Journal of Operations Management 28 (2010) 144–162
Contents lists available at ScienceDirect
Journal of Operations Management
journal homepage: www.elsevier.com/locate/jom
The impact of information technology use on plant structure, practices,
and performance: An exploratory study
Gregory R. Heim 1, David Xiaosong Peng *
Department of Information & Operations Management, Mays Business School at Texas A&M University, 320 Wehner Building, 4217 TAMU, College Station, TX 77843-4217,
United States
A R T I C L E I N F O
A B S T R A C T
Article history:
Received 25 July 2008
Received in revised form 16 September 2009
Accepted 23 September 2009
Available online 1 October 2009
Firms have been investing millions of dollars on information technology (IT) in their manufacturing
plants. However, the research literature is unclear about the extent and scope of the impact of IT use on
plant operations. This study examines the impact of IT use on the structure, practices, and performance
of manufacturing plants. Drawing on information systems and operations management literature, the
study differentiates between plant IT use (i) at the process level, (ii) due to internal process integration,
and (iii) due to customer and supplier collaboration, labeled as process intelligence, integration
intelligence, and collaboration intelligence, respectively. The study also accounts for intelligence
gathering due to statistical process control (SPC) practices. The proposed impacts of IT use are examined
using data from a study sample of manufacturing plants from electronics, machinery and transportation
component industries. Overall, the evidence suggests that SPC has a broader and more significant impact
on many aspects of plant operations than the individual dimensions of IT use. However, the three
dimensions of IT use do exhibit distinct effects on plant structure, practices, and performance. Process
intelligence tends to be associated with plant size and productivity, while integration intelligence and
collaboration intelligence tend to be associated with work practices related to increased organizational
decentralization and a flexible technology focus.
ß 2009 Elsevier B.V. All rights reserved.
Keywords:
Manufacturing
Process control
Information technology
Regression
1. Introduction
The impact of information technology (IT) use on performance
and other organization outcomes is an important topic to both
practitioners and academics. During the 1970s and 1980s,
manufacturers began adopting IT to automate plant operations.
Since the early 1990s there has been a rapid growth in IT
investments for enterprise resource planning and supply chain
management in manufacturing industries. These information
technologies are believed to improve the efficiency of manufacturer shop floor operations, enhance integration across different
functional areas, and facilitate inter-firm collaboration (Akkermans et al., 2003; Banker et al., 2006; Kelley, 1994).
Despite increased adoption and use of advanced IT by
manufacturing firms, to date there is still a lack of systematic
study of the impact of IT on manufacturing operations, particularly at the plant level (Banker et al., 2006). A review of the
Operations Management (OM) literature indicates that prior
* Corresponding author. Tel.: +1 979 845 6996; fax: +1 979 845 5653.
E-mail addresses: [email protected] (G.R. Heim), [email protected]
(D.X. Peng).
1
Tel.: +1 979 845 9218; fax: +1 979 845 5653.
0272-6963/$ – see front matter ß 2009 Elsevier B.V. All rights reserved.
doi:10.1016/j.jom.2009.09.005
studies have largely studied IT performance impact at the firm
level (e.g., Cao and Dowlatshahi, 2005; Dehning et al., 2007;
Hendricks et al., 2007) or the industry sector level (Shah and Shin,
2007). Another stream of the OM literature has examined a
focused type of IT such as new product development and project
management tools (Bardhan et al., 2007; Malhotra et al., 2001),
enterprise resource planning (Masini and Van Wassenhove,
2009), or supply chain collaboration (Hill and Scudder, 2002;
Saeed et al., 2005). However, few studies have examined a wide
spectrum of IT applications both within and across firm
boundaries.
In the Information Systems (IS) literature, a large number of
studies capture the level of IT adoption using highly aggregated IT
investment measures that typically lump together all IT-related
spending, such as investments in computer hardware, software,
and telecommunication infrastructure (e.g., Dewan et al., 2007;
Harris and Katz, 1991). This approach to examining ‘‘IT capital’’
does not allow the isolation of the impact of specific IT
applications (Banker et al., 2006). However, assessing the impact
of specific IT use is important for improving plant operations
because building IT-based competence is an ongoing process that
requires incremental investments in new IT applications in order
to improve the effectiveness and efficiency of operational
processes at different levels.
G.R. Heim, D.X. Peng / Journal of Operations Management 28 (2010) 144–162
Over the past two decades, manufacturing firms have
witnessed increasingly individualized customer demands, leading
to increased customization and proliferation of products and
services. As a result, modern-day manufacturing firms tend to have
multiple product lines targeting different markets and customer
segments. Within a manufacturing firm, individual manufacturing
plants supplying different target markets and customers need to be
supported by a tailored configuration of IT applications (Henderson and Venkatraman, 1999; Kathuria et al., 1999). While many
firms strive to develop a common IT infrastructure, decisions on
specific IT investments to some extent rest on plant management.
Thus, a plant-level analysis of IT use is expected to provide a finegrained understanding of the linkage between IT and organizational outcomes.
Prior research suggests that IT use may not directly impact
overall business or operational performance, but rather affects
certain intermediate variables such as process efficiency and
organization learning (Banker et al., 2006; Devaraj et al., 2007;
Tippins and Sohi, 2003). Simply examining overall performance
impacts may miss important implications of the many roles IT can
play. Thus, researchers need to consider dependent variables other
than performance. ‘‘In some circumstances, adopting the effectiveness of business processes as a dependent variable may be more
appropriate than adopting overall organizational performance as a
dependent variable’’ (Ray et al., 2004, p. 23). In this study we
examine a broad set of dependent variables encompassing measures
for plant structure, operational practices, and performance.
In examining the impact of IT use, researchers have adopted
various theoretical perspectives such as the resource-based view,
transaction cost theory, and organization information-processing
theory. Most of these theories are borrowed from the management
or economics literature, whereas OM theories have barely been
adopted to examine IT use in manufacturing operations. In this
study, we draw on Jaikumar’s (1988) theoretical framework on the
evolution of process control as well as other relevant literature to
study plant-level IT use and its impact. The key idea we draw from
this framework is that broad use of information technology will
enhance an organization’s ability to process information and thus
change how production processes are managed and work
organized, leading to changes in process scope and work practices.
