Article

Examining the Competitive Capabilities of Manufacturing Firms

Abstract and Figures

Manufacturers compete in a complex and uncertain environment with growing global competition, changing and emerging markets, and increasing levels of manufacturing technology. Order winning hinges on their ability to achieve a set of competitive capabilities that have an external, customer orientation and manifest the relative strength of the individual firm against its competitors. This study proposes a framework for research on competitive capabilities, reports on the development of a set of constructs for measuring those capabilities, and tests relations among them. The constructs measure flexible product innovation, quality, delivery dependability, competitive price, and premium price. The constructs are reliable across industries. Tests of a structural model suggest significant relations among the competitive capabilities and significant, positive, and direct-indirect relations between the competitive capabilities and profitability. Results are based on a sample of 244 firms across 4 industries.
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Structural Equation Modeling: A
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Examining the Competitive
Capabilities of Manufacturing
Firms
Xenophon A. Koufteros , Mark A. Vonderembse &
William J. Doll
Published online: 19 Nov 2009.
To cite this article: Xenophon A. Koufteros , Mark A. Vonderembse & William
J. Doll (2002) Examining the Competitive Capabilities of Manufacturing Firms,
Structural Equation Modeling: A Multidisciplinary Journal, 9:2, 256-282, DOI: 10.1207/
S15328007SEM0902_6
To link to this article: http://dx.doi.org/10.1207/S15328007SEM0902_6
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Examining the Competitive Capabilities
of Manufacturing Firms
Xenophon A. Koufteros
Information Technology and Operations Management
Florida Atlantic University
Mark A. Vonderembse and William J. Doll
College of Business
The University of Toledo
Manufacturers compete in a complex and uncertain environment with growing
global competition, changing and emerging markets, and increasing levels of manu-
facturing technology. Order winning hinges on their ability to achieve a set of com-
petitive capabilities that have an external, customer orientation and manifest the rela-
tive strength of the individual firm against its competitors. This study proposes a
framework for research on competitive capabilities, reports on the development of a
set of constructs for measuring those capabilities, and tests relations among them.
The constructs measure flexible product innovation, quality, delivery dependability,
competitive price, and premium price. The constructs are reliable across industries.
Tests of a structural model suggest significant relations among the competitive capa
-
bilities and significant, positive, and direct–indirect relations between the competi
-
tive capabilities and profitability. Results are based on a sample of 244 firms across 4
industries.
Intense global competition, dynamic markets, and the worldwide spread of ad
-
vanced manufacturing technology are creating a complex and uncertain environ
-
ment. Customers expect the rapid introductions of new, high-value, high-quality
products. To win orders, a firm should possess competitive capabilities that have
an external-customer orientation and manifest the relative strength of the firm
against its competitors (Teece & Pisano, 1994; Teece, Pisano, & Shuen, 1997).
STRUCTURAL EQUATION MODELING, 9(2), 256–282
Copyright © 2002, Lawrence Erlbaum Associates, Inc.
Requests for reprints should be sent to Xenophon Koufteros, Information Technology and Opera
-
tions Management, Florida Atlantic University, 111 E. Las Olas Boulevard, Fort Lauderdale, FL
33301. E-mail: kouftero@fau.edu
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These capabilities are not coincidental; they result from strategic actions which
consider customer demands, competitor actions, and supplier capabilities as well
as the firm’s internal strengths and weaknesses (Schroeder & Lahr, 1990). Manu
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facturing strategy, being a part of strategic undertaking, has a determining impact
on the development of competitive capabilities. Several researchers (Menda &
Dilts, 1997; Roth & Miller, 1990; Schroeder & Lahr, 1990; Stonebraker & Leong,
1994; Vickery, 1991; Vickery, Droge, & Markland, 1997; Ward & Duray, 2000;
Ward, Leong, & Snyder, 1990) have contributed toward a better understanding and
appreciation of manufacturing strategy. These models are predominantly hierar
-
chical; they begin with corporate strategy which leads to business strategy and sub
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sequently to manufacturing strategy.
Based on its manufacturing strategy, an organization sets competitive priorities
(goals and objectives) and implements programs and practices to achieve these pri
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orities. When implemented effectively, these action plans foster manufacturing
competencies. These manufacturing competencies are internally measured dimen
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sions of competition (e.g., conformance to design specifications). When properly
organized, managed, and focused on customers, these competencies enable an or-
ganization to build a set of dynamic competitive capabilities which are external di-
mensions of competition (Teece & Pisano, 1994). These external dimensions may
include rapid and flexible product innovation, value-to-customer quality, and fast
and reliable delivery (Deming, 1986; Doll & Vonderembse, 1991; Teece et al.,
1997). Priorities and competencies, including manufacturing flexibility, have ex-
ternal value only as they enable an organization to build competitive capabilities.
Although there is a general consensus that competitive dimensions include
quality, price, flexibility, innovation, and delivery (Nemetz, 1990; Noble, 1995;
Safizadeh, Ritzman, Sharma, & Wood, 1996; Wood, Ritzman, & Sharma, 1990), a
framework that discusses these dimensions across competitive priorities, compe
-
tencies, and competitive capabilities is lacking. Without this framework, organiza
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tions may set competitive priorities and seek internally focused manufacturing
competencies without recognizing how they create capabilities that customers de
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mand. Another consequence of improperly classifying these dimensions are errors
in developing research instruments. From a theoretical and construct validity per
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spective, a serious problem is created when the same items, scales of measure
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ment, and constructs are used to measure priorities, competencies, and competitive
capabilities.
Furthermore, there is relatively little empirical research that examines relations
between the competitive capabilities that are cited in the literature. The “sand
cone” model (De Meyer, Nakane, Miller, & Ferdows, 1989; Ferdows & De Meyer,
1988; Nakane, 1986; Noble, 1995) that has emerged is predominantly “priorities”
based. Due to the fact that competitive capabilities are more direct antecedents to
firm performance, it is essential to examine whether or how competitive capabili
-
ties are related. What are the effects of flexible product innovation on quality and
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delivery dependability competitive capabilities? Does a change in quality enable
an organization to charge a competitive price, premium price, or both? To what ex
-
tent does a delivery dependability competitive capability impact pricing capabil
-
ity? To answer these questions, proper measurement and subsequent testing of for
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mal hypotheses is needed.
This article focuses on the “outward-looking, external dimensions of competi
-
tiveness because they are the direct antecedents to firm performance. The purpose
of this study is to (a) describe a framework that differentiates among competitive
priorities, manufacturing competencies, and competitive capabilities; (b) define
the dimensions of competitive capabilities and develop scales to measure them;
and (c) do preliminary testing of relationships among these capabilities.
THEORY DEVELOPMENT: PRIORITIES,
COMPETENCIES, AND CAPABILITIES
Building competitive capabilities follows a natural sequence, as shown in Figure 1.
This hierarchical sequence received support from our interviews with 10 execu-
tives. Driven by business strategy, a firm sets competitive priorities and develops
action plans. As action plans are implemented, competencies are developed and
these competencies allow a firm to build competitive capabilities that enable it to
compete in the market place. Although this study does not test the framework
shown in Figure 1, its presentation helps to define competitive capabilities and to
contrast them with competitive priorities and competencies. The framework also
enhances construct validity by depicting competitive capabilities as part of a larger
nomological network of constructs. The foundation for the framework is drawn
from Vickery (1991), Schroeder and Lahr (1990), Ward et al. (1990), Roth and
Miller (1990), and Ward and Duray (2000) who proposed a hierarchical paradigm
for determining manufacturing strategy. The process begins with an understanding
of the environment (Ward & Duray) and continues with the development of corpo
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rate and business strategies. From these, the organization develops a manufactur
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ing strategy that involves both setting competitive priorities and building effective
programs and action plans to achieve those priorities. Ward et al. described this as
the “content of manufacturing strategy.” Manufacturing competitive priorities are
goals and objectives (e.g., increasing product variety and lowering product costs)
that guide management actions. Priorities could include enhancing innovation, re
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ducing customer lead time, increasing product variety, improving customer qual
-
ity, and reducing costs. According to Vickery (1991), programs may involve facili
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ties and equipment, process technology, capacity, layout, quality management,
human resource management, and manufacturing planning and control systems.
