Download Modeling the Influence of Proximity, Relationships and

Modeling the Influence of Proximity, Relationships and Communication
on Knowledge Transfer Efficiency in Business-to-Business Networks
Mary T. Holden, Patrick Lynch, and Thomas O’Toole, Waterford Institute of
Technology
Abstract
The conceptual model presented in this paper was developed through a multi-disciplinary
approach, leveraging the interpersonal relationship, communication, knowledge, and network
streams of research. The model centralises proximity, interpersonal relationships and their
communication patterns as key determinants of knowledge transfer efficiency, most
especially, the efficient transfer of tacit knowledge. The purpose of this paper is to present
the model and, in so doing, it is hoped to: (1) highlight the criticality of a network’s sociopsychological dynamics to achieving ‘frictionless’ knowledge transfer, (2) stimulate
researcher interest in investigating networks from the interpersonal level of analysis, and (3)
underline to business researchers the added-value of extending their research lens to include
applicable streams from other schools of thought.
Introduction
There has been keen and substantial academic and practitioner interest in knowledge transfer
and its efficiency during the past decade. Many critical factors impacting the efficiency of
knowledge transfer have been identified (cf. Döring and Schnellenback 2006; Argote and
Ingram 2000) but nearly all researchers have ignored the micro-level, relational aspects of the
social network in which economic transactions are embedded (Granovetter 1973; 1985),
indicating a significant gap exists in extant knowledge. Substantially decreasing this gap is
the goal of the authors’ current study.
A review and synthesis of several streams of research provides the basis for the authors’ first
research stage – the development of a conceptual model. An integration of network theory
with the interpersonal relationship and communication literatures indicates that: (1) although
arm’s-length interpersonal relationships may be an efficient mechanism for the transfer of
codified, public knowledge, close interpersonal relationships are necessary for the transfer of
tacit, private information, (2) the nature of the relationship between individuals has a major
impact on their communication patterns, and (3) the physical proximity of individuals has an
impact on the development of their interpersonal relationship and their communication
patterns. This multi-disciplinary approach should significantly enhance understanding of the
socio-psychological dynamics impacting knowledge transfer efficiency, most particularly the
efficient transfer of tacit knowledge. The conceptual model proposes that the interpersonal
relationships of a network’s interactants and their communication patterns, as well as the
contextual dimension of proximity are interdependent and are key determinants of knowledge
transfer efficiency. To the best of the authors’ knowledge, no researcher has previously
centralised these variables as key determinants of knowledge transfer efficiency or utilised the
authors’ synthesised theoretical approach to investigate knowledge transfer efficiency.
Based on the above, the purpose of this paper is to present the developed conceptual model
and, in so doing, it is hoped to: (1) highlight the criticality of a network’s socio-psychological
dynamics to achieving ‘frictionless’ knowledge transfer, (2) stimulate researcher interest in
investigating networks from the interpersonal level of analysis, and (3) underline to business
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researchers the added-value of extending their research lens to include applicable streams
from other schools of thought.
Knowledge Transfer, Individuals, and Embeddedness
Although dominant themes in the network literature centralise Granovetter’s (1985)
observation that economic exchange is embedded in social interactions and that a firm’s
social network is pivotal in economic exchange and in achieving and leveraging social capital,
most network researchers are concerned with macro-level effects and not with micro-level
interactions. Very few business researchers have utilised a behavioural approach to their
network studies, hence the socio-psychological dynamics which occur in and between
networks through the interactions of boundary-spanners have received little research
attention. This has occurred despite their centrality in the socio-psychological field and that a
network’s structure, content, and process are socially constructed by a set of interacting
individuals. The impact of embeddedness on the efficiency of knowledge transfer can be
realised by a review of the communication literature. Of relevance to this study’s context is
the sharing of information as well as correctly interpreting the information (message)1 sent
from one individual (source) to another (receiver). As argued in the following pages, the
sharing of information pivots on the nature of the individual’s relationship. However, the
correct interpretation of information is based on individuals sharing a highly correlated
‘symbol-referent system,’ that is, interacting individuals will interpret their communications
in the same way if the individuals share equivalent real-world meanings for the same symbols
(Farace et al. 1977).