145
Jaikumar inductively derived a theoretical framework on the
evolution of process control by tracing historical events in the
firearm industry (Fig. 1). He observed that information used to
control processes was initially generated using paper-based and
mechanical tools to provide static intelligence, and later generated
using digital computer systems oriented toward dynamic intelligence. For each evolutionary period, Jaikumar also notes contemporaneous patterns of plant size and structure, work practices,
operational processes, and performance. In summarizing the
changes in the patterns over time, Jaikumar (2005, p. 112)
presented a framework relating the evolution toward information
systems supporting dynamic intelligence to patterns of organizational structure, operational practices and performance.
While Jaikumar’s theoretical framework is longitudinal in
nature, it offers important implications about the cross-sectional
relationships between different dimensions of intelligence
gathering (using various information systems) and plant
structure, practices and performance. To examine these relationships, we analyze data from an international study sample of
over 200 manufacturing plants in electronics, machinery, and
transportation component industries. We view each plant’s
data on information technology use to represent a chosen
configuration of managerial practices and strategies supporting
dynamic intelligence (Leonard-Barton, 1988; Itami and Numagami, 1992). In a randomly selected cross-sectional sample
of plants, plant IT systems are expected to vary considerably
in terms of their sophistication and scope, therefore placing
sample plants at different stages along the evolutionary path
that Jaikumar proposed firms would follow to develop
dynamic intelligence. We also expect plants to exhibit
different process control characteristics along this path. We
use the data to position the plant operations along the
dimensions of IT use. We find some empirical results consistent
with the implications of the model, yet also observe some
results that differ from the proposed outcomes. The paper
contributes to the literature by systematically examining the
impact of IT use at the plant level.
The paper is organized as follows. Section 2 reviews Jaikumar’s
theoretical framework and develops hypotheses. Section 3
describes data, measurement and analysis results. Section 4
Fig. 1. Evolution of process control (Jaikumar, 1988); the right-most column is intended to show how the dynamic intelligence variables are related to Jaikumar’s framework.
These constructs are not part of Jaikumar’s framework.
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G.R. Heim, D.X. Peng / Journal of Operations Management 28 (2010) 144–162
concludes by discussing theoretical and practical implications and
future research directions.
2. Background
2.1. IT and evolution of process control
Since the 1970s, manufacturing industries have observed
growing adoption and use of advanced manufacturing technology
(AMT), enabled largely by modern information technology.
Technologies such as computer aided design (CAD), computer
aided manufacturing (CAM), material requirement planning
(MRP), and flexible manufacturing systems (FMS) have dramatically changed manufacturing processes and their outputs. AMT is
believed to deliver high production flexibility, rapid responses to
changes in demand and product design, greater control and
repeatability of processes, faster throughput, reduced waste, and
distributed process capability (Boyer et al., 1996; Kelley, 1994). As
such, the traditional tradeoff between flexibility and efficiency has
been altered significantly: ‘‘one of the primary benefits ascribed to
AMTs is the capability to combine traditional economies of scale
with economies of scope in an ability to achieve both flexibility and
efficiency’’ (Boyer et al., 1996, p. 298). Indeed, IT has been widely
acclaimed to enable mass customization since it provides ‘‘efficient
flexibility’’ to help firms cope with the dual challenge of ‘‘speed and
flexibility’’ and ‘‘low cost’’ (Allen and Boynton, 1991, p. 436).
The impact of IT is not limited to core production processes.
Broad adoption and use of IT within the firm and across firm
boundaries also may stimulate new work practices and organizational forms, leading to changes beyond core production processes
(Bresnahan et al., 2002). Recently, advances in Internet technology
have enabled firms to monitor, improve, and control operational
processes throughout the supply chain (Banker et al., 2006; Rai
et al., 2006). Interorganizational processes, when enabled by
integrated IT systems, can create capabilities in ‘‘demand sensing,
operations and work flow coordination, and global optimization of
resources’’ (Rai et al., 2006, p. 227).
The historical use of IT for monitoring and controlling
manufacturing processes is documented in detail in Jaikumar’s
industrial history monograph on the evolution of process control
within firearm manufacturing (Jaikumar, 1988, 2005). In particular, the monograph describes how process scope and worker
discretion changed over time in response to the adoption of tools
and information systems used to monitor and control the
production processes for gun manufacture. Jaikumar uses a model
similar to the one presented in Fig. 1 to describe this evolution. The
model is a two-dimensional matrix built upon the dimensions of
static (mechanization) vs. dynamic (intelligence) information supporting the work process, and low process scope/low discretion vs.
high process scope/high discretion in worker decision making. The
path of process control evolution follows a U-shaped pattern
according to the numbered cells in Fig. 1, from cell 1 through cell 6.
The model positions historical process types along an ordinal
timeline spanning from the English system of manufacturing to
modern (late 1980s) flexible manufacturing systems and computer-integrated manufacturing (CIM). Underlying the model is a
view that operations have been evolving from physical control
toward digital control of production systems. Karmarkar and Apte
(2007) characterize this phenomenon as an evolution ‘‘from a
material-based economy to an information-based economy’’ (p.
438) and suggest that the challenges of managing these information-based operations will provide the bulk of interesting future
OM research topics.
In Jaikumar’s (1988, p. 92) model, production evolves from a
static, unchanging world to a ‘‘dynamic, information intensive
world’’. Jaikumar characterizes mechanized technologies as
incorporating simple manual and ‘‘hard’’ mechanical mechanization. In contrast, dynamic technologies require ‘‘softer’’ machine
intelligence – essentially computerized programmable intelligence – that substitutes for intelligent tasks ordinarily performed
by humans. For example, dynamically intelligent machines must
be able to recognize a part they pick up, how to position that part
for processing, and how to adjust the part processing based on tool
wear. The scope dimension relates to the breadth of unique
production capabilities that a process is able to handle. Discretion
relates to the ability of workers or machines to make autonomous
decisions regarding the production activities that they will
undertake.