From its competitive priorities and programs, a firm should be able to develop a set
of manufacturing competencies (e.g., manufacturing flexibility, low scrap rates, and
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high yields). Manufacturingcompetenciesareinwardlyfocusedskillsthatareattained
throughthesuccessfulimplementationofprogramsandplans.Forexample,if an orga-
nization’s goals are to increase product variety and reduce lead time for customers
(competitive priorities), it may develop programs and practices that reduce setup time
and increase equipment reliability. These programs should increase the firm’s ma-
chine flexibility (competence) that could help it achieve fast and dependable delivery
(competitive capability) of products that precisely meet customer demands.
White (1996) suggested it is unlikely that any company can afford to measure
its manufacturing performance by internal standards alone. Only the market pro-
vides a vehicle by which a firm’s competitive capabilities can be judged. Hill
(1994) put particular emphasis on the importance of “order-winning” in his pro
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cess model of manufacturing strategy. In competitive environments, a firm wins
orders by exhibiting superiority in its bundle of capabilities against those of com
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petitors. Competitive capabilities compare a firm’s ability to meet customer expec
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tations to its competitor’s ability to do the same. Capabilities emphasize the role of
strategic management in appropriately adapting, integrating, and configuring
skills and resources to match customer expectations (Teece et al., 1997). Superior
competitive capabilities should lead to increased performance including higher
sales volume and better profit margins. Improved quality and lower costs may al
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low a firm to compete based on low prices and thus increase sales volume and mar
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ket share which, in turn, may lead to higher levels of profitability. Profitability may
also be realized through premium prices via improvements in flexible product in
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novation, product quality, and delivery dependability.
It is also important to acknowledge that functions other than manufacturing
have an impact on competitive capabilities. Competitive capabilities are influ
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enced, for example, by competencies in marketing and engineering which mani
-
MANUFACTURING FIRMS 259
FIGURE 1 Relation among competitive priorities, manufacturing competencies, and competitive ca
-
pabilities.
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fest themselves in superior product designs. Competencies in information systems
planning and processing may provide the basis for coordinating and integrating
management actions that cut response time. In essence, the competitive capabili
-
ties box shown in Figure 1 is dependent on a series of competencies from a variety
of functions. Management’s role is to organize these competencies to create capa
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bilities that customers desire (Teece & Pisano, 1994).
The existing empirical literature can be mapped across priorities, competen
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cies, and competitive capabilities (see Table 1). The classification of dimensions as
priorities, competencies, or capabilities is based on the way the survey items were
written. This was essential because the label given for a particular construct did not
always correspond to the content and scale of the construct being measured. The
unit of analysis used in these studies is the firm. For internal dimensions the focus
could be on a primary product or project, but using the product or project as the
unit of analysis loses profitability as a good overall performance measure.
Researchers conceptualize priorities by asking respondents to attribute a level
of importance to given dimensions. Safizadeh et al. (1996) measured some priori-
ties on an importance scale, whereas others on a performance position relative to
significant competitors. For example, product performance is measured relative to
significant competitor performance, whereas the ability to introduce new products
into production quickly is measured on an importance scale. Yet, in both instances
they portray them as competitive priorities. Bozarth and Edwards (1997) measured
priorities as the relative importance of each performance criterion.
Cleveland, Shroeder, and Anderson (1989) measured competencies as the capa-
bility of the process to achieve a desired objective or meet a particular performance
criterion. Ferdows and De Meyer (1990) captured competencies as the percent per-
formance change for a given dimension over the years, whereas Noble (1995) used
the strength of a given department within the plant or importance of different is
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sues as a measure of competence.
Nemetz (1990) and Wood et al. (1990) distinguished between “intended” criti
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cal success factors or competitive priorities and “realized” success factors or com
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petitive capabilities. According to Wood et al. (1990), “intended” reflects the areas
that are most important and are displayed in the form of competitive priorities that
help to develop and maintain competitive advantage. In a similar fashion, Vickery,
Droge, and Markland (1993) and Vickery et al. (1997) measured priorities in terms
of the importance a firm attaches to each dimension. They measured competitive
capabilities as the firm’s rating relative to its major competitors. Roth and Miller
(1990) captured competitive capabilities by measuring the strength of a firm’s
manufacturing capabilities compared to its competitors.
Researchers often use a firm’s strength relative to its competitors to assess com
-
petitive capabilities. Although a capability should measure a firm’s relative posi
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tion, that is not sufficient to classify a particular dimension as a competitive capa
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bility. Corbett and Van Wassenhove (1993) argued that a competitive capability
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must have an external or customer focus. For example, even though a firm can
measure its routing flexibility against competitors, routing flexibility is not cus
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tomer focused; it has an internal orientation and should be classified as a compe
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tency. Routing flexibility is important when it can be used to create competitive ca
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pabilities which customers value such as enhanced product variety and dependable
delivery (Upton, 1994).
Corbett and Van Wassenhove (1993) further argued that manufacturing related
competitive capabilities represent to a great extent the product, place, and price di
-
mensions of the firm’s marketing mix. Product refers to the physical dimensions of
the product such as quality. Place includes delivery issues and the availability of
innovative products. Price refers to the amount a customer pays for the product or
service and may involve competitive prices or premium prices. Corbett and Van
Wassenhove suggested that these measures of competitiveness have their counter
-
parts in terms of competencies. They stated that competitive capabilities are out
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ward looking and competencies are the inward looking perspectives of the same
concepts. The counterpart for product is internal quality, such as the number of de-
fects. The analogue to place is a time dimension encompassing process and equip-
ment dependability, flexibility, lead time-related factors, and time to market for
new products. The counterpart for price is cost.
Relations Between Competitive Capabilities
The literature described in Table 1 strongly suggests the inclusion of flexible prod-
uct innovation, quality, delivery dependability, and competitive price as measures
of competitive capabilities. Premium pricing has been added here, although the
manufacturing literature has generally ignored it. On the other hand, the marketing
and strategy literatures have long been acknowledging the significance of a pre
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mium pricing capability. They discuss premium pricing as a means of a skimming
strategy and a consequence of differentiation (Porter, 1980). Manufacturing flexi
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bility which was mentioned by Cleveland et al. (1989), Roth and Miller, (1990),
and Safizadeh et al. (1996) was not included because of its internal focus. Cus
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tomers value product innovation and delivery dependability, but manufacturing
flexibility is one competence that makes them possible (Vickery et al., 1997).
To ground this research in the business environment and to provide additional
support for the usefulness of these dimensions, those capabilities were presented to
10 executives from manufacturing firms during one-on-one structured interviews.
The executives were asked to discuss these capabilities with respect to their organi
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zation and customers. They felt that these dimensions adequately represented the
external capabilities that their customers value.
The set of competitive dimensions suggested in the literature and used in this ar
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ticle is not accidental. These capabilities also reflect the dominant theory in the
strategy literature that is best exemplified by Porter’s generic strategies. Porter
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TABLE 1
Measures of Priorities, Competencies, and Competitive Capabilities in Manufacturing Research
Author(s) Priorities Competencies Competitive Capabilities Comments
Cleveland,
Schroeder, &
Anderson
(1989) (n =6)
Adaptive manufacturing,
cost, delivery
performance, logistics,
production economies of
scale, process
technology, quality
performance, throughput
and lead time, vertical
integration
Cost, quality,
dependability, flexibility
Competencies were measured as the capability of
the process to achieve a desired objective or
meet a particular performance criterion.
Competitive capabilities were measured as the
company’s performance relative to
competitor’s.
