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  • 1. The Emerald Research Register for this journal is available at The current issue and full text archive of this journal is available at www.emeraldinsight.com/researchregister www.emeraldinsight.com/0144-3577.htm IJOPM 25,1 The impact of supply chain relationship dynamics on manufacturing performance 6 Brian Fynes Michael Smurfit Graduate School of Business, University College Dublin, Blackrock, Ireland Chris Voss London Business School, London, UK, and ´ ´ Sean de Burca Michael Smurfit Graduate School of Business, University College Dublin, Blackrock, Ireland Abstract Purpose – The purpose of this paper is to investigate how the dynamics of supply chain (SC) relationships impact on manufacturing performance. Design/methodology/approach – A conceptual framework was developed incorporating dimensions of SC relationship dynamics and manufacturing performance. Structural equation modelling was used to test the model with data collected using a postal questionnaire from 200 suppliers in the electronics sector in the Republic of Ireland. Findings – There was mixed support for the impact of SC relationship dynamics on manufacturing performance. Hypotheses in respect of cost and quality were supported but those in respect of flexibility and delivery were not. Research limitations/implications – Using single informants and focal customers in research design. Practical implications – The process of forming and developing SC relationships can be complex and requires firms to be competent in areas such as negotiation skills, conflict management, anticipating problems/finding solutions in advance and joint problem solving. The selection of such managers should be driven by the need to find individuals who possess supply chain management skills and relational competencies. Originality/value – The nature of the relationship between measures of manufacturing performance has been addressed by two dominant theories: the cumulative or “sandcone” theory and the “trade-off” theory. Findings provide only partial support for this theory with respect to quality and cost. In contrast, the model is more aligned with the “trade-off” theory. Keywords Supplier relations, Performance management, Ireland, Electronic engineering Paper type Research paper Introduction Empirical research in the area of supply chain (SC) relationships has primarily sought to explain the nature of relationship processes rather than their effect on International Journal of Operations & manufacturing or business performance (Styles and Ambler, 2000). As a result, Production Management there is a considerable body of work focusing on the interaction between the various Vol. 25 No. 1, 2005 pp. 6-19 dimensions of SC relationships (such as trust, commitment, adaptation, communication q Emerald Group Publishing Limited and collaboration) but far less on the impact of SC relationship dimensions on 0144-3577 DOI 10.1108/01443570510572213 manufacturing performance. In contrast, there is a considerable body of empirical
  • 2. research that has examined the impact of manufacturing practices on manufacturing Supply chain performance (Ferdows and De Meyer, 1990; Pearson et al., 1995; Voss et al., 1995; Ahmed et al., 1996; Flynn et al., 1999; Lau, 1999; Cua et al., 2001). Accordingly, this relationship paper posits the following research question: RQ1. How do the dynamics of SC relationships impact on manufacturing performance? The remainder of this paper is structured as follows: first, we first review the 7 theoretical context and outline our hypotheses; second, we describe our methodology: third we develop and test a model of SC relationships and manufacturing performance; fourth, we reflect on the implications of our study and conclude with some suggestions for future research. Theoretical background and hypotheses Researchers have used a number of different theoretical frameworks in order to explain the nature of SC relationships. These include transaction cost theory, political economy theory, social exchange theory and resource dependence theory (Robicheaux and Coleman, 1994). These theoretical frameworks have all contributed to the modelling of SC relationships both in their identification of the underlying dimensions of relationships and their selection of appropriate units of analysis (such as firm, dyad or network). In addition, different but complementary streams of research have appeared in the somewhat diverse areas of industrial and business-to-business marketing, channel management, relationship marketing, operations management, supply chain management, logistics and purchasing. They include the IMP (Industrial Marketing and Purchasing Group) Interaction Model, Network Models, Channel Models, Process Models and Partnership Models. The primary focus of these approaches has been on the nature of relationship processes rather than the effect of relationships on manufacturing or business