Subjectively, Jaikumar’s work seems to have predicted the
subsequent industrial movement toward IT-based production
operations. During the 20 years since its publication, the world has
experienced a continuing evolution toward IT-based production
systems via (i) broad plant-level use of computerized production
technologies such as FMS, CIM, CAD/CAM, and CAPP, (ii) enterprise
information systems for CRM, ERP and SCM, and (iii) collaboration
via the Internet (Banker et al., 2006; Wu et al., 2003; Caldeira and
Ward, 2002). As manufacturing firms have increasingly adopted a
broad array of information technology, Jaikumar’s model has seen
growing use by academics to motivate the need for new theoretical
frameworks (Melcher et al., 2002) and operations tools (Brooking
et al., 1995).
Yet, firms today are using information technology in varieties
and to an extent that could not have been imagined in 1988, when
Jaikumar proposed the framework. Thus, researchers may need to
enrich the framework’s constructs based on present-day knowledge of how IT use has played out. Jaikumar’s (1988) work focuses
on how process-level IT affected plant activities at various levels. In
comparison, in today’s manufacturing operations information
technology use can span from internal processes out to customers
and suppliers. In this paper, we characterize a manufacturing
plant’s IT use along three dimensions labeled process intelligence,
integration intelligence, and collaboration intelligence. These three
dimensions of IT use represent IT applications supporting
manufacturing operations, integration among different functions,
and collaboration and integration with suppliers and customers,
respectively. This classification of IT use parallels the general
history of the development of modern information systems
(Banker et al., 2006; Wu et al., 2003; Caldeira and Ward, 2002).
Several studies have explored these factors individually (e.g.,
Bardhan et al., 2007; Hill and Scudder, 2002; Saeed et al., 2005;
Masini and Van Wassenhove, 2009), but few have examined them
as separate factors jointly driving changes in plant structure,
practices, and performance.
Jaikumar’s model (Fig. 1) suggests that after a period
characterized by centralization, process automation and economies of scale, firms should exhibit movement toward early
intelligence gathering via statistical process control (SPC), and
subsequent use of IT-based dynamic intelligence gathering. We
propose that SPC and various dimensions of dynamic intelligence
should be observed in a cross-sectional sample of manufacturing
plants. Yet, at the same time, because firms have different internal
traits and operate in different task environments, each firm may
evolve following its own unique path, which may begin in any of
the stages in Jaikumar’s framework or with any dimension of IT
use. As such, we expect that not all manufacturing plants have
evolved through every stage, leading to different degrees of use of
SPC and IT-based dynamic intelligence. While the left-hand side of
Jaikumar’s model (Fig. 1) may be largely unestimable using
contemporary data since it relates to historical developments from
the first half of the 1900s and before, we believe that the right half
of the framework can be analyzed since it relates to ongoing
manufacturing developments. Thus, we focus on empirically
G.R. Heim, D.X. Peng / Journal of Operations Management 28 (2010) 144–162
estimating the impact of moving away from the left side of Fig. 1 by
adopting statistical process control and systems for IT-based
dynamic intelligence.
2.2. Research model and hypotheses
A move toward centralization of job content and a loss of worker
discretion within a manufacturing plant was an early indicator of a
movement toward large operations focused on scale economies
(Jaikumar, 1988). Centralization as a dimension of organizational
social structure has been studied frequently in prior literature,
especially with respect to its causal impact at the firm level (e.g.,
Parthasarthy and Sethi, 1992, 1993; Vickery et al., 1999). In the first
half of Jaikumar’s framework, as centralization increases, one should
observe associated size and productivity increases, whereas
decentralized worker practices and supporting technology should
be observed to decrease. However, by the 1980s the growing use of
competence-destroying IT was expected to reverse these historical
trends, leading to a lower degree of centralization and increasingly
decentralized practices. Jaikumar’s work suggests that certain
operational patterns will be observed in manufacturing firms at
different stages along this evolution path, presented from top to
bottom within each row in Table 1. For example, the control of work
was expected to evolve over time from a mechanical craft to using a
skill set focused on diagnosis and experimentation.
Consistent with changing views on IT in the strategy literature
(Itami and Numagami, 1992), we expect decreased centralization
to be an outcome of information technology evolution rather than
an input. In this study, we focus on the impact of using statistical
process control and information technology supporting plant
intelligence to examine the proposed movement toward decentralized plant structures, discretionary work practices, and a
flexible technology focus. We derive research propositions from
the proposed pattern in Fig. 1, from the rows highlighted by the
rightmost bold column in Table 1, and from knowledge about
modern-day information systems, which we view in terms of the
three dimensions of dynamic intelligence (i.e., dimensions of IT
use). Our research framework and propositions are presented in
Fig. 2 and the rest of this section.
The first research proposition relates to statistical process
control. We examine the impact of SPC at the plant level as a
representative practice relating to information gathering that
contributes to a more effective control of processes (Jaikumar,
1988). Jaikumar suggests that the drive toward gathering SPC
information was an early movement toward dynamic intelligence
within-plant operations. In manufacturing operations, statistical
tools and techniques play an important role in monitoring and
controlling production processes (Benner and Tushman, 2003;
Handfield et al., 1999; Rungtusanatham, 2001). Previous studies
have found positive impacts of SPC or Total Quality Management
(TQM) (e.g., Anderson et al., 1995; Flynn et al., 1995), while other
studies report mixed results depending upon the outcome
variables examined (e.g., Black and Lynch, 2001; Choi and Eboch,
1998). Overall, literature has documented the role of SPC for
supporting JIT practices in standardized larger scale operations,
and the role of TQM in supporting new decentralized work
practices within-plant operations. Thus, we state the following
proposition:
Proposition P1. Statistical process control: Plants exhibiting a higher
level of statistical process control will be positively associated with (i)
operations size, (ii) operations productivity, and (iii) discretionary
work practices related to organizational change. Statistical process
control will be insignificantly or negatively associated with (iv) discretionary work practices related to experimentation and development, and (v) technology focus.
