiving plants.
Next to the personal interactions, formal channels
also exist in the sense of information and communication
systems. These systems can be used to share knowledge
among members of the manufacturing network (Bigliardi
et al., 2010). In this sense, they also build on the interaction
between employees working at sending and receiving
plants.
Finally, literature discusses that knowledge transfer
can be enabled through similarities. These can be strategic
or knowledge similarities (Darr & Kurtzberg, 2000;
Scherrer & Deorin, 2017). Both can help to overcome
the possible lack of the pre-existing stock of knowledge
of the knowledge receiving plant in the sense that also,
for example, similar strategic orientation can enable the
knowledge receiving plant to understand the provided
knowledge. Strategy can be operationalised through
competitive priorities such as cost, quality, exibility,
delivery or innovation (Schoenherr & Narasimhan, 2012;
Szász & Demeter, 2014).

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To measure the result of the knowledge transfer, we
consider two categories. The rst consists of knowledge
outputs such as an increase in product, process, or
technology knowledge (Argote & Ingram, 2000; Darr &
Kurtzberg, 2000; Kang et al., 2010). The second consists of
operational performance measures, such as cost, quality,
exibility, delivery or innovation (Hayes & Wheelwright,
1984; Rosenzweig & Easton, 2010; Schoenherr &
Narasimhan, 2012; Szász & Demeter, 2014).
Figure 1.
Conceptual Framework
Source: own editing
To sum up, Figure 1 shows the conceptual framework
of the research paper at hand. While the source and the
recipient need to have the described preconditions like the
willingness to share knowledge and absorptive capacity
to take the knowledge in, the channel can have dierent
characteristics. Since we propose that these characteristics
inuence the knowledge transfer activities, we group the
elements of the channels into (1) eort that a company
puts into the interaction between knowledge sending
and receiving plants, (2) the provided collaboration tools
(information systems, enterprise systems), as well as (3)
the similarities between the plants. The (4) coordination
mechanisms (i.e. standardisation, centralisation and
autonomy) frame the interplay between the plants within
the manufacturing network.
Methodology
Being a primarily exploratory research, we have chosen
the multiple case study method (Yin, 1988), which gives
the possibility to both discover diverse knowledge roles of
the multiple plants involved and their dierent underlying
capabilities, but also to observe general knowledge
transfer similarities across dierent cases.
The unit of analysis is the manufacturing plant within
the multinational company. To gain an understanding of
lateral knowledge transfer we examined 13 manufacturing
plants located in Switzerland, Romania, Macedonia and
Albania. We used middle-range theory development
(Merton, 1968), by linking theory and empirical work. We
derived dimensions from theory and rened them through
case study research. Eisenhardt and Graebner (2007)
recommend the case study approach for research interests
such as ours, since the topic is not well documented and
relatively unknown. The qualitative research approach
(Eisenhardt, 1989; Eisenhardt & Graebner, 2007; Voss et
al., 2002) provided us with deeper insights into the selected
case plants and allowed us to generate new insights. The
plant level was selected as the unit of analysis to gain
information in the needed level of detail. Moreover, based
Table 1.
Plant’s’ knowledge transfer activities
Source: own editing

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on Voss et al. (2002) who consider that case studies should
contain between 3 and 30 cases, and Eisenhardt (1989) who
reduce this interval to 4-10 cases, an approximately equal
number of cases (three to four) was targeted in each of the
four participating countries, i.e. Switzerland, Romania,
Albania, and Macedonia, resulting in a total number of 13
case studies. More specically, we conducted case study
analysis at 3 manufacturing plants from Switzerland (S1,
S2, S3), 3 from Romania (R1, R2, R3), 4 from Albania
(A1, A2, A3, A4) and 3 from Macedonia (M1, M2, M3).
These plants operate in various industries like automotive,
electronics, food, constructions, rail, steel, cement, and
industrial equipment oering a good variety in terms of
country and industry to ensure a higher validity of cross-
case analysis.
