Logistics service provider–client relationships

Transportation Research Part E 41 (2005) 179–200 www.elsevier.com/locate/tre

Logistics service provider–client relationships Photis M. Panayides

a,*

, Meko So

b

a

Marketing and Shipping Management, Cyprus International Institute of Management, 21 Akademias Avenue, P.O. Box 20378, CY-2151 Nicosia, Cyprus b Department of Mathematics, University of Hong Kong, Pokfulam Road, Hong Kong

Received 15 August 2003; received in revised form 20 April 2004; accepted 20 May 2004

Abstract The paper investigates the effects of relationship orientation in the logistics service provider–client interaction. A conceptual model is developed and six research hypotheses are empirically analysed using structural equation modelling. The results indicate that relationship orientation has a positive influence on key organisational capabilities, like organisational learning and innovation, promoting an improvement in supply chain effectiveness and performance. Theoretical, managerial and research implications are discussed. Ó 2004 Elsevier Ltd. All rights reserved. Keywords: Logistics service providers; Relationship marketing; Learning; Innovation; Supply chain performance

1. Introduction The recent trend towards the outsourcing of the logistics activities has given prominence to the concept of third party logistics service providers (LSPs). An LSP is defined as a provider of logistics services that performs the logistics functions on behalf of their clients (Coyle et al., 1996). As such they have become increasingly influential in the context of supply chains since the extent to

*

Corresponding author. Tel.: +357 22462246/22462212; mobile: +357 99580782; fax: +357 22331121. E-mail address: [email protected] (P.M. Panayides).

1366-5545/$ - see front matter Ó 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.tre.2004.05.001

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which the logistics functions are prudently undertaken will influence effectiveness and consequent performance in the supply chain. This will require a close understanding and collaboration with their clients in order to understand their business and assist them in improving the supply chain process. On this basis it is conceptualised that a closer relationship between LSPs and their clients will lead to an improvement in the performance of the supply chain. The competitiveness of LSPs will also depend to a large extent on their ability to add value to the bottom line of their clients. LSPs can do that effectively through co-operating with their clients, learning their business practices and introducing innovations, all with a view towards improving the performance of the supply chain. The current literature does not address adequately the potential impacts from implementing relational exchange in supply chains (Prahinski and Benton, 2004). Literature gaps are identified in the form of sufficient conceptualisations of how relational orientation may influence effectiveness and performance, as well as empirical investigation of the key concepts and their inter-relationships. In addition, despite the prominence of the LSP sector, few studies have actually addressed issues relevant to LSPs and performance (e.g., Lai, 2004). In an attempt to address such gaps in the literature, the purpose of this research is to empirically assess the impact of relational orientation in LSP–client relationships and their ultimate impact on supply chain effectiveness and performance. The paper also examines the mediating role of specific organisational competencies such as learning and innovation on effectiveness and performance in supply chains. It is envisaged that the results of this study will make incremental contributions to the theoretical literature and will also provide a basis for further research in supply chain relationships. In addition, the results will also be useful to an audience of practitioners interested in the overall improvement of their supply chain. The results of the study will be of value to LSPs and their clients. For LSPs in particular, the results of the study will provide evidence of the efficacy of implementing a relationship orientation programme. The clients will be informed of the potential of improving supply chain performance via outsourcing to LSPs that can build and maintain valued relationships. The following section highlights the importance of LSP–client relationships in supply chains. The conceptual model and research hypotheses are then developed. Subsequently, the research methodology is described. The analysis and results are presented in Section 5. The paper concludes following a discussion of the research implications.

2. Logistic service provider–client relationships The concept of supply chain management has come to re-enforce the narrower subject of logistics through a more comprehensive treatment that spans the entire value system from suppliers to customers (Handfield and Nichols, 2004). The official definition adopted by the Council of Logistics Management is: ‘‘supply chain management encompasses the planning and management of all activities involved in sourcing and procurement, conversion and all logistics management activities. Importantly it also includes co-ordination and collaboration with channel partners, which can be suppliers, intermediaries, third-party service providers, and customers. In essence, supply chain management integrates supply and demand management within and across companies’’.

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The above definition highlights the importance of LSPs and their key role in the context of improving supply chain performance through collaboration and the integration of all logistics management activities. Payne et al. (1999) argue that the dominant form of competition in coming years will be between networks of supply chain relationships rather than between individual organisations. This underpins the importance of examining relationships in the context of achieving effectiveness and competitiveness in supply chains. The examination of relationships is especially applicable to supply chains due to the problems that can surface regarding the sharing of sensitive information between multiple suppliers and intermediaries (Weitz and Jap, 1995). It has also been suggested that relationships are especially important in marketing channels as they may contribute to performance improvements (Jap et al., 1999). Supply chain relationships may play a key role in supply chain integration and effectiveness (Carr and Pearson, 1999; Lambert and Cooper, 2000; Larson and Kulchitsky, 1998; Patterson et al., 2003). In fact, it has been specifically recognised that to create a competitive advantage, supply chain management is increasingly emphasizing cross-functional and inter-organisational co-ordination of activities (Ballou et al., 2000; Tan et al., 1998). Wathne and Heide (2004) state that relationships are the foundation on which an effective supply chain can be built. It has also been stated that ‘‘organisations that identify and develop relational marketing capabilities. . .and operational capabilities to support relational capabilities, set themselves apart from the competition’’ (Zhao and Stank, 2003, p. 163). It is also important to understand the consequences and strategic impacts of relational exchange (Morgan and Hunt, 1999). By examining the impact of relationship orientation in LSP–client relationships, this paper will assess the potentially beneficial outcomes in the context of supply chains as well as the strategic implications that may arise from relationship orientation. The following section discusses the underlying theoretical concepts that give rise to the research hypotheses.

