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It is the paper published as: Author: D. Jarratt Title: Testing a theoretically constructed relationship management capability Journal: European Journal of Marketing ISSN: 0309-0566 Year: 2008 Volume: 42 Issue: 9-10 Pages: 1106-1132 Abstract: Research Purpose The research objective was to test a theoretically derived representation of a Relationship Management Capability. The Relationship Management Capability architecture developed from the literature integrated theory on dynamic capabilities, the Resource-Advantage Theory of Competition, and prior capability research in innovation and information technology management. Research Design The second-order constructs of Relationship Infrastructure, Relationship Learning and Relationship Behaviour argued to represent a Relationship Management Capability (RMC) were assigned measures adapted from the literature, and pilot tested with industry consultants. The final questionnaire was sent to senior executives responsible for customer relationship management in manufacturing and business service firms in the UK. The structural model representing the RMC was shown to be robust with a Comparative Fit Index of 0.91.FindingsAlthough the low response rate and the subjectiveness of respondents encourage caution in interpreting the research findings, the results suggest that relationship management systems, implemented through collaborative and flexible behaviours, and renewed through adaptive and generative knowledge derived from experience and challenging current relationship management assumptions, are key dimensions of a RMC. Value of the Research This framework advances and tests a new theoretical perspective of a Relationship Management Capability that incorporates a capacity for renewal. In addition, it provides managers with a tool to evaluate their organizations Relationship Management Capability at key stakeholder interfaces on attributes that define relationship infrastructure, relationship learning and relationship behaviour, as this capability is renewed over time. Author Address: djarratt@csu.edu.au URL: http://dx.doi.org/10.1108/03090560810891172 http://info.emeraldinsight.com/products/journals/journals.htm?PHPSESSID=nv21ii5ps0bi6n7l0s6ve6sht5&id=ejm http://researchoutput.csu.edu.au/R/-?func=dbin-jump-full&object_id=8155&local_base=GEN01-CSU01 http://bonza.unilinc.edu.au:80/F/?func=direct&doc_number=000099348&local_base=L25XX CRO Number: 8155
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Testing a Theoretically Constructed Relationship Management Capability
Denise Jarratt PhD
Professor of Marketing
Faculty of Business
Charles Sturt University
Bathurst 2795
Australia
Ph: #61 2 63 384 293
Fax: #61 2 63 384 769
e-mail:djarratt@csu.edu.au
Denise Jarratt is Professor of Marketing and Sub-Dean (Research) for the Faculty of Business at Charles Sturt
University. She has worked extensively in industry and has owned and operated a retail business. While at CSU
Dr Jarratt has undertaken a range of consulting projects in consumer behaviour, retailing and strategy for several
private and public institutions. She has published in international journals and books in the areas of competitive
strategy, capabilities, relationship marketing, trust and business networks.
Testing a Theoretically Constructed Relationship Management Capability
Abstract
3
Research Purpose
The research objective was to test a theoretically derived representation of a Relationship
Management Capability. The Relationship Management Capability architecture developed
from the literature integrated theory on dynamic capabilities, the Resource-Advantage Theory
of Competition, and prior capability research in innovation and information technology
management.
Research Design
The second-order constructs of Relationship Infrastructure, Relationship Learning and
Relationship Behaviour argued to represent a Relationship Management Capability (RMC)
were assigned measures adapted from the literature, and pilot tested with industry consultants.
The final questionnaire was sent to senior executives responsible for customer relationship
management in manufacturing and business service firms in the UK. The structural model
representing the RMC was shown to be robust with a Comparative Fit Index of 0.91.
Findings
Although the low response rate and the subjectiveness of respondents encourage caution in
interpreting the research findings, the results suggest that relationship management systems,
implemented through collaborative and flexible behaviours, and renewed through adaptive
and generative knowledge derived from experience and challenging current relationship
management assumptions, are key dimensions of a RMC.
Value of the Research
This framework advances and tests a new theoretical perspective of a Relationship
Management Capability that incorporates a capacity for renewal. In addition, it provides
4
managers with a tool to evaluate their organisation‟s Relationship Management Capability at
key stakeholder interfaces on attributes that define relationship infrastructure, relationship
learning and relationship behaviour, as this capability is renewed over time.
Keywords: Organisational Capabilities, Relationship Management, Relationship Marketing
Research Paper
5
Introduction
Stakeholder relationships are becoming increasingly central to organisational strategy
(Lorenzoni and Lipparini, 1999). Consequently, the capacity to build and manage
relationships with client and other stakeholder organisations has been identified as an
important strategic capability of organisations (Jarratt and Fayed, 2001). Particularly where
organisations have extended their operational scope beyond their core assets, and created an
extended organisational form or network to access assets integral to value creation, a
relationship management capability provides the mechanism to leverage those assets (Sheth
and Parvatiyar, 2000). In this extended organisation context, “relationships are likely to be
complex and long-term” (Hakansson and Ford 2002, p. 133), “the strategy process is
interactive, evolutionary and responsive” (p. 137) and value creation is dependent on the
effectiveness of the organisation in leveraging relational equity, i.e. the wealth creating
potential that resides in its network of stakeholder relationships (Sawhney and Zabin, 2002).
Further, the capability of an organisation to integrate the resources residing within different
network actors has been posited to be as important as its capability to innovate (Grant, 1996;
Lorenzoni and Lipparini, 1999).
While an organisational customer stakeholder relationship has been defined as a market-
based, relational asset (Srivastava, Shervani and Fahey, 1998) that spans participating firms,
the capability to leverage this market-based asset, and consequently affect the value of the
asset, remains within each participating organisation. A relationship management capability in
which routines foster normative behaviour has been linked to decreased transaction costs
resulting from bargaining and monitoring behaviour, increased innovation, building positional
advantage and enhancing performance (Barney and Hansen, 1994).
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However, Brennan‟s (1997) investigation of business relationships in the UK automotive
and telecommunications industries revealed a) a historical legacy of fear of openness limiting
relationship development, b) relationship inertia c) the hidden costs associated with
relationship building such as joint investments in reporting and knowledge sharing systems,
and d) the ongoing investments associated with shared strategy development and problem
solving. Thus, while reducing the number of suppliers and integrating systems between an
organisation and selected suppliers were seen to simplify and accelerate the production
process, cost improvement was not necessarily confirmed as a key relationship objective nor
achievable, particularly short-term, once the total costs of relationship development were
accounted for. Ongoing relationship monitoring against established cost, quality,
innovativeness and speed to market objectives is therefore important to facilitate learning
about relationship management and provide direction for relationship management systems
and practice renewal.