Case plants were selected based on the joint fullment
of the following criteria (Szász et al., 2019): (a) they belong
to a multinational company (MNC) with at least four
Table 2.
Cross-case analysis of case data related to successful projects
Source: own editing
Company S1 S2 S3 R1 R2 R3 A1 A2 A3 A4 M1 M2 M3
JobRotation x x x x x x x
Cross‐functionalinterfaces x x x
CenterofCompetence x
Helpdesk x
Globalmeetings(Centrallycoordinated) x x
Centrallycoordinatedvisitsofotherplants xx x xx xxx x
Centrallycoordinatedtrainings x x x
Standardise ddocumentation x x x x
Informal Ad‐hocsupportbetweenplants(Socialties) x x x x x
InformationSharing/Web xxxxxx xxxxxx
Collaborationplatform xx xx x xx
DocumentManagementSystem xxx xxxxx
Intranet/Newsxx xxxxx x x
DecisionsupportSystem x x x x x x x
Datawarehousing,datamining,&OLAP x xxx xxxxxx
Conferencingtools(eg.,videoconferencing) xxxxxxxxxxxxx
Communicationtools(e.g.,email,wikis,filesharing,etc.
)
xxxxxxxxxxxx
Artific ialintelligencetools/ExpertSystems
xx x
Simulationtools xxxxx x
Similarities
Product xx xxxxxxxxx
Process xxxxxxxxxxxxx
Technology xxxxxxxxxx xx
Management x x x x x
Service xxxx xx
Product x x x
Process x x x
Technology x
Management
Service
High x xxxxxxxx x
Medium x x x
Low
High x xxxxxxx
Medium x x x x
Low x
Headquarters xxxxxxxxxxxxx
Plant
Headquarters x x x
Plant xxxxx xx xxx
Success
Productrelatedknowledgeincr ease
Technologyrelatedknowledgeincreas e x x
Processrelatedincrease xxx x x xx x
Cost x x x
Qualityxx xxx x
Deliveryspeed x x x
Flexibilit y x x x x
Innovation x
Service(pre‐sales)
Priorrelated
knowledge
Operatio nal
decisionautonomy
Strategicdecision
autonomy
Centralisation
Effortstoenableemployee’sknowledgesharinginteraction
Coordinationmechanisms
Formal
Collaborationtools
Knowledge
Knowldge
sending
plants
Knowledgereceivingplants
Standardisation
Knoweldgeoutput
Competitive
Prioritiesincrease

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manufacturing plants, (b) their MNC is a leading company
in its eld, with the headquarters (HQ) in a developed
country, and operations in at least three countries, (c) the
plant to be interviewed is not an isolated player (Vereecke
et al., 2006), i.e. it is actively engaged in knowledge
sending and/or receiving to/from other units from within
the MNC. Companies with no clear knowledge roles for
the analysed plant or companies that were not transparent
enough regarding their knowledge transfer activities and
underlying were excluded from this study.
Within the qualitative research approach, we used
semi-structured interview as the main method of data
collection, with an interview protocol uniformly applied in
each country. Each manufacturing plant was interviewed
at least once, but in some cases follow-up interviews were
conducted to clarify specic knowledge transfer aspects.
Interviews were targeted at the highest managerial level
who oversee the knowledge transfer projects, but also
actively participate in facilitating knowledge transfer
activities. Researchers have participated in multiple
interviews in mixed teams from dierent countries to
enable a uniform understanding of the data collected.
Field data were collected from December 2015 until
March 2017.
