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Relational factors that explain supply chain relationships

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  • CESA | Colegio de Estudios Superiores de Administración

Abstract

Purpose – The purpose of this paper is to gain a better understanding of the types of relationships that exist along the supply chain and the capabilities that are needed to manage them effectively. Design/methodology/approach – This is exploratory research as there has been little empirical research into this area. Quantitative data were gathered by using a self-administered questionnaire, using the Australian road freight industry as the context. There were 132 usable responses. Inferential and descriptive analysis, including factor analysis, confirmatory factor and regression analysis was used to examine the predictive power of relational factors in inter-firm relationships. Findings – Three factors were identified as having significant influence on relationships: sharing, power and interdependency. “Sharing” is the willingness of the organisation to share resources with other members of the supply chain. “Power” relates to exercising control based on experience, knowledge and position in the supply chain. “Interdependency” is the relative levels of dependency along the supply chain. Research limitations/implications – The research only looks at the Australian road freight industry; a wider sample including other industries would help to strengthen the generalisability of the findings. Practical implications – When these factors are correlated to the types of relationship, arm's length, cooperation, collaboration and alliances, managerial implications can be identified. The more road freight businesses place importance on power, the less they will cooperate. The greater the importance of sharing and interdependency, the greater is the likelihood of arm's length relationships. Originality/value – This paper makes a contribution by describing empirical work conducted in an under-researched but important area – supply chain relationships in the Australian road freight industry.
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Asia Pacific Journal of Marketing
and Logistics
Vol. 22 No. 3, 2010
pp. 419-440
#Emerald Group Publishing Limited
1355-5855
DOI 10.1108/13555851011062304
Received March 2009
Revised November 2009
Accepted April 2010
Relational factors that explain
supply chain relationships
Mario Ferrer
Faculty of Business and Informatics, CQ University, Rockhampton, Australia
Ricardo Santa
Faculty of Law and Business, Charles Darwin University, Darwin, Australia
Paul W. Hyland
School of Management, Queensland University of Technology,
Brisbane, Australia, and
Phil Bretherton
School of Law and Business, Charles Darwin University, Darwin, Australia
Abstract
Purpose – The purpose of this paper is to gain a better understanding of the types of relationships
that exist along the supply chain and the capabilities that are needed to manage them effectively.
Design/methodology/approach – This is exploratory research as there has been little empirical
research into this area. Quantitative data were gathered by using a self-administered questionnaire,
using the Australian road freight industry as the context. There were 132 usable responses.
Inferential and descriptive analysis, including factor analysis, confirmatory factor and regression
analysis was used to examine the predictive power of relational factors in inter-firm relationships.
Findings – Three factors were identified as having significant influence on relationships: sharing,
power and interdependency. ‘‘Sharing’’ is the willingness of the organisation to share resources with
other members of the supply chain. ‘‘Power’’ relates to exercising control based on experience,
knowledge and position in the supply chain. ‘‘Interdependency’’ is the relative levels of dependency
along the supply chain.
Research limitations/implications – The research only looks at the Australian road freight
industry; a wider sample including other industries would help to strengthen the generalisability of
the findings.
Practical implications – When these factors are correlated to the types of relationship, arm’s
length, cooperation, collaboration and alliances, managerial implications can be identified. The more
road freight businesses place importance on power, the less they will cooperate. The greater the
importance of sharing and interdependency, the greater is the likelihood of arm’s length relationships.
Originality/value – This paper makes a contribution by describing empirical work conducted in an
under-researched but important area supply chain relationships in the Australian road freight
industry.
Keywords Australia, Channel relationships, Freight forwarding, Supply chain management
Paper type Research paper
1. Introduction
In large developed economies, such as Australia, road freight businesses play a
significant role as products need to be moved across long distances because of the size
of the country (7,686,850 sq km) and the dispersion of its raw materials, production
and consumption centres. To maintain an effective cost structure organisations are
increasingly required to work closely with their suppliers, customers and other
participants in the supply chain in order to strategically compete and integrate the
logistical practices (Morash and Clinton, 1997; Lambert and Cooper, 2000). In Australia,
the freight industry has remained fragmented but its individual sectors are becoming
increasingly concentrated, although in recent years some multimodal operators such as
Patrick and Toll logistics have emerged. The road sector which dominates the
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transport industry (80 per cent of value) had a four-firm concentration ratio of eight per
cent. Small trucking firms with one or two trucks represent almost 65 per cent of the
industry participants and account for nearly 12 per cent of the industry’s operating
income while the sales of the top four firms account for only 15 per cent of the market
share (Bureau of Transport and Regional Economics, 2003). This indicates that the
trucking industry is not homogenous; it approximates perfect competition because it
has minimal entry barriers. A large number of small trucking firms i.e. owner drivers,
are mainly price takers and usually subcontractors of large multimodal freight service
providers. Many small and medium sized companies in the Australian freight industry
have had to act as providers of combined transportation for their clients. They have
been increasingly entering partnerships in order to survive the consolidation trend that
has occurred in developed economies such as the USA; where small size freight players
have managed to scale up through niche market leadership, investment in technology
and execution of a differentiated supply chain strategy (Gordon, 2004). Evidences from
other research studies (Ferrer, 2010) indicate that competition that is sharing
warehouse space and depot staff in capital cities is a practice in the Australian trucking
industry that enables regional trucking firms to minimise the cost of carrying
inventory but also to provide a more flexible service to customers as they can pick their
loads up at a more convenient time. Also, alliances are a type of inter-firm relationships
that large size trucking firms would form with customers to provide dedicated 3PL
service.