performance. We wish to address this gap in the context of the sequence of how relationships evolve and their impact (if any) on manufacturing performance. According to Dwyer et al. (1987), relationships evolve through five general phases: (1) Awareness; (2) Exploration; (3) Expansion; (4) Commitment; and (5) Dissolution (which we do not include for the purpose of this study). We now use this framework to develop our conceptual model of SC dynamics as follows. Awareness refers to A’s recognition that B is a feasible exchange partner; however, interaction between the parties has not yet transpired. Exploration refers to the search and trial phase in relational exchange and communication is a key feature of this phase. Communication is “the formal as well as informal sharing of meaningful and timely information between firms” (Anderson and Narus, 1990, p. 44). Frequent and timely communication is important because it assists in resolving disputes and aligning perceptions and expectations (Morgan and Hunt, 1994). Effective communication is therefore essential for successful relationships (Monczka et al., 1995). There are three aspects of communication behaviour that are important in relationships: the quality of the communication, the form of information sharing and
  • 3. IJOPM the extent to which both parties jointly engage in planning and goal setting (Mohr and 25,1 Nevin, 1990). Expansion is the next phase in relationship development and refers to the continual increase in benefits obtained by exchange partners. With increased communication, parties begin to establish trust. Trust has been defined as “the firm’s belief that another company will perform actions that will result in positive actions for the firm, as well as not take unexpected actions that would result in negative outcomes 8 for the firm” (Anderson and Narus, 1990, p. 45). Assuming the relationship continues to develop and communication increases, the level of trust between both parties will grow (Anderson and Narus, 1990). Formally, this gives: H1. Communication has a positive effect on trust. Commitment is the next phase of relationship development and refers to an implicit or explicit pledge of relational continuity between exchange partners. Communication and trust continue to grow during this phase leading to increased co-operation and goal congruence. Increasing levels of trust reduce the specification and monitoring of contracts, reduce uncertainty and provide material incentives for co-operation (Hill, 1990). This co-operation includes collaboration on matters such as quality, product and process design, information systems and value analysis. Once trust is established, both parties learn that coordinated joint efforts will lead to outcomes that exceed what either would have achieved acting independently. This gives: H2. Trust has a positive effect on co-operation. The co-operation which arises from the presence of trust during the expansion phase now begins to impact on the commitment phase. As the relationship develops and expectations continue to grow, the parties begin to bond themselves in such way as to encourage their continued investment in the relationship (i.e. they become committed). Such bonding is frequently by way of exchanging transaction specific investments (or adaptation). Williamson (1983) argues that the exchange of transaction-specific investments (or adaptation) results from both increased trust and co-operation that gives credibility of commitment to the relationship. This gives: H3. Trust has a positive effect on adaptation (transaction-specific investments). H4. Co-operation has a positive effect on adaptation (transaction-specific investments). Adaptation is frequently bilateral in nature. Suppliers adapt to the needs of specific important customers and customers adapt to the capabilities of specific suppliers (Heide and John, 1988). Adaptations are important for a number of reasons. First, they can represent considerable investments by one or both parties. Second, they may be of critical importance for the conduct of business. Third, the investments frequently cannot be transferred to other SC relationships. Adaptations can have significant consequences on the long-term performance because they enhance the competencies and attractiveness of a particular supplier/customer. More specifically, adaptation involving process and product design, value analysis, cost targeting and design of quality control and delivery systems can directly impact on the key manufacturing performance variables: cost, quality, flexibility and delivery (Heide and John, 1990). In other words:
