An Empirical Investigation of the Impact of Electronic ... - CiteSeerX

Proceedings of the 36th Hawaii International Conference on System Sciences - 2003

An Empirical Investigation of the Impact of Electronic Collaboration Tools on the Performance of a Supply Chain Élisabeth Lefebvre,1 Luc Cassivi,1,2 Louis A. Lefebvre1 and Pierre-Majorique Léger1,3 1- ePoly Centre of Expertise in Electronic Commerce École Polytechnique de Montréal [email protected]

2- Department of Management and Technology Université du Québec à Montréal

Abstract The central premise of this paper is that collaboration, and more specifically e-collaboration, plays a major role in achieving a sustainable competitive edge. In particular, we propose here to examine the relative efficiency of electronic collaboration (e-collaboration) tools and to assess the impacts of these tools on the innovativeness and performance of individual firms positioned along an industry-specific single supply chain. Empirical data from both the upstream and downstream perspectives for firms positioned at different points of one supply chain suggest that e-collaboration and its impacts create a one-sided benefit for the upstream side of the supply chain: first, the overall relative efficiency of e-collaboration tools is higher and, second, the impacts of e-collaboration are more beneficial when used with suppliers than when used with customers. The results also point to a stage model for implementing collaboration tools in a supply chain: the level of efficiency is higher for e-collaboration tools that support more operational than strategic activities (procurement vs. capacity planning). Finally, this research suggests strongly that collaboration tools can have significant impacts on the supply chain and that these tools need to be implemented progressively, both upstream and downstream, thereby yielding different and, most probably, cumulative benefits over time.

1. Introduction Electronic commerce (e-commerce) has generated strong interest from the business community, policy-makers and researchers but we are still struggling to assess its impacts. Surprisingly, “our empirically-grounded understanding of the systematic effects of e-commerce is still at an elementary stage” [24]. The focus of this paper is on business-to-business ecommerce in the context of supply chain management.

3- Department of Information Technologies HEC Montréal

More specifically, we propose to examine the relative efficiency of electronic collaboration (e-collaboration) tools and to assess their impacts on the innovativeness and performance of individual firms positioned along a single industry-specific supply chain. This corresponds to a rather under-investigated but highly relevant area of research [32]. In fact, some authors have argued that competition is increasingly occurring between supply chains rather than between large firms or multinationals [30]: time to market, customization of products, volumes, and even bottom-line profits depend heavily on the collaboration between the business partners along supply chains, although it is acknowledged that in some cases multinationals impose their own vision of supply chain management. The central premise of this paper is that collaboration, and more specifically e-collaboration in a Web-based supply chain environment, plays a major role in achieving a sustainable competitive edge.

2. Theoretical background The literature on the benefits and competitive advantages that can be achieved with interorganizational systems, and in particular electronic data interchange (EDI), is wideranging and deeply rooted in different disciplines, from information systems [8, 9, 35], to production and operations management [31], to organization theory and strategic management [3, 13, 28]. Knowledge gained from previous work on EDI is valuable since EDI was one of the first technologies used to conduct business-to-business (B-to-B) e-commerce, and findings in that area are relevant to e-collaboration tools within the context of supply chains. In parallel, interest in Internet-based collaborative systems and e-collaboration for supply chain management has been rising [5, 6, 11, 15, 27], although there is still little empirical evidence on this issue.

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Electronic collaboration tools represent a multifaceted concept, which includes many types of information exchange tools, from simple e-mail systems to more complex interactive CAD systems, used to exchange product information and specifications. This study focuses on Web-based collaboration tools designed by key players in the telecommunication equipment industry. These tools are mainly used to exchange critical operational information (e.g., data relating to manufacturing, design and logistics activities) and transactional information (e.g., purchase orders) between supply chain partners [19, 38]. A complete definition of each e-collaboration tool is available in Appendix 1. The following sections briefly present some theoretical background related to the specific focus of this paper. 2.1 E-collaboration tools as interactive innovations The relative impacts of e-collaboration tools depend on their diffusion within a supply chain, since these tools are basically interactive innovations. By definition [29], an interactive innovation is of little interest to a business partner unless another business partner also adopts it. As a result, the utility (real or perceived) of an interactive innovation increases for all adopters with each additional adopting partner. An interactive innovation leads to positive network externalities and therefore promotes what Markus [20] calls reciprocal interdependence (as opposed to sequential interdependence). Hence, the relative impacts of e-collaboration tools depend on their level of diffusion, and more importantly, on their relative efficiency. Do e-collaboration tools stimulate, support or lead to innovation? Relational innovations include e-commerce’s already well-known ability to broaden markets and strengthen pools of business partners [23, 25], but they also involve trust (i.e., improving the quality of relations with partners) and loyalty (i.e., increasing the likelihood of retaining business partners) [26, 33]. E-collaboration tools are therefore expected to improve relational innovation. Furthermore, these tools also entail process innovation, for instance, in terms of design (i.e., gearing design processes dynamically to customer feedback) or logistics (i.e., increasing the flexibility and speed of distribution). Based on the above discussion, we formulate the following hypothesis: H1: The relative efficiency of e-collaboration tools is positively related to the firm’s innovativeness, more specifically to the level of relational and process innovations.