In order to analyse our research questions, we rst
asked the interviewees general questions about the plants
(environmental conditions, strategies and involvement
in knowledge transfer activities, embeddedness in
network). Second, they were asked to evaluate their
embeddedness in the knowledge transfer activities within
their networks, more exactly to evaluate in general how
much (1) information and (2) innovation they send and
receive and (3) how much training they oer to employees
from other plants and how much training they received
from other plant sta in comparison to other plants in the
network. These dimensions were adapted from the work
of Vereecke et al. (2006). The rst dimension covers the
amount of information transferred, which needs to be
distinguished from pure data based exchange and refers
to more explicit data concerning day-to-day activities
related to products, processes, technology, management
or services (i.e. meaningful information related to
manufacturing). In addition, the second dimension aims at
capturing innovation, which is related to a more tacit type
of knowledge. Transferring innovation from one plant to
another means that there are no routines established and
most often, its implementation is based on a combination
of knowledge and information. Third, trainings were also
assessed as a frequent form of complex knowledge transfer
within the manufacturing network of MNCs.
Next, in order to derive dierences between benecial
and less benecial knowledge transfer, we asked
interviewees to identify a successful and a less successful
knowledge transfer project and to explain what content
explicitly has been transferred between plants. Altogether
we examined 25 examples of knowledge transfer projects,
as one of the 13 plants refused to identify an unsuccessful
project. By having multiple knowledge transfer projects,
we aimed at getting a better understanding of the specic
content transferred within these processes. We were also
interested in the factors that made the interviewees consider
a knowledge transfer project benecial or not which were
mainly related to the process (e.g. time and resources needed
to transfer knowledge) or the outcome (e.g. new process
technology introduction) of knowledge transfer processes.
We then discussed the dierent prerequisites for
knowledge transfer based on factors derived from
literature. In addition, to gain in-depth insights, we
encouraged the interviewees to discuss additional relevant
factors. It has to be noted that each knowledge transfer
project was investigated from a unilateral perspective, but
we aimed to include both sender and receiver plants in our
sample to gain a better understanding of both roles within
manufacturing networks.
All interviews lasted between two and three hours,
were taped and afterwards transcribed. The contents of all
interviews were summarised into a manuscript containing
the details of each knowledge transfer discussed.
Afterwards, the research team conducted a cross-case
analysis to compare and to gain a proper understanding of
knowledge transfer processes at each manufacturing plant.
Besides the interview data, company documentations,
archive data, manuals, we used industry publications and
personal observations in order to formulate conclusions
(Szász et al., 2019).
Data analysis and findings
Following the analysis of interview data, complemented
by archival data and personal observation, we categorized
the 13 plants in four groups in terms of their involvement
in knowledge sending and knowledge receiving activities
(Table 1):
– Group 1 (Net senders) consists of those plants that
send a lot of knowledge to other plants, but receive
only minimal knowledge from others.
– Group 2 (Balanced actors) and Group 3 (Active
receivers) are intermediate groups. The plants in
group 2 send and receive knowledge approximately
to an equal extent. Plants in group 3 are also involved
in both sending and receiving knowledge, but they
receive somewhat more and send somewhat less than
plants belonging to Group 2.
– Group 4 (Net receivers) consists of those plants that
are mainly knowledge receivers, and engage only
rarely in knowledge sending activities, and if they do
so, they send only low amount of knowledge.
Table 1 further shows that the older plants in the sample
are those who act as knowledge senders. The younger
the plants are, the more they are leaning towards the
net receiving group (Group 4). The intermediate groups
are not fully consistent related to amount of knowledge
transferred and the age. Plant R1 belongs to the active
receivers group despite being one of the youngest plants
in the sample. Plant M3 is also part of the intermediate
group, but with 50 years in age, it is older than the youngest
plant in the net sender group (S1). Consequently, we can

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only partly support the existing results from literature
discussing plant age and the participation in knowledge
transfer activities as correlating factors.