Managing the supply chain involves the management of numerous inter-firm
relationships which have become increasingly important in developing a competitive
position. Supply chains exist on the basis that the participants actively engage in
managing them rather than allowing external forces to direct their actions (Mentzer
et al., 2001). In dynamic and complex industries such as the road freight transport,
members need realise that inter-firm relationships are strategically important (Anand
and Khanna, 2000), that benefits can be obtained from long-term relationships, but not
all relationships can be long-term oriented (Ballou et al., 2000; Golicic et al., 2003). Thus,
the decision about what type of inter-firm relationship is appropriate for a specific
circumstance is complex. There is a spectrum of possible supply chain relationships,
from arm’s length to complex, long-term alliances and each of them demands a
particular degree of managerial attention (Lambert et al., 1996; Bensaou, 1999; Golicic
et al., 2003). Supply chain relationships framework involves not only physical-technical
but also socio-psychological, network management and strategic factors (Oliver and
Ebers, 1998; Nooteboom, 1999; Barrat, 2004). Whilst the strategic factor include sharing
processes, the socio-psychological and network management elements encompass the
power and leadership structures, trust building processes, risk and reward structures,
mutuality and organisational culture. Risk, reward and power affect an organisation’s
commitment to cooperate with other supply chain members, whereas corporate culture
determines the compatibility between members of the different types of relationships
such as partnerships. The different relationship taxonomies supply chain researchers
(Bensaou, 1999; Lambert and Cooper, 2000; Golicic et al., 2003; Varamaki and Vesalainen,
2003; Xu and Beamon, 2006) have put forward are characterised by factors which
develop along a continuum. Thus, as relationship management is a situational approach,
firms need to consider and clearly understand how their situation varies as one of the
determinants for the selection of an inter-firm relationships’ portfolio.
Given there are several types of relationships managers need to better understand
the nature of supply chain relationships and how these can influence the functioning of
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their businesses. Thus, the key objective of this study is to explore the influence
of different relationship factors have on inter-organisational relationships in the
Australian road freight transport industry. The research question addressed in this
paper is: Do power, interdependency, sharing and trust predict the type of relationship
road freight transport firms engage in?
2. Literature review
Inter-firm relationships have traditionally been studied through the use of the
‘‘governance’’ lens. The relationship marketing literature was one of the first to propose
a range of relationships from transactional, short duration and sharp ending by
performance, to relational exchange, longer duration and reflection on ongoing process
(Dwyer et al., 1987; Noordewier et al., 1990). In an attempt at enhancing the spectrum,
some other authors have placed, in an intermediate location, cooperative orientated
relationships (Golicic et al., 2003; Rinehart et al., 2004). With more adversarial oriented
relationships, the likelihood of future exchange between two parties occurring is low.
Conversely, a higher probability of future interactions exists if the relationships are
more collaboratively oriented (Kaufman et al., 2000). Although the market response to
growing competition has for some years been an increasing trend towards integration,
it has been asserted that not all supply chain relationships that companies enter need
to be either collaborative or cooperative (Duclos et al., 2003). Similarly other authors
suggest that supply chain relationships do not need to be ‘‘one size fits all’’ as the
market and product circumstances are dynamically changing (Dyer et al., 1998;
Bensaou, 1999; Lambert and Cooper, 2000).
Four prominent types of inter-firm relationship can be identified in industries like
the road freight transport sector. The first inter-firm relationship type concerns
contractual work arrangements defined as arm’s-length relationships. This type of
relationship can best be described as inter-organisational linkages characterised by
dealings that are at arm’s-length which involve spot transactions, often based on
auctions or auction-like arrangements (Hoyt et al., 2000). In arm’s-length relationships,
detailed written contracts prevent the parties from operating and making decisions
independently (Dore, 1992; Sako, 1992), however, in many cases, particularly in the
road transport industry in Australia, contracts are verbal. When contracts are written
before the initiation of the relationships, they describe each partner’s obligations in
almost every possible scenario. Relationships at arm’s length are characterised by little
or no investment in assets with minimal information exchange in which the parties
would be typically would be providing standardised items not adding to the
differential advantage of the end product (Lambert and Cooper, 2000).
A second type of inter-firm linkage relates to cooperative agreements. Cooperation
involves the coordination of similar or complementary activities carried out by
organisations in business relationships, aiming at attaining enhanced joint results or
individual results with expected reciprocity, as time progresses. The rationale behind
cooperative efforts is based on arrangements to share resources, either tangible or
intangible, as well as the pursuit of other business goals, through the redesign of
processes and products (Cousin, 2002). External and horizontal relationships between
partners characterise cooperative agreements. Cooperative efforts differ from arms-
length relationships in that they rely on higher levels of trust (Mentzer et al., 2001),
moderated levels of power and are more long-term oriented. Cooperation appears to be
one of the inter-firm arrangements affirmed to be directly influenced by relationship
trust between the parties and it is an important motivator for partners to reduce the
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complexityof their environment and gain more control over their external environmental
forces (Zhang et al., 2003). Partners in cooperative arrangements seek to lower
transaction costs by sharing access to goods, manpower, services and information
(Polenske, 2004), and research conducted in the manufacturing industry indicates
operational information may be shared among many cooperative firms, because
cooperation agreements are non-exclusive.
The third component of the inter-firm relationships typology is collaboration, which
appears to be closer to the alliance end of a continuum proposed by several authors
(Golicic et al., 2003; Rinehart et al., 2004). This type of relationship is viewed as a more
durable relationship in which parties bring organisations into a new structure with full
commitment to working more closely, with a shared mission, vision and higher levels
of trust. Such relationships require comprehensive planning, seamless linkages (Krause
and Ellram, 1997), unified seeking of synergies and goals and well-structured
communication channels operating at all levels. Information exchange plays an
important role in improving supply chain collaboration (Lambert and Cooper, 2000). Risk
sharing is greater in inter-firm collaborative relationships because each participant
commits its resources and power can be unequal. Gain and risk sharing capabilities come
from a willingness to share rewards and penalties across the firms involved (Spekman
and Carraway, 2006). While resource sharing cooperative firms have equal access to
these resources, in collaborative agreements the firms tend to gain unique and often
unequal access to some of these resources (Polenske, 2004). This can explain the
motivation offirms to collaborate to not only improve performance by reducing costs but
also by expanding and controlling the market. Alliances are the fourth relationship type
that the literature discusses. Alliances can be described as a structured mode of
inter-organisational arrangement that involve exchange relationships between parties
without necessarily having to create a new entity (Dickson and Weaver, 1997).
Alliances are intended to be long-term, develop new resources or skills, and seek to
enhance the competitive position of each partner. It has been asserted that the success
or failure of a supply chain alliance is driven by commitment and trust between the
parties (Whipple and Frankel, 2000). Trust must exist in an alliance since there is inter-
dependency between the parties to mutually achieve goals which are a pre-requisite for
their initial creation, as a partner may be a competitor or be involved in other alliances
with a firm’s competitors. Likewise, trust needs to exist for allies not only to share
critical strategic information but also for each ally to consider that its long-term need
will be better fulfilled (Moore, 1998). The commitment of the parties involved in an
alliance ideally involves the sharing of risk and reward in a joint effort to create
synergy to gain access to resources, access to new markets, access to technology,
access to capital and access to international and closed markets (Rothkegel et al., 2006).
