Soroosh Saghiri
email: s.saghiri@kingston.ac.uk

A structural approach to assessing postponement strategies: construct development and validation

Keywords: POSTPONEMENT, OPERATIONS CONSTRUCT DEVELOPMENT, CONFIRMATORY FACTOR postponement, shipment postponement, manufacturing postponement, ordering/purchasing postponement, design postponement, measured variables

This paper endeavours to introduce and validate constructs and measured variables for postponement strategies. Although empirical researchers have examined postponement, a consistent set of valid, reliable factors has not been developed and used. The lack of valid construct is a barrier to hypothesis testing and meta-analysis on postponement. Using confirmatory factor analysis (CFA), the validity and reliability of the proposed postponement constructs are examined. This is performed through a pilot study and a large scale survey on a sample of 219 manufacturing firms which represent a wide range of manufacturing operations. The outcomes of this paper establish a set of variables which can measure shipment, manufacturing, purchasing and design postponements.

A structural approach to assessing postponement strategies: construct development and validation

Introduction

Postponement has been recognised as an operations strategy in engineering, logistics and operations management bodies of knowledge 'to resolve issues arising from operating in a mass customised market' [START_REF] Ismail | A balanced approach to building agile supply chains[END_REF]. Postponement tries to manage risks and uncertainties in demand by delaying value-adding activities throughout the supply chain [START_REF] Saghiri | Time Postponement: Modified Models to Improve Delivery Lead Time[END_REF]. The delay can happen in the sequence (form), time or place of activities (Yang et al. 2004a;[START_REF] Wong | Analysis of form postponement based on optimal positioning of the differentiation point and stocking decisions[END_REF]. The literature contains diverse perspectives on the postponement theories and applications.

There, however, is a considerable gap in measuring (measurement instruments for) postponement. In general, measurement is crucial in strategy development efforts.

Measuring the level at which a postponement strategy is employed facilitates the [START_REF] Davila | An empirical test of inventory, service and cost benefits from a postponement strategy[END_REF], inventory cost [START_REF] Aviv | Capacitated multi-item inventory systems with random and seasonally fluctuating demands: implications for postponement strategies[END_REF], delivery lead time [START_REF] Skipworth | Implications of form postponement to manufacturing a customized product[END_REF], supply chain flexibility [START_REF] Nair | Linking manufacturing postponement, centralized distribution and value chain flexibility with performance[END_REF], responsiveness [START_REF] Forza | Form postponement effects on operational performance: a typological theory[END_REF], and cost [START_REF] Davila | An empirical test of inventory, service and cost benefits from a postponement strategy[END_REF]. By measuring postponement, its benefits/costs can be assessed and analysed in more detail.

Moreover, various enablers and barriers may affect the success of a postponement strategy. To find their impacts, the employed postponement strategy should first be understood and measured properly. Factors such as product modularity (Yang et al., 2004b), supply chain structure (Yeung et al., 2007), and market volatility [START_REF] Rabinovich | Postponement Effects on Inventory Performance and the Impact of Information Systems[END_REF] has an impact on postponement implementation. These impacts can be assessed in depth if the level of postponement can properly be measurable. This depends greatly on a valid, reliable measurement system for postponement. There are studies which indicate, for example, product modularity or process re-organisation as enablers of postponement (Yang et al., 2004a;[START_REF] Forza | Form postponement effects on operational performance: a typological theory[END_REF]. They, however, fail to evaluate the effect of enablers as they cannot measure postponement implementation. Despite numerous studies on different aspects of postponement theories and applications, limited measured variables have been developed to assess postponement. [START_REF] Li | Development and validation of a measurement instrument for studying supply chain management practices[END_REF] have developed general measured variables for postponement such as 'product modular design', 'products customisation in distribution centres', 'assemble to order system', and 'delayed assembly to downstream stages of the supply chain'. 3 [START_REF] Rabinovich | Postponement Effects on Inventory Performance and the Impact of Information Systems[END_REF] introduce two constructs of 'Reliance of time postponement' and 'Reliance of form postponement' where evaluation of them has not been well clarified. In [START_REF] Nair | Linking manufacturing postponement, centralized distribution and value chain flexibility with performance[END_REF], manufacturing postponement is addressed by a single variable (i.e. the programmes developed to facilitate postponement of the final product manufacturing, assembly, packaging or labelling), which does not measure all aspects of postponement. [START_REF] Davila | An empirical test of inventory, service and cost benefits from a postponement strategy[END_REF] measure the level of postponement by the percentage of generic products shipped across the supply chain, which is a relevant but not sufficient metric. Finally, [START_REF] Tang | A Fuzzy-GA Decision Support System for Enhancing Postponement Strategies in Supply Chain Management[END_REF] address postponement measurement. However, they only link some operational performance indicators (e.g. inventory holding) to the rate of postponement, where the rate is addressed very vaguely.

The studies reviewed above have a very narrow definition of the postponement constructs and their measured variables. They mostly focus on postponement enablers (e.g. modular design or common components) rather than purely on postponement implementation and performance. This paper tries to develop a measurement instrument for postponement. It identifies and validates postponement constructs and measured variables for each.

The rest of this paper is organised as follows. Section 2 explains the methods of identification and confirmation of the postponement constructs. Section 3 introduces the postponement constructs and their measured variables recommended by this research. Results of the pilot study are presented in section 4, and outcomes of the confirmatory factor analysis on the large scale study are discussed in section 5. Finally,

Methods

Latent variable (LV) approach

The research constructs in many fields of study are often not directly measurable or observable. Those constructs are called 'latent variables' [START_REF] Bagozzi | A prospectus for theory construction in marketing[END_REF]. The latent variable approach is appropriate for this research, as the postponement constructs discussed in this study can be represented by a variety of variables. The literature of postponement has highlighted its multidimensionality. Academicians and practitioners who participated in this research also agree on the suitability of the latent variable approach for this research (see the pilot study in section 4).

Construct and scale development

This research pursues the construct and scale development process introduced and tried out in the literature (see for example Churchill [START_REF] Jr | A paradigm for developing better measures of marketing constructs[END_REF] with the following stages:

• Stage 1: Identification of the constructs and definition of their measured variables.

• Stage 2: Content validity analysis with expert judges.

• Stage 3: Scale purification and assessment of convergent and discriminant validity through the pilot study (Q-sort analysis).

• Stage 4: Assessment of convergent and discriminant validity and unidimensionality through the large-scale study and confirmatory factor analysis. research identifies an initial list of measured variables (items) for each construct. In total, 28 measured variables and 4 constructs have been generated with 7 measured variables for each postponement construct. Each construct and its associated variables are discussed and initially validated with details (see section 3). This will be a basis for confirming the construct content validity.