147
Table 1
Jaikumar’s proposed patterns across evolutionary timeline.
Operational
characteristics
Proposed direction of
change
Number of people
(minimum scale)
Number of machines
Increase (+) followed by
decrease ()
Increase (+) followed by
decrease ()
Increases (+)
Decrease () followed by
increase (+)
Size trends
Productivity increase
Number of products
Nature of work
Standards for work
Discretionary work practices
Control of work
Technology keys
Focus on absolute product
Focus on relative product
Focus on work standards
Focus on process standards
Focus on functional
standards
Focus on technology
standards
Mechanical craft
Repetitive tasks/
diagnostic skill
Experimental skill
Work ethos
Perfection
Satisfied
Reproduce
Monitor
Control
Develop
Organizational change
Break up of guilds
Staff-line separation
Increase functional
specialization
Increase problemsolving teams
Increase cellular control
Increase product-process
collaboration/integration
Staff-to-line ratio
Line workers/machine
Increases (+)
Decreases ()
Instrument of control
From micrometer to
professional computer
workstation
Process focus
Accuracy
Precision:repeatability
Precision:reproducibility
Precision:stability
Adaptability
Versatility
Focus of control
Product functionality
Product conformance
Process capability
Product/process
integration
Process intelligence
Rework
Decreases ()
Technology focus
(Source: Jaikumar, 2005, p. 13).
Rows in italic are not empirically examined. The rightmost column contains
terminology used throughout the remainder of the paper.
We break the theorized evolution along the right-hand side of
Jaikumar’s model in Fig. 1 into three separate factors that represent
the separate evolution due to use of information technologies
supporting process intelligence, integration intelligence, and
collaboration intelligence. Process intelligence represents the
extent to which information technology is adopted for tactical
uses at the process level within manufacturing operations. In this
study, we consider a breadth of practices included within the scope
148
G.R. Heim, D.X. Peng / Journal of Operations Management 28 (2010) 144–162
Fig. 2. Research model.
of process intelligence. These practices span from product design
processes to material procurement activities, plant-floor operational control, and shipping processes. As a representative
dimension of dynamic intelligence, we propose that the increased
use of information technologies supporting process intelligence
will be associated with Jaikumar’s expected outcomes of smaller
plant size, increased discretionary work practices, and technology
focus changes leading to higher flexibility and customization. Thus,
we propose the following:
Proposition P2a. Process intelligence: Higher process intelligence
will be negatively associated with (i) operations size. Higher process
intelligence will be positively associated with (ii) operations productivity, (iii) discretionary work practices related to organizational
change, (iv) discretionary work practices related to experimentation
and development, and (v) technology focus.
Extant literature has found some of these impacts related to
process intelligence. Studies report industry-level evidence that IT
investments are related to smaller firm size (Brynjolfsson et al.,
1994; Mitchell, 2002). At the firm level, IT investments are found to
relate positively to firm financial performance (Bharadwaj et al.,
1999), operational performance (Banker et al., 2006), and productivity (Black and Lynch, 2001; Mukhopadhyay et al., 1997; Bessen,
2002; Comin, 2002). At the process level, IT use is found to improve
process efficiency and conformance quality (Banker et al., 2006;
Kelley, 1994). IT use in new product development projects has the
potential to speed up the development process (Bardhan et al., 2007;
Mallick and Schroeder, 2005), reduce development costs, improve
customer satisfaction (Bardhan et al., 2007; Griffin and Page, 1993),
and ultimately, help achieve new product success (Koufteros et al.,
2005; Mallick and Schroeder, 2005; Swink and Song, 2007). Finally,
prior studies suggest that IT use stimulates organization changes
and innovation, and leads firms to use more skilled workers
(Bresnahan et al., 2002).
Integration intelligence represents the extent to which each of
the tactical information technologies at the process level is
integrated into a single system, in this case an ERP system, in
order to provide plant and firm managers with a system-wide
intelligence about the activities taking place within operations.
Again, for integration intelligence, we include the integration of a
broad set of practices related to product design, procurement, shop
floor operations, and logistics. Since higher integration intelligence
represents an increase toward higher dynamic intelligence, we
propose that the increased integration intelligence will be
associated with Jaikumar’s expected outcomes of smaller plant
size, increased discretionary work practices, and technology focus
changes leading to higher flexibility and customization.
Proposition P2b. Integration intelligence: Higher integration intelligence will be negatively associated with (i) operations size. Higher
integration intelligence will be positively associated with (ii) operations productivity, (iii) discretionary work practices related to organizational change, (iv) discretionary work practices related to
experimentation and development, and (v) technology focus.
Extant literature, however, suggests that mixed outcomes may
result from integration intelligence. Several studies report that ERP
helps firms gain a competitive advantage over non-adopters
(Hunton et al., 2003; Mabert et al., 2003). However, the benefit
varies considerably across firms. Banker et al. (2006) observe
positive associations of integration IT with efficiency, quality
improvement, and time to market. Hitt et al. (2002) find that ERP
adoption is positively related to production output and sales, yet
negatively related to the cost of goods sold. Yet, Akkermans et al.
(2003) identify many shortcomings of ERP systems that directly
impact intelligence gathering and dissemination, leading to
decreased process flexibility and worsening performance. Because
the focus of many ERP systems is on standardizing processes, a
movement toward ERP may lead to findings inconsistent with
Jaikumar’s framework.
Collaboration intelligence represents the extent to which a
plant has adopted information technologies that allow them to
communicate and collaborate with customers and suppliers. The
activities span from marketing and sales activities on the customer
side to planning, design, purchasing and supply activities on the
supplier side. Once again, since higher collaboration intelligence
represents an increase in dynamic intelligence, we propose that
increases in collaboration intelligence will be associated with
Jaikumar’s expected outcomes of smaller plant size, increased
discretionary work practices, and technology focus changes
leading to higher flexibility and customization.