Nemetz (1990)
(n = 30)
Cost, quality, delivery
dependability, product
and volume flexibility
Measured ratings of a firm’s performance relative
to other companies in the industry.
Roth & Miller
(1990) (n = 193)
Quality, low prices,
delivery dependability,
product and volume
flexibility, market scope
Measured strength of the manufacturing firm
compared to its competition with respect to
various manufacturing capabilities.
Wood, Ritzman, &
Sharma (1990)
(n = 144)
Physical/high performance
design of quality,
time/delivery, cost/price,
reputation/quality consistency
Physical/high performance
design of quality,
time/delivery, cost/price,
reputation/quality
consistency
Priorities were captured as the importance
attached on each alternative, manifesting
intended performance. Competitive capabilities
were measured as the relative position against
significant competitors.
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Ferdows & De
Meyer (1990)
(n = 167)
Unit manufacturing cost,
quality conformance,
speed of new product
introduction, delivery
dependability
Percent performance change in each dimension
from 1985 to 1987.
Noble (1995)
(n = 561)
Quality, dependability of
system, delivery, cost,
mix and volume
flexibility, innovation
Measured primarily as the strength of the
corresponding department within the plant.
Safizadeh,
Ritzman,
Sharma, &
Wood (1996)
(n = 144)
Development speed, product
flexibility, volume flexibility
Quality, cost/price,
delivery
time/dependability
They were all intended to be measured as
competitive priorities. However, some of the
constructs were measured based on their
importance (priorities) while other constructs
were measured against the relative position of
significant competitors.
Bozarth &
Edwards (1997)
(n = 24)
Conformance quality, delivery
reliability, cost, delivery speed,
design capability, product
range
Measured as the relative importance of each
performance criterion.
Vickery, Droge, &
Markland
(1997) (n = 65)
Delivery (volume flux, delivery
speed and dependability),
value (cost, product reliability,
conformance to specs),
flexibility (product & process),
innovation (new product
introduction, design/quality
innovation)
Delivery, value, flexibility,
innovation
Priorities were measured as the importance of
each dimension. Competitive capabilities were
measured as the firm’s rating relative to its
major competitors.
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(1980) conceptualized the means to compete in the market place and improve prof
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itability primarily through two strategies: cost leadership and differentiation. The
first approach requires the organization to develop a competitive price capability
through which firms build market share and consequently profitability. The second
approach calls for organizations to differentiate their product offerings and, on that
basis, command premium prices. Although differentiation can assume many
forms, two of the most common and effective strategies are through quality and de
-
livery service (Porter, 1980).
Companies that seek improvements in their quality capabilities develop flexible
product innovation capabilities. In the absence of flexible product innovation, im
-
provements in quality capabilities may be inconsequential, especially in high
change environments. For a differentiation strategy to be viable, a firm must be
able to sustain noticeable differences (Murray, 1988). These differences are fre
-
quently the result of a continuous stream of product innovations. Product innova
-
tion capabilities may also be valuable in building delivery dependability capabili
-
ties. This is expected to be more obvious for make-to-order firms where product
development cycles can significantly impact delivery dependability. The product
innovation capability, as an antecedent for other capabilities, has to be constantly
adjusted. Teece et al. (1997) asserted that successful firms achieve “timely respon-
siveness and rapid and flexible product innovation” (p. 515) coupled with the man-
agement capability to effectively coordinate and redeploy competencies. They re-
fer to this source of competitive advantage as “dynamic capabilities. Dynamic
refers to the shifting character of the environment when time to market is critical
and the pace of innovation is accelerating. Capabilities emphasize management’s
role in adapting, integrating, and reconfiguring organizational skills, resources,
and functional competencies to cope with a changing global environment.
Flexible Product Innovation: It is the extent to which the manufacturing
enterprise is capable of introducing new products and features in the market
place.
The fast pace of technology and the demands of the customers for novel and
better products requires that companies be able to innovate continually and bring
these innovations to the market place quickly (Blackburn, 1991; Clark &
Fujimoto, 1991; Miltenburg, 1995). Innovation is expected to have an important
impact on quality as 80% of quality issues are determined at the product develop
-
ment stage. The continuing efforts in product innovation foster improvements in
process innovation, organizational learning, and time to market (Garvin, 1984). A
reduction in product development time facilitates more frequent flexible product
innovations. Due to frequent innovation, products closely match current customer
demands and expectations and firms can adopt technological advancements as
they become available. Any design or quality deficiencies are overcome quickly,
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resulting in more satisfied customers. Current and future needs of customers are
incorporated into new products expeditiously.
Flexible product innovation may also enhance delivery dependability because
organizations may learn how to innovate more quickly and because flexible prod
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uct innovations are frequently accompanied by process change. Developing a de
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livery dependability capability calls for both a reduction of lead times and a reduc
-
tion of the variability of lead time throughout the supply chain. Activities that have
an adverse effect on lead time and its variability diminish the ability of a firm to
compete on delivery dependability (Fawcett, Calantone, & Smith, 1997). A large
portion of customer lead time, especially in the make-to-order or design-to-order
environments, is consumed by product development efforts. An important reason
for delays in product development is engineering change orders that stem from
poor flexible product innovation capabilities. As flexible product innovation is
linked with process change, the time and costs of these changes can be reduced and
the effectiveness can be increased. The more capable a firm is in developing new
products and features, the more likely it will be in keeping delivery dates.
Hypothesis 1: Flexible product innovation capability is directly and posi-
tively associated with quality capability.
Hypothesis 2: Flexible product innovation capability is directly and posi-
tively associated with delivery dependability capability.
Quality: It is the extent to which the manufacturing enterprise is capable of
offering product quality that would fulfill customer expectations.
Quality gauges the capability of the firm to design and produce products that
would fulfill customer expectations (Doll & Vonderembse, 1991; Hall, Johnson, &
Turney, 1991). Capon, Farley, and Hoenig’s (1990) meta-analysis identified 20
studies examining the relation between the quality of business products or services
and business performance. They found 104 positive versus 8 negative relations be
-
tween quality and firm performance. Quality is expected to have significant effects
on competitive price and premium price capabilities. Higher quality levels usually
lead to lower costs, and lower costs allow firms to compete on prices (Garvin,
1984). Philips, Chang, and Buzzell (1983) found that higher perceived quality was
indeed related to higher market share and lower costs. Fine’s (1983) results indi
-
cated that costs declined more rapidly for firms that produced high-quality prod
-
ucts than for firms that produced low-quality ones.
Ferdows and De Meyer (1990) results showed that conformance quality was
significantly related to delivery dependability but not with production cost and
overhead cost. Sluti (1992) found that conformance quality had a positive relation
with delivery dependability. As a company meets customer expectations, they tend
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to have fewer product returns, fewer defects and warranties claims, and higher cus
-
tomer goodwill.
Firms may also command premium prices based on product differentiation and
improved quality. Indeed, customers are sometimes willing to pay a premium price
to get better quality (Porter, 1980, 1985), whether it is perceived quality or real.
Firms that deliver products that match customer quality requirements may capture
market share and build customer loyalty.
Hypothesis 3: Quality capability is directly and positively associated with de
-
livery dependability capability.
Hypothesis 4: Quality capability is directly and positively associated with
competitive price capability.
Hypothesis 5: Quality capability is directly and positively associated with
premium price capability.
Delivery Dependability: It is the extent to which the manufacturing enter-
prise is capable of meeting customer delivery requirements.
Hall et al. (1991) defined dependability as consistently performing at the time
promised. Maskell (1991) stated that the time to delivery is important. Finally,
White’s (1996) survey and taxonomy of strategy-related performance measures
suggested that there is little disagreement over measures of delivery dependability.