After the definition of the constructs and their variables, the validation process is carried out in stages 2 to 4. Validation refers to the extent to which a construct, which is not observable, is reflected by a variable(s) that measures or manipulates that construct [START_REF] Hair | Multivariate Data Analysis[END_REF]. In stage 2, the content validity is assessed by practitioners and academicians. First, through a group interview and discussion, the definition of the constructs used in this research is presented to five consultants and managers who all agreed to attend a joint meeting. The practitioners are all experts in the areas of strategy, production, logistics, purchasing, and product design. They are selected from the firms in, or related to, the sample frame of the large-scale survey (see section 2.3).

Following the explanation of the basics of the study and research constructs, initial variables of each postponement construct have been introduced and discussed.

Variables that are not agreed to belong to the domain of each construct are removed, new variables are recommended and other items are modified if required. The major changes are also double-checked with the managers involved earlier in the content validation.

In stage 3, the convergent and discriminant validity of the postponement constructs are assessed through the pilot study and the Q-sort method [START_REF] Nahm | The Q-sort method: Assessing reliability and construct validity of questionnaire items at a pretesting stage[END_REF]. The Qsort method is an iterative process for manual factor sorting by judges. The degree of judges' agreement on the variables allocated to each construct, forms the basis of assessing construct validity. In each iteration, the constructs' variable pool is provided to two judges who are chosen from the managerial level of companies of the sample frame or from experts who work with the sample frame companies. Cohen's Kappa [START_REF] Cohen | A Coefficient of Agreement for Nominal Scales[END_REF] and Moor and Benbastas hits ratio (Moor and Benbastas, 1991). Cohen's Kappa is measured as the agreement proportion of joint judgment after the chance agreement is excluded from it. Cohen's Kappa is calculated as (Σ i P ii -Σ i P i+ P +i )/(1-Σ i P i+ P +i ) where P ij is the percentage of agreement/disagreement between two judges (see [START_REF] Nahm | The Q-sort method: Assessing reliability and construct validity of questionnaire items at a pretesting stage[END_REF] for the details). The value of Kappa is between zero and one. Values between 0.40 and 0.75 are usually recognised as fair to good, and values more than 0.76 show an excellent degree of agreement [START_REF] Landis | The measurement of observer agreement for categorical data[END_REF]Moor and Benbastas, 1991). The hit ratio in Moor and Benbastas method is calculated as Σ i P ii /Σ ij P ij where P ij are agreed/disagreed items and P ii are agreed items (see Nahm et al., 2002, for details). Hit ratio is good if it is more than 65%. [START_REF] Sethi | Development of Measures to Assess the Extent to Which an Information Technology Application Provides Competitive Advantage[END_REF]. At the end of each iteration, details of judgments are discussed and necessary changes (i.e. deletion of measured variable or change of wording) are made.

The iterative process is carried out until the variables allocated to the constructs are all agreed -i.e. good Cohen's Kappa and hit ratio are achieved. Details of application and results of the Q-sort method are discussed in section 4.

In stage 4, the large-scale study is conducted by means of confirmatory factor analysis (CFA) to analyse the convergent and discriminant validity. There is a general understanding in the literature that the construct validity largely depends on convergent and discriminant validity and constructs unidimensionality (Carmines and , 1979). CFA is used based on the strong theory provided by the literature, which supports the postponement constructs and their associated measured variables. CFA is recognised as a useful tool with high capability to examine the construct validity and proposed measured variables [START_REF] Campbell | Convergent and discriminant validity by the multitrait-multimethod matrix[END_REF][START_REF] Hair | Multivariate Data Analysis[END_REF].

Data required by CFA in the large-scale study is collected via 219 usable questionnaires (details are discussed in section 2.3). Considering the medium size of the sample, CFA in this study uses the partial least square (PLS) method [START_REF] Tenenhaus | PLS path modeling[END_REF]. Convergent validity of the postponement constructs are assessed by separate LV1 LV1 1 models, and unidimensionality and discriminant validities are measured in paired LV1 LV2 models (see Tenenhaus et al. 2005 for details). Figure 1 shows the research structure and the main outcomes of each stage.

Sampling process and questionnaire design

The sample frame for the large-scale study consists of 593 manufacturers (suppliers) of a wide range of automotive parts and components -the unit of study is the individual firm/company. According to the ISIC Rev. 3.1 2 (UN, 2009), the sample frame covers divisions 17, 25, 26, 27, 28, 31, 32, and 34 of section 'D' 3 . All companies in the sample frame are small and medium-sized companies. Although the sample frame is limited to automotive suppliers, it represents the majority of manufacturing industries.

The target respondents in the firms (i.e. units of study in the sample frame) are senior or middle managers in the areas of operations, purchasing, logistics, quality, production or strategic management. Beside their job position, it is assured that they have both theoretical and practical knowledge of their job as well as the context of the current study (i.e. operations strategy and postponement).

After three rounds of attempts 4 (including reminder emails, letters and telephone contacts), 219 usable questionnaires have been collected. 58 usable questionnaires have been returned after the first round, 92 usable questionnaires have been returned

after the second round, and 69 usable questionnaires have been returned after the third attempt. This indicates a response rate of 36%, which is satisfactory and comparable with similar studies (for example, Das, 2001: 19%;Koste et al., 2004: 11.5%).

Moreover, Power analysis, as the most accurate way to identify the sample size and its power, is also employed [START_REF] Mcquitty | Statistical power and structural equation models in business research[END_REF][START_REF] Faul | G*Power[END_REF]. The sample size of 219 and α = 0.05 in a two-tail assessment have resulted in an extremely high power of 0.99 for a medium effect size (r = 0.30) and 0.85 (high) and 0. Possible differences among different groups of respondents are also assessed.

Usable responses are divided based on two criteria: (i) response mode/channel (manual/hard copy vs. electronic/email) and (ii) the industry group to which a firm belongs (ISIC Group). The t-test for difference between the two samples indicates no significant difference between postal/ hard copy and electronic responses at α = 0.05 level. Responses of the firms, which belong to different industry groups are also classified into eight groups (ISIC groups: 17,25,26,27,28,31,32,and 34). The difference among those responses is assessed by ANOVA test. The results indicate no significant difference among the groups. The results are available from the author.