Although theoretical arguments have been presented linking capabilities, knowledge and
organisational learning to competitive advantage (Fiol, 2001; Wright, Dunford, and Snell,
2001; Besanko, Dranove and Shanley 2000; Hunt, 1999; Teece, Pisano and Shuen, 1997;
Spender and Grant, 1996), few authors have contributed to the debate on how a capability that
has the capacity for renewal might be described and measured (Jarratt, 2004; Makadok, 2001;
Day, 2000; Helfat and Raubitschek, 2000; Verona, 1999; Brown and Eisenhardt, 1997; Helfat,
1997). This research seeks to contribute to this debate. The principal objective of this article is
to test a theoretical representation of a relationship management capability within the context
of the management of customer stakeholder relationships. In light of the increasing
importance of multiple, organisation-wide, inter-firm cooperative relationships to
organisational wealth creation, and the need of organisations to adjust and renew capabilities
in line with emerging tacit and explicit capability knowledge, the two central theoretical
7
frames of reference selected to construct and define measurement of a relationship
management capability are an empirically tested, capability architecture (Gold, Malhotra and
Segars, 2001) and the Resource-Advantage Theory of Competition (Hunt, 1999). The
proposed model is tested at an operational unit level, specifically, in the context of major
organisational customer relationships.
Firstly, literature on dynamic capabilities is reviewed and the link between learning and
capability renewal established. Then, the architecture, constructs and measures of a
relationship management capability are proposed from a synthesis of literature on
organisational capabilities and relationship marketing literature. Hypotheses are developed
and components of relationship management infrastructure, behaviour and learning constructs
described. Next, the method of data collection is presented and, finally, implications of the
results for theory and practice are proposed.
Dynamic Properties of Organisational Capabilities
Capabilities have been defined by Day (1994, p. 38) as “complex bundles of skills and
collective learning, exercised through organisational processes, that ensure superior
coordination of functional activities” and are constantly adjusting to advance capability
configuration and build efficiency and effectiveness (Verona, 1999; Brown and Eisenhardt,
1995). They are embedded in the organisation, are essential for the generation of profit from
organisational resources (Kelly and Amburgey, 1991; Law, Wong and Mobley, 1998; Gold et
al., 2001), and do so through the integration of knowledge, tangible and intangible assets,
processes, human capital and learning (Plakoyiannaki and Tzokas, 2002; Grewal and
Tansuhaj, 2001; Mahoney and Pandian, 1992).
8
The „dynamism‟ in organisational capabilities emerges through the application of learning
to routines and behaviour (Barney, 2001; Barney, Wright and Ketchen, 2001; Karim and
Mitchell, 2000; Teece et al., 1997). Organisations adjust capability embedded routines and
behaviour either reactively or proactively as they strive to respond to, or pre-empt, market
conditions to retain or build competitive advantage (Nelson and Winter, 1982; Hunt, 1999).
Routines that underpin an organisation‟s relationship management capability have only
started to emerge in the literature, although patterns of behaviour displayed as a result of those
routines have been clearly documented (Hult, Hurley, and Giunipero, Nichols, 2000; Jarratt
and O‟Neill, 2002).
Evidence supporting a dynamic capabilities perspective (Teece et al., 1997) was provided
in a longitudinal analysis of three inter-firm manufacturing networks (Lorenzoni and
Lipparini, 1999) where it was revealed that lead firms made changes to interacting platforms
and inter-firm connecting processes to facilitate communication and foster cross-network
learning. The lead firm‟s relationship management capability defined the nature of the
network, and the resultant “interactive process among actors created a greater quantity of
planned and spontaneous information” (p. 333). Deliberate initiatives were implemented to
restructure processes so as to access additional competencies residing in network actors.
Similar initiatives were observed by Brown and Eisenhardt (1997) in organisations that were
successful in enhancing their new product management capability.
Integrating the resource-based view (RBV) and evolutionary economics with theories
explaining the strategic behaviour of the firm, the Resource-Advantage Theory of
Competition (Hunt and Morgan, 1995; Hunt, 1997; 1999; 2000) provides a theoretical
foundation for these knowledge driven applications to advance capability behaviour and
productivity. Consistent with other knowledge theories of the firm, organisational learning is
endogenous to this resource-based competitive system, with the re-investment in capabilities
9
re-enforcing heterogeneity of the capability, and building comparative advantage (Hunt,
2000). Consequently, a relationship management capability will incorporate inter-firm
knowledge sharing routines for exchange of information (explicit) and know-how (tacit), and
knowledge of relationship history (Jarratt, 2004). Learning how to improve relationships will
drive capability re-configuration and deployment (Barney, 1991; Barney, 2001; Eisenhardt
and Martin, 2000; Fiol, 2001; Wright et al., 2001), thus facilitating efficiency and
productivity gains in relationship management practice.
The need for capabilities to be reconfigured to retain competitive advantage (Fiol, 2001),
places emphasis within a relationship management capability on knowledge generation about
how relationships can be better developed and managed. Learning will enable new tasks to be
performed better and faster, and may occur through imitation of others, and/or through
collective contributions to solving complex problems. Constant surveillance of advances in
information and communication technology and a willingness to adopt best practice are
precursors to reconfiguring a capability to achieve competitive advantage (Teece et al., 1997).
A capability is a multi-level phenomenon, existing across firms, at the firm level and at the
operational unit level. Some capabilities are built jointly with stakeholders, while others
emerge in one area of the company and are transferred to other units (Jarratt, 2004;
Birkinshaw, Bresman and Hakanson, 2000; Dyer and Singh, 1998). Thus, in addition to
learning from current relationship dysfunction, current and prior relationship activity across
all functional areas of the organisation, i.e. past experiences and expertise, are also likely to be
important sources of learning. Thus, past relationship experiences residing in organisational
memory and relationship management expertise are important infrastructure resources of a
relationship management capability.
As relationship learning results from resolving issues associated with current inter-firm
interactions and from general knowledge of innovation in relationship management practice,
10
both adaptive and generative learning are important for capability re-configuration and
deployment (Barney, 1991; Barney, 2001; Eisenhardt and Martin, 2000; Fiol, 2001; Wright et
al., 2001). Adaptive learning encourages changes that are consistent with current norms of
thinking and practice, whereas generative learning results in changes that are discontinuous,
such as the application of new systems, new processes, and new management forms (Baker
and Sinkula, 1999; Lukas, Hult, and Ferrell, 1996).
Reflection on current relationship management experience, integrated with a synthesis of
relevant „best practice‟ information and prior learning located in organisational memory, may
result in the development of new relationship governance mechanisms across an organisation
with new performance criteria added, and new technology applied to facilitate interaction.
Thus,
H1 Relationship Infrastructure is a function of Adaptive and Generative Relationship
Learning
Relationship Management Capability: architecture, constructs and measures
Verona (1999) conceptualised a capability as a higher-order structure reflecting functions and
integration. Within this higher-order architecture, functional capabilities were argued to
deepen technical knowledge while integrative capabilities defined adsorptive capacity. A
functional capability was assigned dimensions of expertise (lessons learnt residing in
organisational memory), past experience (tacit knowledge of they way things are done),
routines and processes (Helfat, 1997; Verona, 1999). Integrative capabilities were described
through the managerial processes, structures and culture that facilitate the location,
dissemination and application of new knowledge. Gold et al., (2001) drew on this architecture
in defining and measuring an organisation‟s knowledge management capability. The authors
11
empirically tested a capability that combined a functional capability (technical IT system)
with two integrative capabilities. One integrative dimension described structures that
facilitated the discovery of new knowledge, while the second integrative capability defined
organisational cultural aspects that fostered collaboration and exploration.
