identifying key supply chain capabilities for facets of

IDENTIFYING KEY SUPPLY CHAIN CAPABILITIES FOR FACETS OF FIRM PERFORMANCE

Arun RaiCenter for Process Innovation &

Department of Computer Information Systems J. Mack Robinson College of Business

Georgia State UniversityAtlanta, GA

[email protected]

Ravi PatnayakuniDepartment of Economics and Information Systems

University of Alabama in HuntsvilleHuntsville, AL

[email protected]

Nainika SethDepartment of Economics and Information Systems

University of Alabama in HuntsvilleHuntsville, AL

[email protected]

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IDENTIFYING KEY SUPPLY CHAIN CAPABILITIES FOR FACETS OF FIRM PERFORMANCE

ABSTRACT

Firms need to manage their portfolio of supply chain capabilities in order to realize improved performance. However, different facets of performance may require firms to focus on specific supply chain capabilities. To explore the issue, we first identify the set of supply chain factors related to firm performance from various literature streams. We then pose the questions: (i) which supply chain-related factors have the most explanatory power for different aspects of firm performance, and (ii) how do these factors differ in their impact across these facets of firm performance? We build logistic regression models to establish the relationship between supply chain capabilities with facets of firm performance, to identify capabilities with the most explanatory power in explaining relative firm performance. Our results indicate that different supply chain capabilities impact different facets of firm performance. While improved relationship with customers is found to be related to higher levels of product modularity, and asset specificity of supply chain partners, financial flow integration has a negative influence on this facet of firm performance. Firm performance in terms of revenue growth is found to be strongly related to efficient management of physical flows in the supply chain. Finally improved operational performance has interesting relationship with IT infrastructure capabilities, whereas data consistency has a positive relationship and application integration has a negative relationship with this facet of firm performance. Suggested inferences from negative performance require future investigation of issues such as sub-optimal functional components implemented as part of an integrated ERP or SCM packages as well as process rigidities that impede an firm’s ability to respond to customer or operational needs in a flexible way.

Keywords: supply management, information technology, productivity, technology management

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IDENTIFYING SUPPLY CHAIN CAPABILITIES THAT IMPACT FACETS OF FIRM PERFORMANCE

1. Introduction

Effective management of supply chains is considered to be the next frontier for firms

to improve profitability and competitive position. The Global Supply Chain Forum

defines supply chain management as “the integration of key business processes from end

user through original suppliers that provides products, services, and information that add

value to customers and other stakeholders,” (Lambert, Cooper andPagh, 1998). The broad

definition reflects a significant change in organizational business models where networks

of firms that form a supply chain, rather than individual firms, compete with other such

networks in the marketplace (Evans and Wurster, 1999; Lambert et al., 1998). It also

suggests a shift from managing the traditional logistics of physical goods to the

integration of physical, financial and information flows across supply chains.

An AMR report (Reilly, 2004) suggests that U. S. manufacturers could potentially

realize $488 billion in operating margins by improving their supply chain management

(SCM). Firms can focus their supply chain initiatives to realize different competitive

objectives such as streamlining operations, expanding market share, and improving

customer service (Ramdas and Spekman, 2000). To achieve these objectives, firms are

looking to invest in a variety of supply chain management capabilities from designing

product architecture to integrating supply chains from end-to-end, i.e., from lower tier

suppliers to the final consumer. Challenges associated with such an undertaking span

strategic, operational, and technological issues that need to be understood more

comprehensively.

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Given the potential for improved firm performance and limited systematic

understanding, supply chain capabilities have been researched by scholars in operations

management, logistics, marketing, information systems and strategic management (Otto

and Kotzab, 2003). These investigations have provided for complementary perspectives

through which many important supply chain capabilities have been identified. However,

there has been limited examination of these capabilities in term of their effects on distinct

aspects of performance, i.e., operational performance, revenue growth, and relationship

with customers.

Our objective is to identify and evaluate a comprehensive set of supply chain

capabilities from several literature streams that influence different aspects of

performance. The specific research questions that we investigate are: (i) which supply

chain-related factors have the most explanatory power for different facets of a focal

firm’s performance; specifically operational excellence, revenue growth, and customer

relationship? and (ii) how do these factors differ in their impact across these facets of

performance for a focal firm?

By identifying factors from multiple perspectives on SCM and conducting an

empirical study to evaluate their relative importance for firm performance, our study

makes the following contributions: (i) it consolidates and categorizes existing SCM

research streams to identify the set of potentially critical capabilities for SCM-related

firm performance; (ii) it empirically derives models of capabilities that explain the three

different aspects of SCM-related firm performance, i.e., operational excellence, revenue

growth and customer relationships; and (iii) it isolates the different patterns of factors that

influence each facet of firm performance. Driven by different competitive strategies,

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firms are likely to focus on specific facets of their performance. From a practical

standpoint, our findings should enable firms to focus attention and resources on specific

capabilities that are most important for each aspect of performance.

Our results indicate that different facets of firm performance are impacted by product

architecture, process and relational structures, and IT capabilities. They provide

interesting insights into the make up of supply chain capabilities that are related to facets

of firm performance. We find that operational excellence is influenced by consistency of

data across applications, systems, and partners on the supply and sharing of information

related to demand signals across the supply chain but is negatively impacted by the extent

to which SCM applications are integrated across partners. Revenue growth is found to be

dependent on physical flow integration that represents optimized staging of inventory to

avoid stock-piles and stock outs. Interestingly, while integration of demand-related

information is considered a key solution to alleviate error amplification of forecasts (i.e.,

the bullwhip effect), our results suggest that integration of financial flows and application

integration may negatively impact some facets of firm performance.

The rest of the paper is organized as follows. The next sections discuss the three

facets of firm performance and the supply chain drivers of performance. This is followed

by a presentation of the empirical study and finally and results and conclusion are

presented.

2. The Three Facets of Firm Performance: Operational excellence, Revenue

Growth, and Customer Relationships

Three facets of firm performance, operational excellence, revenue growth and

customer relationship have been recognized as important aspects of firm performance

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(Slywotzky, Morrison andWeber, 2000). Whereas operational performance measures

include time and cost based measures, strategic performance measures include better

relationship with customers, and increased sales from new and existing products.

Operational excellence is an firm’s responsiveness, improvements in productivity, and

effective balancing of operations costs and service levels (Fisher, 1997; Simchi-Levi D.,

Kaminsky P. andE., 2000). Furthermore, firms need to achieve market-focused

performance (Malhotra, Gosain andEl Sawy, 2005) through revenue growth and customer

relationships. Revenue growth is the increase in sales from existing products and from

new products and markets (Zahra and George, 2002). Finally, customer relationships

reflect the bond and loyalty between a firm and its customers, which results in intimate

knowledge about customer preferences.

Prior research suggests that there is evidence that supply chain capabilities impact the

three facets of performance considered in this investigation. For instance, consider

product architecture where the objective of modular product design is to be able to offer

increasing product differentiation, without increasing product complexity (van Hoek,

1998) by using standardized interchangeable components (Lampel and Mintzberg, 1996).

By reducing the complexity of the inbound logistics process and enabling postponed

manufacturing, modular product architecture can impact a firm’s relationship with its

customers and provide opportunities for revenue growth. Similarly IT and resource flow

integration (physical, financial and information) with supply chain partners can

potentially support operational excellence by providing operational visibility and

streamlined flow of goods that compress the time interval between a customer’s request

for a product or service and its delivery (Hult, Ketchen andSlater, 2004; Tyndall, 1998).

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Thus supply chain capabilities can positively influence operational performance (Lee,

Padmanabhan andWhang, 1997a; Simchi-Levi D. et al., 2000), improve customer

relationships, and promote market growth (Rai, Patnayakuni andSeth, 2006; Tyndall,

1998).

3. Potential Supply Chain Determinants of the Three Facets of Firm Performance

Our review of the literature suggests that supply chain capabilities that impact firm

performance may be classified as: (i) product characteristics, (ii) IT infrastructure

capabilities, (iii) relational capabilities, and (iv) process capabilities. The factors

included in our study within each of these four major categories are listed in Table 1and

the rationale for their consideration is discussed in subsequent paragraphs.