2.2 E-collaboration innovation and performance Certain impacts of B-to-B e-commerce can already be traced and evaluated. Reductions in transaction costs and gains in accuracy and speed are well documented [17, 36]. Internet-based systems can be viewed as an “enabling technology” [27] from which operational and even strategic benefits can be derived. Networking and collaboration lead to competitive advantages, especially in the case of supply chain management [4]. The potential benefits derived from supply chain collaboration are numerous: reduced inventory, improved customer service, efficient use of human resources, reduced cycle times, faster time to market for new products, improved shareholder value, enhanced public image, greater trust and interdependence, increased sharing of information, and technological standardization [21]. E-collaboration tools appear to be linked to tangible and intangible benefits and to operational and strategic advantages. When developing supply chain collaborative initiatives, performance indicators should be based mostly on process performance, and not on financial performance [18]. Therefore, it is proposed to investigate the strength of the relationships between e-collaboration and three dimensions of performance specified in Beamon’s work [2], namely input measures, output measures and flexibility measures. Hence, the following hypothesis is proposed: H2: The relative efficiency of e-collaboration tools is positively related to the firm’s performance, more specifically in terms of input, output and flexibility measures. 2.3 Firm size and position on the supply chain as control variables As demonstrated by Kaufman et al. [14], larger suppliers within a supply chain context collaborate more intensively than their smaller counterparts. Since business partners are not all equally well informed of the potential of ecollaboration tools, and since smaller firms may experience difficulties operating in an electronically mediated environment due to a lack of competencies, asymmetries may occur along the supply chain. The position held by a particular upstream or downstream member of the supply chain therefore represents a relevant control variable, as does its size. From these arguments, a third hypothesis is formulated: H3: Relationships between the relative efficiency of ecollaboration tools and a firm’s innovativeness and performance are influenced by the company’s size and position in the supply chain.

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3. Methodology We present here the results of the second phase of a major study carried out in the telecommunications industry. This study consisted of three consecutive and complementary phases: Phase 1: a detailed case study of the business impact of Bto-B e-commerce. This case study was conducted in the context of an international study of e-commerce initiatives coordinated by the OECD. This particular endeavor focused on one large telecommunications equipment manufacturer and some of its strategic first-tier suppliers. Phase 2: an electronic survey of the individual firms acting on the same supply chain for one large telecommunications equipment manufacturer. Phase 3: an international electronic survey of e-commerce practices in the wireless telecommunications industry. We will briefly examine the structure of the telecommunications equipment supply chain (section 3.1). We will then present the data collection strategy and the profile of the responding firms (section 3.2). Finally, we will outline the research variables and the operationalization of their measures. 3.1 The telecommunications equipment supply chain The research design requires a thorough examination of the structure of the telecommunications equipment supply chain. According to APICS [1], “a supply chain is defined as the processes linking supplier and user companies,

from the initial raw materials to the ultimate consumption of the finished product.” Figure 1 builds on this widely accepted generic definition but captures some of the specificities of the telecommunications equipment supply chain. At the heart of the supply chain is the system integrator, which acts as the OEM (original equipment manufacturer). Well-known multinationals such as Alcatel, Ciena, Cisco, Fujitsu, Lucent, NEC, Nortel Networks, Sycamore and Tellabs are considered to be system integrators or OEMs. The OEMs’ customers are the network operators, which are responsible for different types of communication networks, namely transport networks, metropolitan area networks and access networks. Network operators represent the final customers of the supply chain in question; they are facing a rather dynamic, competitive and fierce environment characterized by deregulation in some regions and changes in demand resulting from new services such as the Internet. The pressures created by this rather turbulent environment are channeled upstream to all business partners along the supply chain. OEMs are increasingly outsourcing assembly activities to EMSs (electronic manufacturing specialists), which are also referred to as contract manufacturers. EMSs such as Celestica, Solectron, SCI and Jabil offer both lower costs and greater flexibility (essential to respond to highly fluctuating demand) than OEMs. EMSs act as first-tier suppliers for the OEMs. Second- and third-tier suppliers are responsible for component manufacturing and subsystem assembly