Achieving a shared symbol-referent system is constructed through the social interactions of
individuals (Krone et al. 1987). An example of the affect of the lack of a shared symbolreferent system on knowledge transfer is provided by Reagans and McEvily (2003); they
concluded from their research that “individuals from different areas of expertise find it more
difficult to share knowledge and information with each other…” (p. 265) – their finding
supports interpersonal and communication research which has found that interpersonal
similarity has a major impact on relationship development as well as the interactants’
communication patterns (cf. Knapp and Vangelisti 2005). Further, the interpersonal
relationship literature informs that the closer the relationship between the individuals, the
more efficient their communication becomes (Knapp and Vangelisti 2005) and the more
proprietary (Knapp 1984), thereby implying that closer relationships lead to the sharing of
tacit knowledge. The foregoing is paralleled in the embeddedness literature; Gulati (1998)
argues that strong ties lead to the sharing of sensitive information and Hansen et al. (2004)
inform that individuals that share a strong tie are likely to have “developed a shared
communication frame whereby each party has come to understand how the other party uses
subtle phrases and ways of explaining difficult concepts” (p. 781).
As well as the above, a further driving motivation for the study’s individual level of analysis
in examining knowledge transfer in this study is provided by: Andrews and Delahaye’s
(2000) argument that “...in knowledge-creating companies knowledge is primarily related to
individuals rather than built-in to organizational routines, work practices, machines or
technologies...” (p. 798), Argote and Ingram’s (2000) comment that human interactions are
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The underlying assumption is that not obtaining the same meaning from the message the first time results in
additional communications in order to clear ambiguities surrounding the message’s meaning, thereby leading to
inefficiencies in the transfer of knowledge.
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the “primary source of knowledge and knowledge transfer” (p. 156), and work completed at
the individual level of analysis by Reagans and McEvily (2003).
Knowledge Transfer, Interpersonal Relationships, and Relational Communication
The network literature highlights that there are two types of knowledge: tacit
(complex/relatively un-codified, and personal) and explicit knowledge (readily
understood/codified, and public). Although there are noted difficulties in transferring explicit
knowledge despite its easy codification, the transfer of tacit knowledge is considered highly
problematic, due to its personal, cognitive nature (cf. Zander and Kogut, 1995); in this regard,
and as previously mentioned, the literature indicates that the more tacit the knowledge, the
closer the interpersonal relationship must be to ease its transfer. Hansen (1999) notes that
“When the knowledge being transferred is noncodified and dependent…an established strong
interunit relationship between the two parties to the transfer is likely to be most beneficial. In
a strong interunit tie, the source unit is likely to spend more time articulating the complex
knowledge” (p. 88). Further, findings from Hansen (1999), Uzzi (1997), Ingram and Roberts
(2000) and Inkpen and Tsang (2005) support Szulanski’s (1996) determination that an
“arduous” relationship is a major barrier to tacit knowledge transfer. From a governance
perspective, trust is a necessary component to the transfer of proprietary knowledge (Mohr
and Nevin 1990), and there is a general consensus in several literature streams that the closer
the relationship, the higher the level of trust (cf. Inkpen and Tsang 2005; Ring and Van de
Ven 1992; Dwyer et al. 1987; Altman and Taylor 1973).
Literature from the relational communication field has determined that the nature of the
relationship between individuals has a significant impact on: (1) whether or not an individual
communicates with another individual, and (2) the patterns of the interactants’
communication (cf. Burgoon and Hale 1984 and Dillard et al. 1995). Intrinsically, as
communication (verbal or non-verbal) is the mode by which knowledge is transferred
(whether tacit or explicit), then the nature of the relationship between individuals affects
knowledge transfer’s efficiency. Despite the general consensus in the network literature that
knowledge requires human agency and that an individual’s social relationships “matter” in the
knowledge transfer process, the type of interpersonal relationships between network actors,
that is, ranging from non-affiliative (discrete/arm’s-length) to very affiliative (very
close/bilateral) (cf. Holden and O’Toole 2006; Sias and Cahill 1998; Stohl and Redding
1987), has received little research attention, representing a major gap in extant literature.