----------------

Insert Table 1 here

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3.1 Product characteristics

Fisher (1997) notes that while the supply chain literature has focused on capacity

utilization, efficiency, and cycle time, it has largely overlooked the relationship between

product demand characteristics, design approaches, and performance objectives. Product

demand patterns reflect whether a product is functional or innovative. Functional

products satisfy basic needs, don’t vary over time with stable and predictable demand and

relatively long product life cycles. In contrast, innovative products have short life cycles,

high contribution margins, high product variety and uncertain product demand.

Accordingly, different supply chain capabilities are required for each of these types of

product. Functional products require efficient supply chains as their competitive

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environment is characterized by heightened competition and low profit margins. Supply

chain processes that manage efficient flow of physical goods lead to high inventory

turnover and lower supply chain costs to improve operational excellence and

consequently competitive position of a firm. In contrast, innovative products require

responsive supply chains where speed, flexibility, and quality are emphasized for gains in

revenue and profitability. Their supply chains should provide for responsiveness to shifts

in demand through buffer inventories that avoid stock outs and aggressive lead-time

reduction for order fulfillment. Given that differences in product demand can impact the

different aspects of performance, we define consumer demand predictability as the

degree of forecasting uncertainty in predicting consumer demand and include it as a

factor in our investigation.

The current competitive environment requires that supply chain capabilities be

established not only for responsiveness to changes in demand but also for greater levels

of customization (Bensaou, 1999; Magretta, 1998). Furthermore, supply chains

capabilities should enable firms to compete in aggressively shortening product life cycle

with successive product generations often overlapping each other (Lee, 2000). Firms can

respond to the competitive realities of customization and shorter product life cycles by

focusing on their product architectures. With modular product architectures that use

standardized interchangeable components (Baldwin and Clark, 1997; Lampel and

Mintzberg, 1996), firms can increase product variety without increasing product

complexity (van Hoek and Weken, 1998). Furthermore, it has been suggested that

modular product designs can enable reduction in lead times, transportation costs and

inventory costs through improved coordination (Feitzinger and Lee, 1997). Accordingly,

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we include product modularity defined as the degree to which the products share

common and standardized components and are designed on common product platforms.

3.2 IT Infrastructure

To compete in the digital economy, firms continue to make significant investments in

IT infrastructures for competitive advantage. By digitizing their front and back-end

supply chain activities, firms can realize significant performance benefits (Zhu and

Kraemer, 2005). Such digitization provides a platform for business processes, current

and future, with reduced asset intensity and increased responsiveness to customer needs

(Davenport, 2005; Slywotzky et al., 2000). Thus, IT infrastructures that enable firms to

integrate inter-organizational systems with intra-organizational systems are critical

resources that can shape firm performance.

IT infrastructure has been characterized as an aggregation of its constituent

components (for example hardware, software etc.) to a set of shared IT services for the

firm (Broadbent, Weill andSt. Clair, 1999; Keen, 1991). A well-integrated IT

infrastructure is much more than individual physical components, as it requires standards

for the integration of data, applications, and processes. These standards have to be

negotiated and implemented in order to connect distributed applications (Ross, 2003;

Weill and Broadbent, 1998). Viewed from this perspective, IT infrastructures that

integrate systems within and across firm boundaries can result in reduced transaction

costs and enable collaborative governance through information sharing and joint

decision-making (Clemons, Reddi andRow, 1993).

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From our perspective, an integrated IT infrastructure enables consistent and real-time

transfer of information between SCM-related applications for functions that are

distributed across a focal firm’s supply chain partners. To capture the core properties of

IT infrastructure capabilities relevant to SCM, we identify data consistency, cross-

functional application integration, and reconfigurability as key characteristics of IT

infrastructure.

Data consistency is the degree to which common data definitions and consistency of

data have been established across a focal firm’s supply chain, a capability which has been

suggested as critical for process integration (Chen and Paulraj, 2004; Malone, Yates

andBenjamin, 1987). High levels of data consistency should be characterized by: (a)

common data definitions for key entities, such as customer and product, which have been

established across the systems of a focal firm and its supply chain partners and (b)

automated systems for accurate data capture.

Cross-functional application integration is the degree of real-time communication of

a focal-firm’s function-specific supply chain management applications with each other

and related ERP and CRM applications. We consider integration of applications for

supply chain planning and execution, and their integration with ERP and CRM systems,

all of which characterize the application infrastructure for end-to-end management of

supply chains (Kalakota and Robinson, 1999).

Reconfigurability is the ability of an firm to recombine components of its existing

infrastructure, or to integrate new systems from different vendors into existing

infrastructure in response to changing requirements (Keen, 1991). An IT infrastructure

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that is reconfigurable should enable the redesign of supply chain processes and offer

increasing flexibility for entry into product markets.

3.3 Relational orientation

The use of electronic markets enabled by IT is expected to strengthen the use of

market mechanisms for inter-firm transactions. However, Clemons et al. (1993) put forth

the “move to the middle” hypothesis where they argued that intangibles such as trust and

quality developed with fewer suppliers can play a role in achieving higher levels of

performance. For instance, the integration of supplier’s knowledge in product and

process design through routine interactions of teams was found to be positively correlated

with financial and non-financial performance (Tan, Kannan andHandfield, 1997).

To render an explanation of these performance gains, the relational exchange view of

the firm is posited as an alternative to the transaction exchange perspective for inter-firm

relationships (Zaheer and Venkatraman, 1994). This view suggests that exchange is an

ongoing sociological process where interactions and adaptations result in the

development of shared knowledge over time (Axelrod, 1984; Heide and John, 1992) and

where cooperation takes the place of opportunism (Dwyer, Schurr andOh, 1987). Such

long-term relationships do not have to be necessarily governed by fully-specified formal

contracts (Macaulay, 1963), instead they are governed by relational norms that are based

on internalization and mutual influence (Joshi and Stump, 1999; Macneil, 1980). The

relational orientation is also characterized by the commitment of resources to creating

tangible and intangible relationship specific assets (Dyer, 1994; Dyer, 2000).

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Based on the key aspects identified in the relational view of the firm (Chen and

Paulraj, 2004; Dyer and Singh, 1998; Dyer and Nobeoka, 2000), we focus on three

relational properties: i) relational interaction routines, ii) investment by suppliers in

relationship-specific assets, and iii) long-term orientation between the focal firm and its

supply chain partners.

Relational interaction routines are defined as the degree to which informal and

formal mechanisms are established for the exchange of information and knowledge

between the focal firm and its supply chain partners. This definition is informed by the

relational view of the firm (Dyer and Singh, 1998), which suggests that interaction

routines structure the coordination and communication with supply chain partners so that

information and knowledge are appropriately revealed and effectively combined. These

routines are represented by the recurrent patterns of interaction between partners and the

degree to which knowledge about best practices and new product-markets are shared

among them.

Relational asset specificity is defined as the degree to which a firm makes partner-

specific investments in tangible physical resources and knowledge of partner procedures,

culture, and technological know-how (Williamson, 1985). Relationship-specific assets

can consist of site-specific investments in production facilities, and customized tools and

machinery that the partners develop over a period of time (Williamson, 1985). In addition

to physical assets, investments in social capital can also constitute relationship specific

assets (Cohen and Levinthal, 1990; Tsai and Ghoshal, 1998) such as having intimate

knowledge about partner processes and preferences. Relational asset specificity is

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assessed in terms of site specificity, physical asset specificity and investments in social

capital for existing supply chain relationships.

Long-term orientation is defined as the degree to which long-term considerations,

objectives of mutual gain, and informal governance characterize a firm’s relationships

with its supply chain partners. The expectation of relationship continuity (Noordewier,

Johnson andNevin, 1990) may promote parties to underscore mutual benefits and not let

short-term opportunism disrupt the relationship. Moreover, informal reinforcement

mechanisms can be more effective and less costly than formal ones (Hill, 1995), as they

do not require detailed monitoring and enforcement. Finally, informal mechanisms are

self-reinforcing and difficult to imitate (Dyer and Singh, 1998). Accordingly, long-term

orientation of a focal firm’s supply chain relationships is characterized by trust, goodwill,

and a focus on mutual gain.

3.4 Process Integration

Effective management of supply chains depends on the integration of boundary

spanning organizational processes that include relational and IT capabilities that are

leveraged for performance (Ellram and Choi, 2000; Simchi-Levi D. et al., 2000). These

processes involve upstream and downstream flows of information, goods, and finances,

often across multiple channels. To achieve such levels of integration, firms need to

leverage not only their own but also their partner’s capabilities (Dyer and Singh, 1998).