The questionnaires developed for the large-scale study mainly address the postponements' measured variables (see Table 2) and try to find the application level of the measured variables of the postponement constructs 5 . Questions are grouped into four sets which represent the four postponement constructs. Each question addresses 5 . Details of the questionnaire can be collected from the author. The questions try to quantify the measured variables in different ways, such as using the five-point Likert scale [START_REF] Sethi | Development of Measures to Assess the Extent to Which an Information Technology Application Provides Competitive Advantage[END_REF]. The Likert scale assures the reliability of the questionnaire very well [START_REF] Mitchell | Assessing the Reliability and Validity of Questionnaires: An Empirical Example[END_REF]. Reliability is also examined through the test re-test method [START_REF] Bracken | State of the art procedures for translating, validating and using psycho educational tests in cross cultural assessment[END_REF] which is applied to a small group of randomly selected respondents. The answers are coded as '1' to '5' where '1'

F

and '5' indicate the lowest and highest level of a postponement measure respectively.

To assess the questionnaire validity, it has been pre-tested and reviewed by three academicians to discover any ambiguity in the questions, possible complexity in understanding the questions, problems regarding logical sequences or relations between questions, the time requirements to complete the questionnaire, comments on the contents, and comments on the structure of the questionnaire.

Postponement: diverse types and classifications

Various types of postponement have been defined and addressed in the literature.

Having a review of the postponement definitions in literature (see for example taxonomical studies by [START_REF] Van Hoek | Reconfiguring the supply chain to implement postponed manufacturing[END_REF]Yang et al., 2004b), postponement typologies can be classified into three groups: whether it is about product differentiation or non-differentiation activities. Form postponement is mainly about moving the activities towards supply chain downstream [START_REF] Forza | Form postponement effects on operational performance: a typological theory[END_REF][START_REF] Shao | Evaluation of postponement strategies in mass customization with service guarantees[END_REF], which is not necessarily linked to time and Lee means moving the customer order decoupling point [START_REF] Sharman | The rediscovery of logistics[END_REF] closer to the customer. By logistics postponement, [START_REF] Lee | Postponement for mass customization: Satisfying customer demands for tailor-made products[END_REF] implies doing some operations further along the supply chain. And by form postponement, he means changing the sequence of activities which form the product and/or move the differentiation activities towards supply chain downstream. Form postponement has been defined similarly in other studies (for example see Skipworth and[START_REF] Skipworth | Implications of form postponement to manufacturing: a case study[END_REF][START_REF] Forza | Form postponement effects on operational performance: a typological theory[END_REF]).

Lee's (1998) definition of logistics postponement is mainly linked with moving the place of one or some operations. In that respect, the concept of time postponement has been missed in his classification. In many cases, time postponement is not linked with studies have addressed postponement in production processes (e.g. [START_REF] Waller | Postponement, product customization, and market-oriented supply chain management[END_REF]. Zinn and Bowersox (1988) provides a good explanation of postponement in production processes and discuss that postponement may happen at labelling, packaging, assembly and manufacturing stages. This classification of postponement is restricted to the in-house production processes. Even in the context of production processes, although this classification comprises the main operations, other activities of a production system such as storage and transport could have been considered for This classification is useful as it clarifies the type of value-adding activity which is delayed. However, it does not explain whether the postponement is in time, form or place. Figure 2 summarises the literature of this classification.

<Insert Figure 2

here>

In the third type of classification, postponements are categorised based upon the number of products covered by the postponement. While all products are covered by the postponement strategy, it is called full postponement. However, when only some products or parts of a product demand are postponed, it is called 'partial' or 'hybrid postponement' [START_REF] Graman | Implementation issues influencing the decision to adopt postponement[END_REF]. [START_REF] Lee | Postponement for mass customization: Satisfying customer demands for tailor-made products[END_REF] uses the same labels ('partial' and 'full') for different types of postponement. Although this classification is not comprehensive, it may help define postponement measured variables.

Postponement constructs and initial measured variables

As reviewed earlier, there are very few measured variables recommended and developed for postponement in the literature. Even those mentioned by the literature are mostly vague and inaccurate. As postponement is about delaying activities before anything else, the first measured variable of postponement is the length of time of the postponement. The more the activity is delayed in terms of time, the higher the level at which the postponement strategy is implemented. The impact of the time length of postponement and its effect on cost/benefit of postponement strategy has been outlined by [START_REF] Lee | Modelling the Costs and Benefits of Delayed Product Differentiation[END_REF]. This measure is shown by l in Figure 6. So when l=0, no postponement

happens. An activity can be delayed by the time the real customer order arrives. This will be an upper limit for l. To reflect postponement status in a firm as a whole, rather than for individual activity or product, the average of delays in postponements which are applied to activities/products are considered as a measured variable.

The second suggested variable is the number or share of activities which are delayed. The higher the number of delayed activities (compared to the total number of actives for a particular operation), the more the postponement strategy is put into operation. This variable is shown by n/N, where n is the number of delayed activities and 'N' is the total number of activities. For instance, when out of ten main purchasing For the fourth variable, the value of, or the value added by, the postponed activities is focused upon [START_REF] Lee | Modelling the Costs and Benefits of Delayed Product Differentiation[END_REF]. The more the adding value is delayed the higher the level of postponement is employed. The fourth variable is defined based on the similar rationale provided for the third variable, where instead of time, the value added by activities is considered. This variable is shown by v in Figure 6.

The fifth suggested variable is the percentage or share of products which follow the postponement strategy. The higher the number of products included in postponement strategy, the more the postponement strategy is deployed in an operations system. This The seventh recommended variable refers to the deployment of the postponement strategy in the planning activities. The more the postponement is considered in the firm's planning, the higher the level of postponement implementation is expected. The highest level would apparently be when postponement is maintained in the strategic planning of a firm. Figure 6 summarises the recommended measure variables for postponement.

<Insert Figure 6 here>

Based upon the seven measured variables developed for postponement in general, the measured variables for each type of postponement are identified in Table 2.

<Insert Table 2 here>

Pilot Study

In this section, the measured variables suggested for the postponement constructs (Table 2) are initially assessed by a pilot study for convergent and discriminant validity. As explained earlier in section 2.2, Q-sort method is used to pre-assess the convergent and discriminant validity. In the first round of the pilot study, following the Q-sort method, the overall hits ratio of 68%, the inter-judge raw agreement ratio of 79%, and the Cohen's Kappa score of 73% have been achieved, which are all moderate or good. In order to improve the hits ratio and agreement scores, off-diagonal entries in the Q-sort matrices are reviewed. Feedback from the two first-round judges is also considered. As a result, two variables from DP (DP6 and DP7), two variables from OP (OP6 and OP7), one variable from MP (MP7), and one variable from SP (SP7) have been removed. The modified variables are then entered into the second round.