During the interviews, the interviewees were asked to
id en t i fy su cc ess f ul an d le ss s uc ce ssf ul k no w le d g e tr a nsf e r
projects in order to derive dierences between benecial
and less benecial knowledge transfer. Table 2 shows
the cross-case results of case data related to successful
projects. All units investigated, no matter if they are
k no w le dg e se nd ing o r kn o wl e dg e re cei vi ng pl a nt s, en ga ge
in formal (job rotation, centrally coordinated visits of
other plants, trainings, standardised documentation)
and informal (ad-hoc support between plants) eorts
to facilitate knowledge exchange. Furthermore, all
plants have various types of collaboration tools in
place (such as information sharing tools, web, intranet,
video conferencing, email, le sharing, and even more
complex systems which can facilitate knowledge transfer
activities). When it comes to similarities, all knowledge
exchanging plants have similar process knowledge and
most of them also have similar product, process and/or
technology knowledge. The level of standardisation and
centralisation is overwhelmingly high, with the majority
of st r ategic and operation a l decisions making activit ies at
headquarters. Overall, if the knowledge transfer projects
were successful, the plants reported either a knowledge
output in a process or a technology related knowledge
increase.
In order to dierentiate between benecial and less
benecial knowledge transfer, we asked the interviewees
to identify successful and less successful knowledge
transfer projects and to explain what content explicitly
has been transferred between plants. From this resulted 25
examples of knowledge transfer projects, one of the plants
refusing to comment on unsuccessful projects. Table 3 and
Table 4 highlight some examples of prerequisites leading
Table 3.
Examples of successful knowledge transfer projects
Source: own editing
Company Role Projectdescription Supportingfactors
S1 Knowledge
sender
Duplicationofproduction
lineatsisterplant.
Knowledgesendingplant
providedtechnological
andprocessknowledge
andsupportedemployees
atsisterplantin
implementingand
ramping‐upproduction.
Effort
‐Monthlyvideoconferencescoordinatedbycentral
‐Everysixmonthvis itofemployeesfromknowledgesendingplantatknowledgereceivingplant
‐Higheffortinestablishingpersonaltiesbetweenemployeesofknowledgesendingandreceivingplant
Collaborationtools
‐Allnecessarydocumentationstoredininformationsharingsoftware
Similarities
‐Establishmentofsimilarproductionlineasatsendingplant
‐Nopriorrelatedknowledgeinplace
Coordination mechanism
‐Highlevelofstandardisationindocumentation
Success
‐Technologic alknowledgeoutput
R3 Knowledge
receiver
ImplementingofTPM
system.Otherplant
providedknowledgeof
howtoimplementTPM
andhowtofollowthe
rules.TPMwas
implem entedandrunning
after1,5yearsat
interviewedplant.To
compare:average
implementationtime
withinc ompanyis3years .
Effort
‐CentrallycoordinatedvisitsofotherplantsthathadTPMalreadyimplemented
‐Oneemployeefromknowledgesendingplantservedasaconsultantforreceiverplant
‐Consultantmadefrequentvisitsatknowledgereceivin gplant
Collaborationtools
‐Allnecessarydocumentationstoredininformationsharingsoftware
Similarities
‐Similarprocessesatknowledgesendingandreceivingplant
‐Nopriorrelatedknowledgeinplace
Coordination mechanisms
‐Highlevelofstandardisation
‐Highlevelofcentralisation; AutonomyatHQ
Success
‐ Processknowledgeoutput
M3 Knowledge
receiver
Newprocessknowledge
transferredfromsister
planttoM3.Init,the
sisterplantenabledM3in
howtoimplementanduse
aprocessextensionsto
theexistingproduction
process.
Effort
‐Ad‐hocvisits tootherplantstoseehowtheyconductedprocessstepofinterestforse lectio npurpose
‐Oneemployeefromknowledgesendingplantservedasaconsultantforreceiverplant
‐Jobrotationwithknowledgesendingplantwaskey
‐Networkofpeerscontactedforad‐hocquestions(socialties)
Collaborationtools
‐Informationindatabase
‐Videoconferencingtoolsusedtointeract withknowledgesendingplant
Similarities
‐Similartechnologyknowledgeatsendingandreceivingplant
‐Nopriorrelatedknowledge
Coordination mechanisms
‐Highlevelofstandardisation
‐MediumlevelofcentralisationwithstrategicautonomyatHQandoperationalautonomyatplant
Success
‐Processrelatedknowledgeoutput

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STUDIES AND ARTICLES
to a knowledge transfer success (Table 3) and some, where
the knowledge transfer was unsuccessful (Table 4).