G.R. Heim, D.X. Peng / Journal of Operations Management 28 (2010) 144–162
Proposition P2c. Collaboration intelligence: Higher collaboration
intelligence will be negatively associated with (i) operations size.
Higher collaboration intelligence will be positively associated with
(ii) operations productivity, (iii) discretionary work practices related
to organizational change, (iv) discretionary work practices related to
experimentation and development, and (v) technology focus.
Extant literature reports somewhat mixed results with respect
to collaboration intelligence. Demand chain and supply chain
integration have been shown to be positively associated with firm
performance (Frohlich, 2002; Frohlich and Westbrook, 2002).
Barua et al. (2004) find significant positive links from supplier-side
to customer-side digital collaboration, and ultimately to financial
performance. Likewise, Banker et al. (2006b) find positive
associations of collaboration IT with efficiency, quality improvement, and time to market. In contrast, Malhotra et al. (2005)
observe a negative correlation between partner-enabled market
knowledge creation and operational efficiency measures.
3. Data and methodology
3.1. Data source
The data for our study were collected from 2005 to 2007 as part
of the High Performance Manufacturing (HPM) project (Bozarth
et al., 2009). The HPM project uses a stratified sample consisting of
traditional and high performance manufacturing plants. This
research design ensures a sufficient number of high performing
plants in the sample along with more representative traditional
plants. Variation in plant characteristics is likely to be associated
with different extent and scope of IT use, making it possible to
examine our propositions using data collected from the crosssectional sample of manufacturing plants in the HPM study.
Data were collected using a mail survey. Because data collection
involved multiple countries, the questionnaires were translated
into the native language of each country. The questionnaires were
then translated back to English by a different person to check for
accuracy. This process helps control for country level response bias
due to inaccurate translation of the survey questions.
The sample includes plants in eight countries: Finland, Sweden,
Germany, Austria, Italy, Japan, Korea, and the United States. These
countries were selected to obtain a broad representation of
developed economies across different geographical regions and
149
cultures (Europe, Asia, and North America). The sample plants
represent three industries: machinery, electronics, and transportation components. These industries were selected because of their
significant presence in the countries where the survey was
conducted (Schroeder and Flynn, 2001). Details of the sample
plants are reported in Table 2.
The sample plants were randomly drawn from a master list of
manufacturing plants in each participating country, with approximately 10 plants in each of the three industries (electronics,
machinery, and transportation components) for a total of
approximately 30 plants per country. The main informant for
the dynamic intelligence variables was the information systems
manager. Other informants for this study included accounting
manager, direct labor, human resource manager, quality manager,
inventory manager, plant manager, plant superintendent, product
development manager and process engineer, who responded to
questions measuring plant structure, practice, and performance. A
research coordinator at each participating plant distributed the
questionnaires to the named managers and randomly selected
workers and supervisors, and collected the completed questionnaires in sealed envelopes to protect confidentiality. After all data
were collected, the research team provided each participating
plant with a detailed profile of its own manufacturing operations
and benchmark data in its industry, as promised before the data
collection effort. Altogether 238 manufacturing plants responded
to the survey. Among the plants contacted by the research team,
65% returned the survey questionnaires, representing a response
rate of 65%.
3.2. Construction of variables
Table 3 presents the independent and dependent variables
used in the study, including their data type, a description of the
construct measures, respondents for the construct, and published studies that use the same or similar measures, where
applicable. The content validity of the measurement items is
established based on an extensive review of the relevant
literature. This study employs a mixture of indicator, integer,
dollar, ratio, percentage, and averaged scale variables. The
descriptive statistics and the correlation matrix of the variables
are presented in Table 4. In general, the correlations seem to be
reasonable. No abnormally high correlations or counter-intuitive
correlations are present.
Table 2
Plant profile.
Number of plants
Country
Annual sales revenue (median)
Number of employees (mean)
Average market share of main products
Average product life time
Average number of product models
Percent of manufacturing cost
Direct labor
Materials
Overhead
Industry
Total
Electronics
Machinery
Transportation components
Finland
Sweden
Germany
Italy
Austria
Japan
Korea
United States
14
9
9
10
10
10
10
9
6
13
13
10
7
11
10
11
10
19
19
7
5
13
11
9
30
41
41
27
22
34
31
29
Total
79
78
81
238
$284 million
659
25.8%
7.21 years
120
16.5%
59.6%
19.9%
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G.R. Heim, D.X. Peng / Journal of Operations Management 28 (2010) 144–162
Table 3
Dependent and independent variables uses in analysis.
Variable
Type/respondent
Description
Statistical process control
Naor et al. (2008), Peng et al. (2008)
Mean (scale items)
DL, PE, QMa
Processes in our plant are designed to be ‘‘foolproof.’’c (0.49)
A large percent of the processes on the shop floor are currently under statistical quality control
(0.89)
We make extensive use of statistical techniques to reduce variance in processes (0.89)
We use charts to determine whether our manufacturing processes are in control (0.68)
We monitor our processes using statistical process control (0.91)
Rwg = 0.80b
Process intelligence
Integration intelligence
Collaboration intelligence
Electronics, machinery
USA, Japan, Italy, etc.