Many large retailers and manufacturers in Australia are increasingly establishing
alliances with 3PL firms that are capable of providing fully integrated multiple
services. In today’s freight industry, creating this different way of doing business may
be, in some cases, a matter of survival. In other cases, different approaches to
participating in relationships are adopted to build sustainable competitive advantage,
maximise asset utilisation and increase profitability. Participants such as road freight
service providers need to realise the importance of understanding relational factors,
such as power, sharing, trust and interdependency that influence the establishment of
inter-firm relationships that enable them to leverage the complementary strengths of
other firms within their supply chain, and function efficiently.
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2.1 The notion of power
Power, from one perspective, canbe described as the influence of one party over the other
(Ireland and Webb, 2006). French and Raven (1959) suggested that there are aspects of
control and coercion of the parties’ power which enable the participants to maintain
order and authority but its abuse is a problem and needs to be limited. They also
identified five sources of power which they refer to as: reward power, coercive power,
expert power, referent power and legitimate power. Although Raven (1993) included a
sixth source of power, this has been assumed as a characteristic of expert power
(Dapiran and Hogarth-Scott, 2003). Expert power refers to the ability of a party in a
relationship to hold and control distinctive knowledge, information and skills that are
valuable to the other party an can promote innovation (Cox, 2001); whereas referent
power concerns a party’s desire to be associated with another out of admiration for them
(Zhao et al., 2008). Whilst reward power refers to the ability of one party in the
relationship to mediate tangible or intangible rewards to the other party, coercive power
concerns the ability to mediate punishment and take disciplinary measures over partners
(Rokkan and Haugland, 2002). For instance, in the freight transport industry a party can
exercise expert power over the other by holding market, process or regulatory
knowledge, and consequently, the other party may give up control, believing such
knowledge could lead to better performance and profitable contracts. Likewise,
participants in the freight industry could exercise reward power over the shippers by
offering lower prices, shorter delivery times or improved material handling technology.
Conversely, shippers might offer more long-term contracts or extended loading and
unloading times exercising reward power over the freight service provider. Finally,
legitimate power concerns the recognition of the right to hold authority over the others
which originates from perceived standing or status and is present if one of the parties
believe the other retains the natural privilege to such power (Maloni and Benton, 2000).
The notion of power imbalance is often considered one of the greatest
discouragements and negative influences to maintaining long-term oriented relationships
(Naude
´and Buttle, 2000) and has been identified as a deterrent to trust. Although the
work of Hingley (2005) discusses that earlier research (Svensson, 2001) has presented a
different point of view, not all relationships are based on mutual trust. For instance, the
Japanese automotive industry relationships are highly regarded as being long-term
oriented and highly collaborative relationships. But research indicates that it does not
mean that Japanese car manufacturers’ relationships with suppliers are primarily relying
upon trust (Cox, 1997). Cox (1997) suggests that there are indications that Japanese car
makers often create hierarchies of structural dominance with their suppliers, in which
although the latter regards the relationships as a win-win, the car makers keep effective
control over the supplier relationship wherever possible and there is little need for trust.
Furthermore, Dapiran and Hogarth-Scott (2003) found that the presence of power does
not always inhibit close relationships. They found that the existence of reward and
coercive power in relationships leads to capitulation and desire to exit the relationship
whereas referent and expert power leads to cooperation and trust building.
There is some evidence that suggests that relationships that tend to have power
imbalanced are often less stable than balanced ones such that parties comfortable with
the balance are less likely to seek alternative partnerships (Bretherton and Carswell,
2002). Nevertheless, it is not always feasible to maintain an ideal power balance in inter-
firm relationships and, in many cases, weaker parties are happy to stay in the
relationships to keep their business profitable or a least cash-flow positive (Gummesson,
1991). For example, for a small, regional trucking firm survival could be challenged by the
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closure of markets so that in order to stay in business the freight company is happy to
accept the conditions the remaining dominant firms in the region make until it works out
how to rebalance power either changing the nature of the relationship or seeking
alternatives. This suggests that organisations should not ignore the diversity of
relationships (Bensaou, 1999; Hyland et al., 2005) which are not always initially influenced
by trust but also depend on other factors such as power, sharing and dependency.
H1. Power is a predictor of the type of relationship in inter-firm relationships.
2.2 The importance of sharing
It is increasingly argued that organisations need to view themselves as members of
a supply chain that depend one upon one another to be competitive and survive
(Christopher, 2005). Studies suggest that among the different coordination mechanisms
in the supply chain, sharing is a dimension increasingly sought to maximise the
benefits and minimise the risks that arise from inter-firm relationships (Varamaki and
Vesalainen, 2003; Xu and Beamon, 2006). So in a competitive environment, the success
of businesses depends on their ability to manage and share resources such as
information and assets, costs and risk within their networks of associates (Lambert
and Cooper, 2000).
Sharing information concerns the degree to which information is communicated
between supply chain partners and the nature and type of information. Information
exchange is enhanced by relational factors such as trust and organisational integration
to cope with the effect of lack of visibility which negatively impacts on supply chain
performance (Reichhart and Holweg, 2007). Some of the benefits organisations can
obtain from sharing relevant and accurate information includes increased system
responsiveness, reduced lead-time, improved forecasts, reduced bullwhip effect,
reduced supply chain costs and improved customer service (Simchi-Levi et al., 2000).
Organisations can share information at several levels including strategic, operational
and tactical depending on the type of relationships they are participating in (Mentzer
et al., 2000; Varamaki and Vesalainen, 2003). Sharing operational information is less
problematic than sharing tactical or strategic information (Monczka et al., 1998;
Lamming et al., 2000). Strategic type information is expected to be shared in close long-
term orientated relationships – collaboration, partnerships (Varamaki and Vesalainen,
2003; Hyland et al., 2005). This type of information assists businesses in making
decisions about strategic issues such as supplier selection, product introduction and
location of facilities (Chopra and Meindl, 2001). Tactical level information is usually
shared in more cooperative work arrangements (Hyland et al., 2005; Xu and Beamon,
2006) and helps firms to jointly foresee demand. Tactical information includes
operating costs, inventory costs and aggregate demand. Finally, sharing operational
level information encompasses communicating weekly production, delivery schedules
and order replenishment (Chopra and Meindl, 2001) among supply chain member
participating in cooperative or arm’s length types of relationships (Hyland et al., 2005).