The comparison between Table 3, showing examples
of successful projects, and Table 4, showing examples of
unsuccessful projects, displays that the successful projects
were characterised by a high level of eort in personal
contact (monthly video conferences, frequent employee
visits from knowledge sending plants at knowledge
receiving plants, personal ties between the knowledge
sending and knowledges receiving plants, job rotations
between sending and receiving plants, consultancy
activities, frequent visits of key knowledge sending
personnel at the knowledge receiving plants, ad-hoc
visits to other plants). As the manager of S3 commented:
“Knowledge transfer is a people’s business” or as the plant
manager of M3 summed up: “Systems are not enough.
Network is the key factor …. The network of people.”
Even though in all successful projects, information about
the emphasised projects were in the company databases, they
were only useful if the data quality was appropriate. And
even then, the information alone was not useful if there were
no employees from the knowledge sending plant explaining
the content of the documents and data repositories to their
peers at the knowledge receiving plants.
Prior-related knowledge showed to be of no necessity
for a successful knowledge transfer. In all three examples
provided in Table 3, no prior-related knowledge was in
place. In comparison to this, two out of three examples
of the unsuccessful projects had prior-related knowledge
based on the emphasised project in place.
With regard to the coordination mechanisms, the
companies showed similar characteristics, no matter if
the project was successful or not. All companies have a
medium to high level of standardisation and a medium
to high level of centralisation with strategic decision
autonomy always at headquarters and operational
autonomy partly at headquarters and partly at the plant
level.
Discussion and conclusion
The subsequent paragraphs briey summarise our ndings
in light of the research questions of this paper.
First, related to RQ1.a, our study revealed that both
knowledge sending and knowledge receiving plants
Table 4.
Examples of unsuccessful knowledge transfer projects
Source: own editing
Company Role Projectdesc ription Hind eringfactors
S2 Knowledge
sender
ProducttransferfromS2
tolowcostplant.
Effort
‐HQforcedemployeesofS2toprovidetheirprocessknowledge
‐EmployeesofS2werenotmotivate dtogivetheirknowledgetootherplant
‐Process wasrushed,notenoughmanagementattention
Collaborationtools
‐Allnecessarydocumentationstoredininform ationsharingsoftware,butbaddataquality
Similarities
‐Similarproduct,processandtechnologyknowledge
‐Priorrelatedknowledgerelatedtoproduct,processandtechnology
Coordinationmechanisms
‐Highlevelofstandardisat ion
‐Mediumlevelofcentralis ationwit hstrategicautonomyatHQandoperationalautonomyatplant
Success
‐Nosuccess
A3 Knowledge
receiver
Providinginformatio n
aboutaccidentssothat
thesameaccidentdoes
nothappeninoneofthe
otherplantsaswell.
However,notallaccidents
couldbeavoided.
Effort
‐Formaldocumentationofaccidentandhowitcametoit‐nopersonalexperienceexchange
Collaborationtools
‐Databaseforreport
Similarities
‐Similarproduct,processandtechnologyknowledge
‐Nopriorrelatedknowledge
Coordinationmechanisms
‐Highlevelofstandardisat ion
‐HighlevelofcentralisationwithstrategicautonomyatHQandoperationalautonomyatplant
Success
‐Nosuccess
M2 Knowledge
receiver
Reproductionofaproduct
thatwasproducedatan
otherplantonlybasedon
companydocumentation
ofthatspecificproduct.
Attheend,thefinal
productdidnotperform
wellunderdifferent
humidityconditions.