  • 4. H5. Adaptation has a positive effect on cost. Supply chain H6. Adaptation has a positive effect on quality. relationship H7. Adaptation has a positive effect on flexibility. H8. Adaptation has a positive effect on delivery. Figure 1 shows our conceptual model in a structural equation model operationalising 9 framework. Methodology Survey instrument The instrument used to test the stated hypotheses was a mail survey. A questionnaire based on existing five-point Likert measurement scales anchored by strongly agree/disagree for the relationship constructs and superior/inferior to competitors for the performance constructs was initially drafted (see Appendix). While it could be argued that objective scales are more insightful we have used subjective scales because of the multi-sectoral nature of our survey. In addition, firms can be reluctant to disclose exact performance figures (Ward and Duray, 2000); however, managers well-acquainted with performance data can provide an accurate subjective assessment (Choi and Eboch, 1998). Indeed, past research indicates that managerial evaluations correspond closely to objective data obtained from both internal and external sources (Dess and Robinson, 1984; Venkatraman and Ramanujam, 1986). This draft questionnaire then was pre-tested with academics and practitioners to check its content validity and terminology and modified accordingly. The modified questionnaire was then pilot tested to check its suitability and appropriateness for the target population before mailing. To encourage completion, respondents were promised, and received, a summary of the research findings. Two repeat mailings of the instrument were carried out to improve the overall response rate. For the purposes of this study, we adopted the approach used by Sako et al. (1994), where respondents were asked to reply to questions with respect to the basis of the most important or focal customer-product relationship. Figure 1. Structural equation model operationalising framework
  • 5. IJOPM Sample 25,1 The population chosen for this study were manufacturing companies in the electronics sector in the Republic of Ireland. The reasons for focussing on this sector are twofold. First, electronics has emerged as a leading sector in the Republic of Ireland in terms of adopting manufacturing and SC practices and is not subject to the same level of regulation as other comparable sectors such as pharmaceuticals and chemicals 10 (Dicken, 1998). As such, the sector is predominantly influenced by competitive rather than regulatory forces. Second, the sector is heterogeneous in terms of sub-sectors and product/process complexity. In order to establish the size of the survey population, databases from the Irish Trade Board, the National Standards Association of Ireland, the Industrial Development Authority and Kompass Ireland were consulted. This produced an initial listing of 821 companies. Telephone contact was established with each of these companies and the key informant was also identified at this stage. The key informant was identified by enquiring as to which single individual was responsible for, and capable of responding to questions on SC relationships and manufacturing performance. This step was taken in order to improve the quality and quantity of responses as well as to reduce the impact of potential inaccurate recall, hindsight bias and subconscious attempts to maintain self-esteem that can occur from using a single informant (Kumar et al., 1993). From the initial frame of 821 companies, 283 were removed from the sample as they had either gone into liquidation or were service rather than manufacturing plants. Each of the remaining 538 companies was then sent a copy of the questionnaire. A total of 202 questionnaires were returned, of which 200 were usable giving an overall response rate of 38 per cent. Analysis and findings Descriptive statistics The degree to which the sample is representative of the population was addressed by carrying out a series of standard chi-square goodness-of-fit tests with respect to employee numbers, plant ownership and plant age (see Table I). For each of the characteristics, we found no significant difference between the population percentages and the sample percentages. This suggests that the sample response profile is not significantly different from the population profile and that the sample is broadly representative on key variables. The descriptive data collected (plant size, ownership) confirmed much of what is already known about the electronics sector in Ireland in terms of industry structure. On the one hand, the majority of companies are relatively small, independently owned indigenous operations, and, on the other, there are a smaller number of larger plants that are subsidiaries of overseas companies (Table I). Confirmatory factor analysis and structural path model We conducted a single-step analysis with the simultaneous estimation of both measurement and structural models to test our hypotheses using AMOS 4. The covariance matrix used for data input is shown in the Appendix. The overall fit statistics for the model (x2¼ 509.52, df ¼ 516, x2/df¼ 1.72, p , 0.001, GFI ¼ 0.83, AGFI ¼ 0.80, CFI ¼ 0.87, RMSEA ¼ 0.06) demonstrate an acceptable level of overall fit (Bollen, 1989).