Many researchers have acknowledged and investigated the use of a delivery ca-
pability as a means of differentiation (Bowersox, Daugherty, Droge, Rogers, &
Wradlow, 1989; Fawcett et al., 1997; Shapiro, Rangan, & Sviokla, 1982). Fawcett
et al. pointed to the need for a better understanding of the impact of delivery capa
-
bilities on firm performance and other capabilities. Delivery capabilities are gain
-
ing ground in importance due to the globalization of trade and the use of
Just-in-Time (JIT) principles around the world. Many companies depend on sup
-
plier deliveries instead of depending on inventories. The effectiveness and effi
-
ciency of a supply chain is predicated on the assumption of on-time deliveries.
Fawcett et al. showed through their analysis that delivery dependability has a sig
-
nificant positive relation with performance.
Delivery dependability is hypothesized to have significant effects on competi
-
tive price as well as premium price capabilities. A firm with higher delivery de
-
pendability reduces the need for special arrangements such as new setups, expedit
-
ing, and the use of alternative but more expensive modes of transportation (Fawcett
et al., 1997). The direct and indirect costs often imposed by customers as penalties
for not being dependable can also be avoided. The lower cost that results helps the
firm compete based on prices. Delivery dependability is also a means through
which a firm can differentiate its product offerings and command premium prices
(Porter, 1980, 1985). For many firms operating in a JIT mode, delivery dependabil
-
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ity is essential because the buffer inventory between them and their suppliers has
been cut dramatically. These firms are often willing to pay a premium price or to
buy larger quantities to receive appropriate delivery service.
Hypothesis 6: Delivery dependability capability is directly and positively as
-
sociated with competitive price capability.
Hypothesis 7: Delivery dependability capability is directly and positively as
-
sociated with premium price capability.
Competitive Price: It is the extent to which a firm is capable of competing
based on low prices.
Competitive pricing reflects the ability of an organization to compete against its
major competitors based on low price (Miller, De Meyer, & Nakane, 1992; Wood
et al., 1990). Giffi, Roth, and Seal (1990) and Miller et al. provided some measures
of competitive pricing. Competitive pricing essentially manifests the ability of the
organization to withstand competitive pressure. Quality and time-based practices
may enable a firm to reduce its costs and time and thus improve its capability to
compete on price.
Competitive price is theorized in this article to have a positive relation with
profitability. When an organization chooses to compete based on low prices, it may
increase its profit through volume. Although prices may be relatively low, the in-
crease in market share may boost overall profit.
Hypothesis 8: Competitive price capability is directly and positively associ-
ated with profitability.
Premium Price: It is the extent to which the firm can command superior
prices.
The capability of premium pricing is used by several firms including
time-based competitors. Time-based competition researchers (Blackburn, 1991;
Karagozoglu & Brown, 1993; Stalk, 1988; Stalk & Hout, 1990) suggest that
firms with a high level of flexible product innovation capability and dependable
delivery cycles can charge premium prices. Customers may also see premium
pricing as indicative of a superior product, service, or both. Customers are will
-
ing to pay a premium price to get more reliable deliveries. Also, a better and
more innovative product design and superior product quality give to a firm the
opportunity to achieve premium prices in the market. Commanding premium
prices implies that a company may profit significantly through higher profit mar
-
gins.
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Hypothesis 9: Premium price capability is directly and positively associated
with profitability.
RESEARCH METHODS
Anderson and Gerbing (1982, 1988) and Fornell and Larcker (1981) recom
-
mended that one assess and finalize the measurement model first before the struc
-
tural model is tested. To establish content validity for the measurement model, the
relevant literature cited in Table 1 was reviewed and assessed to identify potential
measures and aspects of each construct. To provide additional support for content
validity, definitions of the five constructs and the initial set of items were presented
to 10 executives during structured interviews. Based on their suggestions, items
were added, changed, or deleted. A 7-point Likert-type scale was used to assess the
capabilities of a firm compared to the average in the industry. To render further
support for content validity, the items for all constructs were reviewed in a formal
pretest study by 14 experts (nine faculty from Business and Engineering Colleges
and five practitioners located in the Midwest region of the United States).
A pilot study was performed to explore the proposed scales. Responses from 34
discrete-part manufacturing firms were used to purify the scales, to assess the in-
ternal product rule of unidimensionality, and to evaluate reliability. Overall, 5
scales and 23 items emerged with scale reliabilities greater than 0.91. The items
that emerged from the pilot study are displayed on Table 2. In the large-scale study,
the items were mixed throughout the instrument to make sure that the results were
not an artifact of the sequence of the questions.
Research Design and Large Sample Characteristics
The Society of ManufacturingEngineers (SME) cosponsored the largesample study
and provided the mailing list and logistical support. SME sent a prenotification card
to the executives 2 weeks prior to mailing the questionnaire. The questionnaires
were sent out using envelopes and stationary from SME and the cover letter was
cosigned by an SME executive. Questionnaires were sent out to executives in 2,500
discrete-part manufacturing firms with more than 100 employees each. Four Stan
-
dard Industrial Classification (SIC) codes were targetedto ensure a sufficientsample
size so that industry analysis could be done at a later time: (a) SIC 34 (fabricated
metal products, except machinery and transportation equipment; (b) SIC 35 (indus
-
trial and commercial machinery); (c) SIC 36 (electronics, electrical equipment, and
components); and (d) SIC 37 (transportation equipment). These specific SICs were
chosen because they are popular with manufacturing researchers and this may allow
the development of a nomological network of constructs for those industries.
There were 244 usable responses from a single mailing. Sample statistics indi
-
cate that 92% of responses came from the targeted SIC codes. The majority of re
-
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spondents were high-level executives from firms with less than 500 employees.
The response rate was 10%, which is not unusual when the unit of analysis is the
firm and the study involves an extensive organizational-level survey. (The study in
-
cluded four other major parts.) For example, Ferdows, Miller, Nakane, and
Vollman (1989) reported that their response rate for the Global Manufacturing Fu
-
tures Survey varied between 10% and 25%. Roth and Van der Velde (1991) re
-
ported an overall response rate of almost 10%.
To evaluate the representativeness of the sample, a chi-square test of differences
was conducted between observed and expected (population) frequencies for two
digit SIC codes and firm size. The χ
2
test showed that the distribution of the sample
fits very well with the distribution of the population for SIC codes (p > .527),
whereas the sample seems to include more large firms than the population would
imply (p < .001).
Confirmatory Factor Analysis
The focus of the confirmatory study is the assessment of measurement properties
and a test of a hypothesized structural model. The approach used here is primarily
adapted from Koufteros (1999). Confirmatory factor analysis is performed on the
entire set of items simultaneously (Anderson, Gerbing, & Hunter, 1987).
Convergent validity is assessed by examining the significance of individual
item loadings through t tests. The overall fit of a hypothesized model is tested us-
ing the maximum likelihood chi-square statistic provided in the LISREL program
and other fit indexes such as the ratio of chi-square to degrees of freedom, Good-
ness-of-Fit Index (GFI; Jöreskog & Sörbom, 1993), Bentler and Bonnet’s (1980)
Nonnormed Fit Index (NNFI) and Normed Fit index (NFI), and Bentler’s (1980)
comparative fit index (CFI).
Potential misspecifications in the measurement model can be examined by re
-
viewing each item’s completely standardized expected change in Λ
x
(i.e., potential
cross loadings). Items exhibiting change in Λ
x
greater than .4 should be investi
-
gated for their lack of unidimensionality and possible misspecifications in the
model.
Discriminant validity can be assessed through Bagozzi and Phillips’s (1982)
methodology and by comparing the average variance extracted (AVE) to the
squared correlation between constructs (Fornell & Larcker, 1981). Reliability esti
-
mation is left for last because, in the absence of a valid construct, reliability is al
-
most irrelevant. The AVE estimate (Fornell & Larcker) is a complimentary mea
-
sure to the measure of composite reliability. It is also generally desirable to
develop scales that have invariant performance across different industries or other
situations. This is an attractive feature because researchers may use such scales
confidently in different contexts. As a first step, composite reliabilities were calcu
-
lated for each scale across four industries.