3. Theory and research hypotheses 3.1. Conceptual model and construct definitions The effects of relational exchange may influence supply chain effectiveness directly or indirectly by promoting organisational learning and innovation. Supply chain effectiveness will have a positive impact on supply chain performance. This underpins the conceptual framework for this study that is depicted in Fig. 1 below. Relationship orientation refers to the proactive creation, development and maintenance of relationships with customers and other parties that would result in mutual exchange and fulfilment of promises at a profit (Harker, 1999). Relationship orientation may be viewed as a philosophy of doing business successfully and as an organisational culture that puts the buyer–seller relationship at the centre of a firmÕs strategic and operational thinking. Organisational learning refers to the organisation-wide activity of creating and using knowledge to enhance competitive advantage. This includes obtaining and sharing information about customer needs, market changes and competitors actions as well as development of new

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RO

OL

INN

SCE

SCP

KEY: RO: Relationship orientation; OL: Organizational learning; INN: Innovation; SCE: Supply chain effectiveness; SCP: Supply chain performance Fig. 1. Conceptual framework.

technologies to create new products that are superior to those of competitors (Hurley et al., 1998; Moorman and Miner, 1998). Organisational learning influences what kind of information is gathered and how it is interpreted, evaluated and shared (Moorman and Miner, 1998; Sinkula et al., 1997). Innovation is defined as the adoption of an idea or behaviour—whether pertaining to a device, system, process, policy, programme, product or service—that is new to the adopting organisation (Zaltman et al., 1973). Hurley et al. (1998) defined firm innovativeness from a collective perspective, that is, openness to new ideas as an aspect of a firmÕs culture. Rogers and Shoemaker (1971) called one person or groupÕs propensity to adopt a new idea or technology early, relative to others, innovativeness. Seminal studies in organisational innovations suggested that innovations at the organisational level are expected to result in an organisational change that might affect the performance of that organisation (Armour and Teece, 1978; Rosner, 1968; Mansfield, 1968). Performance improvement is at the heart of supplier development programmes (Krause et al., 2000). Effectiveness is the extent to which goals are accomplished. Venkatraman and Ramanujam (1986) focused on organisational effectiveness and classified business performance measures either as financial or operational. Operational measures included key competitive success factors (e.g., quality, delivery, service, flexibility) and internal indicators (e.g., defects, schedule realisation). In the current study the LSPÕs effectiveness is an operational measure of competitive success factors, namely quality, timeliness, customer service and flexibility. Supply chain performance is defined in terms of cost and financial improvements. 3.2. Hypotheses In a supply chain management context, a primary requirement is that the supply chain parties develop relationships characterized by willingness to share and receive information and work in a collaborative manner in order to improve effectiveness and reduce cycle time (Hult and Nichols, 1996). The co-ordination of information technology and data is essential for supply chain effectiveness (Handfield and Nichols, 1999; Frohlich and Westbrook, 2001). Inter-organisational relationships would arguably lead to enhanced supply chain performance and greater potential benefits for all parties in the supply chain (Johnson, 1999; Lee et al., 1997). According to Selnes and Sallis (2003, p. 80), ‘‘through relationship learning, both parties in customer–supplier relationships identify ways to reduce or remove redundant costs, to improve quality and reliability and to

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increase speed and flexibility’’. This may also be applicable to the LSP–client relationship as the exchange of data and information is in this case also essential for a seamless supply chain. On the basis of the above it is hypothesised that: H1: Relationship orientation between logistics service provider–client is positively related to the LSPÕs effectiveness in the supply chain. One of the benefits of a relational exchange is the reduction in transaction costs due to the repeated nature of transactions that enables the parties to learn and assimilate each otherÕs business practices. It follows that, in entering into relational exchange with other parties, a firm will develop another key capability that will enable it to draw upon and learn from previous relational exchanges. The importance of learning in the context of inter-organisational relationships had been recognised and explored in previous studies (e.g., Inkpen, 1998; Inkpen and Crossan, 1995). Inkpen and Crossan (1995) indicate that inter-organisational relations in the context of joint ventures not only enable a company to learn from its partner, but also raise the ability to learn to a competitive advantage. Organisational learning culture manifests itself in a behavioural norm that impacts the development and processing of market information (Deshpande and Webster, 1989; Deshpande et al., 1993). In essence learning orientation is reflected by the presence of values that influence the propensity of a firm to proactively pursue new knowledge and challenge the status quo (Sinkula et al., 1997). Relationship orientation promotes organisational learning because it manifests itself in close communication, reciprocity, trust and commitment with buyers and suppliers thus enhancing an organisationÕs ability to develop, acquire and process market information. Liu et al. (2002) conceptualised that customer orientation that focuses on dynamic interactions between the organisation and its customers will lead to improvement of organisation-wide learning values. Their hypothesis of a positive relationship between customer orientation and learning orientation was empirically supported. Learning ability will improve as the partners sustain their business relationship over time and develop a joint understanding that allows uniquely efficient communication and know-how of when to contribute and draw on each otherÕs resources. Barlow and Jashapara (1998) examine the influence of inter-organisational relations in the context of promoting organisational learning. It is shown through case studies of major UK organisations that improved learning at all levels is partly the result of effective communication and information distribution systems, both within and between organisations. On this basis it is hypothesised that: H2: A relationship orientation between logistics service provider–client is positively related to organisational learning. Organisational learning has been discussed in the literature as an antecedent to innovation (e.g., Damanpour, 1991; Verona, 1999). The relationship between organisational learning and innovation has been theoretically conceptualised but not empirically supported in a supply chain context (see Brewer and Speh, 2000). Organisational learning orientation was found to influence firm innovativeness particularly in terms of adopting innovative technologies and processes (Calantone et al., 2002). Since the adoption of new technologies and processes are crucial in a supply chain environment, it can be inferred that organisational learning will be positively related to innovativeness. Hence:

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H3: Organisational learning in logistics service provider–client relationships is positively related to innovation. Supply chain management is contingent on having a culture that fosters swift response to any rising problems, willingness to help customers and ability to handle problems improve customer service, timeliness and fulfil promises made. The above defines effectiveness in the supply chain. In achieving higher levels of supply chain effectiveness it is important to update systems and processes. Organisational innovation is intended to improve effectiveness as organisations attempt to respond to changes in their internal or external environment (Damanpour, 1991). Innovation, particularly in terms of investing in new systems that will enhance supply chain integration and communication, is imperative (Rutner et al., 2003). For instance, establishing advanced links with their customers enables companies to transmit and receive information, which gives potential to speed up the entire transaction and improve efficiency in terms of fulfilling promises to customers (Stefansson, 2002). Openness to new ideas that promote administrative efficiencies and adoption of new process technologies will culminate in an improvement of the LSPÕs effectiveness in the supply chain. To test this proposition it is hypothesised that: H4: Innovation in logistics service provider–client relationships is positively related to the LSPÕs effectiveness in the supply chain. Effective supply chains achieve the timely delivery of products and related information that is accurate. In doing so, it is essential that supply chain operations are effective and any arising problems are dealt with swiftly. Effectively performing supply chains manage to minimize costs reduce cash-to-cash cycle time and improve the rate of utilisation of facilities (Brewer and Speh, 2000; Shin et al., 2000). Supply chain operations entail numerous costs associated with purchasing, holding inventory and delivery failure. Improvement in supply chain cycle efficiency will involve reduction of such costs as greater co-operation and information sharing will avoid duplication of activities and improve on-time performance. Benton and Krajewski (1990) examine in a simulation study the impact of vendor performance in a variety of manufacturing environments. They found that poor delivery performance manifested in on-time delivery results in higher levels of inventory and order backlogs, i.e. poor supply chain performance. In this study, effectiveness in terms of timeliness, information quality and customer service is regarded as fundamental to the improvement of supply chain performance. Therefore it is hypothesised that: H5: The LSPÕs supply chain effectiveness is positively related to supply chain performance. A key aspect of inter-organisational relationships is the ability to adapt to the partnerÕs needs and requirements. Inter-organisational relationships would entail openness to new patterns of behaviour and the adoption of new ideas as an aspect of a firmÕs culture. In consequence, it can be theorized that relationship orientation promotes innovation. This relationship has not been previously examined in the literature although there have been attempts to investigate other aspects of customer orientation (e.g., market orientation) as antecedents to innovation (Hurley

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et al., 1998). In their paper, Hurley et al. (1998) indicate that elements of inter-organisational relationships such as collaboration and communication would have an impact on innovation. On this premise it is hypothesised that: H6: Relationship orientation in logistics service provider–client relationships is positively related to innovation.

4. Methodology 4.1. Construct operationalisation Relationship orientation was measured by adopting a scale developed by Sin et al. (2003). The scale, which was tested in a follow-up study and found to be valid and reliable (Sin et al., 2002), encompasses all the commonly cited attributes that are regarded as central in the formation of relationships. These include the items of communication (Anderson and Narus, 1990), empathy (Berry et al., 1990), trust (Morgan and Hunt, 1994), shared value (Evans and Laskin, 1994), reciprocity (Houston et al., 1992) and bonding (Shani and Chalasani, 1992). Relationship orientation was conceptualised as a second-order factor made up of the six aforementioned items. Lukas et al. (1996) argued that organisational learning cannot be captured by a single unidimensional measure. Organisational learning was measured using a scale developed by Sinkula et al. (1997) and Hult and Ferrell (1997a,b). The scale includes the four central attributes of organisational learning as discussed in the literature, viz. commitment to learning (Sinkula et al., 1997), open-mindedness (Sinkula, 1994), shared vision (Verona, 1999) and intra-organisational knowledge sharing (Moorman and Miner, 1998). The scale was tested and adopted in subsequent studies and found to be valid and reliable (e.g., Calantone et al., 2002). In order to tap the domain of innovation, the scale developed by Hurt and Teigen (1977) and Hurt et al. (1977) was adopted in this study. In fact this scale has been used and validated in a number of other previous studies (see Calantone et al., 2002; Hollenstein, 1996). When considering innovation in an industrial setting, the firm or organisation is the unit of adoption (Rogers, 1995). The LSPÕs supply chain effectiveness was conceptualised as an operational measure of competitive success factors, such as quality, timeliness, customer service and flexibility. Items for measuring these constructs were developed and adopted from Beamon (1999), Gunasekaran et al. (2001), Mentzer et al. (2001) and Stewart (1995). Supply chain performance is defined in terms of cost and financial improvements. All responses were reported on a one to seven Likert-type scale anchored by ‘‘1 = strongly disagree’’ to ‘‘7 = strongly agree’’. 4.2. Sampling and data collection Data were collected via a large-scale industrial survey of third-party logistics service providers in Hong Kong. A survey methodology was chosen because it was deemed to be the most efficient way of reaching a large number of respondents, whereas the data required facilitated the use of a questionnaire with close-ended questions that could be answered via a mail-administered