Parent firm sales
Plant sales
Labor
Sum (0/1), IS
Sum (0/1), IS
Sum (0/1), IS
0/1
0/1
Dollar, PM
Dollar, AC
Integer, AC
Facility size
Integer, AC
Labor productivity
Capital productivity
Orders processed per month
Number of final product configurations
Ratio, AC
Ratio, AC
Integer, PM
Integer, PM
Centralization of authority
Aiken and Hage (1966)
Mean (scale items)
DL, HR, SP
Rwg = 0.78
Even small matters have to be referred to someone higher up for a final answer (0.90)
This plant is a good place for a person who likes to make his own decisions (0.47)*
Any decision I make has to have my boss’s approval (0.84)
There can be little action taken here until a supervisor approves a decision (0.76)
Flatness of organization structure
Nahm et al. (2003), Vickery et al. (1999)
Mean (scale items)
HR, PS, SP
Rwg = 0.93
Our organization structure is relatively flat (0.78)
There are few levels in our organizational hierarchy (0.85)
Our organization is very hierarchical (0.799)*
There are many levels between the lowest level in the organization and top management (0.74)*
Our organizational chart has many levels (0.90)*
Cooperation
Mean (scale items)
IM, PM, SP
We work as a partner with our suppliers, rather than having an adversarial relationship (0.66)
We encourage employees to work together to achieve common goals, rather than encourage
competition among individuals (0.73)
We work as a partner with our customers (0.48)
We believe that cooperative relationships will lead to better performance than adversarial
relationships (0.66)
We believe that the need for cooperative relationships extends to both employees and external
partners (0.54).
We believe that an organization should work as a partner with its surrounding
community (0.63)
Rwg = 0.86
See Appendix A, Table A.1
See Appendix A, Table A.1
See Appendix A, Table A.1
Indicator variables for industry effect
Indicator variables for country effect
Annual sales of parent corporation
Sales value of production ($000)
Sum of hourly personnel and salaried personnel, averaged across present year and 2 years
previous
Space used by the plant (square feet) including production and warehouse space (omits South
Korean plants)
Plant sales/labor
Plant sales/plant size
Orders this plant processes each month, on average
Number of final product configurations sold to customers last year
Employee suggestions
Percent cross trained
Integer, HR
Percent, HR
Suggestions per worker provided annually
Percent of plant workers that have been cross-trained in more than one job
Multi-functional employees
Ahmad and Schroeder (2003)
Mean (scale items)
HR, SP, PS
Rwg = 0.84
Our employees receive training to perform multiple tasks (0.77)
Employees at this plant learn how to perform a variety of tasks (0.88)
The longer an employee has been at this plant, the more tasks they learn to perform (0.520)
Employees are cross-trained at this plant, so that they can fill in for others, if necessary (0.79)
At this plant, each employee only learns how to do one job (0.69)*
Percent employees on problem
solving teams
Small group problem solving
Percent, HR
Percent of the workers in the plant involved in problem solving teams
Mean (scale items)
Rungtusanatham et al. (2005)
DL, QM, SP
Rwg = 0.80
During problem solving sessions, we make an effort to get all team members’ opinions
and ideas before making a decision (0.55)
Our plant forms teams to solve problems (0.81)
In the past 3 years, many problems have been solved through small group sessions (0.83)
Problem solving teams have helped improve manufacturing processes at this plant (0.83)
Employee teams are encouraged to try to solve their own problems, as much as possible (0.63)
We do not use problem solving teams much, in this plant (0.78)*
Knowledge level engineering degrees
Percent, HR
Percent of salaried personnel having engineering or technical degrees
Developing unique practices
Mean (scale items)
PE, SP, PS
Rwg = 0.81
We are known for developing innovative new practices (0.73)
We gain a competitive advantage from our unique practices (0.69)
When we adopt a new practice, our competitors can easily copy it (0.38)*
Willingness to introduce new products
5 point scale
PM
Which term best describes the plant’s posture toward new products?
5-Leader in new products; 4-Among the first to adopt new products, but not the leader
3-Adopts new products when it becomes more or less the general rule
2-Usually among the last to adopt new products; 1-Never adopts new products
New product configurations last year
Average lifetime of product configurations,
Bozarth et al. (2009)
Volume flexibility, Flynn and Flynn (2004)
Integer, PE
Real, PE
Number of new product configurations introduced last year
Average lifetime of a product configuration, in number of years
Percent change
month-to-month, IM
Average percent change in plant output dollars from one month to the next, over the course
of a year
G.R. Heim, D.X. Peng / Journal of Operations Management 28 (2010) 144–162
151
Table 3 (Continued )
Variable
Type/respondent
Description
Customization orientation of process
Cua et al. (2001)
Weighted score, PE
What percent of product volume falls into each category?
Weighted percent = OneOfAKind% 5 + SmallBatch% 4 + LargeBatch% 3 + LineFlow% 2 +
Continuous% 1
Integration between functions
Mean (scale items)
PE, PM, PS
Rwg = 0.84
The
The
The
Our
Our
Inter-functional design efforts
Mean (scale items)
Mishra and Shah (2009)
PD, PE, SP
Direct labor employees are involved to a great extent before introducing new products or
making product changes (0.71)
Manufacturing engineers are involved to a great extent before the introduction of new products
(0.76)
There is little involvement of manufacturing and quality people in the early design or products,
before they reach the plant (0.70)*
We work in teams, with members from a variety of areas (marketing, manufacturing, etc.) to
introduce new products (0.78)
Rwg = 0.79
functions in our plant work well together (0.87)
functions in our plant cooperate to solve conflicts between them, when they arise (0.77)
marketing and finance areas know a great deal about manufacturing (0.45)
plant’s functions coordinate their activities (0.76)
plant’s functions work interactively with each other (0.79)
Scrap rate (Devaraj et al., 2004)
Percent, QM
Percentage of internal scrap and rework
Customer satisfaction with products
and service (referred to as ‘‘customer
satisfaction’’ elsewhere)
Bozarth et al. (2009)
Mean (scale items)
DL, QM, SP
Rwg = 0.83
Our customers are pleased with the products and services we provide for them (0.89)
Our customers seem happy with our responsiveness to their problems (0.73)
We have a large number of repeat customers (0.46)
Customer standards are always met by our plant (0.73)
Our customers have been well satisfied with the quality of our products, over the past 3 years
(0.85)
In general, our plant’s level of quality performance over the past 3 years has been low,
relative to industry norms (0.76)*
a
Respondents: AC, accounting manager; DL, direct labor; HR, human resource manager; IM, inventory manager; IS, information systems manager; PD, member of product
development team; PE, process engineer; PM, plant manager; PS, plant superintendent; QM, quality manager; SP, supervisor.
b
Rwg = ratio coefficient for assessing inter-rater agreement, Rwg > 0.80 indicates acceptable inter-rater agreement.
c
Confirmatory factor analysis loading.