The dynamic capabilities perspective, discussed in the previous section, provides
theoretical support for incorporating a learning dimension (i.e. the discovery of new
knowledge) within a capability. Knowledge to enhance and develop a distinctive capability is
inherent in Bharadwaj‟s (2000) description of an IT capability, Day‟s (2000) grounded
research investigation of a market-relating capability and, implicit in Brown and Eisenhardt‟s
(1995) case examination of a product innovation capability. Bharadwaj (2000) identified
organisational learning as key components of an IT capability, while, Day‟s process
integration and alignment dimension refers to human interrelationships that lead to knowledge
sharing and generation identified in his market-relating capability.
Brown and Eisenhardt‟s (1995) investigation of product innovation capabilities across five
case organisations concluded that processes (for example, probing the future, capturing
innovation relevant knowledge from stakeholders and across projects) and behaviour
(flexibility to adjust in response to change requirements, scheduling multiple products and
orchestrating transitions) were critical for product innovation efficiency and productivity.
Neither adaptive nor reflective learning about how to enhance the integration of the new
product development system were discussed in the research conclusions. The authors,
however, reported that “managers frequently reassessed their transitions, fine-tuning their
processes. For example, managers of one case organisation were considering shortening their
project intervals to nine months to pick up the pace against competitors, while managers of
another case organisation had recently elaborated their transition procedures to match their
increasingly broad product line” (p. 23). Post-study, both case organisations demonstrated
12
substantial market success through growth from new products, providing empirical support
for the link between learning, capability process renewal and competitive advantage.
In addition to a learning dimension, Verona‟s architecture encouraged the integration of
infrastructure (a functional capability comprising expertise, knowledge of past experience
residing in organisational memory, routines and processes), and culture that support the
creation and application of new knowledge (Helfat, 1997; Verona, 1999). Empirical research
has captured infrastructure through processes (Brown and Eisenhardt, 1995), routines,
experience and knowledge residing in organisational memory (Gold et al., 2001), hardware
infrastructure, technical and operational skills and organisational memory (Bharadwaj, 2000)
and experience, databases, management systems and routines (Day, 2000).
Cultural dimensions driving the implementation of capability processes have been captured
empirically as flexibility (Brown and Eisenhardt, 1995), interaction, collaboration, exploration
(Gold et al., 2001), and values, mind-set and norms (Day, 2000). In addition, flexibility and
collaboration are organisational values captured within Day‟s relationship orientation
dimension. Flexibility is an organisational value associated with an adhocracy culture while
collaboration is a value associated with a clan organisational culture and contingent upon
cohesiveness, participation and teamwork (Deshpandé, Farley and Webster, 1993; Deshpandé
and Webster, 1989; and Quinn and Cameron, 1983). These two organisational values have
been identified previously as encouraging strategic integration between organisations and
influencing, positively, relationship outcomes (Jarratt and O‟Neill, 2002; Anderson and
Narus, 1990; Johnson, 1999; Lusch and Brown, 1996; Mohr, Fisher and Nevin, 1996). Thus,
the functional and integrative dimensions of organisational capabilities described by Verona,
empirically tested by Gold et al., (2001) and evidenced in research by Brown and Eisenhardt
(1995), Bharadwaj (2000) and Day (2000) are: learning, infrastructure and behaviour.
13
Following Verona‟s capability architecture and drawing on these empirical studies, in this
research a relationship management capability is identified as: infrastructure i.e the
relationship management system and processes; relationship memory; relationship
experience; learning capturing both generative and adaptive learning; and behaviour that
provides evidence of a cooperative culture and flexibility in relationship development and
management, and the implementation of new relationship management knowledge
(relationship collaboration, relationship flexibility and relationship innovation). Each of these
capability dimensions is represented by the multiple constructs depicting systems, processes,
intangible resources and actions (Figure 1).
________________________
TAKE IN FIGURE 1 HERE
_________________________
It has been previously argued that the „dynamism‟ in organisational capabilities emerges
through the application of learning to routines and behaviour either reactively or proactively
to retain or build market position (Barney, 2001; Barney et al., 2001; Teece et al., 1997; Hunt,
1999). In addition, collaborative behaviours, normative control mechanisms and the
emergence of new practices (social influence theory, Friedkin and Johnson, 1999) will drive
changes within the relationship infrastructure.
Thus, it is posited that:
H2 Relationship Behaviour is a function of Relationship Learning; and
H3 Relationship Infrastructure is a function of Relationship Behaviour
Consistent with the Resource-Advantage Theory of Competition, the three hypotheses linking
the second-order constructs represent a dynamic system, with the dependent construct,
14
relationship infrastructure, becoming the input of a recurrent learning and learning application
cycle (Figure 1).
Capabilities have generally been captured in research through measures that recognise
human expertise and know-how, behavioural aspects, such as flexibility, „hard work‟ and
„rapid response‟, and technical and/or operational excellence (Brown and Eisenhardt, 1995;
Deshpandé and Farley, 1998; Bharadwaj, 2000; Grewal, Comer and Mehta, 2001; Saini and
Jognson, 2005). Measures identified to capture the constructs representing a Relationship
Management Capability follow these recommendations and, predominantly, are selected from
the relationship marketing literature. Where this was not possible, scales developed and tested
in other capability contexts were interpreted in a Relationship Management Capability
context. Both of the accepted approaches to measuring organisational capabilities i.e. direct
measurement of knowledge and skills, and the measurement of observable outcomes (in the
form of behaviour), are employed (Moorman and Slotegraaf, 1999).
Relationship Infrastructure
Relationship Infrastructure is captured through the relationship management system, and the
resources of experience and memory that support generative learning.
Relationship Management System: A dynamic capabilities perspective confirms routines
as the fundamental infrastructure component. Dyer and Singh‟s (1998) relational view of the
firm identified inter- and intra- firm routines for knowledge-sharing as one of the primary
sources of profit generation, while Gold et al.‟s (2001) study of a knowledge management
capability incorporated measures capturing routines that support knowledge capture, synthesis
(mapping) and dissemination. In describing a relationship capability, Day (2000) spoke of
organisational processes that underpin a co-ordinated relationship management system.
Measures adopted to capture evidence of routines that comprise a relationship management
15
system are drawn from research focusing on inter-organisational linking structures that
facilitate inter-organisational communication and knowledge transfer, and facilitate learning
about the relationship, as these aspects have been linked to quality relationship management
practice (Dyer and Singh, 1998). Relationship operating processes (Parikh, 2001; Henricks,
1991; Devlin and Bleackley, 1988 and adapted from Gold et al., 2001) are identified as:
monitoring relationship progress; coordination of relationship management activities; routines
to evaluate potential relationship partners; inter-organisational processes that increase
interaction; systems designed to enhance relationship productivity.