Sharing information in relationships involves cost and risk, which in many cases
these can outweigh the benefits of sharing information (Swaminathan et al., 1997). So it
is argued that organisations need to understand the costs associated with sharing
information in different types of working arrangements. For instance, alliances are
characterised by faster task coordination and execution and less asymmetrically held
information, transaction costs are negligible which leads the effective deployment of
the resources brought to the alliance and transaction costs are kept to a minimum.
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Conversely, in relationships at arm’s length the transaction costs associated with
balancing out information asymmetry might not be as low as desired and can lead to
low flexibility and coordination between the participants in the relationship. Xu and
Beamon (2006) posit that usually, the higher the level of resource sharing the lower the
costs associated with physical flow, but the higher the risk costs. Risks are associated
with sharing information as partners have the possibility of abusing information and
diminishing the benefits of sharing (Maloni and Benton, 1997; Das and Teng, 2001). If
the risk of opportunism in a particular relationship is sufficiently high, considerable
resources must be spent on control and monitoring, resources that could have been
deployed more productively for other purposes (Wathne and Heide, 2000). Examples
of abuse of shared information encompass: voluntary disclosure of confidential
information to competitors, loss of competitive knowledge, loss of data privacy and
data integrity. This suggests that as more detailed information is shared the lower the
transaction costs but the higher the risk.
Organisations enter close relationships such as collaboration and alliances to not
only share risks and rewards but also to share coordination costs (Dyer et al., 1998;
Gulati and Singh, 1998) and resources. The rationale for establishing relationships
involves an understanding of finding ways to make the relationship efficient, the extent
to which coordinating the costs offsets the benefits of the relationship. For instance, an
organisation with a just-in-time production process can be negatively impacted by a
road freight service provider that decides to cut costs by decreasing the frequency of
deliveries. The organisation needs to work with the trucking company to avoid an
increase in the landed costs by transferring the expertise it has developed in its journey
towards just-in-time and find potential improvements – cost coordination – for the
freight company. Researchers have explained the extent of coordination costs by using
a taxonomy of interdependences which include pooled, sequential and reciprocal
interdependencies (Gulati and Singh, 1998). Their work indicates that pooled
interdependence, in which partner organisations deploy resources into a pool and each
of them uses them from the shared pool, has the least coordination costs due to the low
coordination requirements. On the other hand, reciprocal interdependence – partner
organisations pool resources in which outputs are highly connected to the inputs of
each other – this has the highest coordination costs as continuous mutual adjustments
are required to fulfil the needs of the parties.
H2. Sharing is a predictor of the type of relationship in inter-firm relationships.
2.3 The role of interdependency
Power and dependence are related. The seminal work on power by Emerson (1962)
posits that the extent of dependence between the participants in a relationship gives
an indication of their relative power. The theory argues that the various types of
relationships present in the spectrum suggested by Sako (1992) and Spekman et al.
(1998) are differentiated, from others, by the notion of interdependence including
reciprocal interdepedency (Koulikoff-Souviron and Harrison, 2007). The main concern
of supply chain management is how to coordinate the independent players to work
together as a whole to pursue the common goal of chain profitability in changing
market conditions (Simatupang and Sridharan, 2002). Although the Australian road
freight industry is increasingly moving towards rationalisation which allows the larger
organisations to maintain a relatively lop-sided power advantage over small owner
drivers, not all relationships in the freight transport industry can be simply explained
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by a straightforward power motivation. Rather, one can argue that freight companies
need to simultaneously pursue adversarial/competitive and cooperative and
partnering/network approaches although this implies becoming not only dependant
but also interdependent.
Research indicates that interdependency induces cooperative goals between
companies and their suppliers and distributors which develop trusting and continuously
improved relationships, which in turn produce customer satisfaction (Wong et al., 2005).
Mutual dependency prompts companies’ sense of urgency to develop cooperative goals
with suppliers and distributors and working with much concern for the interests of the
others. Interdependency in supply chain concerns the extent to which supply chain
relationship participants’ processes depend on each other to attain their goals and
achieve the overall value creation (Simatupang et al., 2002). Furthermore, a firm’s
cooperative efforts can be based on whether an organisation perceives the relationship as
likely to provide benefits over and above the costs of organisational autonomy:
expenditures of resources and/or concessions of organisational power and authority
(Pfeffer, 1997). Likewise, asset specificity increases dependency – it helps to increase one
party’s value to the other, which makes the latter dependant and minimises opportunistic
behaviour (Nooteboom et al., 2000). However, excessive investment on specific assets
leads to higher levels of vulnerability of the party that deploys the more specific
resources. Partners’ vulnerability involves loss of control over critical resources,
reduction of freedom of choice and increases the costs of seeking alternative partners.
Criticality of a resource and the concept of switching cost are fundamental to
interdependency and are well explained by the resource dependence theory (Pfeffer,
1997) and transaction cost theory (Williamson, 1985). Existence of critical alternative
sources or partners has been regarded as a factor that will establish the cost
of substitutability (El-Ansary and Stern, 1972). Spekman et al. (1998, p. 639) posit that
‘‘criticality is based on the notion of high recognized interdependence’’ as supply chain
members will not act in their own best interest to the disadvantage of the supply chain.
Moreover, a member can possess a critical resource to the supply chain regardless of
what proportion of the total input it represents. For example, when freight service from
remote plantations to distribution centres are interrupted this can cause problems to
the farm business and to the supermarkets. Further, as firms increase cooperation
they become equally dependant upon each other. The switching costs increase and
autonomy decreases. In addition, parties that are involved in highly interdependent
relationships are expected to face high switching costs which are associated with
overcoming the barriers to old working arrangements and the non-recurring expenses
of setting up new relationships. Parties involved in more collaborative arrangements
progressively adapt resources and processes to fulfil the needs of that relationship,
thereby mutually raising the exit barriers and switching costs.