*
Reverse coded items.
Because of the relatively high response rate (65%), nonresponse bias does not appear to be a serious concern. Nevertheless, for each variable with missing responses, we compared
responding and non-responding plants for differences in plant size,
measured by number of employees. The results suggest that
responding and non-responding plants do not exhibit significant
differences in plant size.
In this study, we view each plant’s data to represent a chosen
configuration of managerial practices that places their operations at
certain points along each of the dimensions of IT use. Thus, we use
the proposed dynamic intelligence dimensions as positioning
variables, which allow us to examine whether the proposed
relationships between dynamic intelligence and the outcome
dimensions are exhibited in the empirical data set. The first four
rows in Table 3 present these key statistical process control and
dynamic intelligence variables. The statistical process control
variable, representing the extent of process control intelligence,
was constructed as the average of several items from a multiple item
seven-point scale measuring SPC adoption. The dynamic intelligence variables are each constructed as the arithmetic sum of the set
of individual IT application variables representing each dimension of
dynamic intelligence. Appendix A (Table A.1) presents the IT
application areas included in each dynamic intelligence variable.
Consistent with the literature (Banker et al., 2006; Barczak et al.,
2007; Hitt et al., 2002), we represent each implemented IT
application area within each dimension of dynamic intelligence as
a binary (0–1) variable. An individual IT variable is assigned a value
‘‘0’’ or ‘‘1’’, where ‘‘0’’ denotes that the represented IT functionality
has not been implemented and ‘‘1’’ otherwise. Because each IT
variable represents whether a particular plant functionality is
supported by information technology, the extent to which a plant
uses IT should be reflected in the total number of functionalities
(processes and activities) supported by IT. Thus, to construct each
dynamic intelligence variable, we summed together the values of
the list of underlying dichotomous variables representing the use
of information technology within the scope of that dimension of
dynamic intelligence in the plant.
Because each IT item specifies a separate functionality
supported by information technology, different IT items are not
interchangeable. Thus the IT items can be considered as formative
measures (Diamantopoulos and Winklhofer, 2001, p. 271). An
important criterion for assessing the construct validity of the
formative measures is their content validity (Diamantopoulos and
Winklhofer, 2001; Petter et al., 2007). We chose the measurement
items based on our definition of the dynamic intelligence
constructs. The measurement items capturing the constructs are
well grounded in prior studies. A list of related articles upon which
we built the dynamic intelligence measures can be found in
Appendix A (Table A.1). Another important criterion for formative
items is that they should not exhibit high multicollinearity
(Diamantopoulos and Winklhofer, 2001). In our study, each
dynamic intelligence item has a Variance Inflation Factor (VIF)
below 10, with only two items having a VIF greater than 5. Thus,
multicollinearity does not seem to be a serious concern as the
literature suggests that VIF below 5 (some suggest below 10) is
acceptable (Haan, 2002; Hair et al., 2005).
We also performed Exploratory Factor Analysis (EFA) on each of
the dynamic intelligence measures. Unlike the reflective items,
formative items of the same construct do not have to load on a
single factor (Diamantopoulos and Winklhofer, 2001). The EFA
results indicate that the IT items measuring each type of dynamic
intelligence form logical groups representing plant operational
areas, except for a small number of item cross loadings. These
results to some extent provide support for the validity of the
dynamic intelligence measures. The EFA factor loadings are
presented in Appendix A (Tables A.2–A.4).
This study uses a number of multi-item scales, each scored
along a seven-point scale. The majority of the multi-item scales are
evaluated by three different respondents who have relevant
domain knowledge for the assigned questions (Table 3). Using
152
Table 4
Descriptive statistics and correlation matrix.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
Percent cross trained (%)
Multi-functional employees
Percentage on problem solving team (%)
Small group problem solving
Percentage of engineer degrees (%)
Developing Unique practices
Willingness to introduce products
New products last year
Average product life time (year)
Volume flexibility (%)
Process customization
Functional integration
Inter-functional design efforts
Scrap rate (%)
Customer satisfaction
Mean
Standard deviation
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
4.68
19.25
11.87
4.80
418,804,822
284,445.80
659.39