Based on a review of the literature, process integration can be characterized by; (a)

standardization of supply chain processes, (b) institutionalization of processes that

integrate flows of raw materials and goods (Stevens, 1990), finances (Mabert and

Venkatraman, 1998), and information (Lee, Padmanabhan andWhang, 1997b) across the

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supply chain, and (c) institutionalization of processes that integrate these flows across

multiple channels (Gulati and Garino, 2000). To capture these aspects of process

integration, we focus on five constructs, each of which is now discussed.

Process standardization is defined as the deployment of standardized processes

across multiple product lines. Standardized processes are a formalized set of policies and

procedures that include coordination patterns, task configurations, and communication

patterns that enable routine deployment of best practices leading to better performance of

the process (Alavi and Leidner, 2001). Sequencing of production rules associated with

work processes, and process architectures that define a standardized configuration of

tasks and their interfaces help to achieve predictability, control, and efficiency (Demsetz,

1991; Grant, 1996). Such processes provide an important mechanism for firms to deploy

their knowledge assets to create organizational capabilities (Grant, 1996). Deployment

of standardized processes is assessed by the similarity across product lines in

manufacturing, procurement, management and marketing processes in the supply chain.

Physical flow integration is defined as the degree to which firm’s globally

optimize the stock and flow of materials and finished goods across their supply chains.

Downstream flows consist of raw material, subassemblies and finished goods, while

upstream flows encompass goods that are returned or need to be repaired. Integration of

these flows reduces inventory holdings, improves asset utilization, and customer service

(Gustin, Daugherty andStank, 1995). A variety of techniques, such as just-in-time

delivery (Lowson, King andHunter, 1999), automatic replenishment and vendor-managed

inventory programs (Daugherty, Myers andAutry, 1999; Ellinger, Taylor andDaugherty,

1999), and contracting with third-party logistics providers for inventory management

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services (Richardson, 2001; van Hoek, 2000), have been applied to integrate physical

flows. Accordingly, physical flow integration is assessed as the multi-echelon

optimization of costs, just-in-time deliveries, joint management of inventory with

suppliers and logistics partners, and distribution network configuration for optimal

staging of inventory.

Financial flow integration is defined as the extent to which the exchange of

financial resources between a focal firm and its supply chain partners is driven by

workflow events. The flow of finances was among the earliest business processes to be

reengineered so as to reduce delays, improve productivity, and eliminate redundant tasks

(Hammer, 1990). It has been suggested that streamlining financial flows by using event-

based triggers for payables and receivables can result in better working capital efficiency

and cash flow management (Greenfield, Patel andFenner, 2001); it can also improve

revenue growth by making resources available for alternate use and improve customer

relationships (Sabath and Frentzel, 1997). Integrating these flows requires management

of downstream flows such as pricing, invoices and credit terms, and upstream flows such

as payments and account payables. Thus, financial flow integration is assessed as the

extent to which accounts receivables and payables are automatically triggered by supply

chain events.

Information flow integration is defined as the extent to which operational, tactical,

and strategic information is shared by a firm with its supply chain partners. Sharing

operational information about in-transit and in-storage inventory can reduce total

inventory in the supply chain (Lee et al., 1997b; Seidmann and Sundarajan, 1997).

Similarly, sharing production and delivery schedules can enhance operational efficiencies

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through improved coordination of resource allocation, and execution of activities and

roles across the supply chain (Lee, So andTang, 2000). Moreover, information sharing

related to demand signals allows retailers, manufacturers, and suppliers to improve

forecasts, synchronize production and delivery, coordinate inventory-related decisions,

and develop a shared understanding of performance bottlenecks (Lee, 2000; Simchi-Levi

D. et al., 2000). Information flow integration is assessed in this study by the extent to

which a focal firm and its supply chain partners share demand-related information,

inventory and sales positions, production and delivery schedules, and performance

metrics as indicators of information flow integration.

Clicks-and-bricks integration is defined as the extent to which a focal firm uses

common supply chain processes (inbound and outbound) and resources (for example IT

infrastructure) for its online and brick-and-mortar channels. Integration of traditional

brick and mortar supply chains with online supply chains can provide benefits of cross

promotion, shared information, purchasing leverage and distribution economies (Gulati

and Garino, 2000). Accordingly, integration levels may range from total integration of

online and offline channels, to total separation between these channels.

4. The Empirical Study

4.1 Instrument Development

A survey instrument was developed and administered to supply chain and

logistics managers. This instrument was developed using guidelines in Straub (1989) and

Sethi and King (1991). For each of the capability constructs, we developed multi-item

measures that are listed in Appendix A. These measured were developed by drawing

upon prior research, industry reports, and discussions with several members in the

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practice community involved with the management of IT-enabled supply chain

improvement initiatives. The survey was subjected to a two-stage pilot test with IS

researchers and subsequently with supply chain and logistics managers.

Most items in the survey instrument used a seven-item Likert type scale where

respondents were asked to state their agreement with a given statement on a scale that

ranged from “strongly agree” to “strongly disagree” with its midpoint anchored as

“neither agree nor disagree.” Respondents were asked to respond to survey questions

with reference to the firm’s primary product(s) or product line(s). Primary product(s) or

product line(s) were defined as those that command a significant proportion of company

revenues, usually 15 to 20 percent, or greater, of revenues. The survey also asked

respondents to compare their firm’s performance in terms of operational excellence,

revenue growth, and customer relationships, relative to competing firms. The use of

subjective measures overcomes difficulties posed in comparing financial measures owing

to differences in accounting conventions (Powell and Dent-Micallef, 1997).

Furthermore, some of the performance measures, such as timeliness and customer

relationships, do not have equivalent accounting-based performance measures. A

semantic comparison scale was used for e firm performance where respondents were

asked to rate the performance of their firm as “much better than average,” “better than

average,” “same as competitors-average,” “slightly less than average” or “much less than

average” in comparison to their competitors. Standardized values for all variables were

used for subsequent analyses.

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4.2 Data Collection

The survey was mailed to supply chain and logistics managers who were also

attendees of the Annual Conference of the Council of Logistics Management1. A list of

approximately 1800 names from the set of attendees was randomly selected. All firms

that did not belong to manufacturing or retail industries (the first two digits of SIC codes

in the range of 20 to 39 and 52 to 59) were removed from the sample. The survey was

targeted to senior and middle level managers of the supply chain management or logistics

function in the organization. Names of manager whose titles most resembled the target

respondent profile were selected from each firm, which resulted in a final list of 432

manufacturing and retail firms. The first round of surveys was mailed out by

conventional mail followed by e-mail reminders were sent, providing respondents the

option of receiving another copy of the survey by regular mail or completing the survey

online. After accounting for undelivered surveys and invalid mailing addresses and

incorrect e-mail addresses, the effective number of surveys sent to supply chain and

logistics managers in 360 firms. We received a total of 110 combined responses via

return mail, web and e-mail. The effective response rate was 30.55%, which is

considered an acceptable response rate for survey-based research. The data were

examined for non-response bias using analysis of variance techniques. The last group of

respondents was considered as most likely to be similar to non-respondents, a comparison

of the first 25% of responses to the last 25% was used to test for response bias in the

sample as suggested by (Armstrong and Overton, 1977). The tests did not reveal any

response bias across study and demographic variables.

1 Now known as the Council of Supply Chain Professionals

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The median firm size was 4000 employees and the median firm revenue was 1.5

billion dollars with close to 60% of the respondents reporting revenues greater than 1

billion dollars. Majority of the respondents were from publicly listed firms (62.5%) with

the rest from private (15.4%) and subsidiaries of publicly listed firms (22.19%). Forty-

five percent of the respondents were from the logistics function, seventeen percent each

from the supply chain and distribution functions, twelve percent had responsibility for IT

pertaining to the supply chain, six percent specified that their direct responsibility

focused on e-commerce and digitization to support the supply chain, and three percent

belonged to the purchasing function. Collectively, our respondents appear to hold

positions that are well aligned to the subject matter of the survey and are thus likely to be

well informed about supply chain initiatives in their firms.

4.3 Measurement Validation

In the first stage of our analysis, we assessed measurement properties of the

constructs. Each construct was factor analyzed to determine if the items mapped onto

more than one underlying construct. All items loaded on to their respective constructs,

captured more than 50% of the variance in the construct and had a Cronbach’s alpha of .