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270
TABLE 2
Completely Standardized Parameter Estimates,
t
Values, Error Terms, and Fit Statistics
Latent Variable Item M SD
Completely
Standardized
Factor Loading (λ) t Values
Flexible product
innovation (ξ1)
X1. Our capability of developing customized products is 5.57 1.17 .51
a
X2. Our capability of developing unique features is 5.31 1.25 .70 7.24
X3. Our capability of developing a number of “new” features is 5.29 1.22 .86 7.81
X4. Our capability of developing a number of “new” products is 5.00 1.31 .81 7.70
Quality (ξ2) X5. Our capability of offering products that function according to
customer needs over a reasonable lifetime is
6.04 0.87 .66
a
X6. Our capability of offering a high-value product to the customers
is
5.96 0.94 .67 9.26
X7. Our capability of offering safe-to-use products that meet
customer needs is
5.91 0.99 .72 9.85
X8. Our capability of offering reliable products that meet customer
needs is
5.96 0.84 .78 10.48
X9. Our capability of offering durable products that meet customer
needs is
5.98 0.90 .80 10.73
X10. Our capability of offering quality products that meet customer
expectations is
5.94 0.93 .83 11.01
X11. Our capability of offering high-performance products that meet
customer needs is
5.76 1.16 .76 10.25
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271
Delivery dependability
(ξ3)
X12. Our capability of providing dependable deliveries is 5.25 1.42 .95
a
X13. Our capability of providing on-time deliveries is 5.20 1.47 .95 30.82
X14. Our capability of delivering the kind of products needed on
time
5.27 1.31 .88 23.26
X15. Our capability of delivering the correct quantity of products
needed on time is
5.31 1.33 .88 23.58
Competitive price (ξ4) X16. Our capability of offering prices as low or lower than
competitors’ prices is
4.26 1.46 .85
a
X17. Our capability of offering prices that are competitive is 4.69 1.31 .90 18.80
X18. Our capability of competing based on prices is 4.34 1.45 .89 18.45
X19. Our capability of offering prices that match competition is 4.59 1.38 .94 20.56
Premium price (ξ5) X20. Our capability of commanding premium prices is 5.14 1.25 .85
a
X21. Our capability of selling at prices above average is 5.07 1.17 .90 17.08
X22. Our capability of selling at high prices that only a few firms
can achieve is
4.91 1.33 .84 15.93
Note. Items were measured on a 7-point scales ranging from 1 (much below industry average)to7(much above industry average). Profitability was
measured by a single item (i.e., What is your profitability relative to the average in the industry?) using a 7-point scale ranging from 1 (considerably be-
low)to7(considerably above). M = 4.73, SD = 1.37. χ
2
= 365.71, df = 199, χ
2
/df = 1.84; GFI = .89; NNFI = 0.95; NFI = 0.91; CFI = .96.
a
Indicates a parameter fixed at 1.0 in the original solution.
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To assess the fit of the structural model, chi-square, degrees of freedom, GFI,
NNFI, NFI, and CFI were evaluated. If the data fits the model adequately, the t val
-
ues of the structural coefficients (i.e., gamma and beta) will be evaluated to test the
research hypotheses.
RESULTS
A list of the 23 items of competitive capabilities and a measure of profitability pre
-
sented in Table 2 along with means and standard deviations. Using as input 244 ob
-
servations, the examination of the t values (see Table 2) associated with each of the
loadings indicates that they are all statistically significant. Thus, all indicators are
significantly related to their specified constructs verifying the posited relations be
-
tween indicators and constructs (latent variables).
The data analysis shows an overall well-fitting model (see Table 2). Chi-square
per degrees of freedom = 1.84, GFI = 0.89, NNFI = 0.95, NFI = 0.91, and CFI =
0.96. Consequently, the model fits well and satisfies the internal and external prod-
uct rules of consistency for unidimensionality assessment. In addition, an evalua-
tion of the completely standardized expected change in Λ
x
shows that there is no
need for respecification as there is no item with expected change in Λ
x
greater than
0.40. In fact, there is only one item with expected change in Λ
x
greater than 0.15.
In terms of discriminant validity, the differences between the fixed and free so-
lutions (see Table 3) for all models tested were significant. The lowest difference in
chi-square values was 7.36 (p < .01, df = 1). The data provides evidence of
discriminant validity. This conclusion is corroborated when the second method of
evaluating discriminant validity was employed. The squared correlations between
latent constructs were significantly less than the corresponding average variances
extracted. The highest squared correlation was observed between flexible product
innovation and quality and it stood at 0.29. The corresponding AVE were 0.56 and
0.64, respectively.
Overall, the data provided support for the proposed model in terms of conver
-
gent validity, model fit and unidimensionality, and discriminant validity. Con
-
sidering these results, composite reliability was evaluated. Composite reliabilities
are shown on Table 3. The lowest composite reliability is 0.87, whereas the rest are
above 0.90. The estimates of AVE (see Table 3) are above 0.56, providing further
evidence of reliability. Reliability was also assessed by calculating reliability for
each scale across four industries (see Table 3). The minimum reliability observed
for any scale was 0.82 for premium price in SIC 37 and the majority of scales have
reliabilities greater than 0.90. All scales exhibit good reliabilities across different
industries. It is also evident from Table 3 that reliabilities are stable across the in
-
dustries.
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The results of fitting the structural model to the data (see Table 4) indicate that
the model had a good fit as indicated by chi-square and degrees of freedom (1.74),
GFI (0.89), NNFI (0.96), NFI (0.96), and CFI (0.96). The specified relation be
-
tween flexible product innovation and quality (see Hypothesis 1) was supported by
the data as indicated by the high positive t value (t = 6.88). In fact, this structural
coefficient was the strongest amongst all coefficients in the model. A higher level
in flexible product innovation capability may lead to a higher level of quality. Flex
-
ible product innovation was also hypothesized to affect delivery dependability (see
Hypothesis 2). There was not sufficient evidence to indicate that flexible product
innovation affects delivery dependability (t = –1.66). The literature suggests that
firms that have high levels of flexible product innovation could develop delivery
dependability capabilities. This may be true in make-to-order environments where
product development lead time takes a significant portion of the customer lead
time. The absence of product innovation capabilities may lead to longer and more
unpredictable product development lead times which will subsequently have ad
-
verse effects on delivery performance.
Quality was hypothesized to have an impact on delivery dependability (see Hy
-
pothesis 3). Indeed, there was ample evidence to indicate that quality has positive
effects on delivery dependability. The t value (t = 5.24) for the unstandardized co
-
MANUFACTURING FIRMS 273
TABLE 3
Descriptive Statistics, Correlations, Composite Reliability, Average Variance
Extracted, Discriminant Validity Tests, and Reliability by SIC Code
Constructs M SD
Flexible
Product
Innovation Quality
Delivery
Dependability
Competitive
Price
Premium
Price
Flexible product
innovation
21.17
(4 items)
4.20 .87
a
[.56]
b
Quality 41.56
(7 items)
5.21 .54**
(51.70
c
)
.90 [.64]
Delivery
dependability
21.03
(4 items)
5.19 .16*
(67.57
c
)
.38**
(47.05
c
)
.95 [.84]
Competitive price 17.88
(4 items)
5.16 .12
(57.97
c
)
.20**
(75.85
c
)
.36**
(7.36
c
)
.93 [.80]
Premium price 15.13
(3 items)
3.48 .30**
(43.56
c
)
.37**
(59.19
c
)
.16*
(29.91
c
)
.02
(52.53
c
)
.90 [.74]
Composite SIC34 n = 92 .84 .88 .95 .93 .92
Reliability SIC35 n = 68 .86 .91 .94 .94 .87
By SIC SIC36 n = 29 .90 .91 .97 .95 .91
SIC37 n = 33 .88 .89 .94 .94 .82
a
Composite reliabilities are on the diagonal.
b
Average variances extracted are on the diagonal in brackets.
c
χ
2
differences are indicated in parentheses. Differences in χ
2
for 1 df are significant at 0.001.