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questionnaire. Sample firms were identified from the highly respected directory of the Hong Kong Trade and Development Council that serves as a resource centre for business information and maintains relevant databases of industrial companies. In an effort to improve response rate and reduce non-response bias, Dillman (1978, 2000) authoritative suggestions were adhered. This included the enclosure of a stamped addressed envelope with the questionnaire, assurances of confidentiality and anonymity and a promise that a report of the results and managerial implications will be sent to the respondents after completion of the study. Two follow-up reminders with enclosed questionnaires were sent 3 and 5 weeks after the initial mailing. A total of 1083 questionnaires were sent resulting in 251 useable responses after the two follow-ups, representing a response rate of 23.17%. The key informant technique was implemented in order to facilitate response rate from selected individuals deemed to be knowledgeable of the aspects under scrutiny (Phillips, 1981). Our claim that key informants were carefully selected is supported by the descriptive statistics of the final sample: about 44% of the respondents consisted of ‘‘Directors’’, 34% were ‘‘Managing Directors/ General Managers’’, about 9% were in the position of ‘‘President’’ and another 5% ‘‘CEOs’’ whereas the rest described themselves as marketing executives or owners of the firm. People at the top of the management hierarchy are regarded as knowledgeable of the issues under scrutiny in organisational studies of this type. In addition, the average time they have spent at management level within the organisation was 6.5 years and none of the respondents had less than 2 years at this level. Non-response bias was assessed in accordance to the procedure recommended by Armstrong and Overton (1977). This involved t-tests which revealed no statistically significant differences among the items in the responses of early and late waves of returned surveys. In terms of size, 24% of the companies had up to 15 employees, 27% had between 16 and 50 employees, 27% had between 51 and 200 employees and 22% more than 200 employees. 4.3. Construct testing Before testing the hypotheses, we discuss the set of questionnaire items for each construct. All constructs were measured with multiple-item scales. Scale purification was undertaken prior to hypothesis testing. The scales were grounded in the literature (OÕLeary-Kelly and Vokurka, 1998) and confirmatory factor analysis (CFA) was used to ensure reliability (Kim and Mueller, 1978). Following basic descriptive analyses (examination for coding errors, normality, skewness, kurtosis, means and standard deviations) the data were subjected to confirmatory factor analysis (CFA) by means of LISREL 8.52 (Jo¨reskog and So¨rbom, 1993). The results are presented in the following section. The fit statistics, modification indices and standardized residuals indicated potentially problematic items, which were deleted on the basis of statistical as well as substantive grounds (Anderson and Gerbing, 1988, 1991; MacCallum, 1986). During a pre-test, logistics managers and academics were asked to indicate the extent to which certain measures tap the domain of the construct and to recommend modifications. The substantive validity pre-test resulted in six items being removed and a number of modifications to the retained measures. All items used can be seen in Appendix A. The items removed are shown in italics. These items were removed because, as indicated by the statistical diagnostics for convergent validity and reliability, their loadings were low and their removal strengthened overall model fit.

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Following the survey, all the items were subjected to confirmatory factor analysis. The initial model was modified by examining the LISREL diagnostics and by assessing the conceptual impact of removing an item, taking into account the need to tap all relevant aspects of the focal construct (MacCallum, 1986). Based on the criteria certain measures were deleted from the model; this included removal of the ‘‘reciprocity’’ item due to poor fit. The resulting model provided an adequate model fit (v2 = 202.02; df = 113; p < 0.001; RMSEA = 0.061; NNFI = 0.99; CFI = 0.99; GFI = 0.91; AGFI = 0.87). All itemsÕ loadings on their corresponding construct were high (ranging from 0.61 to 0.95) and significant at the 0.05 level, demonstrating adequate convergent validity. Small residuals indicated that uni-dimensionality was also achieved. Cronbach alpha reliability coefficients were as follows: trust = 0.93; communication = 0.80; empathy = 0.87; bonding = 0.80; and shared value = 0.90. Similar to the relationship orientation construct, this is a second-order construct and the measures were subjected to confirmatory factor analysis utilising the same process as described above. The LISREL diagnostics indicated a good model fit suggesting the accurate measurement of the central attributes of organisational learning (v2 = 146.60; df = 75; P = 0.00; RMSEA = 0.060; NNFI = 0.99; CFI = 0.99; GFI = 0.93; AGFI = 0.89). All itemsÕ loadings on their corresponding construct were high (ranging from 0.71 to 0.86) and significant at the 0.05 level, demonstrating adequate convergent validity. Small modification indices and residuals indicated that uni-dimensionality was also achieved. Cronbach alpha values also indicate reliability as follows: commitment to learning = 0.82; shared vision = 0.88; intra-organisational knowledge sharing = 0.81; and open-mindedness = 0.82.