67 and above, providing evidence of convergent validity (Appendix A). Discriminant

validity was assessed by examining the variance shared by a construct with its indicators

as compared to the variance shared with other constructs in the model. The analysis

shows that all constructs share a greater amount of variance with their measurement items

than with other constructs in the study (Appendix B). The highest correlation, r = 0.597,

between information flow integration and physical flow integration, indicates that

multicollinearity problems are unlikely to be manifest in the data (Hair, Anderson,

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Tathem andBlack, 1998). In addition, low correlations are observed among customer

relationships, revenue growth, and operational excellence, suggesting that each of these

three measures captures independent or nonoverlapping dimension of supply chain

performance. Finally, we examined the item-total correlations for these constructs. The

correlation pattern indicates that an item posited to form a construct has stronger

correlation with the multivariate mean of items in the construct than with multivariate

means of other constructs providing further evidence of discriminant and convergent

validity (Appendix C).

Although the constructs demonstrate good measurement properties, convergent

validity and internal consistency are required for formative constructs, which the

constructs in this study are. To briefly elaborate on this important issue, constructs are

considered as reflective, when each measurement item draws on to the same underlying

construct as opposed to formative, where each measurement item captures different facets

of the underlying construct. Jarvis et al.(2003) note that this is a common

misspecification in marketing where constructs that should be modeled as formative are

modeled as reflective. The criteria to model constructs as formative or reflective is based

on (i) direction of causality from construct to indicators, (ii) interchangeability of

indicators, (iii) co-variation among indicators, and (iv) nomological net of construct

indicators (Jarvis et al., 2003). For formative constructs indicators are considered to

‘form’ as opposed to ‘reflect’ constructs, are not necessarily interchangeable, need not

co-vary, and can be drawn from different nomological networks. The opposite conditions

apply in the case of reflective constructs. Based on the set of criteria, all constructs in

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this empirical study may be considered as formative and hence do not require to exhibit

convergence or internal consistency.

For subsequent analysis, unit means of items for each formative construct are used.

This is the recommended approach when new measures are developed and transferability

is a consideration (Hair et al., 1998). Moreover, linear composite scores based on

different weighting schemes are highly correlated when items are internally consistent

(Rozeboom, 1979), as is the case here.

4.4 Common method bias issues

Survey data based on perceptions of single respondent rather than actual states raises

concerns of potential common method bias in the data. One procedure that can be used

to assess the extent of this problem is Harmon's one-factor test (Podsakoff and Organ,

1986). The procedure requires that all items used to measure the dependent and

independent variables be entered into a single exploratory factor analysis. If a single

factor accounts for most of the variance in the data then common method bias is likely to

be a significant issue in the dataset. Factor analysis using all items produced 16 factors,

each of which with an eigenvalue greater than 1.0. Collectively these factors accounted

for 76% of the variance in the data where the first factor explained only about 19% of the

total variance. A similar factor analysis using only linear composites accounted for 67%

of the variance and the first factor explained only 27% of the total variance. Since a

single factor did not account for most of the variance, these results suggest that common

method bias is unlikely to be a significant issue in the collected data.

Further confidence in the data was gathered by examining the correlation between

firm revenue data reported by respondents and objective data reported in COMPUSTAT.

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Data for 57 companies were obtained from COMPUSTAT, as this subset of companies in

the sample were both publicly traded and for which respondents had provided SIC codes.

Self-reported revenue data and publicly reported COMPUSTAT data had a correlation

of .92 (p=.00) lending confidence in the survey data. Finally, the results of the analysis,

as reported below, show different levels of significance for constructs within and across

models that were tested. Thus, while the presence of common method bias cannot be

ruled out, the results for the Harmon-factor test, strong correlation between self-reported

and objective measures of firm revenue, and the different sets of constructs that are

significant in different models suggest that common-method bias is not a significant issue

in our study.

4.5 Analysis and results

After eliminating missing values, a total useful sample size of 82 remained. The three

facets of performance formed the dependent variables namely, customer relationships,

revenue growth, and operational excellence. Since our objective is to investigate the

effect of supply chain capabilities on the probability that the firm will be more successful

on each of the three key facets of performance, we dichotomize the dependent variables

by using the median as the cutoff point. This approach enables us to select the most

important variables that enhance the probability of high firm performance. Additionally,

the scales for measurement used are ordinal: while dichotomization reduces the power of

the tests, it reduces the risks of a Type I error. This is consistent with our objective,

which is to identify the most important variables and not just those that are significantly

related to dimensions of performance. In terms of the coding scheme used, a case is

coded as ‘1’ if its dependent variable is greater than the cutoff point and coded as ‘0’

21

otherwise. By using this approach, we create two groups of companies for each

performance variable with group 1 (code 1) representing “above average performance”,

and group 2 (code 0) “equal to or below average” performance.”

For each dependent variable, we build two logistic regression models. The first

model (Model 1) predicts the likelihood of enhanced performance based on product

characteristics (i.e., product modularity and consumer demand predictability) while the

second model (Model 2) also incorporates the remaining variables. This two-step

approach enabled us to first evaluate the effects of product modularity, which are

relatively less in the control of a single firm and tend to correlate with industry structure

and market dynamics (Fine, 1998; Ulrich, 1995). Moreover, the effects of product

modularity, especially on customization, operational requirements, and market-based

outcomes (Fine, 1998; Worren, Moore andCardona, 2002), have received more attention

than other advanced integration capabilities in terms of IT, processes, and relationship

management. Subsequent to the examination of the effects of product modularity, we

evaluate the effects of these other capabilities. We now discuss the results for each of the

three dimensions of firm performance.

Customer Relationships

Table 2 reports the results of the logistic regression analysis regarding the effects of

independent variables on the likelihood of enhancing customer relationships. Overall,

both models are significant based on Hosmer-Lemeshow goodness of fit test. As Model 1

shows, product modularity has a strong effect on the probability of enhanced customer

relationships. The higher the product modularity, the more likely the firm will have

22

enhanced customer relationships (Exp(B) = 32.66, p=0.03). However, consumer demand

predictability does not impact customer relationship significantly.

----------------

Insert Table 2 here

-----------------

Results from Model 2 suggest that product modularity, relational asset specificity,

and financial flow integration have a significant effect on the likelihood of enhanced

customer relations at the 0.10 significance level. Thus, the probability of quality of

customer relationships is enhanced though higher product modularity and asset

specificity. These approaches enable firms to generate greater product variety, possibly

approximating the actual customer requirements better, and to achieve closer

coordination with customer’s supply chain processes through asset integration. However,

the higher the financial flow integration, the lower the probability of enhanced customer

relationship. This may possibly be due to the implication of higher levels of financial

flow integration requiring faster payments from their customers and less flexible payment

terms.

Revenue Growth

Table 3 shows the results for the dependant variable of revenue. Model 1 suggests

that the two product characteristics do not have a strong effect on revenue enhancement.

Model 2, on the other hand, suggests that physical flow integration is the only variable

that has a significant impact on the likelihood of increased revenue. The odds ratio

suggests that the higher level of physical flow integration, the higher the probability that

the firm will generate higher revenue (Exp(B)=100.09, p=0.02). Thus, our results

suggest that by globally optimizing the stock and flow of materials and finished goods is

23

a key capability in realizing revenue growth from existing product lines and markets.

Although other capabilities do not have a direct association with this facet of firm

performance, it is quite conceivable that higher levels of physical flow integration are

achieved by leveraging modularity and IT capabilities.

----------------

Insert Table 3 here

-----------------

Operational Excellence

Table 4 reports the results for operational excellence. We find from Model 1 that

product modularity has a significant effect on the likelihood of improving operational

excellence. The higher the modularity, the more likely companies will achieve

operational excellence (Exp(B)=21.71, p=0.05). However, the effect of product

modularity is not robust to the inclusion of other process-related variables. Model 2

shows that with the introduction of other process variables, product modularity is not

significant. Two variables of data integration and cross-functional application integration

from the IT infrastructure category are significantly related to the likelihood of

operational excellence. However, their roles in achieving operational excellence are

different. The higher the data integration level, the greater the probability of operational

excellence (Exp(B)=34.73, p=0.05), while the higher the application integration level, the

lower the probability of operational excellence (Exp(B)=0.02, p=0.02). This is a

particularly interesting finding suggesting that application integration may establish

rigidities that constrain adaptation, while standards for the exchange of consistent data

plays a critical enabler role for the coordination of supply chain operations.