Two different judges participate in the second round. The results have shown a good improvement in overall hits ratio. At the end of the second round, the overall hits ratio is 95%, the inter-judge raw agreement ratio is 91%, and the Cohen's Kappa score is 88%. All the ratios are interpreted as a good level of agreement. To improve the ratios, the feedback of the judges in the second round has been discussed and the modified set of variables has been entered into the third round (MP6 and SP6 have been dropped). Two new judges take part in the third round. They receive a brief clarification on variables and details of their definition. At the end of round three, the overall hits ratio is improved to 98%, the inter-judge raw agreement ratio is 95%, and For example, finding the number of customers to whom postponed manufacturing is applied changes continuously. Similarly, understanding how postponement as a strategy is deployed across the firm and affects its plans is almost impossible (at least in the sample frame of this research).

<Insert Table 3 here> 5. Confirmatory factor analysis

Convergent validity and reliability analysis

This section runs the CFA process for each construct and its associated variables. An LV1 LV1 measurement model is developed for each construct, measured variables' loadings are calculated by PLS, and convergent validity is discussed accordingly. In the case of low loading, i.e. less than 0.7, the measured variable with low loading is reviewed with one academician and one practitioner (from those people who have been involved in the pilot study). Then, the measured variable with low loading is deleted (after agreeing on it), and the CFA process is run again for the construct. This iterative process is carried out until the construct with all significant loadings (≥ 0.7) results.

Cronbach's Alpha measures are also calculated to measure the constructs' reliability.

CFA: design and development postponement (DP)

The CFA results for the product design postponement The CFA results for the ordering/purchasing postponement (OP) construct indicates high loading for all measured variables except one which is 0.388 for OP4 (i.e. The amount of ordering work -in terms of value it makes/adds, which is postponed, compared to the total value made in ordering). After discussion on this measured variable, it has been found that the variable is very difficult to understand and measure.

Moreover, some aspects of it exist in OP3 (i.e. the amount of ordering work in terms of time, which is postponed, compared to the total time spent in ordering). So, by removing this variable, major theoretical aspects of the OP construct are still kept.

Thus, it has been decided to delete OP4. This improves the convergent validity of OP.

The second round of CFA indicates high loadings for all measured variables. Hence, the OP convergent validity is confirmed. Two rounds of CFA are shown in Discriminant validity is tested by comparing the square of the correlation between two constructs and the average variance extracted (AVE) of each construct [START_REF] Chin | Structural equation modeling analysis with small samples using partial least squares. Statistical strategies for small sample research[END_REF]. If the variance extracted is higher than the squared correlation, the discriminant validity test is passed. Moreover, unidimensionality is assessed through cross-loadings analysis. A high cross-loading may indicate that a measured variable does not belong to the construct which it is supposed to. To make those assessments, all research constructs and their variables, which have been confirmed earlier, pass a dual comparison process. The process consists of forming an LV1 LV2 path model and calculating AVE, correlation and cross-loadings. Table 8 and Table 9 show the results. The path model diagrams are also provided in Appendix A. As indicated in Table 8, all AVEs are higher than the associated correlations. Beside, no significant cross-loading is found in Table 9 which confirms the constructs' unidimensionality.

<Insert Table 8 here> <Insert Table 9 here>

Implication of the results

The confirmatory factor analysis arrives at a list of validated measured variables for the postponement constructs identified by this research. There are some variables which have been dropped during the pilot study and the confirmatory factor analysis.

These final variables are not necessarily exclusive, but reflect postponement measures to a reliable extent. The results indicate that all four postponement constructs are measured by the duration of the delay, durations of the delayed activity, share of the delayed activities (compared to all activities), and share of the products postponement applied to them (compared to all the firm's products). For the manufacturing postponement, value of the delayed activity should also be added to the measured variables. This has been highlighted (not been dropped) for the manufacturing postponement as there is not necessarily a direct link between the duration and the value of an activity in manufacturing processes. Moreover, measuring the value of manufacturing activities is much easier than design, ordering or shipment activities.

Thus, when the postponement is considered at the activity level in design, ordering/ purchasing or shipment operations, it can be measured by two key variables: duration of the delay and duration of the delayed activity. So the postponement level of a product design activity, as an example, depends on how lengthy the activity is and how long it is delayed. For manufacturing postponement, value of the delayed activity should also be considered. To measure postponement at a higher level, which includes all activities of a specific stage of the value chain (e.g. product design or shipment), share of the delayed activities is also considered as a postponement measured variable.

Finally, to expand the postponement measurement instrument to a firm-wide scale,

Discussion and Conclusions

The main objective of this research was to develop valid and reliable constructs for postponement. Four domains of postponement have been identified based on the literature review. Seven measured variables have been developed for each construct and their validity and reliability have been tested systematically. The content validity, reliability, unidimensionality, convergent validity and discriminant validity, have been assessed through a pilot study and a large-scale study using Q-sort method and confirmatory factor analysis.

Based on the results, the four postponement constructs -i.e. shipment (SP), Many empirical studies of postponement have addressed indirect measures for postponement (as shown in Table 1). [START_REF] Davila | An empirical test of inventory, service and cost benefits from a postponement strategy[END_REF] or impacts of, postponement (for example [START_REF] Nair | Linking manufacturing postponement, centralized distribution and value chain flexibility with performance[END_REF]Yang et al., 2005;[START_REF] Davila | An empirical test of inventory, service and cost benefits from a postponement strategy[END_REF][START_REF] Shao | Evaluation of postponement strategies in mass customization with service guarantees[END_REF], there are extremely limited measures developed or suggested for postponement [START_REF] Rabinovich | Postponement Effects on Inventory Performance and the Impact of Information Systems[END_REF][START_REF] Nair | Linking manufacturing postponement, centralized distribution and value chain flexibility with performance[END_REF]. Even the few suggested measures for postponement are very rough and limited. By developing and validating postponement constructs and their measured variables, this paper has contributed significantly to the postponement practical body of knowledge. When a postponement strategy is measureable, its link with performance factors on which it may have impact (e.g. flexibility) can be examined more efficiently. For example, in an E-tailing system, shipment to the customer's home address is postponed until the customer's online order arrives. To find the impact of postponement on the inventory cost in the distribution centres and retail shops, the postponement level should be measured. The measurement instrument developed in this paper can identify the extent to which shipment postponement is employed. It measures the shipment duration, the time length of the delay in shipment, the shipment activities which are delayed and the products the postponement applies to them. By measuring those variables, the level of postponement is determined more accurately. Then, the link between shipment postponement and inventory cost can be evaluated analytically and in more detail.