  • 6. Characteristic Population (%) Sample (%) x2 Supply chain relationship No. of employees Fewer than 20 21.9 16.5 20 but fewer than 50 41.2 40.0 50 but fewer than 100 15.6 20.5 100 but fewer than 200 11.0 11.5 11 200 or more 10.3 11.5 NS Plant ownership Irish 55.0 52.0 UK 5.0 2.5 Other European 14.0 14.5 USA 20.5 25.0 Japan 2.0 3.5 Other 3.5 2.5 NS Plant age Less than 5 years 10.8 14.0 6 but less than 11 years 18.5 22.0 11 but less than 20 years 47.1 42.0 20 but less than 50 years 21.2 19.0 Table I. 50 years or more 2.4 3.0 NS Population and sample Note: NS ¼ Not significant profiles Confirmatory factor analysis (CFA) was performed to evaluate the measurement properties of the model constructs. The factor loadings (l), standard errors, t-values and Cronbach a values are shown in Table II. All but one of the items (i.e. A1) have high (l . 0.60) and significant (t . 1.96) loadings (Chin, 1998). However, we retain A1, given its centrality to our argument and also that its factor loading (0.50) is reasonably high and also significant ( p , 0.01). In addition, the reliability of each scale was satisfactory with Cronbach a values of at least 0.70 achieved in all cases (Nunally, 1978) (Table II). Table III shows the standardized path estimates (g), standard errors and t-values for the path (structural) model. With the exception of H7 and H8, all path estimates are both high (g . 0.20) and significant (t . 1.96) (Chin, 1998). The results thus provide empirical support for all but two of our hypotheses. We reflect on our findings and their implications in the next section (Table III). Discussion and implications We reflect on our results with regard to first, SC relationship dynamics and second, the effect of SC dynamics on manufacturing performance. Using Dwyer et al.’s (1987) relationship development process incorporating the sequential phases of awareness, exploration, expansion and commitment we developed a conceptual framework which posited that the initial activity in relationship development was communication. As communication grows in terms of frequency and intensity, trust develops which in turn leads to increased co-operation and adaptation. Our analysis provides empirical support for this sequence of events in relationship development. In addition, our results are consistent with previous studies that have focussed on specific phases of relationship development such as communication ! trust (Anderson and Narus, 1990), trust!co-operation (Styles and Ambler, 2000), and co-operation!adaptation
  • 7. IJOPM Construct Standardised loading l Standard error t-value a 25,1 Communication 0.76 CM1* 0.62 CM2 0.65 0.12 7.22 CM3 0.80 0.15 8.18 12 CM4 0.62 0.12 6.97 Trust 0.82 T1* 0.66 T2 0.68 0.12 8.24 T3 0.84 0.14 9.62 T4 0.75 0.11 8.92 Quality 0.78 Q1* 0.68 Q2 0.75 0.17 5.63 Flexibility 0.79 F1* 0.65 F2 0.65 0.38 2.97 Co-operation 0.76 CL1* 0.60 CL2 0.71 0.16 7.14 CL3 0.78 0.16 7.39 CL4 0.60 0.13 6.34 Adaptation 0.78 A1* 0.50 A2 0.82 0.24 6.69 A3 0.63 0.22 5.97 A4 0.77 0.24 6.61 Cost 0.82 C1* 0.59 C2 0.83 0.35 4.43 Delivery 0.75 Table II. D1* 0.99 Confirmatory factor D2 0.84 0.28 2.53 analysis and reliabilities Note: * The corresponding parameter is set to 1 (unstandardised) to fix the scale of measurement Standardized path estimate Standard Hypothesis Path g error t-value Result H1 Communication ! trust 0.79 0.12 6.57 Supported H2 Trust ! co-operation 0.43 0.16 4.34 Supported H3 Trust ! adaptation 0.36 0.10 3.51 Supported H4 Co-operation ! adaptation 0.34 0.06 2.47 Supported H5 Adaptation ! quality 0.61 0.20 4.56 Supported H6 Adaptation ! cost 0.52 0.16 3.63 Supported Table III. H7 Adaptation ! flexibility 0.38 0.14 1.82 Not supported Path model coefficients H8 Adaptation ! delivery 0.13 0.12 1.58 Not supported