Relationship Experience: Verona‟s (1999) functional capability also embraces the ability
of an organisation to build an effective integrated capability through experience. Those
organisations experienced in relationships, and have credibility as a relationship partner, are
more likely to be sought out as partners and are more likely to create value through the
relationship (Day, 1995; Birkinshaw et al., 2000). Following Birkinshaw et al., (2000) and
Day (1995), measures adopted to capture relationship experience are: effectiveness of inter-
organisational knowledge sharing systems, effectiveness of relationship building and process
skills and, comparison of relationship management ability with competitors.
Relationship Memory: Memory provides the infrastructure for capability learning
(Verona, 1999). Moorman and Miner (1997, p. 93) defined organisational memory as
collective beliefs, behavioural routines, or physical artefacts and identified memories as an
important resource for change. Following this definition, Hult et al., (2000) established
measures to capture organisational memory in studying organisational learning in global
purchasing. Their measures have been adapted to reflect a relationship context for this study:
organisational conversations keep relationship management lessons alive; audit unsuccessful
relationship endeavours; share lessons learned; challenging currently held assumptions about
relationship management.
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Relationship Learning
Learning about how to enhance relationships between an organisation and its customers may
occur as a result of current relationship management experience, lessons learned from the
experiences of others, or engagement with relationship memory (Hult and Ferrell, 1997;
Slater and Narver, 1995). Measures employed to represent generative learning about
relationship management systems and practice were adapted from Hult et al., (2000).
Generative learning is captured through: discussion of future relationship needs; seek new
ideas to improve relationship management; discuss ideas for relationship improvement at
interdepartmental meetings. Adaptive learning measures were drawn from Dickson‟s (1992)
notion of the need to learn and adapt quickly: quick to detect changes in relationship
performance; quick to detect changes in relationship interactions; discussing ideas for
potential relationship improvement with partners.
Relationship Behaviour
Behaviours that support relationship longevity are those that demonstrate that the relationship
is important to an organisation, and that the organisation is open to new possibilities for
relationship building. Behaviours demonstrating that the relationship is important are those
that facilitate an understanding of the partner‟s needs, skills, resources, and views on new
opportunities, and behaviours confirming openness are those that demonstrate a willingness to
adapt to meet those needs and address emerging opportunities (Day, 2000). As learning
involves the development of new knowledge (Slater and Narver, 1995), a relationship
behavioral capability will also exhibit behavioral practices that confirm the implementation of
that new knowledge, thus facilitating capability renewal. Therefore, the behavioural
dimension of a relationship management capability will be captured through behaviour that
17
facilitates learning and the dissemination of learning (collaboration), a cooperative culture
(flexibility), and innovation in relationship management (innovation).
Relationship collaboration: “Collaborations are designed to expand the size of the joint
benefit „pie‟ and give each party a share of an incrementally greater pie that could not be
generated by either firm in isolation” (Jap, 1999, p. 462). Collaboration is a reflection of
relationship interconnectedness and a mechanism through which knowledge can be generated
and distributed. Drawing on Jarratt and O‟Neill (2002), Dahlstrom, McNeilly and Speh
(1996), Morgan and Hunt (1994), and Weitz and Jap (1995), collaboration measures are:
balanced representation on the management team, multiple inter-organisational links,
collaborative decision-making, and idea exchange.
Relationship flexibility: “Adapting to an exchange partner's unique needs and operations
creates a dependency relationship and builds switching costs” (Cannon, Achrol and Gundlach,
2000, p. 182). Flexibility, i.e. the ability and willingness to adapt, is a meta-level capability,
and is reflective of the culture of an organisation that has discarded its internal hierarchical
form, linking with others to build unique value propositions (Birkinshaw et al., 2000).
Flexibility was a key dimension of Brown and Eisenhardt‟s (1995) “semi-structure” linked to
the development of successful new product portfolios. Flexibility captures the notion that it is
expected that the contractual relationship “will be modified as the market, the exchange
relationship, and the fortunes of the parties evolve” (Cannon et al., 2000, p. 185). Following
Jarratt and O‟Neil (2002), Dahlstrom et al., (1996), Cannon et al., (2000) and Weitz and Jap
(1995), flexibility is measured through: willingness of both parties to adjust the ongoing
relationship, willingness to alter prior commitments, greater emphasis on normative control.
Relationship Innovation: Relationship innovation reflects the implementation of adaptive
and generative learning, confirming both the dissemination of new relationship management
knowledge and resulting action. Measures of relationship innovation are adapted from Baker
18
and Sinkula (1999), Hurley and Hult (1998), and Vorhies and Harker (2000): setting standards
in relationship management practice, being first to adopt innovative relationships management
practices, and constant improvement in policies governing relationships.
Research Method
The research objective, to test the robustness of a theoretical representation of a relationship
management capability in a customer stakeholder context encouraged a quantitative
investigation. A cross-sectional design was selected as an appropriate approach to achieve the
research objective, since, at a single point in time, an organisation‟s relationship infrastructure
will reflect a system that has adapted as a result of learning implemented through behaviours
recognised to support successful relationship management, i.e. the current cycle of
relationship management capability renewal. Cross-sectional data was collected from business
enterprises on measures representing the three second-order constructs that collectively
represent a relationship management capability renewal cycle.
The questionnaire contained measures described in the literature and previously tested with
marketing or management personnel. The questionnaire was pilot tested to establish face
validity with five academics who actively consult to industry and are knowledgeable in the
area. Although all measures in the questionnaire had been derived from previous research,
each item was discussed with the selected industry consultants to confirm face validity. When
suggestions for change were made, the item was retested in the context of linked scale items
to ensure clarity. For one item, “we have the experience to effectively implement knowledge
sharing systems with our relationship partners” examples were added in parentheses (e.g.
linked intranets, linked data warehouses) to enhance face validity. The questionnaire was able
to be completed in the time specified and the final form retested to ensure questions were
19
assessed as clear in meaning, and were appropriate. Consistent with the view that the ability
to leverage stakeholder-based resources remains within each firm (Srivastava et al., 1998;
Sawhney and Zabin, 2002), those overseeing the management of key customer relationships
for their organisation were the target population for the research.
The final form of the questionnaire was mailed to the marketing director or managing
director (where no marketing director was nominated) of every second top 3000
manufacturing, business supply and business service firms in the UK (1500 questionnaires
sent). Business services, business suppliers and manufacturing firms were included in the
study, as prior qualitative research had established the importance of a relationship
management capability to proactive firms in these sectors (Jarratt and Fayed, 2001). It is
posited that successful organisations in these sectors are more likely to place greater emphasis
on the management of relationships than unsuccessful firms.