24

----------------

Insert Table 4 here

-----------------

5. Conclusions

Researchers in operations management, logistics, marketing, information systems and

strategic management (Otto and Kotzab, 2003) have identified a variety of supply chain

capabilities that are likely to influence firm performance. Different firms may have

different performance objectives based on their competitive strategy and competitive

position in the market. In managing their resources firms may need to focus on those

supply chain capabilities that are likely to have an impact on specific performance

outcomes that complement their strategy. We explore this issue by reviewing the

literature and identifying supply chain factors under four categories as (i) product

architecture, (ii) IT infrastructure, (iii) relational orientation and (iv) process integration

that could potentially influence different aspects of firm performance. A total of 13

different supply chain capabilities were included in the study. The three facets of firm

performance considered relevant in assessing firm performance (Slywotzky et al., 2000)

and included in the study were operational excellence, revenue growth, and customer

relationships. We used logistic regression models to establish the relationship between

supply chain capabilities with facets of firm performance to identify capabilities with the

most explanatory power in explaining relative firm performance.

----------------

Insert Table 5 here

-----------------

The pattern of relationships that emerges from our analysis is summarized below

(Table 5). Our results provide evidence that the three facets of performance need to be

25

managed by establishing capabilities that span product characteristics, relational

orientation, IT infrastructure, and process characteristics. By establishing modular

products and relationship-specific assets, firms can improve customer relationships

through configuration and customization of offerings that better align with the

requirements of customers. Additionally, revenue growth can be enabled by establishing

agile and efficient physical flow capabilities that fulfill demand for new products in

existing markets or for existing products in new markets. Finally, standards for the

exchange of consistent data play a major role in the coordination of supply chain

operations, while integrated applications and financial flows can lead to constrained

operations and customer relationships.

Negative impacts of integration and performance may be attributed to

implementation of integrated SCM or ERP systems that are inflexible. Seamless end-to-

end application integration specifically may trap firms in rigid workflow and exchange

processes that may not be easy to modify. This can constrain changes in vendors, supply

network memberships, or agility in response to changes in industry or market structure.

It is important, then, for supply chain managers to explore integration approaches that

enable coordination and in addition enable adaptation of processes and relationships

based on shifting requirements. For example, process interface protocols (PIPs) that

enforce interfaces for data exchange but render degrees of freedom on choices related to

applications and process design, may provide a mechanism to achieve coordination with

supply chain partners while retaining flexibility for adaptation.

The study and its conclusions are subject to limitations that need to be recognized

for cautious and appropriate interpretation of results. The study sample is limited to only

26

manufacturing and retail firms limiting its generalizability to other sectors. A self-report

survey instrument based on perceptual measures is used for data collection. We focused

on a single firm as the unit of analysis and a single respondent in each firm to report

information about the firm’s supply chain. Additional points of analysis for each sample

point would improve the reliability of our results.

References

Alavi M.and Leidner D. E., 2001. Review: Knowledge management and knowledge management systems: Conceptual foundations and research issues. MIS Quarterly 25(1), 107-136.

Armstrong J.and Overton T., 1977. Estimating nonresponse bias in mail surveys. Journal of Marketing Research 14(3), 396-402.

Axelrod R., 1984. The evolution of cooperation. Basic Books, New York.

Baldwin C. Y.and Clark K. B., 1997. Managing in an age of modularity. Harvard Business Review 75(5), 84-93.

Bensaou M., 1999. Portfolios of buyer-supplier relationships. Sloan Management Review 40(4), 35-44.

Broadbent M., Weill P.and St. Clair D., 1999. The implications of information technology infrastructure for business process redesign. MIS Quarterly 23(2), 159-182.

Chen I. J.and Paulraj A., 2004. Towards a theory of supply chain management: The constucts and measurements. Journal of Operations Management 22(2), 119-150.

Clemons E. K., Reddi S. P.and Row M. C., 1993. The impact of information technology on the organization of economic activity: The `move to the middle' hypothesis. Journal of Management Information Systems 10(2), 9-35.

Cohen W. M.and Levinthal D. A., 1990. Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly 35(1), 128-152.

Daugherty P. J., Myers M. B.and Autry C. W., 1999. Automatic replenishment programs. Journal of Business Logistics 20(2), 63-82.

Davenport T. H., 2005. The coming commoditization of processes. Harvard Business Review 83(6), 101-110.

Demsetz H., 1991. The theory of the firm revisited. In:(Williamson O. E.,Winter S. G., Eds), The nature of the firm. Oxford University Press, New York, pp 159-178.

Dwyer F., Schurr P.and Oh S., 1987. Developing buyer-supplier relationships. Journal of Marketing 51(2), 11-27.

27

Dyer J. H., 1994. Dedicated assets: Japan's manufacturing edge. Harvard Business Review 72(6), 174-178.

Dyer J. H., 2000. Collaborative advantage : Winning through extended enterprise supplier networks. Oxford University Press, New York, New York, U.S.A.

Dyer J. H.and Singh H., 1998. The relational view: Cooperative strategy and sources of interorganizational competitive advantage. Academy of Management Review 23(4), 660-679.

Dyer J. H.and Nobeoka K., 2000. Creating and managing a high performance knowledge-sharing network: The toyota case. Strategic Management Journal 21(3), 345-367.

Ellinger A. E., Taylor J. C.and Daugherty P. J., 1999. Automatic replenishment programs and level of involvement: Performance implications. The International Journal of Logistics Management 10(1), 25-36.

Ellram L. M.and Choi T., 2000. Supply management for value enhancement. National Association of Purchasing Management, Tempe.

Evans P.and Wurster T. S., 1999. Blown to bits: How the new economics of information transforms strategy. Harvard Business School Publishing, Inc., Cambridge, Massachusetts, U.S.A.

Feitzinger E.and Lee H. L., 1997. Mass customization at hewlett packard: The power of postponement. Harvard Business Review 75(1), 116-121.

Fine C., 1998. Clockspeed: Winning industry control in the age of temporary advantage. Perseus Books, Reading, MA.

Fisher M. L., 1997. What is the right supply chain for your product. Harvard Business Review 75(2), 105-116.

Grant R., 1996. Prospering in dynamically competitive environments: Organizational capability as knowledge integration. Organization Science 7, 375-387.

Greenfield A., Patel J.and Fenner J., 2001. Online invoicing ready for business-to-business users. Information Week, pp 80-82.

Gulati R.and Garino J., 2000. Get the right mix of bricks and clicks. Harvard Business Review 78(3), 107-114.

Gustin C. M., Daugherty P. J.and Stank T. P., 1995. The effects of information availability on logistics integration. Journal of Business Logistics 16(1), 1-12.

Hair J. F., Anderson R. E., Tathem R. L.and Black W. C., 1998. Multivariate data analysis. Prentice Hall, Upper Saddle River, New Jersey, U.S.A.

Hammer M., 1990. Reengineering work: Don't automate, obliterate. Harvard Business Review 68(4), 104-114.

Heide J. B.and John G., 1992. Do norms matter in marketing relationships? Journal of Marketing 56(2), 32-44.

Hill C. W. L., 1995. National institutional structures, transaction cost economizing, and competitive advantage: The case of japan. Organization Science 6(1), 119-131.

28

Hult G. T., Ketchen D. J.and Slater S. F., 2004. Information processing, knowledge development, and strategic supply chain performance. Academy of Management Journal 47(2), 243-253.

Jarvis C. B., Mackenzie S. B.and Podsakoff P. M., 2003. A critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of Consumer Research 30(2), 199-218.

Joshi A. W.and Stump R. L., 1999. Determinants of commitment and opportunism: Integrating and extending insights from transaction cost economics and relational exchange theory. Canadian Journal of Administrative Sciences 16(4), 334-352.

Kalakota D. R.and Robinson M., 1999. E-business: Roadmap for success. Addison-Wesley, Reading.

Keen P. G. W., 1991. Shaping the future: Business design through information technology. Harvard Business School Press, Cambridge, MA.

Lambert D. M., Cooper M. C.and Pagh J. D., 1998. Supply chain management: Implementation issues and research opportunities. The International Journal of Logistics Management 9(2), 1-19.

Lampel J.and Mintzberg H., 1996. Customizing customization. Sloan Management Review 38(1), 21-30.