5. Empirical analysis and results 5.1. Descriptive statistics and analysis of the measurement model Table 1 shows the correlation matrix and descriptive statistics for the research variables in the model. The variable means are below 6 (M = 5.3) on the basis of 7-point Likert scales. The standard deviations for the variables range from 0.81 to 1.09 (M = 0.93) indicating considerable amount of variation in the responses. The values of the correlations range from 0.32 to 0.62, with

Table 1 Means, standard deviations and correlation matrix Variable

M

SD

RO

OL

INN

SCE

SCP

RO OL INN SCE SCP

5.18 5.22 5.12 5.67 5.17

0.83 0.92 1.09 0.81 0.98

1 0.57** 0.37** 0.45** 0.32**

1 0.62** 0.50** 0.38**

1 0.40** 0.37**

1 0.57**

1

Note: RO: Relationship orientation; OL: Organisational learning; INN: Innovation; SCE: Supply chain effectiveness; SCP: Supply chain performance. ** All significant at p < 0.01.

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the mean being 0.46. The correlations provide an initial indication of the validity of the conceptualisation that relationship orientation in logistics service provider–buyer relationships will have an impact on supply chain performance. For a much stronger test, the hypotheses will be tested using structural equation modelling (SEM) via LISREL. Structural equation modelling is an applicable method for analysing the hypotheses as it allows the analysis of multiple relationships simultaneously and provides measures of overall model fit. It also explains the significance of each of the relationships between the variables (Kline, 1998). The recommended two-step procedure was adopted where a measurement model is developed in order to examine the relationship between the underlying variables and the factors they are supposed to measure prior to development of the structural model that includes causal paths based on the hypothesised relationships between the variables in the model (Anderson and Gerbing, 1988). According to Bollen (1989) identification of the estimated model is necessary. The estimated model was over-identified (t < s) therefore, one set of estimates could be used to test the model. To achieve higher levels of measurement quality, validity and reliability, the research followed established procedures and the data were assessed thoroughly. Content validity represents the sufficiency with which a specific domain of content (i.e., a construct) was sampled (Nunnally, 1978). Although content validity is subjective and judgmental (Flynn et al., 1990) it can be used in conjunction with other methods of validity assessment to provide corroborating evidence based on two standards suggested by Nunnally: does the instrument contain a representative set of measures, and were sensible methods of scale construction used? In this study the items used to measure the constructs were adopted from previous scales found to be valid and reliable and pilot tested with logistics managers to ensure that the items tapped the domain of the construct. Overall measurement quality was assessed using confirmatory factor analysis (Anderson and Gerbing, 1991). Although measurement quality is sometimes assessed factor by factor, each multiple-item indicator was considered simultaneously to provide for the fullest test of convergent and discriminant validity. A number of indices were used to determine if the fit of the data to the model is adequate. In addition, it was desired that all of the indicator variables for each factor in the measurement model have a t-statistic of 2.0 or greater. Also it was important that no standard errors associated with the t-statistics be near zero (such as 0.0003). As can be seen from Table 2, all loadings exceed 0.6 and each indicatorÕs t-value exceeds 10.0 (p < 0.001). Standard errors were 0.05 and above. Co-efficient alpha is above 0.8 for each scale, exceeding the recommended cut-off point of 0.7 suggested by Nunnally (1978). The overall fit supports the measurement model. The v2 statistic is 344.21 with 234 degrees of freedom (p < 0.01), something which suggests discrepancies between the data and the proposed overall model. However, this may be attributed to sample size and the highly restrictive assumption of perfect model fit in the population. The v2 to degrees of freedom statistic is 1.47 (below the recommended value of 3.0 suggested by Bollen, 1989). A number of goodness-of-fit indices were used to assess practical significance. The LISREL 8.52 output showed an adequate model fit as follows: (1) GFI was 0.91, AGFI was 0.90, CFI was 0.99, NFI was 0.97, NNFI was 0.99 and PNFI was 0.82; (2) RMSEA was 0.036 indicating a close model fit (Baumgartner and Homburg, 1996; Browne and Cudek, 1993). The model Akaike Information Criterion (AIC) was also less than for the saturated model indicative of achieving model fit without ‘‘over-fitting’’ with too many coefficients.