The method of data capture provides a subjective opinion of only one participant in the
business relationship. However, it is argued that the opinion of a manager responsible for the
continued operation of a major relationship is in itself important. Such individuals will be
knowledgeable about the learning that is applied to enhancing relations, the infrastructure
supporting them and the level of collaboration, flexibility and innovation. The key informant
technique (Mitchell, 1994; Phillips, 1981) has been widely used in similar studies. Further,
there was very strong agreement by respondents with the statements: “my knowledge about
our major customer relationships” is high (93.7%), “my involvement in the development or
management of our major customer relationships is high” (94.2%), “my confidence in
answering this questionnaire is high” (91.1%).
The response rate of 12.67% was disappointing, particularly as attention was given to
personally addressing envelopes, personally signing the accompanying letter, using University
letterhead, offering to send an executive summary, sending a reminder mailing and phoning
20
approximately one third of the sample population. Although the low response rate is a limiting
factor of this study, it is consistent with that of other similar surveys. Hult et al., (2000)
experienced a response rate of 10.7% in collecting data from external purchasing
organisations located in different multinational corporations, in comparison with an internal
SBU response rate of 85.3%; Grewal et al., (2001) reported a response rate of approximately
14%, while Farrell (2000) and Homburg and Pflesser (2000) a response rate of approximately
15 %. Homburg and Pflesser proposed that the length of a questionnaire required to
investigate complex organisational phenomena and the level of respondent targeted limits the
response rate, a factor also experienced by Diamantopoulos and Schlegelmilch (1996) and
Harzing (1997).
Descriptive statistics revealed that the sample represented all targeted industry categories
(manufacturers of consumer and industrial goods, R&D organisations, business service
providers, technology organisations, raw material providers, office supplies). The sample
included 33% business service firms, 34% manufacturing firms and 30% business supply
organisations (R&D, technology and office supply companies), with 3% failing to record a
category of operation. Thus, even through the response rate was low, the sample is
diversified, containing representation from all targeted population groups and is therefore
argued to support „robustness of the relational finding‟ (Blair and Zinkhan, 2006 p. 6). All
questionnaires were completed by a senior executive: marketing directors/managers or
managing directors. Blair and Zinkham‟s (2006) recommend testing for non-response bias
through the systematic comparison on key attributes of those responding from an alternative
communication mechanism. As those responses received last resulted from telephone requests
to complete the survey following a second distribution of the questionnaire or from
completion of a faxed survey following telephone contact, it was appropriate to employ
Armstrong and Overton‟s (1977) test for non-response bias, comparing the first and last thirty
21
responses on key attributes. No significant differences were found across the range of
measures. The length of the questionnaire restricted the ability of the researcher to capture the
data via telephone. Although some contacted by phone agreed to complete a faxed copy of the
questionnaire or complete the copy already received, negative comments received during this
third, telephone phase of data collection (“I have already indicated by not responding that I do
not want to be involved”) discouraged further attempts to encourage survey completion. The
lack of significant differences on key measures between those who responded first and those
who completed the survey following phone contact indicates that non-response bias was not a
problem in these data.
Construct validation was achieved through application of Confirmatory Factor Analysis
(CFA). One construct, Relationship Experience, was found to be unreliable. Therefore, all
items representing Relationship Infrastructure through the constructs relationship
management system, experience and memory were subjected to exploratory factor analysis. A
two factor solution (determined by examining eigen values and the scree plot) encouraged
retaining relationship memory and collapsing relationship management system and experience
within a single relationship management system construct. Items within this new construct
with low correlations were deleted, however, taking care to retain the theoretical significance
of relationship experience within the construct (Moorman, Zaltman, and Deshpandé 1992).
Reliability coefficients of the remaining latent constructs reported by Cronbach‟s
coefficients were all greater than 0.77. This approach is consistent with that adopted by
Siguaw, Simpson and Baker (1998), Hurley and Hult (1998) and Farrell (2000), who
employed analyses of item and item-total inter-correlations, EFA and CFA to validate scales
and to eliminate poorly performing items. Table 1 provides the results of CFA on latent
constructs tested, items contributing to their measurement, the strength of each item‟s
contribution to the construct, CFIs, and chi-square values with degrees of freedom. Goodness
22
of Fit measures (Comparative Fit Indices CFI) indicated high levels of model fit for these
constructs (Hair Jr, Black, Babin, Anderson, and Tatham, 2006).
A principal component analysis using varimax rotation was conducted to determine the
underlying relationships within the data. Relationship Management Learning, containing
items representing both adaptive and generative learning, explained 48.9% of the variance
across all constructs of the model, with an eigen value of 11.72. Collaborative behaviours
contributed 5.7% of the variance, relationship management system offered 5.1%, flexibility
4.6%, practice innovation 3.7% and relationship memory 3.5%, with a cumulative variance of
71.8%.
________________________
TAKE IN TABLE 1 HERE
_________________________
Results
The first stage of measurement estimation comprised testing convergent and discriminant
validity of each construct and the three second-order factors: Relationship Infrastructure,
Relationship Learning and Relationship Behaviour. Confirmatory Factor Analysis (CFA) on
all first and second-order constructs using EQS 6.1 indicated high levels of model fit (Hair Jr
et al., 2006). Factor loadings were all higher than 0.4, with all Comparative Fit Indices falling
above 0.92. Following recommendations of Anderson and Gerbing (1988), items were
considered for deletion if they demonstrated several large residuals against other items in the
construct, had an insignificant loading or shared common variance with items in other
constructs. However, theoretical considerations played an important role in item deletion
considerations. No items were deleted from the constructs at this stage. Path coefficients
23
between the three second-order constructs and each of their component factors were all
significant at the = 0.05 level. Table 2 identifies the second-order factors, CFI‟s, chi-squares
with degrees of freedom and reliability coefficients.
________________________
TAKE IN TABLE 2 HERE
_________________________
Each second-order construct was examined against criteria specified to assess formative or
reflective characteristics. For example, the constructs Relationship Collaboration,
Relationship Flexibility and Relationship Innovation each contain items that describe a similar
theme and are manifestations of the construct Relationship Behaviour. A change in one
indicator in these constructs is unlikely to cause changes in the construct they represent.
Further, these constructs are highly correlated and thus meet Jarvis, MacKenzie and
Podsakoff‟s (2003) criteria of a Type 1 second-order factor (reflective first order and
reflective second-order).
Discriminant validity of measures was assessed through both pairwise confirmatory factor
analyses and comparing factor correlations to the variance extracted. Forcing items of
different latent constructs into a single factor decreased model fit when compared to the two-
construct solution for each pair of constructs. The chi-square difference for each pair of latent
constructs was significant in each case, confirming discriminant validity (Anderson and
Gerbing, 1988). A second test of discriminant validity involved comparison of the average of
the sum of the squared standardized loadings on constructs to the squared correlation between
constructs (Hair Jr et al., 2006, p. 778). The averages of the squared loadings were all found
to be higher than the squared correlations except in three cases: Case 1 - Relationship
Management System (RMS) and Relationship Memory (RM); Case 2 - Adaptive and
24
Generative Learning; and Case 3 - RMS and Relationship Innovation (RI). RMS and RM
form the second-order construct Relationship Infrastructure, while Adaptive and Generative
Learning also contribute to the variance in the same second-order construct, Relationship
Learning. In Case 3 the squared correlation (0.608) between RMS and RI was lower than the
average of the squared loadings for RI (.761), but marginally higher than that of RMS (.534).