Lee H., Padmanabhan V.and Whang S., 1997a. The bullwhip effect in supply chains. Sloan Management Review 38(3), 93-102.

Lee H., So K.and Tang C., 2000. The value of information sharing in a two-level supply chain. Management Science 46(5), 626-643.

Lee H. L., 2000. Creating value through supply chain integration. Supply Chain Management Review, pp 30-36.

Lee H. L., Padmanabhan V.and Whang S., 1997b. Information distortion in supply chain: The bullwhip effect. Management Science 43(4), 546-558.

Lowson B., King R.and Hunter A., 1999. Quick response: Management of the supply chain to meet consumer demand. John Wiley & Sons, London, UK.

Mabert V. A.and Venkatraman M. A., 1998. Special research focus on supply chain linkages: Challenges for design and management in the 21st century. Decision Sciences 29(3), 537-550.

Macaulay S., 1963. Non-contractual relations in business: A preliminary study. American Sociology Review 28(1), 55-67.

Macneil I. R., 1980. The new social contract: An inquiry into modern contractual relations. Yale University Press, New Haven, CT.

Magretta J., 1998. The power of virtual integration: An interview with dell computer's michael dell. Harvard Business Review 76(2), 72-84.

Malhotra A., Gosain S.and El Sawy O. A., 2005. Absorptive capacity configurations in supply chains: Gearing for partner-enabled market knowledge creation. MIS Quarterly 29(1), 145-187.

29

Malone T., Yates J.and Benjamin R. I., 1987. Electronic markets and electronic hierarchies. Communications fo the ACM 30(6), 484-497.

Noordewier T. G., Johnson G.and Nevin J. R., 1990. Performance outcomes of purchasing arrangements in industrial buyer-vendor relationships. Journal of Marketing 54(4), 80-93.

Otto A.and Kotzab H., 2003. Does supply chain management really pay? Six perspectives to measure the performance of managing a supply chain. European Journal of Operational Research 144(2), 306-320.

Podsakoff P. M.and Organ D. W., 1986. Self-reports in organizational research: Problems and prospects. Journal of Management 12(4), 531-544.

Powell T. C.and Dent-Micallef A., 1997. Information technology as competitive advantage: The role of human, business, and technology resources. Strategic Management Journal 18(5), 375-405.

Rai A., Patnayakuni R.and Seth N., 2006. Firm performance impacts of digitally-enabled supply chain integration capabilities. MIS Quarterly 30(2), 225-246.

Ramdas K.and Spekman R. E., 2000. Chain or shackles: Understanding what drives supply-chain performance. Interfaces 30(4), 3-21.

Reilly K., 2004. Amr research finds $488 billion in new operating margin available to u. S. Manufacturers. AMR Research.

Richardson H. L., 2001. Where's the value? Transportation and Distribution, pp 55-58.

Ross J. W., 2003. Creating a strategic it architecture competency: Learning in stages. MISQ Executive 2(1), 31-43.

Rozeboom W. W., 1979. Sensitivity of a linear composite predictor items to differential item weighting. Psychometrika 44(3), 289-296.

Sabath R. E.and Frentzel D. G., 1997. Go for growth: Supply chain management's role in growing revenues. Supply Chain Management Review 1(2), 16-24.

Seidmann A.and Sundarajan A., 1997. Building and sustaining interorganizational information sharing relationships: The competitive impact of interfacing supply chain operations in marketing strategy. In:(Degross J.,Kumar K., Eds), International Conference on Information Systems, Atlanta, GA, 205-222.

Sethi V.and King W. R., 1991. Construct measurement in information systems research: An illustration in strategic systems. Decision Sciences 22(3), 455-472.

Simchi-Levi D., Kaminsky P.and E. S.-L., 2000. Designing and managing the supply chain: Concepts, strategies, and case studies. Irwin/McGraw-Hill, New York.

Slywotzky A. J., Morrison D. J.and Weber K., 2000. How digital is your business? Crown Business., New York, NY.

Stevens G. C., 1990. Successful supply chain management. Management Decision 28(8), 25-30.

Straub D., 1989. Validating instruments in mis research. MIS Quarterly 13(2), 147-169.

30

Tan C., Kannan V. R.and Handfield R. B., 1997. Supply chain management: Supplier performance and firm performance. International Journal of Purchasing and Materials Management 34(3), 2-9.

Tsai W.and Ghoshal S., 1998. Social capital and value creation: The role of intrafirm networks. Acadmey of Management Journal 41(4), 464-476.

Tyndall G. P., Gopal, C., Partsch, W., and Kamauff, J.W., 1998. Supercharging supply chains: New ways to increase value through global operational excellence. John Wiley & Sons, New York, NY.

Ulrich K., 1995. The role of product architecture in the manufacturing firm. Research Policy 24, 419-440.

Van Hoek R. I., 1998. Reconfiguring the supply chain to implement postponed manufacturing. International Journal of Logistics Management 9(1), 95-110.

Van Hoek R. I., 2000. The role of third party logistics providers in mass customization. The International Journal of Physical Distribution and Logistics Management 11(1), 37-46.

Van Hoek R. I.and Weken A. M., 1998. The impact of modular production on the dynamics of supply chains. International Journal of Logistics Management 9(2), 35-50.

Weill P.and Broadbent M., 1998. Leveraging the new infrastructure:How market leaders capitalize on information technology. Harvard Business School Press, Cambridge, MA.

Williamson O. E., 1985. The economic institutions of capitalism. Free Press, New York, NY.

Worren N., Moore K.and Cardona P., 2002. Modularity, strategic flexibility and firm performance: A study of the home appliance industry. Strategic Management Journal 23(12), 1123-1140.

Zaheer A.and Venkatraman N., 1994. Determinants of electronic integration in the insurance industry: An empirical test. Management Science 40(5), 549-566.

Zahra S. A.and George G., 2002. The net-enabled business innovatin cycle and the evolution of dynamic capabilities. Information Systems Research 13(2), 147-150.

Zhu K.and Kraemer K. L., 2005. Post-adoption variations in usage and value of e-business by organizations: Cross-country evidence from the retail industry. Information Systems Research 16(1), 61-84.

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Appendix A: Measurement Properties of Scale Items

Item Factor Loading

Product Characteristics: Consumer Demand Predictability

CDP1 There is a high margin of error in product forecasts. .797

CDP2 Products have a short life cycle (< 1 year). .797

Cronbach’s Alpha NA

Variance Explained 63.4%

Product Characteristics: Product Modularity

PM1 Uniqueness in product parts and designs has been minimized. .661

PM2 Product parts and subassemblies are shared across many products. .804

PM3 Production processes are shared across many products. .849

PM4 Products have a modular design. .749

Cronbach’s Alpha .71


IT Infrastructure: Reconfigurability

ITR1 The processing capability of our IT infrastructure (transaction processing) can be scaled up or down as needed. .606

ITR2 Our IT infrastructure constrains us in redesigning supply chain processes. .870

ITR3 Our IT infrastructure prevents us from changing our supply chain partners (e.g. suppliers, customers, logistics partners). .865

ITR4 Our IT infrastructure prevents us from changing communication and reporting linkages across departments and the supply chain. .860



IT Infrastructure: Data Consistency

ITD1 Automatic data capture systems are used (e.g. bar code) across the supply chain. .702

ITD2 Definitions of key data elements (e.g. customer, order, and part number) are common across the supply chain. .891

ITD3 Same data (e.g. order status) stored in different databases across the supply chain is consistent. .817


Variance Explained65.14%

IT Infrastructure: Cross Functional Application Integration

The following applications communicate in real-time:

32


Item Factor Loading

ITA1 Supply chain planning applications (e.g. Demand planning, transportation planning, manufacturing planning). .820

ITA2 Supply chain transaction applications (Order management, procurement, manufacturing and distribution). .864

ITA3 Supply chain applications with internal applications of our organization (such as enterprise resource planning). .855

ITA4 Customer relationship applications with internal applications of our organization. .793



Relational Orientation: Relational Interaction Routines

RIR1 We have created formal and informal arrangements for information exchange with our partners. .805

RIR2 Partners are involved in quality and improvement initiatives. .891

RIR3 We share best practices with our partners. .897

RIR4 We learn about new technologies and markets from our partners. .846



Relational Orientation: Long Term Orientation

LTO1 We have long-term relationships with strategic partners. .766

LTO2 In key partner relationships, trust and goodwill have the same, or greater, significance as formal contracts. .867

LTO3 Both sides in the relationship do not make any demands that can hurt the relationship. .745



Relational Orientation: Relational Asset Specificity

RAS1 Partner tools and machinery are customized to our needs. .715

RAS2 Partners have dedicated significant investment and capacity to our relationship. .851

RAS3 Partner knowledge of our procedures, culture and technological know-how is difficult to replace. .772



Process Integration: Information Flow Integration

IFI1 Production and delivery schedules are shared across the supply .748

33


Item Factor Loading

chain.