This research addresses the notion of mutual dependence or interdependence for
supply chain relationships in which criticality, substitutability and switching cost
are key determinants. Interdependent relationships are enhanced by sharing of key
resources, engaging in joint cooperative planning and long-term orientation (Bowersox
and Closs, 1996; Mentzer et al., 2001; Zeng and Chen, 2003). Managing supply chain
relationships in today’s competitive markets involves participants seeking close, long-
term working relationships with one or two partners (competitors, suppliers and
customers) who depend on one another for much of their business; developing
interactive relationships with partners who share information freely, working together
when trying to solve common problems and when designing new products. Also
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jointly planning for the future, and making their success interdependent on other
parties (Krause and Ellram, 1997).
H3. Interdependency is a predictor of the type of relationship in inter-firm
relationships.
2.4 Trust does matter
Effective supply chains rely on shared information and trust among partners and they
are essential requirement for successful supply chain relationships. La Londe (2002)
supports this by stating that issues of trust and risk can be considerably more significant
in supply chain relationships, because supply chain relationships involve in many cases
a higher level of interdependency between competitors. Trust can be best defined as to
the extent to which a party fulfils an agreement, meets the expected professional
obligations and can be viewed as not behaving opportunistically (Sako, 1992; Gulati and
Singh, 1998). Although there has been some empirical research suggesting that there are
unexpected high trust levels in early stages of interactions between the parties
(McKnight et al., 1998), it is asserted that trust is a behavioural attitude that evolves and
is an outcome of gradual consistent effort over time (Bstieler, 2006).
The literature provides insights into three interconnected roles that trust plays in inter-
organisational exchanges. First, trust is, in many cases, an effective means of allowing a
firm to minimise the risks of opportunistic behaviour as it is expected that parties will
forgo short-term individual gains in favour of the long-term interests of the inter-
organisational exchange (Das and Teng, 1998). Corsten and Kumar (2005) posit that trust
results in greater openness between suppliers and retailers and thus greater knowledge
and appreciation of each other’s contribution to the relationship. Second, trust can be a
source of competitive advantage in inter-organisational relationships formed by parties
that behave trustworthily and do not act against values, standards and principles of
behaviour. Sako (1998) found that high quality (source of competitive advantage) can be
consistently maintained in high-trust production systems. Third, trust influences
performance by reducing transaction costs, encouraging investment with future returns
and motivating continuous improvement and learning (Sako, 1992; Whipple and Frankel,
2000).
The literature offers several categorisations of trust. For example, Sako (1992)
measured three types of trust: contractual trust, competence trust and goodwill trust.
More recent studies present types of trust such as credibility or competence and
benevolence (Nooteboom et al., 1997) and dispositional, institutional and trusting
beliefs (Korczynski, 2000). This research is based on the categorisation made by (Sako,
1992) which has reported trust as a relationship-oriented variable, influencing the
formations of inter-firm relationships. For instance, the notion of trust among supply
chain partners was found as a vital ingredient for success in more long-term orientated
inter-firm relationships such as collaborative arrangement and alliances in which firms
make great commitments. They demand joint processes supported by a high degree of
goodwill type of trust to smoothly ease conflicts when they arise (Dyer and Chu, 2003).
Goodwill trust motivates participants in closer and mature inter-firm relationships to
work interdependently, with a shared mission, vision, seamless planning, seeking
synergies (Steendahl et al., 2004) and undertaking, if needed, activities that were
not agreed (Sako, 1992).
H4. Trust is a predictor of the type of relationship in inter-firm relationships.
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3. Research methodology
This research is undertaken with an exploratory purpose as the problem of inter-firm
relationships among freight businesses has had little previous empirical investigation.
According to Sekaran (2003) exploratory study is undertaken when there is a lack
of understanding of the problem which leads to an unstructured problem design.
Quantitative data were gathered through a self administered mail questionnaire of road
freight businesses operating throughout Australia.
The structure of the self-administered questionnaire was 15-pages, eight sections and
pre-specified multiple choices. The six broad topics within the survey related to
the manger’s perceptions of the influence of trust, power, sharing and dependency on
relationships such as arm’s length, cooperation, collaboration and alliances. Based on
previous studies, Likert-type scales were adapted to measure the extent that road freight
transport businesses and their supply chain partners interact with each other. The trust
construct was initially measured by nine items that were derived from the extant
literature (Dwyer and Welsh, 1985; Koenig, 1989; Green, 2003). For instance, respondents
were asked to rate their level of agreement (from strongly agree to strongly disagree on a
five point scale) on whether a party in the trucking industry would act fairly and would
not take unfair advantage of the other, even given the chance. The assessment of road
freight transport supply chain partner’s power sources utilised 12 items. Respondents
were asked to rate the importance (from very important to not very important on a five
point scale) of power in relationships. For example, trucking organisations were asked if
the organisation would consider power importance to comply, with a request, as a result
of a belief that supply chain partners possessed the ability to penalise them. The extent
of sharing between members of the road transport supply chain was measured through a
44-item Likert-type scale instrument, adapted from the extant literature (Koenig, 1989;
Das and Teng, 1999; Oliver and Delbridge, 2002), devised to reflect the opinions of
participants in the road freight transport industry regarding the importance of sharing
resources, information, assets and risk/rewards respondents were asked to rate their
perceptions from very important to not important. Finally, the interdependency
construct was measured by eight items though. Sample item is ‘‘Our supply chain
partner would find it difficult to recoup their investments in us if our relationship were to
end’’ respondents were asked to rate their level of agreement from strongly agree to
strongly disagree on a five-point scale.
Road freight transport was chosen as the research setting to examine inter-
organisational relationships since this is an industry in which inter-firm relationships, in
many cases, are exhibiting competitive patterns but many operators are facing problems
because profit margins are becoming much tighter over time. A sample of 1,000 trucking
firms was identified. The research was designed to target respondents with knowledge
of inter-firm relationships. The mail questionnaire, with telephone follow-up, allowed
contact with the respondents to facilitate the process of clarification. It is acknowledged
that response rate and turn around are issues with these types of data collection
methods. Therefore, this study minimised these limitations by also using a drop-off and
pick-up technique that enabled the researcher to visit a purposive sample of respondents
to gain their commitment to complete the questionnaire. This increased the response rate
by 71.4-12.8 per cent and the turnaround was five days faster than the mail questionnaire
with telephone follow-up technique.