Effort
‐Nopersonalcontact,experienceexchangewouldhavehinderedprojectfailure
Collaborationtools
‐ Productdocumentationincompanydatabase
Similarities
‐Sameproduct,processandtechnologyknowledge
‐Priorprocessrelatedknowledge
Coordinationmechanism
‐Mediumlevelofstandardisation
‐Mediumlevelofcentralis ationwit hstrategicautonomyatHQandoperationalautonomyatplant
Success
‐ Nosuccess

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STUDIES AND ARTICLES
engage in formal and informal eorts to facilitate
knowledge exchange. The knowledge transfer is more
successful in case of similar process knowledge or similar
product/technology knowledge base. Moreover, all
companies have a medium to high level of standardisation
and a medium to high level of centralisation with strategic
decision autonomy always at headquarters and operational
autonomy partly at headquarters and partly at the plant
level. We align with Scherrer and Deorin (2017), who
consider that these aspects (strategic similarities and
product/product family/technology similarities) are
prerequisites for a knowledge transfer to be benecial.
Second, related to RQ1.b, despite the often-discussed
fact that the information systems are necessary for a
successful knowledge transfer, our data mirror a dierent
perspective. Only if a personal interaction between the
knowledge sending and receiving plant is established,
the knowledge transfer has been considered as benecial.
In line with this, several authors have criticised the
general assumption that information systems can support
the knowledge transfer within organisations. Authors
claim that if there is no overlap between the knowledge
sender and receiver in their underlying knowledge base,
knowledge transfer based on information systems alone
does not work (Alavi & Leidner, 2001). As stated above,
our data do not reveal the importance of prior knowledge
stock. Instead, our conclusion is more in line with the
statement of Roberts (2000), saying that information
systems will never be able to entirely replace face-to-
face interactions (RQ1.a) between knowledge sender and
receiver.
Third, in relation to RQ1.c, we conclude based on our
data that the pre-existing stock of knowledge, claimed
to be necessary to have the ability to absorb provided
knowledge, does not hold true in all of our cases. With
this, our data do not support the necessity of causal
ambiguity for a benecial knowledge transfer (Gupta
& Govindarajan, 2000; Szulanski, 2000). Instead, the
personal contact seems to be able to overcome non-existing
prior knowledge stocks, which is an important implication
for newly established plants with less knowledge or plants
located in countries with lower levels of pre-existing
knowledge base.
Fourth, related to RQ2, we conclude that at least a
medium level of standardisation needs to be in place
to enable the possibility of knowledge transfer, but the
coordination mechanisms alone seem not to be responsible
for a successful knowledge transfer.
To sum up, managers seeking benecial knowledge
transfers within manufacturing networks should take the
importance of personal interactions during the knowledge
exchange process into account. It is worth to invest
in personal exchange activities, such as mutual plant
visits, joint projects, informal meetings, joint training
programs and team-buildings, to bind social ties even
prior to knowledge exchange activities, as these social
ties support a higher frequency of interactions and a
higher willingness to participate in knowledge exchange
activities, and ultimately can secure the success of
transferring knowledge between peer plants belonging to
the internal network of the same MNC.
Overall, our study contributes to operations and
knowledge management literature by exploring the
role of some plant-level and network-level factors in
successful knowledge transfers, but it is also limited in
its generalizability given that case study research was
conducted. Nevertheless, the variance between the cases
in terms of country, industry and age oers a good basis
to formulate propositions for further research attempts.
Further research on a larger scale should verify our results
and quantify the importance of personal factors compared
to structural factors in successful knowledge transfers.
Future research should also involve additional factors,
such as disseminative capability, absorptive capability
dimensions, to investigate whether there is an interaction
between these factors and the ones involved in our study
that can further improve successful knowledge transfers.
Another highly relevant future research issue is related
to the COVID-19 pandemic as it inuences the way
employees work at companies and poses an important
limit to personal interactions which was deemed as the
most inuential prerequisite of successful knowledge
transfer. How changing work habits will inuence
knowledge transfer projects remains, thus, an issue for
further investigation.
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