IFI2 Performance metrics are shared across the supply chain. .842

IFI3 Supply chain members collaborate in arriving at demand forecasts. .820

IFI4 Our downstream partners (e.g. distributors, wholesalers, retailers) share their actual sales data with us. .665

IFI5 Inventory data are visible at all steps across the supply chain. .701



Process Integration: Physical Flow Integration

PFI1 Inventory holdings are minimized across the supply chain. .781

PFI2 Supply chain wide inventory is jointly managed with suppliers and logistics partners (e.g. UPS, FedEx). .780

PFI3 Suppliers and logistics partners deliver products and materials just in time. .689

PFI4 Distribution networks are configured to minimize total supply chain –wide inventory costs. .630



Process Integration: Financial Flow Integration

FFI1 Account receivables processes are automatically triggered when we ship to our customers. .828

FF2 Account payable processes are automatically triggered when we receive supplies from our suppliers. .828



Process Integration: Integration of Clicks and Bricks

ICB1Our online channels and traditional channels use the same supply chain for production, inventory management, warehousing, and distribution.

.875

ICB2 Our online channels and traditional channels use the same supply chain for marketing, sales, and customer relationship management. .901

ICB3Our online channels and traditional channels use the same information technology infrastructure (hardware, software, networks, and operating systems).

.745



Process Integration: Process Standardization

34


Item Factor Loading

Please indicate how similar the following are across the primary product(s) or product lines:

PS1 Technical know-how and manufacturing processes. .757

PS2 Procurement processes. .839

PS3 Management processes. .916

PS4 Sales, marketing and, distribution. .711


Variance Explained 65.52

Firm Performance: Operational Excellence

OE1 Product delivery cycle time. .744

OE2 Timeliness of after sales service. .842

OE3 Productivity improvements (e.g. assets, operating costs, labor costs). .741



Firm Performance: Revenue Growth

RG1 Increasing sales of existing products. .869

RG2 Finding new revenue streams (e.g. new products, new markets). .869



Firm Performance: Customer Relationships

CR1 Strong and continuous bond with customers. .873

CR2 Precise knowledge of customer buying patterns. .873



35

Appendix B: Discriminant Validity and Descriptives

CDP PM ITR ITD ITA RIR LTO RAS IFI PFI FFI ICB PS OE RG CR

Consumer Demand Predictability (CDP) .797*

Product Modularity (PM) .298 .769IT Reconfigurability (ITR) -.054 -.039 .808Data Consistency (ITD) .304 .261 -.424 .807Cross Functional Application Integration (ITA) .144 .122 -.388 .541 .833

Relational Interaction Routines (RIR) .064 .275 -.203 .362 .285 .860

Long Term Orientation (LTO) .091 .260 -.263 .226 .182 .408 .794Relational Asset Specificity (RAS) -.043 .115 .003 .095 .182 .385 .301 .781Information Flow Integration (IFI) .093 .212 -.304 .560 .423 .498 .285 .272 .758Physical Flow Integration (PFI) .165 .294 -.210 .372 .264 .535 .462 .407 .557 .723Financial Flow Integration (FFI) .070 .111 -.319 .205 .178 .248 .188 .255 .213 .268 .828Integration of Clicks and Bricks (ICB) -.039 .087 -.195 .052 .126 .130 .185 .199 .090 .034 .102 .843

Process Standardization (PS) .065 .507 -.068 .132 .032 .132 .284 .181 .025 .058 .194 .520 .809Operational Excellence (OE) -.010 .103 .355 .284 .234 .303 .306 .164 .294 .307 .243 .078 .005 .777Revenue Growth (RG) -.042 .137 -.012 .155 .157 .253 .134 .112 .280 .309 -.039 .009 .035 .252 .869Customer Relationships (CR) .071 .118 -.201 .172 .126 .241 .359 .323 .156 .337 .142 .168 .091 .398 .316 .873Mean 4.32 4.31 3.90 4.57 4.18 4.94 5.04 4.61 4.06 3.93 5.12 4.60 3.52 3.63 3.49 3.78Standard Deviation 1.02 .96 1.40 1.47 1.50 1.14 1.05 1.17 1.24 1.25 1.34 1.55 .62 .67 .79 .73

36

Appendix C: Item Construct Correlations


CDP1 .735 .207 .013 .122 .026 -.061 .046 -.081 -.012 .034 -.031 -.171 -.036 -.056 -.024 -.070CDP2 .851 .265 -.087 .337 .184 .138 .093 .002 .141 .208 .123 .078 .124 .029 -.042 .155PM1 .224 .626 -.068 .249 .129 .118 .052 .148 .230 .205 .141 .098 .309 .122 -.057 .049PM2 .207 .781 -.103 .167 .071 .183 .180 .068 .097 .208 .041 .056 .444 .053 .175 .141PM3 .234 .810 -.065 .230 .073 .245 .293 .033 .146 .228 .072 .088 .350 .102 .139 .146PM4 .210 .712 .115 .122 .082 .259 .241 .081 .144 .219 .070 .017 .403 .028 .146 .017ITR1 .012 -.023 .666 -.333 -.407 -.258 -.204 -.025 -.187 -.236 -.332 -.115 -.077 -.248 -.043 -.097ITR2 -.068 .050 .858 -.340 -.279 -.107 -.223 .090 -.258 -.130 -.200 -.136 .126 -.323 .001 -.193ITR3 -.086 -.048 .840 -.253 -.232 -.073 -.204 -.054 -.194 -.133 -.179 -.160 -.121 -.283 -.047 -.220ITR4 -.035 -.106 .845 -.429 -.326 -.209 -.210 -.006 .331 -.175 -.310 .216 .158 -.287 .049 -.139ITD1 .229 .177 -.201 .751 .319 .149 .053 .068 .305 .216 .083 -.025 .091 .155 .102 .087ITD2 .306 .229 -.409 .870 .513 .375 .239 .088 .531 .368 .204 .097 .123 .281 .110 .167ITD3 .192 .216 -.424 .787 .481 .355 .258 .070 .522 .314 .215 .055 .096 .255 .166 .163ITA1 .117 .164 -.279 .518 .833 .185 .100 .174 .447 .321 .051 .185 .111 .264 .215 .175ITA2 .199 .086 -.255 .482 .862 .211 .164 .130 .251 .171 .121 .091 .047 .186 .053 .075ITA3 .157 .047 -.409 .440 .858 .317 .201 .184 .333 .197 .279 .109 -.027 .146 .156 .076ITA4 -.004 .108 -.353 .354 .787 .235 .170 .149 .387 .218 .209 .043 -.043 .196 .085 .102RIR1 .115 .193 -.206 .202 .199 .809 .395 .274 .317 .400 .208 .198 .168 .265 .184 .161RIR2 .028 .239 -.157 .335 .243 .883 .284 .376 .428 .447 .186 .039 .056 .281 .196 .235RIR3 .077 .291 -.195 .348 .336 .898 .390 .375 .461 .530 .203 .129 .129 .223 .222 .209RIR4 .000 .221 -.142 .357 .195 .848 .338 .305 .500 .469 .258 .080 .093 .280 .268 .229LTO1 .105 .245 -.276 .158 .016 .405 .744 .338 .174 .404 .206 .187 .408 .178 .110 .335LTO2 .048 .150 -.150 .166 .189 .214 .869 .144 .155 .207 .024 .098 .242 .204 .100 .207LTO3 .071 .239 -.241 .214 .213 .374 .765 .244 .345 .484 .197 .174 .043 .323 .087 .308RAS1 .078 .096 .026 .076 .104 .202 .173 .751 .275 .322 .197 .106 .057 .120 .021 .166RAS2 -.112 .079 -.140 .144 .144 .428 .297 .827 .226 .349 .219 .216 .197 .203 .123 .322RAS3 -.077 .089 .116 .010 .010 .278 .233 .764 .134 .276 .176 .142 .164 .068 .124 .274