  • 8. (Wilson and Mummalaneni, 1986). Our findings are also consistent with those of Supply chain Humphries and Wilding (2003). relationship The managerial implications in respect of SC relationship dynamics are that firms need to identify at what stage they are at in terms of relationship development and act accordingly. As with personal relationships, the process of forming and developing SC relationships can be complex and requires firms to be competent in areas such as negotiation skills, conflict management, anticipating problems/finding solutions in 13 advance and joint problem-solving. Managers can monitor and measure relationship building using a staged model that covers awareness, exploration, expansion and commitment. The selection and training of such managers should be driven by the need to find individuals who not only possess supply chain management skills but also relational competencies and an extensive personal network (or the ability to develop one). Recognising that SC relationships are close, trusting and requiring investment is a different perspective from viewing them for the purposes of manipulation. However, the development and nurturing of these skills is still at a relatively low level in respect of our study population. Indeed, training and development programmes in the electronics sector still focus on skills such as sales training rather than relationship management (Fynes and Ainamo, 1998). Our findings in respect of the relationship between the dynamics of SC relationships and manufacturing performance are more mixed. More specifically, our findings suggest that adaptation (or investment in transaction-specific investments) leads to an improvement in product quality (H5) and a reduction in product cost (H6) but has no effect on flexibility (H7) or delivery performance (H8). In this regard it is useful to think of quality efforts as reducing variance while flexibility efforts accommodate variance (Jayaram et al., 1999). For example, if managing the dynamics of a relationship are focused on achieving very high conformance to specifications, it can be more difficult to achieve performance on flexibility dimensions such as rapidly accommodating a sudden increase in demand (volume flexibility) or frequent or rapid changes in the product mix (variety flexibility). Accommodating these sources of variation can be more challenging when conformance standards are higher and managers are highly focused on achieving them as compared to when quality standards are lower. This could also explain the lack of support for H8 (delivery) as accommodating variance is critical here also. What then are the implications of our study for the understanding manufacturing performance? The nature of the relationship between measures of manufacturing performance has been addressed by two dominant theories: the cumulative or “sandcone” theory and the “trade-off” theory. The “sandcone” theory, as developed by Ferdows and De Meyer (1990) is based on the proposition that competencies are cumulative rather than mutually exclusive. They suggest that lasting improvements in performance always involve the same sequence of first quality improvement, followed by dependability, speed and finally cost efficiency. Our findings provide only partial support for this theory with respect to quality and cost. In contrast, our model is more aligned with Skinner’s (1969) “trade-off” theory. This theory is based on the proposition that trade-offs between measures of manufacturing performance occur because factories are technology-based systems which are limited in what they can do with their equipment, materials, information systems and management systems. As a result, trade-offs will occur. Our findings support this view. In this regard, Skinner (1992, p. 21) argues that trade-offs are dynamic and notes that “in many
  • 9. IJOPM technologically-based relationships between outcomes, generally called trade-offs, the 25,1 variables may run in parallel or counter to one another at different levels of amplitude”. How the changing dynamics of a SC relationship impact on this is an interesting avenue for future research. Finally, the items used to measure adaptation (see Appendix) primarily reflect an emphasis on adapting production processes and tooling to match the requirements of the 14 customer. It is not surprising that that such adaptations lead to improvements by reducing variation and improving quality. As a result, the cost of poor quality is reduced and the firm becomes more cost competitive. On the other hand, it would