Based on a behavioural perspective, the relational communication approach provides both an
extensive conceptualisation and operationalisation of the dimensions of an interpersonal
relationship (Burgoon and Hale 1987). Utilising a spectrum of ‘friendship’ has been
previously identified as too ambiguous – the use of a relational communication approach
circumvents the innate classification problems involved with the term ‘friend’ as this term has
no universal meaning due to its subjectiveness (Welch and Rubin 2002).
Knowledge Transfer, Interpersonal Relationships,
Proximity and Communication Patterns
Sias and Perry’s (2004) comments that “Communication constitutes and essentializes
relationships; therefore, relational transformation is a communicative process” (p. 591)
denotes the critical role of communication in the development of interpersonal relationships.
Both the communication and the interpersonal relationship literatures highlight that
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communication is central to relationship development and as interpersonal relationships
become closer, communication becomes more frequent, easier, informal, broader and deeper
(moving from work-related to non-work related topics and a further expansion of issues
within each category), as well as less cautious (Sias and Cahill 1998; Knapp 1984; Altman
and Taylor 1973). The foregoing is also reflected in the network literature, especially in
relation to tacit knowledge transfer. Szulanski (1996), drawing from Nonaka (1994), argues
that tacit knowledge transfer requires: (1) numerous interpersonal exchanges, (2) ease of
communication, and (3) close proximity of the source and recipient units; the foregoing
parallels the viewpoints of communication and interpersonal relationship researchers and
theorists. In particular, the issue of close proximity is not only important to developing
interpersonal relationships and communication between individuals but is also important to
the efficiency of knowledge transfer due to knowledge’s known characteristics of stickiness
and leakiness. In the innovation literature it has been identified that knowledge doesn’t travel
very far as “it is intelligible only among close groups who are all pushing at the same frontier
and imagining the same inchoate practices,” hence “innovative knowledge and knowledgebased growth, like industrial growth, still cluster. They cluster because innovative people
tend to cluster, staying close to those who share their visions, understand their insights, and
advance their ideas” (Brown and Duguid 2002, p. 430).
Based on findings from many studies, Altman and Taylor’s (1973) social penetration theory
highlights that “The closer the distance between people, the greater their social contact and
bonds” (p. 159). From a communication perspective, Knapp and Vangelisti (2005) highlight
that proximity is pivotal to developing close relationships as it enhances the flow and depth
and breadth of information between individuals (see also Monge and Contractor 2003).
Drawing from interpersonal attraction theory and relational communication theory, proximity
can have a positive or detrimental effect, that is, individuals may come to like or dislike one
another (Berscheid and Walster1978) and, in turn, these feelings impact the communication
patterns between individuals (see further discussion below). The tacit nature of knowledge
may also require proximity if its transfer occurs through observation (indicating the criticality
of face-to-face immediacy); indeed, because knowledge transfer can occur through
observation, it is perceived that the nature of the interpersonal relationship may, at times, have
no bearing on its transfer (see Figure 1). For the purposes of this study, and drawing from
Brown and Duguid (2002), proximity will be operationalised to reflect if the network’s firms
are local and provide opportunities for close, interpersonal contact.