*Correlation is significant at 0.05. **Correlation is significant at 0.01.
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efficient illustrates the positive effects. When a company enjoys high-quality capa
-
bilities it may enjoy more dependable deliveries to its customers. When the rela
-
tion between quality and competitive price was examined (see Hypothesis 4), the t
value was not significant (t = 1.14). The manufacturing literature, however, sup
-
ports the view that quality has a beneficial impact on cost and should allow a firm
to compete based on prices. This may be true when quality is conceptualized
through internal measures of quality, such as defect rate and rework (Garvin,
1984). When quality is conceptualized through external measures, such as in this
research, the effects of quality on competitive prices may be negligible. On the
other hand, there is sufficient evidence to indicate that quality is related to pre
-
mium pricing (see Hypothesis 5). The relation is strong as represented by a high t
value (t = 5.29). This finding reflects the majority of the literature that suggests that
firms differentiate their products through quality and then command premium
prices.
Similarly, interesting results emerged when the effects of delivery depend
-
ability on competitive price (see Hypothesis 6) and premium price capabilities
274
KOUFTEROS, VONDEREMBSE, DOLL
TABLE 4
Decomposition of Effects: Unstandardized Coefficients and Fit Statistics
Constructs
Flexible
Product
Innovation
(ξ1) Quality (η1)
Delivery
Dependability
(η2)
Competitive
Price (η3)
Premium
Price (η4)
Quality (η1) 0.41 (6.88**)
a
b
0.41 (6.88**)
c
Delivery
dependability (η2)
–0.24 (–1.66) 1.14 (5.24**)
0.47 (4.59**)
0.24 (2.03*) 1.14 (5.24**)
Competitive price
(η3)
0.17 (1.14) 0.30 (4.86**)
0.14 (2.06*) 0.34 (3.60**)
0.14 (2.06*) 0.51 (3.38**) 0.30 (4.86**)
Premium price (η4) 0.84 (5.29**) 0.00 (0.08)
0.35 (4.98**) 0.00 (0.08)
0.35 (4.98**) 0.84 (5.75**) 0.00 (0.08)
Profitability (η5) 0.35 (5.10**) 0.48 (6.40**)
0.21 (4.22**) 0.58 (5.18**) 0.11 (2.64**)
0.21 (4.22**) 0.58 (5.18**) 0.11 (2.64**) 0.35 (5.10**) 0.48 (6.40**)
Note. Hypothesized effects are the direct effects; t values are in parentheses. χ
2
= 387.32, df = 222, χ
2
/df =
1.74; GFI = 0.89; NNFI = .96; NFI = .96; CFI = 0.96.
a
Direct effect.
b
Indirect effect.
c
Total Effect
*t value is significant at 0.05. **t value is significant at 0.005.
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(see Hypothesis 7) were tested. Significant relations with both pricing capabili
-
ties were expected, but only the effects on competitive price (t = 4.86) were sig
-
nificant. The effects on premium price were actually negligible (t = 0.08). The
literature suggests that companies can differentiate their service through delivery
dependability and achieve premium prices. The data illustrates that firms that
have high-delivery dependability do not necessarily build a premium pricing ca
-
pability. It is possible that in many industries delivery service has become an or
-
der qualifier where companies see delivery service as given. The possible effect
of delivery dependability on competitive prices has not received much attention
in the literature and yet the data provides strong evidence that the relation is pos
-
itive. Firms that exhibit high levels of delivery dependability have high levels of
competitive price capability.
The effects of competitive price (see Hypothesis 8) and premium price capa
-
bilities (see Hypothesis 9) on profitability were very significant (t = 5.10 and
6.40, respectively). This is also an interesting finding. Firms with high levels of
competitive price capability have high levels of profitability. On the other hand,
firms with high levels of premium price capability also have high profitability.
This could be explained because in the first place, firms that compete through
competitive prices do so in order to build market share and become profitable
through volume. Other firms differentiate their quality or service and command
premium prices. Profitability is achieved by higher profit margins. This is not to
say however that a firm cannot build both a competitive price and a premium
price capability. (See Figure 2.)
DISCUSSION
Although most of the hypotheses are supported by this research, some hypothe
-
sized relations were not. The literature suggests that higher capabilities in flexible
product innovation should lead to higher delivery dependability capabilities. This
hypothesis is not supported by the data. However, Table 4 reveals that the total ef
-
fects of flexible product innovation on delivery dependability are statistically sig
-
nificant. This is caused by the indirect effect that flexible product innovation has
on the quality capability. In other words, flexible product innovation affects quality
which then affects delivery dependability. It is also possible that environmental in
-
fluences can affect this relation. Such environmental settings include
make-to-stock versus make-to-order conditions, small versus large firms, and low
complexity versus high complexity. Table 4 also reveals that the total effects of
flexible product innovation are statistically significant with competitive price, pre
-
mium price, and profitability. These effects are due to indirect effects. Although in
-
direct relations have not been hypothesized in this article, the far-reaching effects
of flexible product innovation seem clear.
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Quality does not have a significant impact on the competitive price capability.
Although the direction of the relation is positive, the magnitude of the relation falls
short of significance, even at .10. It is important to note that the total effect of qual
-
ity on competitive price is statistically significant. This is explained by the signifi
-
cant relation that has been observed between delivery dependability and competi
-
tive price. Quality is essential for all products, and it is the basis for creating more
competitive manufacturing systems. Firms that desire to charge premium prices
must develop a level of quality that delights the customer. In addition, quality
seems to have significant indirect effects with profitability.
Delivery dependability did not have significant effects on the premium price ca
-
pability. It is possible that delivery dependability is becoming more of an order
qualifying criterion rather than an order-winning criterion. Delivery dependability
is an important variable for a competitive price capability and it had significant in
-
direct effects on profitability. This points to the potentially important role of deliv
-
ery dependability.
Both competitive price and premium price capabilities are strong predictors of
profitability. From a business strategy perspective, these represent the cost leader-
ship and differentiation strategies. Over the last 2 decades there has been a debate
on the wisdom of trying to compete in the market place by employing both a cost
leadership and a differentiation strategy. Porter (1980), being the most vocal advo-
cate of one group, suggested that firms should avoid competing on both strategies
simultaneously. Some manufacturing researchers echoed this view and supported
the tradeoffs model. On the other hand, the opposition suggests that competing
based on multiple dimensions cannot be precluded because it is doable and reward-
ing. Miller (1992) pointed to the dangers associated with pursuing a single generic
strategy. Even Porter (1985) eventually conceded that “a cost leader must achieve
parity or proximity in the bases of differentiation relative to its competitors to be an
above average performer, even though it relies on cost leadership for its competi
-
tive advantage” (p. 13). Likewise, he stated that “a differentiator cannot ignore its
cost position, because its premium prices will be nullified by a markedly inferior
cost position” (p. 14).
Preliminary evidence presented here suggests that firms that compete based on
more than one strategy may outperform their competitors in terms of profitability.
Examination of the sample results indicates that fewer than 6% of the firms attach
more than 75% importance to any one of the competitive capabilities. About one
third of the firms attach more than 50% importance to any one them. The sample
was separated in two groups. The first group includes firms with more than 50%
importance attached to any dimension. The second group includes firms that are
competing using multiple dimensions, no single priority has more than 50% of im
-
portance. Two multiple regression models were developed, one for each group,
with profitability as the dependent variable and competitive capabilities as the in
-
dependent variables. The model of firms competing through one dominant dimen
-
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sion generated an adjusted R
2
of .162 for profitability. The model of firms that are
competing based on more than one strategy generated an adjusted R
2
of .265 for
profitability. The results seem to confirm Miller and Friesen’s (1986a, 1986b) re
-
search that suggests that success seems to be caused by the possession of strategic
advantages, the more the better, rather than strict adherence to Porter’s (1980)
types.