appear that investment in transaction-specific investments has no effect on flexibility or delivery performance. This may very well be explained by the fact that none of the items used to measure adaptation had an explicit delivery/flexibility dimension. As such, the construct items could be expanded to include such dimensions in future studies. Limitations and future research The limitations associated with this study primarily relate to the use of the focal or “most important” customer and reliance on supplier perceptions of SC relationships. Using the concept of the focal customer has a number of disadvantages. First, the approach assumes the existence of a single focal relationship whereas, in practice, a company may have a selection of equally important customer relationships that influence one another. Second, there might not necessarily be a uniform perception within a supplier company as to which customer is the most important. Hence, using a single key informant could produce some bias in our study. Third, respondents were not given a specific set of criteria on which to base their selection of a focal customer relationship in our study; instead, they were asked to respond with respect to their “most important customer relationship”. This approach was adopted so as to facilitate a flexible interpretation of the term “most important”. Equally, however, such flexibility can give rise to bias if respondents used a wide variety of ways to define “most important”. Relying on supplier perceptions of analysis is also a limitation. It can be argued that the perceptions of relationship in our study are somewhat one-sided in that they represent the views of just one party and ignore the views of customers. This limitation implicitly suggests a significantly different research design based on the relationship dyad (in itself, not without difficulties in terms of sample size, dyad access, confidentiality and accuracy of response). As is often the case, longitudinal research could provide valuable contributions to theory development and refinement in the fields of SC relationships. There is a significant temporal dimension to how buyer-seller relationships develop. Accordingly, tracking the development of SC relationships could help clarify cause and effect relationships between variables. A research design, described by Anderson (1995) as “cross-sectional research which is longitudinal in character” presents a potentially interesting approach to data collection in this regard. This involves identifying critical indicators of each stage of SC relationship development from a set of relationship dyads at pre-ordained points in time. These critical indicators could then be used as a basis on which to separate the relationships into those that are at similar stages of development. These sub-samples could then be analysed separately, and the effects of temporal constructs assessed empirically. Empirical studies of the customer’s perspective would complement and add to the findings of this study. Finally, future
  • 10. research could examine issues such as customer perceptions of the dimensions of Supply chain nature of SC relationships and manufacturing performance. relationship References Ahmed, N.U., Montago, R.V. and Firenze, R.J. (1996), “Operations strategy and organisational 15 performance: an empirical study”, International Journal of Operations & Production Management, Vol. 16 No. 5, pp. 41-53. Anderson, J.C. (1995), “Relationships in business markets: exchange episodes, value creation and their empirical assessment”, Journal of Academy of Marketing Science, Vol. 29 No. 4, pp. 346-50. Anderson, J.C. and Narus, J.A. (1990), “A model of distributor firm and manufacturer firm working partnerships”, Journal of Marketing, Vol. 54 No. 1, pp. 42-58. Bollen, K.A. (1989), Structural Equations with Latent Variables, John Wiley & Sons, New York, NY. Chin, W.W. (1998), “Issues and opinion on structural equation modelling”, MIS Quarterly, Vol. 22 No. 1, pp. 7-16. Choi, T.Y. and Eboch, K. (1998), “The TQM paradox: relations among TQM practices, plant performance, and customer satisfaction”, Journal of Operations Management, Vol. 