Regarding respondents profile, the smallest companies (12 per cent) were trucking
companies with an annual turnover of less than $1 million. The largest companies
(10 per cent) were freight service providers with an annual turnover of between
Supply
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429
$50.1 million and $1 billion. This indicates the sample is not restricted by firm size. For
the categoryof service provided, 35.9 per cent of the responding firms carried heavy bulk
cargo and chemicals, 19.2 per cent carried containers, 13.3 per cent carried refrigerated
cargo, 7.5 per cent shipped vehicles and 29 per cent transported other types of cargo. This
is not a representative sample of the industry as some sub-sectors were deliberately
excluded. As the research was looking at businesses that were likely to be in supply
chain relationships with a variety of trucking businesses, i.e. removalists were not taken
into consideration as their work arrangements are based essentially on contract and are
unlikely to form more complex relationships. Although there is no census data for the
road-freight, transport industry, the distribution of the respondents was representative of
the information compiled in ‘‘Who goes where edition 2006’’, which is a comprehensive
commercial directory of freight service providers in Australia.
The main purpose of the study was to assess the power of relational factors in
predicting the type of inter-firm relationship. The first step in the data analysis was to
refine the scales, i.e. assessment of constructs validity and structure, by performing
exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) (Hair et al.,
2010). CFA-based methodologies require fewer assumptions than the more traditional
methods of assessment and, therefore, are more accommodating to most empirical data
(O’Leary-Kelly and Vokurka, 1998). Next, regression analysis was run to find the
predicting power that relational factors can have on the engagement in supply chain
relationships.
Some limitations have been identified in this study which should caution the reader
regarding the generalisability of the findings. The limitations include the small sample
size; the research setting which only included Australian firms, the participants were
only selected from the road freight sector and did not included customers or suppliers
from other sectors; and that only four factors were tested although other factors such
as size of business, ownership structure need to be examined in further research. Also
further research is needed on relationships with customers and suppliers of road
freight firms and it should examine how they view the interrelationships and what
factors effect those relationships.
4. Results and analysis
Of the 1,000 managing directors, managers and chief executive officers, 132 responded,
yielding a 13.2 per cent response rate. All the responses to the questionnaire came
directly from individuals involved in managing inter-firm relationships. Of the entire
sample, approximately 25.8 per cent of respondents acted in the capacity of CEOs,
29.2 per cent were managing directors, 18.3 per cent were operations manager, 3.3 per
cent were owner drivers and 19.2 per cent performed the role of regional manager.
The first step of the data analysis was the refinement of the scale assessment of the
different components of construct validity. Establishing construct validity, involves the
empirical assessment of the adequacy of a measure and requires that three essential
components be established: unidimensionality, reliability and validity (Hair et al., 2010).
Scale refinement was conducted for the trust, power, sharing and interdependency
scales used in this study.
4.1 Trust scale validity
The results from Table I show that the loading values of six of the nine items of this
scale exceeded the cut-off level of 0.4 so these are the good items that will be further
examined with CFA.
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Acceptable goodness of fit measures and average variance extracted (AVE) values
for a construct model indicate construct unidimensionality and convergent validity,
respectively (Hair et al., 1998). Unidimensionality was confirmed with CFA by
examining the fit of the one-dimensional models for the trust construct. Some
commonly used measures to evaluate model fit include the relative chi-square
statistics, root square mean error approximation, comparative fit index; and Tucker-
Lewis index (Ho, 2006). A series of goodness-of-fit indices, i.e. CFI > 0.9, TLI > 0.9 and
RMSEA < 0.8 provide evidence of convergent validity (Hair et al., 1998). The trust
scale regression model fit as indicated by the CFI (0.98), TLI (9.74), RMSEA (0.05) and
CMIN/DF (1.47) appears good which confirms unidimensionality. With CFA, the AVE
is calculated as the mean variance extracted for the items lading on a construct. So for
the trust construct, AVE is computed as the total of squared multiple correlations
divided by six items that form this construct. An AVE of 0.5 or higher is a good rule of
thumb suggesting adequate convergence (Hair et al., 2010). The AVE was 0.58 for the
trust scale.
4.2 Power scale validity
The factor analysis was performed using the maximum likelihood extraction method
and oblique rotation to select the items that will participate in the power scale
regression model to be tested with CFA. As shown in Table II the factor loadings of
the items are 0.50 or higher. This suggested that the 12-item scale measures exhibits
unidimensionality. The 12 items loaded on one underlying factor. The scale items were
then further evaluated for unidimensionality with CFA. The fit indices for the one
factor model as indicated by the CFI (0.81) and the RMSEA (0.12) were not acceptable.
The standardised loadings and the R
2
obtained for each item were examined to further
test the reliability of the scale via CFA. Items related to the importance of expert power,
referent and reward (PIsourcinf, PIknowledge and PIhasasay) had standardised
loading of less than 0.50 and R
2
of less than 0.2 (Sharma, 1994). This low level of item
reliability indicates they are not good measures of the power construct. Furthermore,
Tabl e I.
Results of factor
analysis for the
trust scale
Factor loadings
Tpartapprov 0.84
Tlittleact 0.79
Tcheckpart 0.76
Tconsulpart 0.70
Tsmallmatdi 0.44
Tignorules 0.47
Table II.
Results of factor
analysis for the
power scale
Factor loadings Factor loadings
PIbenefit 0.76 PIpenalize 0.59
PIharmsug 0.66 PIsourcinf 0.59
PIexpertcorr 0.65 PIwitholinf 0.55
PIrightinflu 0.64 PIexaggerat 0.52
PIoblfolsug 0.63 PIhasasay 0.50
PIlotexperi 0.61 PIknowledg 0.50
Supply
chain
relationships
431
these items (PIsourcinf, PIknowledge and PIhasasay) possessed item-to-total
correlations that are relative lower (0.4) than the remaining scale items. If these items
are dropped, the Cronbach’s alpha (0.86) for the power scale would not change. After
removing these items (PIsourcinf, PIknowledge and PIhasasay), the one factor model
fit measures as indicated by the RMSEA (0.06), CFI (0.96) and TLI (0.951), improved
significantly. Convergent validity for this construct was tested through CFA. The
average variance extracted was 0.495 which is slightly below the cutoff value of 0.5.
4.3 Sharing scale validity
Factor analysis (maximum likelihood extraction and oblique rotation) is used to see
whether or not the three domains are valid, and how much the items have loading on
each domain. Because the multi-item construct measures each variable, factor analysis
with rotated factor matrix checks unideminsionality among the items; and those with
factor loading values lower than 0.4 are eliminated (Field, 2000; Hair et al., 2010).