Considering the research findings and limitations, there are opportunities for research in future. This research has arrived at some measured variables for four postponement constructs. The measured variables are the result of a validation process which has been conducted on the initial list of variables ( ). OP2: The percentage of ordering activities which are postponed. OP3: The amount of ordering work (in terms of time) which is postponed (compared to the total time spent in ordering operations). OP4: The amount of ordering work (in terms of value it makes/adds) which is postponed (compared to the total value made in ordering operations). OP5: Percentage of products, postponement applies to their ordering operations. OP6: Percentage of customers for whom ordering postponement is applied to their product(s).

Ordering postponement (OP):

OP7: Level of deployment of postponement in the firm's ordering planning tasks. MP1: Average postponement time in manufacturing (the postponement time varies between the (no delay (lowest postponement level), and delay until receiving customer order (highest postponement level)). MP2: The percentage of manufacturing processes which is postponed. MP3: The amount of manufacturing work (in terms of time) which is postponed (compared to total time spent in manufacturing). MP4: The amount of manufacturing work (in terms of value it makes/adds) which is postponed (compared to total value made in manufacturing). MP5: Percentage of products, postponement applies to their manufacturing processes. MP6: Percentage of customers for whom manufacturing postponement is applied to their product.

Manufacturing postponement (MP):

MP7: Level of deployment of postponement in the firm's manufacturing planning tasks. SP1: Average postponement time in shipment (the postponement time varies between the no delay (lowest postponement level), and delay until receiving customer order (highest postponement level)). SP2: The percentage of shipment operations which is postponed. SP3: The amount of shipment (in terms of time) which is postponed (compared to total time spent in shipment). SP4: The amount of manufacturing work (in terms of value it makes/adds) which are postponed (compared to total value made in shipment). SP5: Percentage of products, postponement applies to their shipment. SP6: Percentage of customers for whom shipment postponement is applied to their product.

  outcomes and their links with postponement. Postponement may affect the inventory level

For stage 1 ,

 1 an extensive literature review has lead to identification of the constructs and measured variables for postponement. Considering the definitions and details provided for the research constructs in the literature, constructs in this research are originated based on the research conducted and published recently. Accordingly, this

  part of the content validation process, copies of the postponement constructs definitions and the associated variables have been distributed to eight faculty members in fields of operations and supply chain management, and five have replied. They could recommend changes in the construct definition and variables. This includes adding or deleting variables of each construct. If changes are major, academicians are requested to appoint a time for an interview to discuss the details.

  typology, constructs and measured variablesThis section first reviews different classifications provided by the literature for postponement. This helps initial identification of postponement constructs and measured variables. A list of postponement constructs and their measured variables are then proposed. Content validity of the recommended constructs is also discussed with expert judges. These form the construct and scale development's stages 1 and 2.

  (i) classification according to the function of postponement; (ii) classification according to the stage of the value-adding process in the supply chain; and (iii) classification according to the scope of products covered by the postponement strategy. The first type of postponement classification categorises postponements according to their main function or purpose. Van Hoek (1998) discusses that postponement can occur in form, time or place. He defines form postponement as delaying activities which determine the function and form of the products until receiving more information about the demand. 'Delay' is addressed vaguely in this definition. If it referred to delay in terms of time, it would be the definition of time postponement,

  [START_REF] Van Hoek | Reconfiguring the supply chain to implement postponed manufacturing[END_REF] defines time postponement as delaying activities which do not determine the function and form of the products until receiving more information about the demand. This definition has shortfalls as it tries to discriminate between activities which do and do not determine the function and form of the products, and to link the second group to the time postponement. This is not always true as time postponement can be applied to differentiation activities as well.[START_REF] Van Hoek | Reconfiguring the supply chain to implement postponed manufacturing[END_REF] definition of place postponement refers to delaying the flow of material towards the supply chain downstream until receiving more information about the demand. Again, this definition is mixed up with time postponement (when it refers to 'until receiving more information'). In place postponement, one or more activities are performed in a place nearer to the market, and could be coupled with time and form postponement.In this type of postponement classification (based on the postponement function),[START_REF] Lee | Postponement for mass customization: Satisfying customer demands for tailor-made products[END_REF] introduces 'pull postponement', 'logistics postponement' and 'form postponement' as the three main categories of postponement. By pull postponement,

  other words, time postponement may occur at one stage (or place) of the supply chain (not moving activities from one stage to the other). In his categorisation,[START_REF] Lee | Postponement for mass customization: Satisfying customer demands for tailor-made products[END_REF] links postponement to customer order decoupling point (pull postponement) where these two concepts are not directly equivalent or necessarily linked[START_REF] Van Hoek | The rediscovery of postponement: a literature review and directions for research[END_REF].Similar discussions may apply to other studies which partially consider form, logistics, time and place postponement.[START_REF] Pagh | Supply chain postponement and speculation strategies: how to choose the right strategy[END_REF] explain that 'logistics postponement' is performed 'by direct distribution of fully finalised products from a centralised inventory to final retailer/customer.' In their classification, logistics postponement refers to time and place. Conversely, Van Hoek (1998) and Bowersox, et al. (1999) distinguish and discuss two categories of 'time postponement' and 'place postponement'. As a conclusion for this type of classification, form, time and place postponements can be recognised as the main categories of this classification. The second type of postponement classification categorises postponements, based on the stage in the supply chain (the value-adding activity) where the postponement occurs, or based on the state of inventories that Yang et al. (2007) define. Many

  et al. (2004a) discuss that postponement may happen in the 'logistics', 'production', 'purchasing' or 'product design and development' stages.

  To address postponement directly and measure it precisely, this fundamental question should be answered first: postponing of what? The literature mentions the postponement of manufacturing, assembly, packaging, purchasing, shipment, and the like. The common factor among all those operations is value. In fact, postponement in the context of operations management is about delaying one or some value-adding activities. Hence, to define postponement constructs and their measured variables, this study tries to find what type of values are delayed, how many of them are delayed, how much they are delayed by, and so on. Moreover, as postponement is about delay, measuring postponement is linked with measuring the time-length of postponement and duration of the postponed operations.In this respect, the current research considers the value-adding activities, which move the product closer to the form or location desired by the customer, and the relevant delays throughout the supply chain. The main value-adding activities of a supply chain may consist of purchasing, production and distribution[START_REF] Chen | Understanding supply chain management: critical research and a theoretical framework[END_REF].Bowersox et al. (2010: p. 6) define supply chain primary activities as procurement, manufacturing, customer accommodation and distribution. Yang et al.(2004a) identify a wider range of main activities including product design, purchasing, production and shipment. Similarly,Simchi-Levi et al. (2009: p. 4) highlight the main elements of supply chain as design, sourcing, production, distribution and sale.