CONCLUSIONS AND FUTURE RESEARCH
This article presents a nomological network of constructs that relates a firm’s strat
-
egy, competitive priorities, manufacturing programs and action plans, manufactur
-
ing competencies, and competitive capabilities. This network provides a founda
-
tion for research in the area. It develops a theoretical rationale for a set of
competitive capabilities, defines these variables, creates a framework that relates
these variables to each other, and considers their impacts on profitability. The final
scales, listed in Table 2, exceed generally accepted validity and reliability stan-
dards for basic research. The generic nature of the competitive capabilities scales
should allow for broad application. To a relatively small degree, the
generalizability of the scales is supported by acceptable reliabilities (above 0.82)
across four industries. These scales should be revalidated in the same industries;
they should also be validated in other industries.
Structural model testing indicates that flexible product innovation has signifi-
cant direct effects on quality and significant indirect effects on delivery depend-
ability, competitive pricing, premium pricing, and profitability. This statistical re
-
sult along with strong evidence in the literature reinforce the central role of product
innovation and product development in organizational success. Quality and deliv
-
ery dependability impact the organization’s ability to set price and, in turn, to attain
profitability.
Environmental, industry, and size effects have not been considered in this study
and may alter the conclusions if included in the model. To briefly examine the per
-
formance of the structural model under different environmental conditions, we
tested it under high and low environmental change. The model fit and structural co
-
efficients are comparable and point to the invariance of the model, at least when
change in the environment is considered.
Alternative structural models were not tested here. Such models may be capable
of producing equally appealing fit indexes and structural coefficients. Future re
-
search, for example, could investigate whether the data can support a sand-cone
like model. The sand-cone models are primarily based on competitive priorities. It
is plausible that competitive capabilities as well as competencies follow the same
causal sequence as competitive priorities do.
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This study uses a single measure of profitability as a surrogate of firm perfor
-
mance. Originally, this measure of profitability was to be used to assess criterion
validity. When building the model in Figure 2, it was decided to use this profitabil
-
ity measure to determine if competitive capabilities had an impact on firm perfor
-
mance. Although this weakness does not diminish the result of the measurement
model or the structural relations among the competitive capabilities, it may impact
the relations between competitive capabilities and profitability. It is important that
future research studies use multiple indicators to measure firm performance or
profitability. Vickery et al. (1997) provided some insights for these measures. In
addition, we would like to indicate that we targeted a single respondent from each
firm. This may induce common variance problems and future research should con
-
sider seeking responses from additional executives from each firm.
The development of a competitive capabilities scale opens up promising ave
-
nues for research. They can be used to investigate how various antecedents are af
-
fecting dependent variables and how several consequences or dependent variables
are affected by antecedents. In other words, more effort is needed to develop a
nomological network of manufacturing strategy constructs. Ward and Duray
(2000) pointed out that the predominant conceptual model of manufacturing strat-
egy remains largely untested and subsequently unsubstantiated. They tested a
model that incorporates relations among the environment, competitive strategy,
and performance. The findings suggest that competitive strategy acts as a mediator
between the environment and manufacturing strategy. The results also suggest that
manufacturing strategy mediates the effects of competitive strategy on perfor-
mance. Manufacturing strategy was conceptualized in terms of competitive priori-
ties. This research was valuable as one of the first to test several links of the manu-
facturing strategy process. An important element missing, however, is the
mediating role that competencies and competitive capabilities have on the effects
of priorities on firm performance. Due to the fact that the conceptual model of
manufacturing strategy has not been adequately tested and thus validated, it would
be prudent that scholars of manufacturing strategy consider other explanations of
strategy, such as Mintzberg’s (1979) emergent strategies. From a practical point of
view, however, we can foresee difficulties in forming a paradigm for emergent
strategies, because such strategies materialize in a nonsystematic fashion.
Another important research question for manufacturing researchers and practi
-
tioners is whether action plans in the form of manufacturing practices do matter.
They would matter if there is evidence that these practices do indeed have an im
-
pact on competitive capabilities and ultimately profitability. Practices such as pull
production, cellular manufacturing, setup improvement, preventive maintenance,
employee involvement in problem solving, and quality improvement are fre
-
quently cited as being important (Koufteros, Vonderembse, & Doll, 1998). Pre
-
sumably, such practices are adopted as action plans based on the firm’s competitive
priorities. Although no hypotheses have been advanced for the nature of such rela
-
278
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tions, the following serves as food for thought for future research. An aggregate
score of manufacturing practices was correlated with competitive capabilities and
profitability. The correlations were positive and statistically significant with flexi-
ble product innovation (p < .001), quality (p < .001), delivery dependability (p <
.001), competitive price (p < .001), premium price (p < .067), and profitability (p <
.024). The correlations suggest that there is sufficient evidence to investigate these
relations further. From an academic and practitioner point of view, it would be of
great interest to identify the strongest antecedents for each competitive capability.
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... The core of a manufacturing firm is to serve its customers by creating value for them and, through these actions, build a long-lasting competitive advantage [1,2]. To achieve this, the manufacturing firm must identify, develop, and continuously enhance the most critical manufacturing capabilities (that is, work with manufacturing strategy implementation) [3,4]. ...
... The firm makes decisions on the basis of the priorities in combination with the available resources, which generate the firm's manufacturing capabilities [8]. Thus, manufacturing capabilities play a key role in terms of strategically aligning skills and resources to fulfil customer needs [2]. They are also key for business-model changes and innovation [1]. ...
... As mentioned above, successful firms in developed countries respond to the current competition through differentiation strategies. Some firms differentiate themselves through quality-related capabilities [2], which allow them to provide high-performing products, while others differentiate themselves through time-related capabilities that allow them to rapidly satisfy customer needs [76]. A third option is to differentiate on the basis of innovation-related capabilities [77]. ...
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... Within the US context, a study by Koufteros, Vonderembse and Doll (2002) who used SEM analysis showed that linkages between the manufacturing strategy and the firm's overall strategy led to higher performance. In their case, the quality dimension of a manufacturing strategy strongly linked with performance outcomes. ...
... The studies on manufacturing strategy configurations (Youndt et al., 1996;Akgul et al., 2015) have identified that various strategic configurations being used by the manufacturing firms (Ward & Duray, 2000;Tracey et al., 1999;Youndt et al., 1996) while other have highlighted and linked the impact of various strategic configurations on performance (Amoako-Gyampah & Acquaah, 2008;Koufteros et al., 2002). Other studies have identified that the moderate effect (Youndt et al., 1996, Ward & Duray, 2000 and mediated effects (Tracey et al., 1999) of manufacturing configurations on firm performance. ...
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Thesis
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... Corbett and Claridge [20] denote capabilities as the ability of a firm to apply resources to do something and further states that capabilities form the primary basis for competition between firms. In the manufacturing strategy literature, capabilities are often conceptualized as a business unit's intended or realized competitive performance or operational strengths [6,14,15,[21][22][23][24] and are therefore assessed using measures of operational performance, which typically includes cost, quality, flexibility, and delivery measures. Swink and Hegarty [25] suggest that the performance-based approach to capabilities is conceptually aggregated to clearly direct the proper use of manufacturing resources. ...
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Manufacturing strategy research aims at providing a structured decision-making approach to improve the economics of manufacturing and to make companies more competitive. The overall objective of this paper is to investigate how manufacturing companies make use of different manufacturing practices or bundles of manufacturing practices to develop certain sets of capabilities, with the ultimate goal of supporting the market requirements. We propose a technique that can effectively take managerial preferences and subjective data into consideration, along with quantitative factors. The tool that is proposed here relies on the use of a more effective version of the Analytical Hierarchy Process (AHP) called the Analytical Network Process (ANP) to help integrate managerial evaluations into a more quantitatively based decision tool, data envelopment analysis (DEA). In this paper, these two techniques, when used together, can provide subjective and objective evaluations for manufacturing strategy decision makers. An illustrative example provides some insights into the application of this methodology. The research contributes to several insights to the research area of manufacturing strategy and to practitioners in manufacturing operations. A model that investigates process improvement investments, assuming that alternative process improvement initiatives exist, is then presented.