As previously indicated, relational communication theory concerns the study of interpersonal
relationships. The underlying assumption of this paradigm is that the content of exchange
messages is bi-dimensional. Ruesch and Bateson (1951) (cited by Rogers and Farace 1975)
argued that “every message has two levels of meaning: (1) a report or content aspect, which
conveys information, and (2) a command or relational aspect, which defines the nature of the
relationship between the interactors” (p. 226). The basic premise of relational communication
is that the relationship between individuals is defined through the relational aspects of the
dyad’s exchanged messages (Burgoon and Hale 1984), that is, when two individuals interact,
they make judgements about the nature of their relationship (Dillard et al. 1995). In turn, the
defined nature of the interpersonal relationship impacts the content aspect of the
communication. For example, researchers have found that in a dominant relationship type
(such as hierarchical inter-firm relationships or superior-subordinate relationships in an intrafirm context), communication is one-way as the hierarchical structure restrains the upward
flow of communication and if low levels of trust exist in these relationships, information is
distorted and poor in quality (O’Reilly et al. 1987). Distortion involves gate-keeping,
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summarisation, changing emphasis within a message, withholding and modifying the nature
of the information (Stohl and Redding 1987). Relationships characterised by dislike can
result in withdrawal (Dillard et al 1999). Withdrawal can manifest itself in many ways such as
infrequent, if any, communication, no feedback, the use of more formal channels, no
participation, and limited information sharing – in other words, a transactional approach to
exchange. Furthermore, because communication is the means by which power is exercised,
dominance in a relationship may result in a pursuit of self-interest over mutual interest, hence
dominance can result in manipulation of communication (such as distortion) and its media
(Frost 1987). In this study and drawing from J. Mohr and colleagues’ communication work
(cf. Mohr et al. 1996; Mohr and Spekman 1994; Mohr and Nevin 1990), communication
patterns will be operationalised as involving its: quality, frequency, directionality, formality,
influence type, and level of confidentiality.
Conceptual Framework and Propositions
It is perceived that in order to understand the role of each network tie in knowledge transfer
and its association to the efficiency of knowledge transfer, the relational components
influencing the transfer of knowledge as well as the proximity of individuals must be
examined.The research methodology is cross-industrial, quantitative and will involve a mail
survey of respondents representing varying types of interpersonal relationships in business-tobusiness networks (spectrum: totally non-affiliative (arm’s-length) to totally affiliative (very
close)). Due to space constraints, the underlying assumption of the propositions and
conceptual model is that the network’s interpersonal relationships are affiliative and that
knowledge is tacit.** As outlined in Figure 1, where interpersonal relationships are
affiliative:
P1-3:
P4-5:
P6 :
Proximity will have a positive affect on:
• Tacit knowledge transfer efficiency.
• The development of affiliative interpersonal relationships.
• Interpersonal communication patterns.
Affiliative interpersonal relationships will positively enhance:
• Interpersonal communication patterns.
• Tacit knowledge transfer efficiency.
Interpersonal communication patterns will have a positive affect on tacit knowledge
transfer efficiency.
Proximity
Interpersonal
Communication
Patterns
Tacit Knowledge
Transfer
Efficiency
Figure 1: Conceptual Model
Affiliative
Interpersonal
Relationship
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Conclusion
This paper’s basic premise is that the interpersonal socio-psychological dimension of human
interaction has a major impact on a network’s knowledge transfer efficiency and that the
leveraging of differing schools of thought provides a more holistic and informed approach to
studying business phenomenon. The conceptual model centralises proximity, the
interpersonal relationship and its relevant communication patterns as key determinants of
knowledge transfer efficiency. As there has been little network research that has centralised
these variables or used the individual level of analysis, it is perceived that the results from
testing the study’s model in the next stage of the authors’ study will add considerably to
substantive knowledge.
*In the future, competitive advantage of firms will not be determined primarily by the efficiency of production
factors used, but by the firm’s ability to exploit available resources in the network (OECD Conference on
Innovation and Growth in Tourism 2003).
**As indicated, the data collected will represent both non-affiliative (discrete/arm’s-length) and very affiliative
(relational/bilateral) relationships; it will also exemplify two different types of knowledge, tacit and explicit.
Hence, the analysis will involve the testing of several models for comparative purposes, for example, ceteris
paribus, a further model would examine the relationship between non-affiliative interpersonal relationships and
tacit knowledge transfer efficiency.
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