Figure 1 – The telecommunications equipment supply chain Web-based information exchange (downstream)

3rd-tier suppliers

2nd-tier suppliers

Subassemblers and 1st-tier suppliers

Assemblers or EMSs

System integrator or OEM

Network operators

Web-based information exchange (upstream) Direct information exchange Multi-tier information exchange Pilot projects

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activities. The size and status of the suppliers vary widely: some are subsidiaries of large multinationals with a global reach while others are SMEs (small and medium enterprises) with just one or two major customers. The complexities of supply chain interactions cannot be fully represented since business partners along this supply chain may also be actively involved with different supply chains dominated by other OEMs. However, Figure 1 gives a clear illustration of one supply chain dominated by one OEM, from the suppliers’ suppliers (i.e., third-tier suppliers) to the customers’ customers (i.e., network operators). 3.2 Data collection strategy and responding firms Based on the results of the case study, an electronic questionnaire was developed to facilitate the input of information by both the managers involved in the upstream activities of the supply chain (supplier-related functions such as procurement and design) and the executives involved in the downstream activities (customer-related functions, for instance, sales and marketing). Via e-mail, a total of 130 companies doing business in the electronic equipment industry were asked to complete the electronic questionnaire posted on the Internet by one major OEM. The sample of companies was identified by the OEM and included its most important supply chain partners. Just over 76% of the 130 suppliers are based in the United States, 12% in Canada and 12% in the rest of the world. The request to answer the questionnaire was sent out by the OEM twice over a two-month period. A total of 53 companies participated in the Web survey, for a 40.8% response rate. Each responding firm completed both the supplier-related and the customerrelated questions. The responding firms are deeply involved in collaboration with different levels of suppliers: 40% deal with three or more levels of suppliers whereas another 40% deal with two levels of suppliers. The responding firms also proved to be internationalized, carrying out both sales and procurement activities in external markets. 3.3 Research variables The research variables can be grouped into three sets. The first reflects the level of efficiency of the Web-based collaboration tools used by the different business partners along the chosen supply chain. The second captures the level of innovativeness of responding firms and their performance. The third consists of the two control variables. These three sets of variables are described in further detail below; the operationalization of the research

variables and their theoretical justification are presented in Appendix 2. 3.3.1 Web-based collaboration tools Eight Web-based collaboration tools were identified from the case study conducted with the OEM and its strategic first-tier suppliers (phase 1 of the study). These tools, which are used to exchange critical information among supply chain partners, are summarized in Appendix 1. The responding firms were asked to assess the relative level of efficiency of each of the eight Web-based collaboration tools when used with their own customers and suppliers. The Web-based collaboration tools to be used by the OEM and the network operators are mostly in a pilot-testing phase (figure 1). As a result, ecollaboration has led to a truncated supply chain from which network operators are absent. 3.3.2 Innovativeness and performance Measures for the level of innovativeness were adapted from the literature review and from previous survey instruments [22, 24, 33]. Building on Beamon's work [2], three dimensions of performance are proposed: input, output and flexibility measures. All variables were refined, tested and validated during the case study. Table 1 displays the number of items and the Cronbach alpha coefficients for each research variable. The construct reliability proved to be quite satisfactory for all variables, with Cronbach alpha coefficients ranging from 0.69 to 0.94. Table 1 – Construct reliability RESEARCH VARIABLES Level of innovativeness Process innovation – upstream Relational innovation – upstream Process innovation – downstream Relational innovation – downstream Performance Input measures Output measures Flexibility measures

Number of items

α

7 5 7 5

0.90 0.92 0.89 0.94

5 9 4

0.89 0.69 0.79

3.3.3 Control variables As mentioned in section 2, the position of a firm in the supply chain and its size are considered as potentially significant control variables. The position held by a particular firm in the supply chain is measured using an ordinal scale from 1 to 5, where 1 is the highest position downstream and 5 is the lowest position upstream. Firm size is simply captured by the number of full-time employees.