  literature, this study focuses on four main value-adding activities of the supply chain: product design, purchasing (including sourcing, ordering and procurement), manufacturing (including main production of components, assembly, packaging, labelling, etc.) and shipment (including distribution). Accordingly, it defines four constructs for postponement: shipment postponement (SP), manufacturing postponement (MP), ordering/purchasing postponement (OM) and product design postponement (DP). The selected postponement typology addresses the value-adding operations and includes aspects of form, time and place postponements. In shipment postponement, the value is in moving the product to a closer location desired by the customer. Companies may limit their distribution channels, centralise their stock points and delay shipment of products to the next stage of the supply chain (e.g. retail shops or customers' home address) until they receive more accurate information about the demand. In general, there should be less cost in keeping the inventory in a centralised warehouse than the supply chain downstream (e.g. a high street retail shop). Moreover, by making more accurate shipments, the over-stock cost will be reduced and the lead-time or stock-out costs may be increased. The cost and benefits of shipment postponement should be analysed in each case independently, and measurement of the shipment postponement facilitates that analysis. In manufacturing postponement the product is held unfinished in a general status which gives more flexibility, less inventory cost and less risk of obsolete finished goods. The manufacturing postponement largely depends on the configuration of the product and the production process. By having the right measure for manufacturing postponement, its enablers and barriers can be explored in more detail. In design and purchasing value is in developing the right product and procuring the right material. Delaying these value-adding activities, manages the risk of mismatch between customer desire and the value generated to meet it at source. Design and purchasing postponement are critical activities which affect an extensive number of operations across the supply chain. Their impacts can be examined appropriately when they are measured more accurately. Considering the above explanations, measured variables of the postponement constructs are defined as follows.

  of them (for example, specifying the purchasing items' requirements and releasing the order) are delayed, the design postponement measure is 0.2. The third measured variable for postponement strategy can be the overall size (in terms of the time) of activities which are delayed (i.e. total duration of the delayed activities). The larger the duration of the delayed activities, the higher the level of postponement. This variable is shown by d in Figure4. A small d indicates the postponement of short activities, a large d specifies the postponement of lengthy activities, and the latter one represents a higher level of postponement. The rationale for choosing this variable is based on the role we expect from postponement to mitigate the risk. If lengthy activities are done in advance (not postponed) and the output product does not match the customer order which arrives later, lengthy rework may be required. To alleviate this risk, more work (in terms of the operation time) can be delayed. This measure can be considered firm-wide (not only for one product or operation), when the average of durations (of the postponed activities) is worked out.

  shown by shown by m/M, where m is the number of products included in postponement strategy and M is the total number of products.The sixth variable is suggested as the percentage or share of customers for which postponement strategy is pursued. Expansion of postponement strategy for a wide range of customers indicates the high level of implementation of postponement strategy. This variable can be shown by c/C, where c is the number of customers included in postponement strategy and C is the total number of products.

  Passing the convergent validity and discriminant validity assessment through the CFA process in this section, the final postponement constructs and measured variables are DP (DP1, DP2, DP3, DP5), OP (OP1, OP2, OP3, OP5), MP(MP1, MP2, MP3, MP4, MP5), SP (SP1, SP2, SP3, SP5).

  products postponement applied to them should be added to the other variables.

  manufacturing (MP), ordering/purchasing (OM) and product design (DP) -have content validity. As indicated by the values of Cronbach's alpha, all postponement constructs are reliable. High measured variables' loadings, communalities and AVEs in CFA indicate convergent validity of the constructs and their final measured variables. Moreover, non-significant correlations between the constructs compared to AVEs approves the discriminant validity. Finally, very low cross-loadings support the unidimensionality of the constructs and proposed measured variables. The results indicate that the research objective has mostly been accomplished and the postponement constructs can be used confidently in future studies. The contribution of the research outcomes and their significance are discussed in the rest of this section.The literature of postponement indicates that postponement is not measured directly and appropriately. The four postponement constructs and their measured variables paper represent a conceptualisation for postponement measurement.

  use product configuration factors such as generic components to measure postponement.[START_REF] Li | Development and validation of a measurement instrument for studying supply chain management practices[END_REF] similarly use product modular design as a postponement measure. They also refer to delaying processes such as assembly to articulate postponement. Other studies such as[START_REF] Nair | Linking manufacturing postponement, centralized distribution and value chain flexibility with performance[END_REF] use very general measures such as 'programmes developed to facilitate postponement' to evaluate postponement. The validated postponement constructs and measured variables proposed by this research concentrate directly on postponement, not on its enabler, barriers or impacts. Consistent with that, the validated postponement constructs and their variables address postponement in three levels of single activity, main operations (group of activities), and the whole firm. Proposing and rigorously validating constructs and measured variables for postponement is the main contribution of this paper. Moreover, measuring postponement in multiple layers is the second contribution of this research. The instrument developed in this paper is practical. It can be of use to researchers for further studies of postponement practices, in the context of operations and supply chain management, and their relationships with other operations strategies and operational performances. The development of the measurement instrument for postponement is expected to motivate and facilitate further theory development and testing in this field. The validation of the postponement constructs also has important implications for managers. Identification of the level at which a postponement strategy is employed is the factors affecting postponement and postponement impacts on other performance factors. Although there are many studies on influencing factors on,

Figure 6 .

 6 Figure 3. Postponement in terms of time length of the delay (l). t+l t

  length of time of the postponement. n/N: share of activities which are delayed. d 1 : time length of the postponed activity. v n : the value added by the postponed activity. m/M: percentage or share of products which follow postponement strategy. c/C: the percentage or share of customers for whom postponement strategy is pursued l: index of postponement time, n: index of activity, m: index of product, c: index of customer summary of postponement measured variables recommended by the literature.Product-based MeasuresGeneral Measures[START_REF] Li | Development and validation of a measurement instrument for studying supply chain management practices[END_REF]: -Product modular design -Products customisation in distribution centres -Assemble to order system -Delayed assembly to the supply chain downstream[START_REF] Davila | An empirical test of inventory, service and cost benefits from a postponement strategy[END_REF]: -The percentage of generic products shipped across the supply

  of deployment of postponement in the firm's shipment planning tasks.