Removal of the item representing relationship experience from the revised construct
Relationship Management System addressed this limitation, however, there was marginal
change in overall structural model robustness and hypothesis support. The theoretical
importance of relationship experience to a relationship management capability, the
significance of the chi-square difference for each RMS and RI constructs, and the marginal
change in structural model robustness and hypothesis support, encouraged retaining this item.
Convergent validity is established where all items are significantly related to their
hypothesized factors (p<0.05) and where there are no high cross-loadings. The item
“Constantly improving relationship policies” cross-loaded on relationship learning (0.485),
however, the importance of retaining three items per construct and evidence from theory
encouraged retaining this item. In addition, the average variances extracted were all equal to
or higher than 0.5 (Hair Jr. et al., 2006). Table 3 identifies composite reliability and variance
extracted for constructs included in the structural model. In using the approach to estimate
composite reliability noted in Table 3 footnote b, no constructs incorporated in the structural
model fell short of the recommended level of 0.60 (Bagozzi and Yi, 1988). In fact all were
over 0.7, indicating adequate convergence (Hair Jr et al., 2006).
The proposed conceptual model was tested through structural equation modelling. The
disturbances within each of the second-order factors were equally constrained within the
structural model. Fit statistics of the model containing three second-order factors and one
containing no second-order factors are compared (Table 4). The second-order factor model
25
was superior on all statistics and converged on 12 iterations. For the model containing the
three second-order factors, a CFI of .91 (with chi-square of 534.86 based on 240 degrees of
freedom indicating a high degree of malfit between the hypothesized model and the null
model) was achieved. The RMSEA of 0.08 fell just within the acceptable range, however the
parsimonious fit measure ²(/df) at 2.23 fell just outside the recommended value. The path
coefficients between the second-order constructs and associated t statistics provided evidence
that the three hypotheses were supported.
______________________________
TAKE IN TABLES 3 AND 4 HERE
_______________________________
Discussion and Implications
The principal objective of this article was to test a theoretical representation of a relationship
management capability within an organisational stakeholder context, specifically, the
customer stakeholder. A framework was constructed that drew on theory of dynamic
capabilities, organisational capability architecture, learning theory and relationship
management practice (Hult et al., 2000; Sinkula, Baker, and Noordewier, 1997; Verona,
1999).
An important theoretical contribution of this study is the identification and testing of
measures representing constructs depicting a Relationship Management Capability. Previous
research on organisational capabilities had incorporated measures of expertise and know-how,
behaviour, and operational excellence. The relationship marketing literature was examined to
identify appropriate measures to represent these dimensions. Where these were not available,
measures contained in the broader capability literature (for example, Brown and Eisenhardt,
26
1995; Deshpandé and Farley, 1998; Bharadwaj, 2000; Grewal, et al. 2001; Saini and Jognson,
2005) were interpreted within a relationship management context. Although cross-loadings
encouraged the removal of two items describing monitoring and coordination of relationship
management activities from the construct Relationship Management System, alternative
representations of these measures were captured within the Adaptive Learning Construct
through items reflecting the speedy identification of changes in relationship interaction and
relationship performance. As relationship inertia and lack of relationship monitoring had
been previously identified as negatively affecting relationship performance and renewal
(Brennan, 1997), it was theoretically important for these measures to be incorporated within
constructs depicting a relationship management capability.
Confirmatory Factor Analysis revealed that the measures derived from research by Day
(1995) and Birkinshaw et al., (2000) to measure „Relationship Experience‟ contributed to less
than 50% of the variance of the construct. A subsequent exploratory factor analysis of all
items representing Relationship Infrastructure encouraged collapsing items measuring
experience and relations management system into one construct. Thus, further qualitative
research is required to establish a set of robust measures to represent „relationship
experience‟. All other constructs demonstrated acceptable validity and reliability, as
evidenced through Cronbach‟s coefficients, composite reliability and variance extracted
calculated from standardised loadings.
A second contribution of this study is testing the theoretical interrelationships between
constructs depicting a relationship management capability. A second-order construct,
Relationship Learning, representing Generative and Adaptive Learning was found to
positively influence Relationship Infrastructure both directly and indirectly through
Relationship Behaviour. The research has established that Generative and Adaptive Learning
will add to the Relationship Memory Resource, and positively influence collaborative,
27
flexible and innovative relationship behaviour. Consistent with Hunt‟s (1999) Resource-
Advantage Theory of Competition, renewal of the Relationship Management System is a
consequence of both learning and behaviour change.
The structural model containing second-order constructs of Relationship Infrastructure,
Relationship Learning and Relationship Behaviour was found to be superior to a model
containing no second-order constructs and hypotheses developed from prior theory were
supported. This finding is consistent with the view of Gold et al., (2001, p. 211) that second-
order factor structures provide the “best empirical model for capturing the variances” among
specific measures. The second-order constructs representing infrastructure, behaviour and
learning capture the dimensions of capabilities contained in prior definitions i.e. knowledge,
tangible and intangible assets, processes, human capital, learning, and the application of
learning to routines and behaviour (Plakoyiannaki and Tzokas, 2002; Grewal and Tansuhaj,
2001; Barney, 2001; Barney et al., 2001; Karim and Mitchell, 2000; Teece et al., 1997;
Mahoney and Pandian, 1992). It was anticipated that high performing organisations operating
in the business-to-business sector would exhibit, to a varying extent, a relationship
management capability that underpins on-going client organisation interactions and
transactions. In this regard the research is confirmatory, with the findings exhibiting
theoretical generalization (Blair and Zinkhan, 2006).
The architecture of a higher-order capability as recommended by Verona (1999) was
shown to be valuable structure for understanding the key components of a relationship
management capability and their interaction. Inherent in this model are three building blocks
that are prerequisites of an organisational capability. Infrastructure, the first building block, is
defined through the functional capability of Relationship Management Systems representing
processes, system integration (experience) and knowledge of relationship management
lessons. This is consistent with Dyer and Singh‟s (1998) relational view of the firm that
28
relationship-specific investments, inter- and intra-firm routines for knowledge-sharing,
complementary resource endowments and effective governance are important sources of
additional profit for the firm.
However, in implementing a relationship management strategy, managers must recognise
the cultural orientation required to energise inter-firm communication and knowledge sharing
routines and build innovation and joint action. Infrastructure alone will not build relationship
bridges with strategic partners nor leverage the resources that lie within those partners.