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Table 2 – Strength of the relationships between ecollaboration, innovativeness and performance (Pearson correlation coefficients)

4. Results and discussion Each firm along the supply chain was asked to evaluate the relative efficiency of e-collaboration tools when they are used with their suppliers (upstream on the supply chain) and with their customers (downstream on the supply chain): this corresponds to the graphic representation of the Web-based information exchange in figure 1 (i.e., two large arrows).

Innovativeness

Figure 2 presents the relative efficiency of these tools, which are ranked in descending order.

Process innovation 0.67**** Relational innovation 0.72****

E-COLLABORATION With suppliers

With customers

p

0.54**** 0.39***

NS **

0.08 0.14 0.40***

* * NS

Performance Overall, the e-collaboration tools are perceived as fairly efficient since the mean for each tool on both sides of supply chain is slightly above the middle of the 7-point Likert scale. Collaborative design receives the highest score from the supplier perspective and the second highest score from the customer perspective, as could be expected in this high-tech industry where product life cycles are short and costs of components are high. A closer look at figure 2 reveals some interesting observations. First, seven out of eight collaboration tools are more efficient when used with suppliers than with customers. The only notable exception is “delivering and tracking,” which receives a higher score when it is used with customers. Second, the ranking of these tools does not seem to indicate a long-term strategic focus: business strategy, capacity planning, and forecasting received low rankings (respectively 8th, 7th and 6th ranks when used with suppliers and 8th, 7th and 5th ranks when used with customers). Third, the rankings of these tools are rather similar for both sides. In fact, there is almost complete agreement between the two sides (p = .0644 for Kendall’s test of concordance in which p = 0 indicates complete agreement and p = 1 complete disagreement). Is there a link between e-collaboration, and, innovativeness and performance? Table 2 gives us some answers:

Input measures Output measures Flexibility measures

0.31** 0.43*** 0.43***

p = level of significance of Pearson correlation coefficients – unilateral tests p = level of significance for the test of difference between the correlation coefficients (suppliers vs. customers) – unilateral tests * p < .10; ** p < .05; *** p < .01; **** p < .001

i)

E-collaboration upstream and downstream is positively related to innovativeness and, to a lesser extent, to the three key dimensions of performance: all twelve correlation coefficients are positive and most are significant. The highest coefficient (r21 = 0.72) occurs between e-collaboration with suppliers and relational innovation.

ii) When examining for the strongest coefficients for both sides (supplier and customer), e-collaboration is significantly related to process innovation (r11 = 0.67 and r12 = 0.54) and to flexibility (r51 = 0.43 and r52 = 0.40). Potential benefits from e-collaboration therefore seem to support the concept of seamless interoperable supply chains.

Figure 2 – The relative efficiency of e-collaboration tools

When dealing with

Suppliers

Customers

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iii) When contrasting the two perspectives (suppliers vs. customers), e-collaboration systematically translates into greater innovativeness and stronger performance for the upstream side than for the downstream side of the supply chain. One partial explanation for this result may rest on the top-down approach in the implementation of these e-collaboration tools, where the flow of decisions moves from the customer’s customer to the supplier’s supplier. Going one step further, we tested whether the Pearson correlation coefficients are indeed significantly higher for the supplier perspective than for the customer perspective, using the following simple formula, which takes into account the present situation involving non-independent correlations: ( r1 3 − r 23 ) (n − 1) (1+ r 12 )

t (n − 3 ) =

(n − 1) ( r 2K + (n − 3 )

where

23

+r

2

13

) (1− r

12

)

3

4

K = 1 − r122 − r132 − r 232 + 2 r12 r13 r 23 .

The results of this test are presented in the last column of table 2. When used with suppliers, e-collaboration tools contribute significantly more to relational innovation and to two performance measures, namely input and output measures, than when these tools are used with customers. Hence, the potential benefits for e-collaboration appear to be one-sided in favor of the upstream side of the supply chain, at least with respect to these three variables. Table 3 displays the Pearson correlation coefficients between e-collaboration, innovativeness and performance when controlling for position (second and fifth columns)

and for firm size (third and sixth columns). The first and third columns are identical to the information presented in table 2 in order to facilitate comparison. Table 3 clearly demonstrates that the strength of the relationships between e-collaboration, innovativeness and performance is not affected by the position held by the firm along the supply chain or by its size: coefficients are remarkably stable with the introduction of the two control variables although size has a slight but non-significant effect on the relationship between e-collaboration and input and output performance measures.