  a blank box in front of each. Judges are supposed to allocate the measured variables to one of the research constructs by writing SP, MP, OP or DP in the blank box or just writing n/a (not available) if they believe the variable is not related to any of the constructs. The level of agreement between judges is measured by two methods;

	order) with
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They have a good knowledge and understanding of the theories and practices of operations and supply chain management. The current research and previous stages are briefed to them, and their questions are answered. Judges are not the same in different iterations. This process is done through face-to-face meetings or via email communication. Two tables on separate sheets are provided to the judges. The first table lists the research constructs and their abbreviations (i.e. shipment postponement: SP, manufacturing postponement: MP, ordering/purchasing postponement: OP, and design postponement: DP) and the second table lists the recommended measured variables (in no particular

  (of those which passed the pilot study, as listed in Table3) and tries to find to what extent it is employed by the firm. Two examples of questions are

	one measured variable as follows:
	MP2: Describe the percentage of manufacturing/assembly activities (in your firm)
	which are postponed by the time more accurate information from the customer is
	received (choices: <20%; 21-40%; 41-60%; 61-80%; and 81-100%).
	o
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MP3: Describe the amount of manufacturing/assembly work (in terms of time) which is postponed, compared to total time spent in manufacturing (choices: <20%; 21-40%; 41-60%; 61-80%, and 81-100%).

Table 1

 1

	indicates the postponement measures used in

<Insert Table 1 here> As shown by Table 1, the measured variables recommended in the literature are either very indistinct (i.e. general measures) or not directly measuring postponement

  After discussion on this measured variable, it has been found that the variable is very difficult to understand and measure. Moreover, some aspects of it exist in DP3 (i.e. the amount of design and development work in terms of time which is postponed compared to the total time spent on design and development). By removing this variable, major theoretical aspects of DP construct are still kept. Thus, DP4 is deleted. This improves the convergent validity. All other loadings in the first round of CFA have been high. In the second round, all loadings are high. Hence, the DP convergent validity is confirmed. Two rounds of CFA are shown in Table4. Hence,

	F o r P development). four measured variables, DP1, DP2, DP3 and DP5 are passed to the next stage e (discriminant validity assessment). e r <Insert Table 4 here>
	R v w e i e 5.1.2 CFA: ordering/purchasing postponement (OP)
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(DP) construct indicates a low loading (0.596) for DP4 (i.e. the amount of design and development work in terms of value it makes/adds, which is postponed, compared to total value made on design and

Table 5 .

 5 The CFA results for the manufacturing postponement (MP) construct indicates high loadings for all measured variables. Results are shown in Table6. Also, the high AVE and Cronbach's Alpha measures indicate the high validity and reliability of the MP measurement model. Hence, five measured variables, MP1, MP2, MP3, MP4 and MP5 are passed to the next stage (discriminant validity assessment).

	The high average variance extracted (AVE) and Cronbach's Alpha measures also
	indicate the high validity and reliability of the OP measurement model. Hence, four
	measured variables, OP1, OP2, OP3 and OP5 are passed to the next stage
	(discriminant validity assessment).
	<Insert Table 5 here>
	F o 5.1.3. CFA: manufacturing postponement (MP)
	r
	P
	e e r
	R e <Insert Table 6 here> v i 5.1.4. CFA: shipment postponement (SP) e w The CFA results for the shipment postponement (SP) construct indicates high loading
	for all measured variables except one, which is 0.412 for SP4. After discussion on this O measured variable, it has been found that the variable may be very difficult to interpret n and measure. Moreover, SP3 may cover some aspects of it, and by removing SP4, l y major theoretical aspects of the SP construct are still kept. Thus, it has been decided to
	delete OP4. This improves the convergent validity of SP. The second round of CFA
	indicates high loadings for all measured variables. Hence, the SP convergent validity is
	24

confirmed. Two rounds of CFA are shown in Table

7

. The high AVE and Cronbach's Alpha measures also indicate the high validity and reliability of the OP measurement

Table 2

 2 the literature on postponement and the final validated variables can be re-tested by future studies. Although the current results can be a useful tool to measure postponement, future studies may conduct a further confirmatory analysis in other sample frames. Separate case studies can also test the suitability and relevance of the postponement measured variables for industries which have not been addressed in this study. This will provide a comprehensive set of postponement measured variables for operations strategists and managers. Finally, although the sample size has been sufficient for the structural model analysis (PLS analysis) and has passed the power analysis test, the larger sample which covers multiple industrial sectors supports the generalisation of the results.

	). The postponement

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  Burns, N.D. and Backhouse, C.J., 2004a. Management of uncertainty through postponement. International Journal of Production Research, 42(6), 1049-1064. Yang, B., Burns, N.D. and Backhouse, C.J., 2004b. Postponement: a review and an integrated framework. International Journal of Operations and Production

	Appendix A CFA: Measurement Models for the Research Constructs Discriminant Validity (loadings and Yang, B., Management Production Management, 24(5), 468-487. cross-loadings)
	Yang, B., Burns, N.D. and Backhouse, C.J., 2005. An empirical investigation into the DP MP
	barriers to postponement. International Journal of Production Research, 43(5), 991 -1005. perspective. International Journal of Production Research, 45(4), 971-988. 0.898 0.847 0.953 0.915 0.957 Yang, B., Yang, Y. and Wijngaard, J., 2007. Postponement: an inter-organizational 0.874 0.933 0.934 0.816
	F o F o F o F o Yeung, J., Selen, W., Deming, Z. and Min, Z. (2007). Postponement strategy from a supply chain perspective: cases from China. International Journal of Physical Distribution & Logistics Management 37(4): 331-356. Zinn, W. and Bowersox, D.J, 1988. Planning Physical Distribution with the Principle DP OP o MP1 MP2 MP3 MP4 MP5 DP1 DP2 DP3 DP5 F 0.005 0.118 0.097 0.089 0.091 0.033 0.107 0.116 0.086
	r r r r of Postponement. Journal of Business Logistics, 9(2), 117-136. r 0.896 0.922 0.880
	DP1 DP1	0.209 0.072 0.228	P e e r R e v i e w P e e r R e v i e w P e e r R e v i e w P e e r R e v i e w 0.901 0.940 P e 0.253 0.154 0.166 0.249 0.177 0.247 w 0.947 0.927 0.923 0.880 0.019 0.192 0.885 0.833 0.899 0.909 0.910 0.934 0.911 0.918 i MP OP v SP1 SP2 SP3 SP5 DP2 DP3 DP5 e 0.332 0.223 0.313 0.219 0.045 0.136 0.123 R 0.873 0.871 0.867 0.932 0.959 0.881 0.821 0.941 r DP SP e OP1 OP2 OP3 OP5 DP2 DP3 DP5 e 0.263 0.324 0.279 0.193 0.290 0.297 0.269
	MP1	0.177	0.194 0.836 0.912 0.211 0.878 0.871 0.911 MP MP2 MP3 MP3 0.214	0.185 MP5	0.083 OP1	0.174	O n l y O n l y O n l y O n l y l 0.138 0.202 y 0.872 0.888 0.883 0.877 n SP O OP2 OP3 OP5
	MP1		MP2	MP3	MP3		MP5		SP1	SP2	SP3	SP5
				OP					SP
				0.912	0.905					0.896
				0.874 0.938					0.897	0.893 0.907
		0.045		0.143	0.072		0.132	0.164	0.204	0.167	0.154
	OP1		OP2	OP3	OP5		SP1	SP2	SP3	SP5
										34 35