Relational norms and values reflecting cultural dimensions of flexibility and collaboration
(Day, 2000) facilitate knowledge generation, knowledge transfer, the collective definition of
opportunities for further joint action, and a willingness to move beyond contractual
agreements to meet changing market demands (Cannon et al., 2000).
The third building block is the generation of knowledge to enhance relationship
management practice, through changing processes and behaviour. Dynamism in capabilities
(Teece et al., 1997) is evidenced through learning how to enhance efficiency and productivity,
and through, subsequently, implementing change. Both generative and adaptive learning were
shown to be important components of a dynamic relationship management capability.
Generative learning occurs through reconsideration of the assumptions residing in relationship
memory and inherent in current relationship approaches, or reflection about actions accepted
as appropriate at specific stages of relationship development (Dickson, 1996). Adaptive
learning results in advances to current relationship systems, norms of thinking and behaviour.
Learning about how to improve relationships will drive capability re-configuration and
deployment. Thus, managers will need to ensure that processes are in place to stimulate new
ideas about relationship management and to capture current knowledge about relationship
practice and productivity. Organisations will need to continually re-invest in each of these
foundation building blocks to ensure relationship processes, relationship intellectual capital,
29
relationship norms and relationship learning combine to create a relationship management
capability that can continue to deliver a comparative advantage (Hunt, 1999).
Those organisations that have adopted an enterprise-wide Relationship Management
Capability will have moved their relationship management practice from a stakeholder focus
to a stakeholder orientation, drawing together, centrally, knowledge about the management of
business relationships gained through practice, and from external sources (Birkinshaw et al.,
2000). Further, they will be applying that knowledge through work practices, structures and
decision support systems at each stakeholder interface (Parikh, 2001).
This current project tested a Relationship Management Capability in the context of
organisational customer relationships. Given that capabilities exist across firms, at the firm
level and at the operational unit level (Birkinshaw et al., 2000; Birkinshaw and Hagstrom,
2000), it is probable that capability systems, behaviour and learning will vary across
operational units, with adaptive and generative learning bounded within the operational unit.
To test the level of relationship management capability applied in managing upstream
stakeholders, data will need to be captured from key upstream and downstream relationship
managers within the same organisation.
What distinguishes an organisation with a Relationship Management Capability from one
applying relationship marketing principles and practice is the existence of routines within
processes that support learning about managing major stakeholder relationships, and the
subsequent adjustments made to systems, support structures and norms of interaction
throughout the stages of relationship formation, management and exit. The framework
presented here provides managers with a tool to evaluate, over time, the development of a
Relationship Management Capability within various functional areas of their organisation and
across all key stakeholder relationships on attributes that define relationship infrastructure,
relationship learning and relationship behaviour.
30
Research Limitations
An important limitation of this research is the low response rate. This study is a cross-
sectional research design in which data was obtained through a self-reporting questionnaire.
It has been argued that such approaches may lead to overestimation of investigated linkages
and that longitudinal data should be used to establish causality (Ehrenberg, 1997; Verhoef,
2003). Although those responsible for relationship management (i.e. constantly assessing
their customers‟ levels of satisfaction and implementing change to enhance that satisfaction,
as well as monitoring market performance and profitability) are in a position to provide
information on the operationalisation of those relationships, it is possible that their subjective
opinions will overestimate their own performance in anticipating and responding to customer
relationship management needs. An initial trial of the questionnaire sought multiple responses
from within each company to collect data on relationship management activity across multiple
stakeholders from multiple perspectives. The low response rate from this trial encouraged the
concentration of data collection on a senior executive responsible for important customer
stakeholder relationship. However, the response rate from these executives, while an
improvement on the initial trial response rate, still fell below that anticipated, and resulted in
the ratio of the parameters to be tested to the number of cases (1:3) falling below acceptable
limits (1:5) (Bentler and Chou, 1995).
Utilising an alternative analysis approach, second order factors were entered into separate
simple regression models. Relationship Learning's contribution to the variance in Relationship
Behaviour as a simple regression was 0.71 under SEM conditions and 0.81 (standardised
coefficient) (F = 351.84 p<0.05) in a simple regression model. Relationship Behaviour‟s
contribution to Research Infrastructure was 0.77 under SEM conditions and 0.80 (F = 320.57,
31
p<0.05) as a simple regression. A stepwise regression of Relationship Learning and
Relationship Behaviour on Relationship Infrastructure as the dependent variable reveals
Relationship Learning's contribution to the variance in Relationship Infrastructure as 0.28
(standardised coefficient) (0.27 under SEM conditions) and Relationship Behaviour‟s
contribution to Research Infrastructure as 0.57 (0.77 under SEM conditions) (F=178.89,
p<0.05). This latter result is not surprising given that the indirect effect of Relationship
Learning on Relationship Infrastructure is not simultaneously calculated in the regression. It is
important to note that the standardised coefficients observed in the simple regressions are
similar to those calculated through SEM (once an additional indirect pathway of Relationship
Learning on Relationship Infrastructure through Relationship Behaviour is accounted for).
In addition, first-order factors formed the basis of seven separate regression models, i.e.
two for each dependent construct representing Relationship Infrastructure (i.e. Relationship
Management System and Memory) and three with Relationship Behaviour constructs as
dependent constructs (i.e. innovation, collaboration and flexibility) and adaptive and
generative learning constructs as independent constructs. All contributions to dependent
constructs were significant (p<0.05) in all cases except one. When both adaptive and
generative learning were entered into a stepwise regression with Relationship Management
System as the dependent variable, both made a significant contribution to the variance in the
dependent construct (0.231 and 0.444 respectively F = 62.385 p<0.05). When adaptive and
generative learning were combined with flexibility, innovation and collaboration constructs
with Relationship Management System as the dependent variable all constructs except for
adaptive learning made a significant contribution to the variance in the dependent variable.
Overall, however, findings from the separate regression models for both first- and second-
order constructs support the conclusions drawn from the simultaneous regression solutions
derived through SEM.