5. Conclusion The tight and very focused research design constitutes a major strength of this study. Considerable efforts were made to capture empirical data from both the upstream and downstream perspectives for firms positioned at different points along one supply chain. However, the results presented in this paper must be interpreted in the light of the particular context of the telecommunications equipment supply chain. This industry is characterized by short product life cycles, high demand volatility and high levels of component subcontracting, all of which may limit the generalizability of our results. Nevertheless, the telecommunication industry is considered to be at the forefront of B-to-B e-commerce, with strong ecollaboration already under way. The results of this study could assist in forecasting e-collaboration benefits in other industries with similar characteristics. This research initiative needs to be replicated and extended to other contexts. Furthermore, complementary data collection strategies could be envisioned. For instance, an interesting complementary procedure might be the measurement of supply chain performance with operational data obtained from the system integrator’s enterprise resource planning

Table 3 - Strength of the relationship between e-collaboration and innovativeness and performance, controlling for position and size E-COLLABORATION WITH SUPPLIERS E-COLLABORATION WITH CUSTOMERS No control variable

Position as a control variable

0.67**** 0.72****

0.66**** 0.72****

0.31** 0.43*** 0.43***

0.29** 0.41*** 0.42***

Size as a control variable

No control variable

Position as a control variable

0.68**** 0.72****

0.54**** 0.39***

0.54**** 0.42***

0.57**** 0.39***

0.32** 0.43*** 0.44***

0.08 0.14 0.40***

0.10 0.15 0.40***

–0.02 0.22* 0.41***

Size as a control variable

Innovativeness Process innovation Relational innovation Performance Input measures Output measures Flexibility measures

p = level of significance of Pearson correlation coefficients (first and fourth columns) – unilateral tests r i p = level of significance of partial correlation coefficients (other columns) where r

* p < .10; ** p < .05; *** p < .01; **** p < .00

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ijk

=

j

− r

(1 − r 2

k i

k i

r

) (1

k j

−

r 2

k j

)

6


system. Another research possibility involves monitoring the impact of e-collaboration tools at all stages of one specific product’s life cycle (from design to recuperation activities). The unit of analysis would therefore be the product rather than the individual firms acting along the supply chain. One important result of this study is the lack of support for the role of the two control variables: the position held by a particular firm upstream or downstream on the supply chain and its size do not affect the strength of the relationships between e-collaboration and its impacts (innovativeness and performance). This clearly suggests that the relevant question is not who benefits from ecollaboration, but rather with whom is e-collaboration more beneficial. In fact, the results of this study suggest that e-collaboration and its impacts create a one-sided benefit for the upstream side of the supply chain: first, the overall relative efficiency of e-collaboration tools is higher and, second, the impacts of e-collaboration are more beneficial when the tools are used with suppliers than with customers. The observed difference between the upstream and downstream sides of the supply chain may be partially explained by the fact that the information exchanged with suppliers is more operational and less complex than the information exchanged with customers. Furthermore, the level of control (real or perceived) that a firm has over the Web-based information depends on its destination and varies with the type of business partners (suppliers vs. customers). This duality needs to be explored in more detail in future research. The results also point to a stage model for implementing collaboration tools in a supply chain: the level of efficiency is higher for e-collaboration tools that support operational rather than strategic activities (e.g., procurement vs. capacity planning). It appears that in first implementing collaboration tools in a supply chain, these operationally oriented tools may be favored because of the lower level of involvement and integration required from all parties. This also explains why the results mostly take the form of process and relational innovations since these are prerequisites to the performance measures. In fact, performance seems to be first observed with respect to flexibility measures, which is what is expected in supply chain optimization. Only then can bottom-line performance measures be grasped. Finally, this research suggests strongly that collaboration tools can have significant impacts on the supply chain and that these tools need to be implemented progressively, both upstream and downstream, yielding different and, most probably, cumulative benefits over time. Acknowledgments: Helpful comments from anonymous reviewers are greatly appreciated. The authors gratefully acknowledge financial support from SSHRC and FCAR.

6.

References

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Appendix 1 – Description of the Web-based collaboration tools The design tool: Enables the use of interactive engineering drawing and storage of computer-assisted design (CAD) designs by all the key supply chain stakeholders involved in the product design activity. The collaborative design tool may be used to ensure that the final design meets all of the stakeholders’ requirements. Meanwhile, it should help reduce the time to market while maximizing the quality and minimizing the costs of the product.