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Table 2 Standardized measurement co-efficients and T-values resulting from CFA Item abbreviation

Relationship orientation Organisational learning Innovation

RO1 RO2 RO3 RO4 RO5 OL1 OL2 OL3 OL4 INN1 INN2 INN3 INN4 INN5 SCE1 SCE2 SCE3 SCE4 SCE5 SCE6 SCE7 SCP1 SCP2 SCP3

0.68 0.76 0.83 0.68 0.74

Cronbach alpha 0.87 Composite reliability 0.83 Average variance 0.50 extracted

SCE

SCP

(11.46) (13.44) (15.18) (11.52) (12.87) 0.77 0.86 0.87 0.82

(14.07) (16.71) (16.97) (15.40) 0.82 0.85 0.90 0.71 0.63

(15.20) (16.14) (17.55) (12.54) (10.55) 0.80 0.82 0.78 0.77 0.80 0.80 0.86

(14.72) (15.34) (14.27) (14.11) (14.90) (14.63) (16.16) 0.83 (15.18) 0.88 (16.58) 0.74 (13.06)

0.90 0.80 0.50

0.89 0.83 0.50

0.93 0.87 0.50

0.85 0.75 0.50

n = 251, v2 = 344.21, df = 234 (p 0.01), GFI = 0.91, CFI = 0.99, NFI = 0.97, PNFI = 0.82, RMSEA = 0.036. T-values are shown in parentheses. All are significant (p < 0.01).

Convergent validity was established from a review of the t-tests for the factor loadings. All the t-tests are significant, effectively indicating that all indicators measure the same construct (Anderson and Gerbing, 1988). Table 2 shows that all of the t-values are greater than 2.0 and significant at p < 0.01 (Steenkamp and van Trijp, 1991). Moreover, each itemÕs co-efficient is greater than twice its standard error indicating convergent validity. In addition to assessing the reliability of the individual indicators, the composite reliability value for each latent variable (also known as construct reliability) was calculated using Fornell and LarckerÕs (1981) measure of composite reliability. Values greater than 0.6 are desirable (Bagozzi and Yi, 1988). The values of the composite reliability index are shown in Table 2 and support construct reliability for the variables (the measurement of the construct is reliable). A complementary measure to composite reliability is the average variance extracted, qv. The average variance extracted shows directly the amount of variance that is captured by the construct

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in relation to the amount of variance due to measurement error. Values of 0.50 and above are desirable (Fornell and Larcker, 1981). The results indicated in Table 2 provide additional confidence in the operationalisation of the variables. Discriminant validity was assessed by determining the confidence interval around the correlation for each pair of factors. The confidence interval is equal to plus or minus two standard errors of the respective correlation co-efficient. If the confidence interval does not include 1.0 then discriminant validity is demonstrated (Anderson and Gerbing, 1988). The test indicated a high level of discriminant validity for the measures. Another test for discriminant validity is to examine if the correlations between measures of different factors using the same method of measurement are lower than the reliability coefficients (Crocker and Algina, 1986). The correlation co-efficients were lower than the reliability co-efficients for all measures (except INN3), suggesting that the measures have discriminant validity. 5.2. The structural models and hypothesis testing Testing of the research hypotheses involved examination of four structural models assessing the respective causal paths as shown in Table 3. The results indicate that all four models achieve acceptable fit. The model diagnostics are shown in Table 4. The Q-plot for the models was approximately linear with a slope near 1 suggesting the absence of major model misspecifications (Bagozzi and Yi, 1988; Bentler, 1990; Jo¨reskog and So¨rbom, 1993). Given the satisfactory fit of the models, the hypotheses were evaluated by examining the estimated structural co-efficients. The results are shown in Table 5. The results indicate the support for Hypotheses H1–H5. Hypothesis H6 that is tested in two of the models is not supported. For Hypotheses H1–H5, the path loadings between the latent variables had positive parameters and magnitudes that support the theoretical causal relationships. All parameters are significant as indicated by their t-values, which are well in excess of 2.00 (t-values for path co-efficients greater than 1.65 are significant at p < 0.10; t-values greater than 1.96 are significant at p < 0.05; t-values greater than 2.58 are significant at p < 0.01).

Table 3 Structural models Model no.

Causal paths examined

Model I

RO ! OL ! INN ! SCE ! SCP

Model II

RO ! OL ! INN ! SCE ! SCP RO ! SCE

Model III

RO ! OL ! INN ! SCE ! SCP RO ! INN

Model IV

RO ! OL ! INN ! SCE ! SCP RO ! INN RO ! SCE

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Table 4 Model diagnostics Model diagnostics 2

v df v2/df GFI CFI PNFI NNFI RMSEA

Model I

Model II

Model III

Model IV

386.96 240 1.61 0.90 0.99 0.84 0.99 0.041

357.15 239 1.49 0.90 0.99 0.84 0.99 0.037

389.78 239 1.63 0.90 0.99 0.84 0.98 0.042

356.39 238 1.50 0.99 0.99 0.84 0.99 0.037

Table 5 Summary of test results for structural models Linkages in the model

Hypothesis

Hypotheses supported

Number sign

Estimate

Model I RO–OL OL–INN INN–SCE SCE–SCP

t-Value

H2 H3 H4 H5

Yes Yes Yes Yes

+ + + +

0.65 0.73 0.50 0.63

8.29 8.92 6.81 8.33

Model II RO–SCE RO–OL OL–INN INN–SCE SCE–SCP

H1 H2 H3 H4 H5

Yes Yes Yes Yes Yes

+ + + + +

0.38 0.65 0.71 0.31 0.63

5.15 8.35 8.81 4.30 8.32

Model III RO–OL OL–INN INN–SCE SCE–SCP RO–INN

H2 H3 H4 H5 H6

Yes Yes Yes Yes No

+ + + + 

0.65 0.75 0.49 0.63 0.03

8.25 7.53 6.79 8.33 (0.43)