Table III shows three domains of sharing are valid and their loading factor on their
loading factor on their items are greater than 0.40.
Confirmatory factor analysis of the refined construct consisting of three factors.
Factor one was named sharing risk as it relates closely to the road freight firms’
willingness to share risk with members of their supply chains. Factor two was named
sharing resources as it relates closely to the road freight firms’ willingness to share
resources with members of their supply chains. Factor three was named sharing assets
as it relates closely to the road freight firms’ willingness to share assets with members of
their supply chains. These three factors resulted in significant standardised loading, as
well as acceptable fit indices and variance extracted (Table IV). Thus, convergent validity
exists for the sharing scale. In order to establish discriminant validity among the three
factors, the dimensions are needed to be shown a non-related in reality (Hair et al.,2010).
A CFA-based approach to discriminant validity is to run the model unconstrained
(the correlation between two constructs is free) and also constraining the correlation
between constructs to 1.0. If the two models do not differ significantly on a chi-square
difference test, the researcher fails to conclude that the constructs differ (Bagozzi et al.,
1991). In this procedure, if there are more than two constructs, one must employ a
Table III.
Results of factor
analysis for the
sharing scale
Factor 1 Factor loadings Factor 2 Factor loadings Factor 3 Factor loadings
SRKlcovrisk 0.69 SRlprovreso 0.75 SAldepcap 0.85
SRKlweaken 0.68 SRldedicass 0.74 SAlwarecap 0.84
Sllinfnewdev 0.62 SRlcapabil 0.66 SAlfleetcap 0.70
Sllperiorep 0.60 SRlnotprovreso 0.65 SAlcargcap 0.69
SRKlbenprob 0.58 SRlsamereso 0.65 SAlinfocap 0.45
Slloperatinfo 0.57 SRlcomitreso 0.61 Slltactinfo 0.45
SRKlnoenharep 0.57 Sllstrateinfo 0.46
SClredcostbe 0.56 SRltechcap 0.44
SClrelredcost 0.56 SClctrlsavcost 0.42
SRKlfindother 0.54
SCljoint 0.52
SRKleqshar 0.52
SRKllosscust 0.51
SRKlmajdec 0.50
SRKllowret 0.45
SRKlotherexp 0.41
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similar analysis on each pair of constructs, constraining the constructs to be perfectly
correlated and then freeing the constraints. Table V shows the fit measures for each
pair of factors tested separately, first with correlation unconstrained and then with the
correlation constrained to 1.0. It was demonstrated for each pair that the constrained
model is significantly inferior in fit.
4.4 Interdependency scale validity
The results from factor analysis indicated that four of the eight items had acceptable
high loadings on one common factor. CFA of the interdependency scale provided
high, standardised loading and high R
2
for only three of the eight items (INrecopuinv,
INdiffreplapart and INteamemb). Consequently, five items were removed from the
scale. Upon, deletion of these items, the improved construct demonstrated convergent
validity with an AVE of 0.65 but the fit indices could not be examined to demonstrate
unidemensionality. Since, there were three scale items for this construct, the model is
just-identified, and the fit indices are not valid.
Multiple regression analysis was used to analyse if there is a predictive relationship
between the independent variable and the dependent variables (Ho, 2006). The sign of
the coefficient assesses the direction of the relationship. H1-H4 were tested using
stepwise models which were generated at the p< 0.05 level. The results for each
hypothesis are exhibited below:
H1. Power is a predictor of the type of relationship in inter-firm relationships.
Tabl e IV.
Results of confirmatory
factor analysis for the
sharing scale
Factor 1
Standardised
regression
weight Factor 2
Standardised
regression
weight Factor 3
Standardised
regression
weight
SRKIcovrisk 0.86 SRIproverso 0.84 SAIdepcap 0.84
SIlinfinewdev 0.72 SRIdedicass 0.88 SAIwarecap 0.79
SRKIbenprob 0.72 SRIcapabil 0.87 SAIfleetcap 0.86
SCIredcostbe 0.73 SRInotprovreso 0.63 SAIcargcap 0.81
SCIrelredcost 0.70 SRIsamereso 0.76
SCIjoint 0.71 SRIcomitreso 0.70
SRKieqshar 0.60
SRKImajdec 0.54
AVE 0.51 AVE 0.58 AVE 0.68
RMSEA 0.73 RMSEA 0.08 RMSEA 0.09
CFI 0.97 CFI 0.96 CFI 0.96
TLI 0.96 TLI 0.94 TLI 0.93
CMIN/DF 1.76 CMIN/DF 1.88 CMIN/DF 2.11
Tabl e V.
Results of discriminant
validity test for the
sharing scale
Normed
Chi-sq Df pCFI TLI RMSEA
Unconstrained Factor 2 Factor 3 2.85 31 0.00 0.94 0.91 0.09
Constrained Factor 2 Factor 3 5.04 32 0.00 0.85 0.79 0.18
Unconstrained Factor 3 Factor 1 1.79 50 0.00 0.95 0.94 0.08
Constrained Factor 3 Factor 1 3.84 51 0.00 0.83 0.78 0.15
Unconstrained Factor 2 Factor 1 1.95 85 0.00 0.92 0.91 0.08
Constrained Factor 2 Factor 1 3.08 86 0.00 0.83 0.80 0.13
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The first hypothesis examines the direct influence that power exerts on inter-
organisational relationships in the Australian road freight transport industry. Linear
regression results indicate strong support based on the value of ß(Table VI).
Looking at Table VI, it can be seen that ‘‘power’’ is only a significant predictor
of cooperative relationships formation ( p< 0.05). A value of ß¼0.22, F(1, 117) ¼5.471,
p< 0.05 for the predictor ‘‘power’’ means that there is a direct relationship between power
and cooperative relationships such that the greater the importance placed on power the
lesser the chances to engage in cooperative relationships.
H2. Sharing is a predictor of the type of relationship in inter-firm relationships.
The third hypothesis examines the influence of ‘‘sharing’’ on inter-organisational
relationships (Table VI). Models indicate slight support for H2, demonstrating that the
importance road freight businesses place on sharing influence engaging in
relationships at arms’ length (ß¼0.23, F(1, 118) ¼6.961, p< 0.05).
H3. Interdependency is a predictor of the type of relationship in inter-firm
relationships.