The capacity planning tool: Determines the amount of capacity required to produce. It establishes measures and adjusts the levels of capacity in terms of labor, machine resources and material necessary to accomplish the operational tasks. For instance, a supplier, with the increased visibility of capacity planning information, monitors its inventory levels according to its customer’s inventory targets.

The delivery and tracking tool: Generates a payment and a delivery request automatically when a product goes from a supplier to its customer. It is also designed to collect shipping information from the third-party logistic providers. It reduces the number of communications between partners and is tightly linked to the direct procurement tool to automatically close purchase orders.

The projected shortages tool: Scans the buyer’s production plan to project expected component or material shortages. Suppliers access the tool frequently (weekly) and provide the delivery schedules of items with potential shortages. The tool also provides real-time information for manufacturing and supply management units and reduces the response time for communication between them.

The replenishment tool: Drives an ordering system from the shop floor. When material is needed on a production line, an order is placed through the replenishment system. The supplier usually has a specific amount of time to deliver ordered material either to the production line or to a stockroom.

The forecasting tool: Frequently exchanges the forecast information provided by both the buyer and supplier. The forecast, which is a prediction of sales and use of products in order to purchase the appropriate quantities in advance, is usually obtained from ERP software or from an advanced planning and scheduling (APS) tool.

The direct procurement tool: Forwards purchase orders (POs) to pre-qualified suppliers. Mostly Web-based, direct procurement replaces fax systems, is usually an alternative to EDI and is often linked to an enterprise resource planning (ERP) system. The supplier usually posts an acknowledgment of receipt and a confirmation of quantity, date and price on the direct procurement tool.

The business strategy tool: Collects and shares the actions that need to be taken to support the objectives and mission of the supply chain (or networked enterprise). Typically, general supply chain objectives are drawn from each organization’s goals for participating in a collaborative relationship.

Sources: [7, 16]

Appendix 2 – Operational measures and their theoretical justification Variables

Items

Theoretical justification

Operational measures

Level of efficiency of e-collaboration (upstream and downstream) The extent to which these collaboration tools are efficient at exchanging information with your suppliers and clients Web-based collaboration tools

All 8 tools as described in Appendix 1

[7, 16]

Likert scales where 1 = disagree and 7 = agree

Innovativeness: The extent to which collaboration activities have made it possible to:

Relational innovation

1. target new groups of business partners

[24, 33]

2. retain partners, customers and suppliers

[24] [24, 22]

3. build trust 4. enrich quality of relations

[24]

5. improve loyalty

[24]

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Likert scales where 1 = disagree and 7 = agree

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Appendix 2 – Operational measures and their theoretical justification (continued) Variables

Items

Theoretical justification

Operational measures

Innovativeness: The extent to which collaboration activities have made it possible to: 1. generate new design processes 2. gear processes to customer requirements 3. improve logistic processes Process innovation

4. increase flexibility of distribution

[24]

5. enhance responsiveness of distribution

Likert scale where 1 = disagree and 7 = agree

6. design new production processes 7. increase production flexibility Performance measures The extent to which collaboration activities have allowed a decrease or increase in:

Input measures

1. inventory levels

[2, 12]

2. inventory costs

[34, 10]

3. operational costs

[34, 10]

4. personnel required

[2]

5. equipment costs

[2]

1. time required to manufacture

[2] [2, 32]

2. fulfillment time 3. lead times to manufacture 4. number of stock-outs Output measures

5. number of items produced 6. number of on-time deliveries

[2] [2, 32, 18, 12]

8. quality of product

[2, 32]

9. customer satisfaction

[2, 34]

1. flexibility to offer volume 2. flexibility to deliver

[2,37] [2, 37]

4. new product introduction

[2, 18]

Position held in the supply chain

Likert scales where 1 = decrease and 7 = increase

[2, 37, 18]

3. variety of products

Control variables Size

Likert scales where 1 = decrease and 7 = increase (reversed scales)

[2, 10]

[ 10, 18]

7. fill rate

Flexibility measures

[32, 10, 18]

Likert scales where 1 = decrease and 7 = increase (reversed scales)

Likert scales where 1 = decrease and 7 = increase

Usual measure

Factual, number of employees

Usual measure

Factual, ordinal scale where 1 = 1st-tier supplier, 2 = 2nd-tier, 3 = 3rd-tier, 4 = 4th- and + tier

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