Model IV RO–SCE RO–OL OL–INN INN–SCE SCE–SCP RO–INN

H1 H2 H3 H4 H5 H6

Yes Yes Yes Yes Yes No

+ + + + + 

0.39 0.66 0.76 0.31 0.63 0.07

5.28 8.33 7.49 4.43 8.30 (0.87)

Relationship orientation in LSP-client relationships has a positive influence on supply chain effectiveness (H1) and organisational learning (H2). Organisational learning has a positive influence on innovation (H3), whereas innovation has a strong positive influence on supply chain

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effectiveness (H4). Improvement in supply chain effectiveness is found to have strong positive influence on supply chain performance (H5). Overall the findings suggest that relationship orientation is extremely beneficial in the context of supply chains. However, relationship orientation will not lead to greater innovation in supply chains (H6) but will only lead to innovation indirectly via organisational learning.

6. Discussion and implications 6.1. Theoretical implications Supply chain management improvements have been largely viewed from an operational standpoint. There has been less attention placed on organisational factors that may influence supply chain performance. A contribution of this paper is that it provides supporting evidence about the influence of organisational factors in improving supply chain effectiveness and performance. In particular it is found that inter-organisational relationships in the LSP–client context, through adoption of relational exchange will have an influence on learning and innovation that can affect the performance of the supply chain. Following this empirical investigation it can be said that relationship orientation cultivates the ‘‘ability to learn’’ in the LSP–client relationship. From a strategic perspective, cultivation of this key organisational competence may lead to sustainable competitive advantage (see also Hurley et al., 1998; Mone et al., 1998). Hence, relationship orientation in LSP–client relationships may influence performance directly as well as indirectly through the development of key organisational competencies that give rise to sustainable competitive advantage. It has also been recognised in the literature that the collection, creation, management, and communication of information are critical to the efficiency, effectiveness and competitive advantage of any supply chain (e.g., Mentzer et al., 2000; Novack et al., 1994; Stern et al., 1996). The empirical investigation indicates that a relationship orientation may actually play an instrumental role in promoting the accurate collection, management, and communication of information that will enable the LSP to improve supply chain effectiveness and that this will lead to improved performance in the supply chain. An important theoretical contribution of this study is that it replicates the association between organisational learning and innovation that was previously conceptualised but not empirically tested in the context of supply chain management (Brewer and Speh, 2000). It follows that improving the ability to learn will be beneficial for a company aspiring to improve the process of material and information flow. Innovation in supply chains is a broad process of learning and implementing new ideas, procedures and technologies. The results of the study reveal a mediating relationship between relationship orientation, organisational learning and innovation. Relationship orientation will not lead to innovation directly but indirectly via organisational learning. This relationship has not been previously discussed in the literature. Relationship orientation is, therefore, essential for a positive learning climate, which in turn will contribute towards innovation in the supply chain, that may include incorporation of new techniques and processes in supply chain operations. Our findings suggest that innovation will actually lead to improved LSP effectiveness in the supply chain. It can be inferred that as the supply chain parties become more innovative in terms of adopting

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new processes, operational routines and invest in new technological systems, supply chain effectiveness in terms of ability to fulfil what was promised, meet standards and solve problems will improve. Given the importance placed by LSPs on the timely delivery of products, which is also essential for inventory control, then investment in a relational strategy that will influence effectiveness should in turn, contribute to an improvement in supply chain performance as found in this study. 6.2. Limitations and further research As a caveat to the findings of this study and an enticement towards the commencement of further research, certain limitations must be acknowledged. The empirical analysis was undertaken with data collected from logistics service providers. Future research may also seek to collect data from the clientÕs side or even adopt a dyadic approach. Although validity and reliability assessments showed strong support for the constructs in this study, future studies may test additional items for operationalising the constructs. Certain items in this study had to be dropped following the results of confirmatory factor analysis. By the same token, supply chain performance may be measured using a number of other dimensions including strategic, tactical and operational measures (Gunasekaran et al., 2001). The results of this study may provide a basis for further examination of potential inter-relationships between these and other variables related to the influence of organisational capabilities on supply chain performance. The usual caveats for cross-sectional research also apply. A longitudinal study of supplier-logistics service provider relationships may provide more insights as to how relationships develop and their effect on organisational learning, innovation and supply chain performance as they unfold. 6.3. Managerial implications The findings of this study are important for LSPs as the sector is faced with increasing competitive pressures in a highly fragmented, high growth market, and challenges that threaten its viability as evidenced by recent consolidations (Gordon, 2003). Strong, enduring and flexible inter-firm relationships can and should play a significant role in a firmÕs competitive posture. This is because the firm can respond to customer needs in a timely fashion and also adapt to market needs more effectively than its competitors. LSPs will be judged on their ability to find ways for accelerating the flow of materials and information along the supply chain. This study reveals that improvement in this flow requires managers to address organisational as well as operational aspects. Managers may be confident of high returns for building client relationships. Not only will a strong relationship influence the development of organisational competencies but will also have a direct influence on the LSPÕs effectiveness and supply chain performance. LSPs may implement various measures in an effort to improve relationships with their clients. LSPs may seek to routinely engage in personal interaction and communication with their clients. Such interaction may take the form of personal visits, telephone calls or e-mail communication. Key players from the partner firm should meet regularly and seek ways to improve performance co-operatively. The parties should work together to identify improvement areas and introduce innovations that will facilitate timely performance and efficient information exchange. Investment

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in technology and systems that will improve integration and contribute towards the longevity of a relationship will be beneficial.