The third hypothesis examines the influence of ‘‘interdependency’’ on inter-
organisational relationships (Table VI). Values of ß¼0.26, F(2, 118) ¼8.51, p< 0.05
and ß¼0.21, F(1, 117) ¼5.47, p< 0.05 for the predictor Interdependence and a value
of for the predictor ‘interdependency’’ suggests that there is a direct relationship
between interdependency and the formation of both arm’s length relationships and
cooperative relationships such as the more the parties work interdependently with
each other and meet the business expectations the greater the chances of participating
in these type of work arrangements.
H4. Trust is a predictor of the type of relationship in inter-firm relationships.
The fourth hypothesis examines the influence of ‘‘sharing’’ on inter-organisational
relationships (Table VI). Models indicate rejection for H4, demonstrating that the
Table VI.
Regression estimate
of power, sharing,
interdependency, and
trust on inter-firm
relationships
Dependent
variable
Independent
variable
R
2
change
model 1
R
2
change
model 2
Standardised
coefficient (ß) Sig.
Arms’ length Power Excluded variables
Cooperative r Power 0.45 0.43 –0.22 0.01
Collaborative r Power Excluded variables
Alliance Power Excluded variables
Arms’ length Sharing 0.23 0.05 0.23 0.04
Cooperative r Sharing 0.45 0.43 –0.1 0.95
Collaborative r Sharing Excluded variables
Alliance Sharing Excluded variables
Arm’s length Interdependency 0.67 0.26 0.04
Cooperative r Interdependency 0.45 0.43 0.21 0.02
Collaborative r Interdependency Excluded variables
Alliance Interdependency Excluded variables
Arm’s length Trust Excluded variables
Cooperative r Trust Excluded variables
Collaborative r Trust Excluded variables
Alliance Trust Excluded variables
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importance road freight businesses place on trust does not influence engaging in inter-
organisational relationships.
5. Discussions and conclusions
Understanding relational factors such as power, interdependency, sharing and
trust and their influence on inter-firm working arrangements has had considerable
academic interest and is of importance to managers involved in inter-organisational
relationships. In spite of the general consensus about the importance of empirically
researching these factors, existing results in the literature have not shown how these
factors can predict the engagement of firms in different types of relationships.
While there have been many findings in a number of studies regarding trust being a
precursor of inter-firm relationships (Sako, 1992; Karahannas and Jones, 1999; Ballou
et al., 2000; Whipple and Frankel, 2000; Jagdev and Thoben, 2001; Green, 2003),
trucking firms in Australia nevertheless perceive that this is not a key factor that
determines their engagement in inter-firm working arrangements. Given the
predominance of a price driven commodity sector, fierce competition and tighter profit
margins, the lack of trust is not be surprising.
This study aimed to identify the relational factors that explained engagement,
by looking at the nature of road freight transport industry inter-firm working
arrangements. It was expected that the underlying characteristics in inter-firm
relationships would be identified through inferential analysis of responses from
questionnaire items regarding a number of supply chain relationships statements. The
study was able to identify three prominent relational factors that typify inter-firm
relationships in the Australian road freight transport industry. They are sharing,
power and interdependency.
One of the expected findings in our study is the significant inverse relationship
between the exercise of power and the formation of more close relationships
(ß¼0.22, p< 0.01). The extant literature has demonstrated that power imbalance
has a negative influence on the formation of relationships (Dapiran and Hogarth-Scott,
2003; Polenske, 2004). This sample, however, indicated that this is only a factor for
cooperative relationships and there was no indication of either a positive or negative
impact of power on the formation of other types of relationships. As respondents
indicated, the use of power has negative influence on cooperative freight transport
organisations which still tend to view use of penalties, bargaining power and expertise
as sources of power that might prevent them from cooperating with other firms in the
trucking industry.
In general terms, relationships in the trucking industry are strongly influenced by
the interdependence and the nature of power. In particular, managers of trucking
organisations perceived that to engage in cooperative arrangements it is important to
approach the relationships as beneficial until it impacts on organisation’s autonomy
(Spekman et al., 1998; Ballou et al., 2000). This type of approach leads to
interdependency. Interestingly, respondents in the road freight transport in Australia,
regard interdependency as a relational factor that explains the engagement in
relationships at arm’s length. Perhaps, this is explained by the interest that trucking
firms such as small to mid size operators have in surviving in the industry. Survival
in a country of the size of Australia and can be secured by providing service to
organisations that can secure significant contracts due to their bargaining power but
that do not find it operationally feasible to dedicate a fleet to cover remote areas.
Supply
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435
When looking at the explanatory power of the factor ‘‘sharing’’ an interesting
finding emerges. The literature suggests that greater sharing of risks, costs and
information is a characteristic of more complex and long-term orientated relationships
(Monczka et al., 1998; Lamming et al., 2000). Nevertheless, the values of regression
between sharing and the four types of working arrangements in this study slightly
support the theory. The findings show that in the industry, placing importance on
sharing is critical to engaging in relationships in which actions are primarily regulated
by contracts (Sako, 1992). The nature of the industry prompts the participants to sign
agreements that enable them to share assets such as depot and warehouse spaces and
information systems to have a greater geographical coverage. This supports the
findings about interdependency and ensures provision of the service in remote areas
for the large to mid size operators and in capital cities for the small operators.
The results also suggest that freight operators see power as discouraging their
involvement in cooperative relationships. This supports what has been theorised about
the influence of unbalanced power on working arrangements that are not contract
oriented (Kumar et al., 1998). This indicates that trucking firms holding powers are not
enforcing their power through punitive means as this harms their relationships. For
instance it might be the case that participants in this industry do not approve reward/
penalty-driven performance. However, if they exist, they are not used to ensure
improvements in their working relationships.
This study provides some insights into the factors that explain the engagement of
firms in different work arrangements within the Australian road freight transport
industry. It demonstrated that in less mature inter-firm relationships, the dominant
type of relational factors are sharing and interdependency. It is also demonstrated
that the importance that freight managers place on power does not encourage
their engagement in cooperative relationships. Trucking firm managers need to
acknowledge that sharing and interdependency influence their relationships and they
need to better understand how these relational factors influence the operational
effectiveness of their individual businesses. The evidence of the influence of the
negative relationships between power and cooperative relationships establishes the
need of fu rther research to explore what type power has influences relationships.
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Corresponding author
Mario Ferrer can be contacted at: m.ferrer@cqu.edu.au
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