37

Appendix C: Item Construct Correlations


IFI1 .073 .220 -.348 .399 .313 .376 .201 .216 .745 .487 .208 .080 .048 .160 .252 .065IFI2 .037 .136 -.241 .430 .210 .408 .178 .220 .821 .448 .175 -.019 -.040 .250 .208 .098IFI3 .094 .101 -.233 .448 .220 .415 .241 .222 .807 .382 .096 .129 .141 .271 .140 .213IFI4 -.013 .115 -.092 .296 .235 .292 .170 .257 .697 .376 .179 .023 .020 .203 .187 .139IFI5 .160 .235 -.234 .539 .596 .391 .280 .116 .727 .412 .135 .137 -.063 .249 .280 .085PFI1 .042 .196 -.217 .265 .279 .422 .341 .340 .328 .770 .248 .226 .143 .168 .261 .238PFI2 .228 .315 -.046 .214 .092 .353 .319 .332 .446 .760 .284 -.086 .022 .193 .160 .178PFI3 .062 .112 -.028 .267 .126 .378 .244 .267 .372 .694 .247 -.052 .006 .205 .228 .188PFI4 .164 .186 -.312 .327 .247 .374 .409 .218 .468 .657 -.011 -.023 -.041 .308 .242 .362FFI1 -.015 .035 -.183 .067 .029 .147 .052 .254 .009 .069 .784 .008 .158 .116 -.175 .114FFI2 .113 .134 -.331 .252 .243 .254 .240 .179 .312 .345 .867 .145 .156 .270 .081 .121ICB1 -.031 .032 -.157 -.054 .037 .131 .208 .208 -.024 .054 .047 .863 .437 .112 -.016 .244ICB2 -.015 .128 -.113 .072 .096 .148 .134 .175 .124 .061 .098 .879 .424 .030 -.012 .101ICB3 -.050 .067 -.217 .114 .183 .053 .121 .120 .130 -.026 .112 .782 .430 .051 .048 .074PS1 .093 .438 -.095 .137 .020 .114 .279 .116 .136 .130 .202 .387 .783 .065 .101 .167PS2 .090 .447 -.053 .143 .058 .177 .267 .228 .051 .084 .081 .411 .822 .078 -.004 .023PS3 .057 .383 -.031 .106 -.059 .111 .243 .208 -.024 .009 .186 .464 .899 -.016 -.012 .096PS4 -.038 .381 -.033 .034 .086 .023 .119 .036 -.098 -.049 .147 .416 .720 -.120 .017 -.008OE1 .032 .060 -.206 .163 .155 .183 .202 .119 .207 .205 .109 .021 -.028 .780 .234 .318OE2 -.070 .075 -.343 .245 .228 .239 .336 .190 .261 .229 .293 .169 .054 .807 .119 .371OE3 .006 .105 -.291 .260 .167 .289 .184 .076 .222 .283 .180 .004 -.006 .738 .223 .239RG1 -.104 .188 -.106 .190 .163 .255 .232 .102 .341 .287 .031 .012 .149 .350 .850 .427RG2 .022 .056 .074 .087 .114 .187 .015 .093 .158 .252 -.092 .005 -.071 .105 .887 .141CR1 .099 .168 -.137 .168 .053 .207 .334 .276 .065 .250 .204 .163 .183 .419 .254 .860CR2 .027 .040 -.211 .134 .161 .212 .292 .286 .201 .334 .050 .131 -.019 .282 .296 .885

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Table 1: Construct definitions

Construct Definitions

Product Architecture

Product Modularity The degree to which the products share common and standardized components and are designed on common product platforms.

Consumer demand predictability The degree of forecasting uncertainty in predicting consumer demand.

IT Infrastructure

Reconfigurability The degree to which components of the IT infrastructure can be recombined and integrated to create new systems in response to changing requirements.

Data Consistency The degree to which common data definitions and consistent data is present.

Cross functional application integration

The degree of real-time communication of a focal-firm’s function-specific supply chain management applications with each other and related ERP and CRM applications.

Relational orientation

Relational interaction routines

The degree to which informal and formal mechanisms are established for the exchange of information and knowledge between a focal firm and its supply chain partners.

Long-term orientation

The degree to which long-term considerations, mutual gains, and informal governance characterize a focal firm’s relationships with its partners

Relational asset specificity

The degree to which a focal firm’s suppliers make partner-specific investments in tangible physical resources and intangible know how.

Process Integration

Information flow integration

The degree to which operational, tactical and strategic information and shared.

Physical flow integration

The degree to which inventory and physical flow of goods is globally optimized.

Financial flow integration The degree to which financial flows are driven by workflow events.

Clicks-and-bricks Integration

The degree to which common supply chain processes are deployed for online and brick-and-mortar channels.

Process standardization

The degree to which business processes are standardized across product lines.

Firm Performance


The degree to which a focal firm is better than its competitors in its responsiveness and in generating productivity improvements.

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Table 1: Construct definitions

Construct Definitions

Customer Relationship

The degree to which the focal firm’s relationship with customers and information about their preferences is better than its competitors.

Revenue Growth The degree to which the focal firm’s increase in revenue from current and new products and markets is more than its competitors.

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Table 2. Logistic regression results for Customer Relationships

Model 1 Model 2Variables B Wald Sig. Exp(B) B Wald Sig. Exp(B)

Product

Product Modularity 3.49 4.61 0.03 32.66 3.92 3.12 0.08 50.30Consumer Demand Predictability 0.43 0.09 0.76 1.53 -0.83 0.23 0.63 0.43

IT Infrastructure

ReconfigurabilityData consistencyCross Functional Application Integration

0.02 0.00 0.99 1.021.86 1.29 0.26 6.41

0.79 0.29 0.59 2.21

RelationalRelational Interaction routinesLong-term OrientationRelational Asset Specificity

-3.13 2.46 0.12 0.042.65 1.94 0.16 14.09

3.64 3.46 0.06 38.21

ProcessInformation flow IntegrationPhysical flow IntegrationFinancial flow IntegrationClicks-and-bricks IntegrationProcess Standardization

-1.22 0.44 0.51 0.301.32 0.46 0.50 3.75-2.78 3.67 0.06 0.061.62 1.00 0.32 5.05

-0.77 0.11 0.74 0.46

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Table 3. Logistic regression results for Revenue Growth

Model 1 Model 2Variables B Wald Sig. Exp(B) B Wald Sig. Exp(B)

Product

Product Modularity 1.47 0.92 0.34 4.35 0.09 0.00 0.96 1.10Consumer Demand Predictability 0.71 0.26 0.61 2.03 0.75 0.20 0.65 2.11

IT Infrastructure

ReconfigurabilityData consistencyCross Functional Application Integration

-1.15 0.63 0.43 0.32-0.67 0.18 0.67 0.51

0.25 0.03 0.85 1.29


-0.13 0.00 0.94 0.880.33 0.03 0.85 1.39

-0.26 0.02 0.88 0.77


0.66 0.13 0.72 1.944.61 5.32 0.02 100.09-1.27 0.84 0.36 0.280.41 0.07 0.79 1.51

1.03 0.22 0.64 2.79

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Table 4. Logistic regression results for Operational Excellence

VariablesModel 1 Model 2

B Wald Sig. Exp(B) B Wald Sig. Exp(B)ProductProduct Modularity 3.08 3.70 0.05 21.71 3.45 2.02 0.15 31.47Consumer Demand Predictability 0.04 0.00 0.98 1.04 -0.59 0.10 0.75 0.55

IT InfrastructureReconfigurabilityData consistencyCross Functional Application Integration

1.30 0.62 0.43 3.663.55 3.88 0.05 34.73

-4.03 5.04 0.02 0.02


-0.23 0.01 0.92 0.790.59 0.09 0.77 1.81

1.81 0.79 0.37 6.12


1.91 0.83 0.36 6.783.21 2.15 0.14 24.741.63 1.17 0.28 5.08-1.67 0.91 0.34 0.19

-3.53 1.72 0.19 0.03

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Table 5: Enabling the Three Facets of Firm Performance through SCM

Customer Relationships

Revenue Growth


Product Modularity Positive Positive; not robust

Asset Specificity Positive

Financial Flow Integration Negative

Physical Flow Integration Positive

Data Consistency Positive

Application Integration Negative

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identifying key supply chain capabilities for facets of

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