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	Supply Chain Stage: Literature Review Studies	Product Development	Ordering / Purchasing	Manufacturing / Assembly	Packaging / Labelling	Shipment / Logistics
	Zinn and Bowersox, 1988
	Content Validation
		Pilot study
	Construct
	Convergent
	F o Validity F o
	r r
	P Figure 1. The overall structure of the construct validity assessment. P
	e e r e e r
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Table 2 .

 2 Proposed postponement constructs and measured variables. Average postponement time in development of a new product or modification of the design of an existing product (the postponement time varies between the no delay (lowest postponement level) and delay until receiving customer order (highest postponement level)). DP2: The percentage of product design and development activities which are postponed. DP3: The amount of design and development work (in terms of time) which is postponed (compared to the total time spent on design and development). DP4: The amount of design and development work (in terms of value it makes/adds) which is postponed (compared to total value made in the product design and development). DP5: Percentage of products, postponement applies to their design and development process. DP6: Percentage of customers for whom design and development postponement is applied to their product. Average postponement time in ordering purchasing items -i.e. raw materials or components (the postponement time varies between the no delay (lowest postponement level), and delay until receiving customer order (highest postponement level)

	Construct	Measure Variable
	DP1: Product
	design
	postponement
	(DP):
		DP7: Level of deployment of postponement in the firm's design and development planning
		tasks.
		OP1:

Table 3 .

 3 The postponement constructs and variables: outcomes of the pilot study.

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	Construct	Variable	Construct	Variable	Construct Variable Construct Variable
	DP:	DP1	OP:	OP1	MP:	MP1	SP:	SP1
		DP2		OP2		MP2		SP2
		DP3		OP3		MP3		SP3
		DP4		OP4		MP4		SP4
		DP5		OP5		MP5		SP5

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Table 4 .

 4 CFA process for the design & development postponement (DP) construct.

	Round Items Loadings Communality AVE	Cronbach's Alpha
	1	DP1	0.930	0.865
		DP2	0.889	0.791
		DP3	0.932	0.869	0.721	0.897
		DP4	0.596	0.355
		DP5	0.853	0.727
	2	DP1	0.931	0.867
		DP2	0.900	0.811	0.831	0.932
		DP3	0.942	0.888
		DP5	0.870	0.756

Table 5 .

 5 CFA process for the ordering/purchasing postponement (OP) construct.

	Round Items Loadings Communality AVE	Cronbach's Alpha
	1	OP1	0.897	0.804
		OP2	0.899	0.808
		OP3	0.904	0.818	0.681	0.865
		OP4	0.388	0.151
		OP5	0.908	0.824
	2	OP1	0.897	0.804
		OP2 OP3	0.910 0.922	0.829 0.849	0.824	0.929
		OP5	0.901	0.812

Table 6 .

 6 CFA process for the manufacturing postponement (MP) construct.

	Round Items Loadings Communality AVE	Cronbach's Alpha
	1	MP1 0.919	0.845	0.841	0.953
		MP2 0.915	0.837
		MP3 0.930	0.865
		MP4 0.899	0.809
		MP5 0.921	0.849

Table 7 .

 7 CFA process for the shipment postponement (SP) construct.

	Round Items Loadings Communality AVE	Cronbach's Alpha
	1	SP1 0.917	0.841
		SP2 0.872	0.760
		SP3 0.864	0.747	0.665	0.806
		SP4 0.412	0.170
		SP5 0.885	0.783
	2	SP1 0.932	0.869
		SP2 0.889 SP3 0.876	0.790 0.767	0.806	0.907
		SP5 0.891	0.794

Table 8 .

 8 Assessment of discriminant validity: comparing AVE and correlation square.

	Path Model	Correlation 2 Construct AVE	AVE > Correlation 2
	DP-MP	0.01	DP	0.82	YES
			MP	0.81	YES
	DP-OP	0.09	DP	0.83	YES
			OP	0.82	YES
	DP-SP	0.02	DP	0.82	YES
			MP	0.81	YES
	MP-OP	0.06	MP	0.84	YES
			OP	0.80	YES
	MP-SP	0.08	MP	0.81	YES
			SP	0.81	YES
	OP-SP	0.02	OP	0.81	YES
			SP	0.81	YES

Table 9 .

 9 Research constructs cross-loadingsunidimensionality analysis.

	DP	MP	DP	OP	DP	SP
	DP1 0.934	0.086	DP1 0.922 0.228	DP1 0.941 0.072
	DP2 0.915	0.107	DP2 0.901 0.269	DP2 0.932 0.123
	DP3 0.957	0.116	DP3 0.94	0.297	DP3 0.959 0.136
	DP5 0.816	0.033	DP5 0.88	0.290	DP5 0.821 0.045
	MP1 0.005	0.874	OP1 0.263 0.896	SP1 0.332 0.881
	MP2 -0.018 0.847	OP2 0.324 0.927	SP2 0.223 0.873
	MP3 0.097	0.953	OP3 0.279 0.923	SP3 0.313 0.871
	MP4 0.089	0.898	OP5 0.193 0.880	SP5 0.219 0.867
	MP5 0.091	0.933
	MP	OP	MP	SP	OP	SP
	MP1 0.918	0.209	MP1 0.878 0.177	OP1 0.912 0.045
	MP2 0.909	0.177	MP2 0.912 0.211	OP2 0.874 0.143
	MP3 0.934	0.249	MP3 0.836 0.194	OP3 0.938 0.072
	MP4 0.910	0.247	MP4 0.911 0.214	OP5 0.905 0.132
	MP5 0.911	0.166	MP5 0.871 0.185	SP1 0.164 0.896
	OP1 0.253	0.947	SP1 0.083 0.872	SP2 0.204 0.897
	OP2 0.154	0.885	SP2 0.174 0.888	SP3 0.167 0.893
	OP3 -0.019 0.833	SP3 0.138 0.883	SP5 0.154 0.907
	OP5 0.192	0.899	SP5 0.202 0.877

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