17 No. 1, pp. 59-75. Cua, K.O., McKone, K.E. and Schroeder, R.G. (2001), “Relationships between implementation of TQM, JIT, and TPM and manufacturing performance”, Journal of Operations Management, Vol. 19 No. 6, pp. 675-94. Dess, G.S. and Robinson, R.B. (1984), “Measuring organizational performance in the absence of objective measures”, Strategic Management Research, Vol. 5 No. 3, pp. 265-73. Dicken, P. (1998), Global Shift: Transforming the World Economy, Paul Chapman Publishing, London. Dixon, J.R. (1992), “Measuring manufacturing flexibility: an empirical investigation”, European Journal of Operational Research, Vol. 60 No. 2, pp. 131-43. Dwyer, F.R., Schurr, P.H. and Oh, S. (1987), “Developing buyer-seller relationships”, Journal of Marketing, Vol. 51 No. 2, pp. 11-27. Ferdows, K. and de Meyer, A. (1990), “Lasting improvements in manufacturing performance: in search of a new theory”, Journal of Operations Management, Vol. 9 No. 2, pp. 168-84. Flynn, B.B., Schroeder, R.G. and Flynn, E.J. (1999), “World class manufacturing: an investigation of Hayes and Wheelwright’s Foundation”, Journal of Operations Management, Vol. 17 No. 3, pp. 249-69. Fynes, B. and Ainamo, A. (1998), “Organisational learning and lean supply: the case of Apple Ireland and its suppliers”, Supply Chain Management: An International Journal, Vol. 3 No. 2, pp. 96-107. Fynes, B. and Voss, C. (2001), “A path analytic model of quality practices, quality performance and business performance”, Production and Operations Management, Vol. 10 No. 4, pp. 494-513. Heide, J.B. and John, G. (1988), “The role of dependence balancing in safeguarding transaction-specific assets in conventional channels”, Journal of Marketing, Vol. 52 No. 1, pp. 20-35.
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  • 12. Voss, C., Blackmon, K., Hanson, P. and Oak, B. (1995), “The competitiveness of European Supply chain manufacturing – a four-country study”, Business Strategy Review, Vol. 6 No. 1, pp. 1-25. Ward, P.T. and Duray, R. (2000), “Manufacturing strategy in context: environment, competitive relationship strategy and manufacturing strategy”, Journal of Operations Management, Vol. 18 No. 3, pp. 123-38. Williamson, O.E. (1983), “Credible commitments: using hostages to support exchanges”, American Economics Review, Vol. 73 No. 4, pp. 519-40. 17 Wilson, D.T. and Mummalaneni, V. (1986), “Bonding and commitment in buyer-seller relationships: a preliminary conceptualisation”, Industrial Marketing & Purchasing, Vol. 1 No. 3, pp. 44-58. Appendix Construct measures and sources (anchored by a strongly agree/disagree five-point scale). Please also see Table AI for sample covariances (estimates): (1) Quality performance (customer satisfaction) (Voss and Blackmon, 1994): . Q1 – Frequency of customer complaints. . Q2 – Adequacy of customer complaint tracking/feedback systems. (2) Delivery performance (Choi and Eboch, 1998): . D1 – Speed of delivery relative to competitors. . D2 – Percentage of orders delivered on-time. (3) Cost performance (Fynes and Voss, 2001): . C1 – Unit cost of product relative to competitors. . C2 – Unit cost of product over life cycle. (4) Flexibility performance (Dixon, 1992): . F1 – Volume flexibility. . F2 – Variety (product line) flexibility. (5) Communication (Heide and John, 1992): . CM1 – Exchange of information in this relationship takes place frequently and informally, and not only according to a pre-specified agreement. . CM2 – In this relationship, any information that might help the other party will be provided for them. . CM3 – Both parties in the relationship will provide proprietary information if it can help the other party. . CM4 – Both parties keep each other informed about events or changes that may affect the other party. (6) Co-operation (Morgan and Hunt, 1994): . CL1 – We co-operate extensively with this customer with respect to product design. . CL2 – We co-operate extensively with this customer with respect to process design. . CL3 – We co-operate extensively with this customer with respect to forecasting and production planning. . CL4 – We co-operate extensively with this customer with respect to quality practices.