7. Conclusion The prominence of outsourcing the logistics functions and the significance of investigating supply chain relationships renders LSP–client relationships worthy of scholarly attention. The empirical analysis and results reported in this paper make contributions to the literature and to management practice. In terms of theoretical contributions, this research examined for the first time, the impact of certain key organisational competencies like relationship orientation, organisational learning and innovation on effectiveness and performance in supply chains. Positive relationships are reported, which suggests that supply chain improvements are as much an outcome of technical and operational measures as they are of managerial, organisational and inter-organisational capabilities. In addition, it is found that innovation in supply chains can be significantly affected by closer relationships and learning between LSPs and their clients. The results support the importance of relational capabilities, and indicate the value of organisational learning and innovation in terms of improving the LSPsÕ supply chain effectiveness and supply chain performance. The study paves the way for further research that would integrate organisational capabilities and operational factors in the context of analysing improvements in supply chain performance. Management of LSPs is also informed of the potentially beneficial effects of relationship orientation in supply chains.

Appendix A Construct

First-order factor

Items

Relationship orientation (RO)

Trust (RO1)

1. We trust each other 2. They are trustworthy on important things 3. According to our past business relationship my company things they are trustworthy persons 4. My company trusts them

Bonding (RO2)

1. We both try very hard to establish a long-term relationship 2. We work in close co-operation 3. We keep in touch constantly

Communication (RO3)

1. We communicate and express our opinions to each other frequently (continued on next page)

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Appendix A (continued) Construct

First-order factor

Items 2. We can show our discontent towards each other through communication 3. We can communicate honestly

Shared value (RO4)

Empathy (RO5)

Reciprocity (RO6)

Organisational learning (OL)

1. We share the same 2. We share the same 3. We share the same things around us 4. We share the same

world view opinion about most things feelings towards values

1. 2. 3. 4. 1.

We always see things from each otherÕs view We know how each other feels We understand each otherÕs values and goals We care about each otherÕs feeling My company regards Ônever forget a good turnÕ as our business motto 2. We keep promises to each other in any situation 3. If our customer gave assistance when my company had difficulties then I would repay their kindness

Commitment to learning (OL1)

1. Managers basically agree that our organisationÕs ability to learn is the key to our competitive advantage 2. The basic values of this organisation include learning as key to improvement 3. The sense around here is that employee learning is an investment not an expense 4. Learning in my organisation is seen as a key commodity necessary to guarantee organisational survival 5. Our culture is one that does not make employee learning a key priority

Shared vision (OL2)

1. There is commonality of purpose in my organisation 2. There is total agreement on our organisational vision across all levels, functions and divisions 3. All employees are committed to the goals of this organisation 4. Employees view themselves as partners in charting the direction of the organisation (continued on next page)

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Appendix A (continued) Construct

First-order factor

Items

Open-mindedness (OL3)

1. We are not afraid to reflect critically on the shared assumptions we have made about the way we do business 2. Personnel in this enterprise realise that the very way they perceive the marketplace must be continually questioned 3. We continually judge the quality of our decisions and activities taken over time 4. We rarely collectively question our own bias about the way we interpret customer information

Intra-organisational knowledge sharing (OL4)

1. There is a good deal of organisational conversation that keeps alive the lessons learned from history 2. We always analyse unsuccessful organisational endeavours and communicate the lessons learned widely 3. We have specific mechanisms for sharing lessons learned in organisational activities from department to department 4. Top management repeatedly emphasizes the importance of knowledge sharing in our company 5. We put great effort in sharing lessons and experiences

Innovation (INN)

1. Our company frequently tries out new ideas (INN1) 2. Our company seeks out new ways to do things (INN2) 3. Our company is creative in its methods of operation (INN3) 4. Our company is often the first to market with new products and services (INN4) 5. Our new product/service introduction has increased over the last 5 years (INN5)

Supply chain effectiveness (SCE)

1. 2. 3. 4.

On-time service delivery (SCE1) Timely response to clientsÕ requests (SCE2) Accurate client record keeping (SCE3) Accurate information delivery to clients (SCE4)

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Appendix A (continued) Construct

First-order factor

Items 5. Fulfil promises to clients (SCE5) 6. Solve clientsÕ problems (SCE6) 7. Willingness to help clients (SCE7)

Supply chain performance (SCP)

1. Ability to reduce costs (SCP1) 2. Improvement in the rate of utilisation of facilities (SCP2) 3. Improvement of cash-to-cash cycle time (SCP3)

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