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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

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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

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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).

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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

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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,

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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

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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

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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

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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