  • 13. 18 25,1 estimates IJOPM Table AI. Sample covariances – C1 C2 F1 F2 D1 D2 Q1 Q2 CM1 CM2 CM3 CM4 A1 A2 A3 A4 T1 T2 T3 T4 CL1 CL2 CL3 CL4 C1 0.91 C2 0.48 1.06 F1 0.19 0.12 0.98 F2 0.08 0.12 0.36 0.77 D1 0.08 0.05 0.07 0.11 0.41 D2 0.12 0.07 0.08 0.16 0.41 0.59 Q1 0.25 0.51 0.13 0.05 0.02 0.04 0.97 Q2 0.46 0.52 0.06 0.11 -0.02 0.05 0.58 1.35 CM1 0.19 0.22 0.23 0.13 0.07 0.09 0.09 0.14 0.73 CM2 0.11 0.14 0.16 0.1 0.06 0.03 0.05 0.09 0.22 0.46 CM3 0.24 0.3 0.26 0.12 0.01 0 0.15 0.25 0.35 0.29 0.63 CM4 0.13 0.22 0.17 0.1 0.08 0.06 0.15 0.07 0.23 0.17 0.27 0.49 A1 0.23 0.21 0.28 0.21 0.1 0.1 0.15 0.19 0.27 0.16 0.22 0.09 1.07 A2 0.19 0.28 0.14 0.15 0.05 0.06 0.32 0.31 0.1 0.15 0.23 0.12 0.36 0.99 A3 0.25 0.36 0.16 0.07 -0.02 0.01 0.36 0.35 0.16 0.09 0.25 0.1 0.49 0.51 1.11 A4 0.12 0.27 0.11 0.15 0.04 0.06 0.33 0.38 0.08 0.12 0.21 0.09 0.36 0.75 0.48 1.05 T1 0.07 0.14 0.23 0.15 0.11 0.1 -0.01 0.03 0.19 0.17 0.22 0.18 0.18 0.11 0.11 0.12 0.66 T2 0.18 0.27 0.26 0.13 0.01 0.03 0.14 0.16 0.23 0.18 0.3 0.2 0.26 0.18 0.3 0.16 0.24 0.59 T3 0.09 0.21 0.3 0.2 0.06 0.08 0.12 0.17 0.27 0.23 0.3 0.25 0.22 0.18 0.24 0.2 0.43 0.38 0.7 T4 0.12 0.2 0.21 0.12 0.06 0.08 0.14 0.19 0.23 0.23 0.29 0.2 0.23 0.18 0.17 0.21 0.31 0.26 0.39 0.55 CL1 0.12 0.18 0.29 0.27 0.06 0.08 0.05 0.12 0.28 0.15 0.26 0.23 0.39 0.24 0.25 0.18 0.25 0.2 0.24 0.3 2.15 CL2 0.06 0.18 0.21 0.13 -0.04 -0.03 0.09 0.12 0.28 0.23 0.29 0.3 0.26 0.35 0.32 0.21 0.14 0.22 0.18 0.18 0.89 1.89 CL3 0.22 0.33 0.36 0.28 -0.05 -0.02 0.19 0.2 0.3 0.2 0.35 0.25 0.3 0.34 0.27 0.24 0.14 0.35 0.24 0.24 0.77 1.06 1.7 CL4 0.26 0.26 0.23 0.19 -0.03 -0.03 0.17 0.19 0.24 0.12 0.16 0.26 0.23 0.29 0.19 0.12 0.23 0.23 0.17 0.19 0.84 0.6 0.8 1.58
  • 14. (7) Adaptation (Heide and John, 1992): Supply chain . A1 – Gearing up to deal with this customer requires highly specialised tools and relationship equipment. . A2 – Our production system has been tailored to meet the requirement of this customer. . A3 – We have made significant investments in tooling and equipment that are dedicated to our relationship with this customer. 19 . A4 – Our production system has been tailored to produce the items supplied to this customer. (8) Trust (Larzelere and Huston, 1980): . T1 – Based on your past and present experience, how would you characterise the level of trust your firm has in its working relationship with this customer. . T2 – We feel that this customer can be counted on to help us. . T3 – We feel that we can trust this customer completely. . T4 – This customer has a high level of integrity.