... Additionally, Saloner perceives CA as a condition where a company innovates products or services to meet stakeholders' needs and ensure their continued existence in the market (Arniati, Puspita, Amin & Pirzada, 2019). Koufteros et al. (2002) describe a framework for competitive capabilities and define competitive and premium pricing, value to customer quality, dependable delivery, and innovating production. The aspects of the CA constructs used in this study include price/cost, quality, delivery dependability, product innovation, and time to market. ...
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Pazarlama yeniliği girişimciliğin bir aracı olarak ele alınabilmektedir. Pazarlama yeniliği yeni süreçler ve farklılık meydana getirmektedir. Bu araştırmada pazarlama yeniliği ile pazarlama performansı ilişkisi açıklanmaya çalışılacaktır. Bu kapsamda Süleyman Demirel Üniversitesi Olimpik Yüzme Havuzunun pazarlama stratejisi ele alınarak bir vaka çalışması yapılmıştır. Araştırmada pazarlama yeniliği değişkenleri olarak ürün, fiyat ve tutundurma faaliyetleri; pazarlama performansı göstergesi olarak satışlar ve müşteri memnuniyeti ele alınmıştır. Araştırma sonucunda pazarlama yeniliği ile pazarlama performansı arasında anlamlı bir ilişkinin olduğu söylenebilir.
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Case and industry studies have increased our understanding of time‐base manufacturing and focused our attention on its key component practices. Despite important contributions by Blackburn (1991) and Stalk and Hout (1990), we lack a clear definition of time‐based manufacturing and its relationship to Just‐in‐time (JIT). This study proposes a framework for research on time‐based manufacturing, reports on the development of a set of seven instruments for measuring the key practices, and tests relationships among these practices. The instruments are valid, reliable, and generalizable across industries and firm size. Tests of the structural model confirm Monden's (1983) notion that shop‐floor employee involvement leads to improved manufacturing practices which, in turn, lead to pull production.
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The measurement of unobservable (latent) variables has been a recent phenomenon in the manufacturing research area. Most available empirical research in manufacturing has been exploratory in nature and has borrowed its methods extensively from other fields such as psychology, sociology, and marketing. Traditional exploratory techniques have been used to provide preliminary scales and assess measurement properties. Manufacturing researchers have, however, overlooked the assessment of unidimensionality, an essential measurement property and a basic assumption of measurement theory. An explicit evaluation of unidimensionality can be accomplished with a confirmatory factor analysis (CFA) of individual measures as specified by a multiple‐indicator measurement model. A paradigm for scale evaluation incorporating CFA for the assessment of unidimensionality is outlined here along with methodology to assess other measurement properties such as convergent validity, discriminant validity, composite reliability, and average variance extracted. A measurement model is tested first followed by a structural model of interest. The hypothesized structural model relates pull production with two of its antecedents, setup improvement and preventive maintenance practices. It further relates pull production to one of its consequences, delivery dependability. Responses from 244 firms are used to test the measurement and structural model.
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This paper explores dimensions of manufacturing competitive strength in the furniture industry. A theoretically relevant set of manufacturing competitive priorities is identified from the operations literature and factor analyzed to determine the core dimensions of manufacturing performance. Relationships between these core dimensions of manufacturing strength and overall business performance are examined. The results identify four dimensions of manufacturing strength in the furniture industry: innovation, delivery, flexibility, and value, with the latter encompassing the combined effects of quality and cost. The study supports innovation as a key order winner in the furniture industry.
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This paper presents a competitive service strategy paradigm which explicitly considers the strategic role of operations as a competitive weapon. This service strategy paradigm draws upon the prevailing manufacturing strategy literature in its definition of strategic operations choices and critical success factors. We show that to make a service delivery system a potential marketing tool, critical success factor criteria must be based upon the explicit service task or mission which coincides with a service operations strategy. We illustrate how critical success factors are the linchpin between operations and marketing in service organizations. Assessing critical success factors is the first step of a process which determines the strategic role that operations can play in a service firm. Using a sample of 117 retail banks, our paper explores industry critical success factors along two dimensions, one is market‐oriented and the other is competitor‐oriented. We derive a framework, which we label the Customer/Account Base (CAB) matrix, to serve as a decision‐aiding tool to evaluate the relative competitive positioning of a service firm. Our analyses show that quadrants on the CAB matrix coincide with four stages of capability development, similar to those found in manufacturing by Hayes and Wheelwright (1984), reflecting the strategic role a service delivery system design plays in meeting the competition. We go on to empirically link the competitive priorities of retail banks with operations strategy contents of structure, infrastructure and integration choices. Using our service strategy paradigm, we empirically show that the pattern of operations choices varies by competitive priority. As anticipated, the pattern of operations choices linked to relationship banking, one of the most difficult capabilities to achieve and one that requires a high degree of customer contact, is characterized by the most holistic and integrative operations strategy. In conclusion, our exploratory findings illustrate how the prevailing manufacturing strategy framework can be adopted in service strategy delivery system design and the moderating role that customer contact exerts in service strategy formation.
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While there has been considerable research on the content of manufacturing strategy, there is a paucity of literature concerning the process of manufacturing strategy formulation [Ward, P.T., Brickford, D.J., Leong, G.K., 1996. Configuration of manufacturing strategy, business strategy, environment, and structure, J. Manage., 22(4) 597–626; Leong, G.K., Snyder, D.L., Ward, P.T., 1990. Research in the process and content of manufacturing strategy, Omega, 18(2) 109–122]. Many researchers have highlighted the need to overcome this deficiency by studying the process of developing manufacturing strategy [Adam, E.E., Swamidass, P.M., 1989. Assessing operations management from a strategic perspective, J. Manage., 15(2) 181–203; Anderson et al., 1989; Leong, G.K., Snyder, D.L., Ward, P.T., 1990. Research in the process and content of manufacturing strategy, Omega, 18(2) 109–122]. To effectively link the manufacturing strategy of a firm to the needs of the marketplace, critical competitive factors or order‐winning criteria must be understood and agreed upon both by operations and marketing managers [Hill, T.J., 1983. Manufacturing's strategic role, J. Operational Res. Soc., 34(9) 853–860; Hill, T.J., 1994. Manufacturing Strategy—Text and Cases, 2nd edn., Irwin, Homewood, IL]. For this study, we created and examined a process of establishing a set of order‐winning criteria for a consumer pharmaceuticals firm which involved the participation of sixteen managers from seven functional areas over four months. The foundation of the process was developed by Hill [Hill, T.J., 1989. Manufacturing Strategy—Text and Cases. Irwin, Homewood, IL; Hill, T.J., 1994. Manufacturing Strategy—Text and Cases, 2nd edn., Irwin, Homewood, IL], however it was soon evident that additional steps were required. The expanded process we developed both exposed significantly differing views among the managers and raised several questions with important managerial and research implications.
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This study uses a causal modelling methodology to examine competing methodological and theoretical hypotheses concerning the effects of product quality on direct costs and business unit return on investment (ROI). Results show that the PIMS’ measures under study exhibit high reliability across all samples. The findings fail to support the widely held view that a high relative quality position is incompatible with achieving a low relative cost position in an industry.
Chapter
Research in manufacturing strategy is necessary to build knowledge about how to compete in a global economy. Many new techniques and technologies are being touted in the literature as cures for industrial performance inertia. However, these techniques and technologies cannot be properly evaluated because of strategic performance measurement problems. This article describes problems with measuring strategic outcomes. A method is then presented for conducting research that requires strategic outcome measurements. One of the major themes is that this method should be temporary; it is far more important for university-industry ties to become closer, and for efforts to be directed at finding practical, standardized methods for measuring strategic outcomes.