32
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Figure 1. The Relationship Management Capability Renewal Cycle
0.84 0.79
0.77
H3
0.71
H2
0. 27
H1
0.93
bbbb
0.87
bb
0.89
Relationship
Management
System
Relationship
Memory
Relationship
Generative
Learning
Relationship
Adaptive
Learning
Relationship
Collaboration
Relationship
Flexibility
Relationship
Innovation
Relationship
Infrastructure
Relationship
Behaviour
0.92
0.92
Relationship
Learning
43
Table 1. Latent Constructs – CFA Measurement Model
Constructs Variables Variable
Contribution
(Standardised
solution) **
CFI and
reliability
coefficient ( )
Independence
model
Chi-square
Relationship
Management
System
Better ability than
competitors
Experience of
implementing knowledge
sharing systems
Monitor relationship
progress
Coordinate relationship
activities
Support interaction
Enhance productivity
0.50 (6.69)
0.57 (8.96)
0.82 (12.04)
0.85 (13.10)
0.71 (11.09)
0.72 (11.30)
CFI = 0.92
= .85
488.191 on 15
degrees of
freedom
Relationship
Memory
Conversation
Audit problems
Share lessons
Challenge assumptions
0.65 (8.87)
0.69 (9.00)
0.77 (10.00)
0.76 (10.15)
CFI = 1.00
= .81
237.292 on 6
degrees of
freedom
Relationship
Generative
Learning
Discuss future needs
Seek new ideas
Discuss change internally
0.81(10.19)
0.83(7.88)
0.74(7.28)
CFI = 0.94
= .81
204.084 on 3
degrees of
freedom
44
Relationship
Adaptive
Learning
Detect performance
changes
Detect interaction changes
Discuss changes with
partners
0.93(9.04)
0.95(19.73)
0.69(9.04)
CFI = 0.98
= .87
379.535 on 3
degrees of
freedom
Relationship
Collaboration
Build multiple links
Balanced management
Collaborative decision-
making
Idea exchanges
0.82 (15.40)
0.86 (16.48)
0.84 (17.47)
0.81 (17.09)
CFI = 0.98
= .90
471.021 on 6
degrees of
freedom
Relationship
Flexibility
Normative control
Making adjustments
Willingness to accept
change
0.61 (6.15)
0.86 (6.14)
0.68 (8.89)
CFI = 0.99
= .77
152.372 on 3
degrees of
freedom
Relationship
Management
Change
Set standards
First to adopt
Constant improvement
0.91 (22.49)
0.95 (22.49)
0.73 (11.09)
CFI = 0.99
= .89
369.74 on 3
degrees of
freedom
** T values in parentheses – ROBUST statistics ROBUST performs better than uncorrected statistics where the
normal distribution is false
45
Table 2. Second–order Factors – CFA Measurement Model
Second-order
Factors
Constructs Variable
Contribution -
Standardized
solution **
CFI and
reliability
coefficient
( )
Independence
model
Chi-square
Relationship
Infrastructure
Relationship
management system
Relationship memory
0.91 (6.44)
0.85 (5.50)
CFI = .92
= .87
804.682 on 36
degrees of
freedom
Relationship
Learning
Relationship
generative learning
Relationship adaptive
learning
0.93 (9.21)
0.85 (11.76)
CFI = 0.98
= .90
1044.17 on 21
degrees of
freedom
Relationship
Behaviour
Relationship
collaboration
Relationship
flexibility
Relationship
innovation
0.90 (7.02)
0.78 (5.56)
0.67 (7.08)
CFI = .94
= .90
1217.313 on 45
degrees of
freedom
** T values in parentheses – ROBUST statistics - ROBUST performs better than uncorrected statistics where
the normal distribution is false
46
Table 3. Latent Constructs – Structural Model
Constructs Variables Variable
Contribution-
Standardized
solution (a)
Composite
Reliability
Average
Variance
Extracted (c)
Relationship
Management
System
Experience
knowledge sharing
Support interaction
Enhance productivity
0.58
0.74
0.82
0.80
0.80 53.4%
Relationship
Memory
Conversation
Audit problems
Share lessons
Challenge assumptions
0.70
0.70
0.79
0.78
0.79 55.3%
Relationship
Generative
Learning
Discuss future needs
Seek new ideas
Interdepartmental
meetings to discuss change
0.81
0.83
0.71
0.76 61.6%
Relationship
Adaptive
Learning
Quick to detect
performance changes
Detect interaction changes
Discuss relationship with
partners
0.93
0.95
0.63
0.82 72.2%
47
Relationship
Collaboration
Build multiple links
Balanced management
Collaborative decision-
making
Idea exchanges
0.83
0.86
0.83
0.82
0.84 67.2%
Relationship
Flexibility
Normative control
Making adjustments
Willingness to accept
change
0.68
0.76
0.77
0.70 54.4%
Relationship
Innovation
Set standards
First to adopt
Constant improvement
0.91
0.92
0.78
0.82 76.1%
* removal of low loading measures to enhance variance extracted
a: Standardized loading. Squared multiple correlations for each measure can be calculated by squaring the
standardized solution for the item
b: Composite reliability: (sum of standardized loadings)²/
{(Sum of standardized loadings)² + Sum of indicator measurement error}
c: variance extracted: sum of squared standardized loadings/no of items
48
Table 4. Goodness of Fit Indices and Structural Model Linkages
Absolute Fit
Measures
Fit Criteria Model containing
three second-
order factors
Model containing
no second-order
factors
² Low ² with
high df
534.868 (240df) 615.529 (238df)
CFI >0.9 0.91 0.87
RMSEA >0.05 and<
0.08
0.08 0.092
Incremental Fit
NNFI Highest 0.89 0.85
Parsimonious Fit
²(/df) > 1 and <2.0 2.23 2.58
Model Linkages
Hypothesis
Standardized
Solution
Significance
t statistic
RM Learning →
RM Infrastructure
H1 0.27 2.528 (p<.05)
RM Learning →
RM Behaviour
H2 0.71 6.237 (p<.05)
RM Behaviour →
RM Infrastructure
H3 0.77 6.626 (p<.05)
49
Appendix
Measures Contained in the Questionnaire
1. Relationship experience of this company
*We have the experience to implement effective knowledge sharing systems with our relationship partners
(e.g. linked intranets, linked data warehouses)
*We lack the relationship building and process skills necessary for effective relationships
Few of our competitors can match our ability to manage business-to-business relationships
2. Relationship management system
*We actively monitor our relationships‟ progress
*We co-ordinate all our relationship management activities
We structure our inter-organizational systems to enhance our ability to jointly acquire and create knowledge
Inter-firm routines increase electronic and/or personal interaction
Our systems are designed to enhance the productivity of our relationships, no matter what the scale or scope
of transactions
3. Relationship Memory
Organizational conversation keeps alive the lessons learned from relationship history
We always audit unsuccessful or problematic relationship endeavours to understand how we can improve
Through discussions and/or circulating reports we share lessons learned from current and past relationships
We are willing to challenge currently held assumptions about our relationships and their management
* items removed as a consequence of poor or cross loading
50
Relationship management knowledge generation
We spend time discussing future relationship needs
We search in-house to find information to enhance our relationships
We actively seek new ideas that will improve the management of our relationships
We are fast to detect changes in the performance of our relationships
We are fast to detect changes in the way we interact with our partner organizations (e.g. increasing conflict)
We meet regularly with our partners to find out how our business relationships might be improved
4. Relationship management Behaviour
Our approach to relationship management supports…..
Various divisions of partners collaborating on projects
A balanced representation on the management team between the partners
Collaborative decision making
Extensive opportunity for idea exchange
As our relationships progress there is less emphasis on contractual control and greater emphasis on control
through agreed principles
When required, we are willing to alter prior commitments
We expect that our relationship partners will be willing to adjust ongoing relationships to cope with change
In our industry, we set the standard in relationship management practice
We are first to adopt innovative relationship management practices
We are constantly improving the policies that govern our relationships
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