332,004.37
473.62
7.10
56,516.85
2,263.50
3.34
4.54
5.82
5.74
54.55
5.30
31.05
5.12
28.51
4.58
2.36
43.12
7.21
22.84
3.28
5.13
4.89
7.01
5.32
0.94
7.41
6.96
2.81
1,649,040,382
476388.73
918.82
1,970,848.65
590.62
12.75
364,425.99
12,459.46
0.73
0.99
0.42
12.19
32.73
0.61
32.27
0.65
24.78
0.65
1.31
95.53
5.73
24.82
1.24
0.60
0.76
11.46
0.53
0.17
0.03
0.04
0.11
0.01
0.17
0.00
0.10
0.01
0.15
0.17
0.16
0.14
0.35
0.04
0.19
0.23
0.20
0.53
0.07
0.29
0.26
0.08
0.27
0.01
0.25
0.38
0.36
0.15
0.32
0.25
0.20
0.14
0.01
0.18
0.07
0.13
0.13
0.07
0.02
0.12
0.11
0.13
0.07
0.08
0.16
0.11
0.08
0.09
0.17
0.16
0.29
0.03
0.02
0.09
0.14
0.13
0.11
0.11
0.14
0.01
0.12
0.01
0.24
0.08
0.10
0.25
0.00
0.29
0.26
0.09
0.23
0.05
0.18
0.01
0.01
0.05
0.14
0.01
0.11
0.06
0.16
0.13
0.05
0.04
0.03
0.14
0.16
0.19
0.03
0.12
0.12
0.10
0.02
0.16
0.01
0.00
0.15
0.14
0.01
0.11
0.09
0.15
0.09
0.12
0.01
0.07
0.11
0.01
0.09
0.20
0.03
0.07
0.17
0.24
0.05
0.29
0.28
0.11
0.05
0.20
0.22
0.19
0.09
0.22
0.15
0.08
0.06
0.08
0.03
0.06
0.26
0.00
0.16
0.17
0.18
0.02
0.04
0.05
0.00
0.53
0.55
0.86
0.45
0.24
0.08
0.09
0.11
0.03
0.23
0.23
0.09
0.17
0.13
0.07
0.12
0.23
0.03
0.03
0.05
0.30
0.11
0.14
0.18
0.12
0.63
0.06
0.04
0.32
0.07
0.11
0.21
0.08
0.07
0.01
0.02
0.13
0.17
0.05
0.16
0.16
0.11
0.05
0.13
0.29
0.12
0.25
0.06
0.16
0.18
0.45
0.27
0.15
0.25
0.33
0.03
0.20
0.08
0.13
0.05
0.00
0.09
0.06
0.02
0.22
0.07
0.04
0.14
0.09
0.05
0.09
0.20
0.77
0.09
0.07
0.02
0.05
0.00
0.21
0.23
0.12
0.14
0.09
0.05
0.07
0.17
0.10
0.02
0.05
0.21
0.01
0.09
0.18
0.04
0.02
0.00
0.20
0.25
0.05
0.04
0.30
0.23
0.17
0.14
0.10
0.18
0.16
0.23
0.02
0.07
0.17
0.06
0.09
0.15
0.13
0.08
0.27
0.20
0.03
0.30
0.13
0.04
0.06
0.12
0.08
0.06
0.09
0.01
0.04
0.02
0.17
0.20
0.21
0.18
0.10
0.01
0.10
0.09
0.24
0.09
0.01
0.22
0.19
0.07
0.11
0.14
0.12
0.03
0.02
0.13
0.02
0.04
0.18
0.16
0.60
0.36
0.35
0.35
0.53
0.07
0.30
0.06
0.33
0.11
0.06
0.07
0.09
0.02
0.25
0.17
0.14
0.42
0.34
0.23
0.29
0.45
0.00
0.35
0.10
0.20
0.02
0.02
0.07
0.15
0.02
0.22
0.21
0.12
0.39
0.02
0.26
0.47
0.11
0.49
0.03
0.33
0.01
0.09
0.05
0.06
0.01
0.39
0.27
0.01
0.38
Mean
Standard deviation
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
54.55
5.30
31.05
5.12
28.51
4.58
2.36
43.12
7.21
22.84
3.28
5.13
4.89
7.01
5.32
32.73
0.61
32.27
0.65
24.78
0.65
1.31
95.53
5.73
24.82
1.24
0.60
0.76
11.46
0.53
0.13
0.06
0.04
0.11
0.04
0.01
0.04
0.03
0.03
0.01
0.10
0.25
0.09
0.11
0.06
0.49
0.03
0.32
0.02
0.04
0.04
0.05
0.05
0.08
0.24
0.22
0.21
0.06
0.18
0.17
0.48
0.09
0.38
0.09
0.01
0.04
0.03
0.11
0.32
0.34
0.09
0.46
0.26
0.05
0.19
0.04
0.01
0.06
0.02
0.13
0.08
0.20
0.14
0.13
0.07
0.39
0.10
0.01
0.17
0.01
0.15
0.36
0.47
0.07
0.56
0.00
0.11
0.00
0.01
0.09
0.09
0.09
0.00
0.03
0.15
0.00
0.07
0.08
0.09
0.05
0.36
0.32
0.08
0.36
0.10
0.04
0.07
0.08
0.10
0.16
0.19
0.01
0.30
0.07
0.08
0.02
0.05
0.09
0.02
0.09
0.11
0.08
0.07
0.02
0.11
0.08
0.06
0.02
0.09
0.08
0.13
0.24
0.11
0.01
0.42
0.12
0.33
0.04
0.27
0.02
p < 0.01 in bold italic. p < 0.05 in bold. p < 0.10 in italic.
G.R. Heim, D.X. Peng / Journal of Operations Management 28 (2010) 144–162
1. Statistical Process control
2. Process intelligence
3. Integration intelligence
4. Collaboration intelligence
5. Parent firm size ($)
6. Plant sales ($000)
7. Number of employees
8. Facility size
9. Labor productivity
10. Capital productivity
11. Orders processed per month
12. Number of final products configurations
13. Centralization
14. Organizational structure flatness
15. Cooperation
16. Employee suggestions
17. Percent cross trained (%)
18. Multi-functional employees
19. Percentage on problem solving team (%)
20. Small group problem solving
21. Percentage of engineer degrees (%)
22. Developing Unique practices
23. Willingness to introduce products
24. New products last year
25. Average product life time (year)
26. Volume flexibility (%)
27. Process customization
28. Functional integration
29. Inter-functional design efforts
30. Scrap rate (%)
31. Customer satisfaction
G.R. Heim, D.X. Peng / Journal of Operations Management 28 (2010) 144–162
multiple respondents to evaluate the same scale questions has the
advantage of mitigating Common Method Variance (CMV), a
widely cited concern for self-report surveys (Podsakoff et al.,
2003). In our study, CMV is further mitigated by having different
respondents for the items measuring the independent and the
dependent variables. To assess inter-rater agreement among
multiple respondents, for each scale evaluated by multiple
respondents, we computed the Rwg coefficients according to the
ratio method (James et al., 1984). The resulting inter-rater
agreement coefficients range from 0.78 to 0.93 (Table 3), and
are in line with the guidance on inter-rater reliability for
operations management research (Rwg > 0.80) suggested by Boyer
and Verma (2000), thus indicating acceptable agreement among
different respondents.
To establish the reliability and validity of the scale measures, we
ran a Confirmatory Factor Analysis (CFA) including all of the multiitem scales. The CFA fit indices suggest acceptable model fit
(RMSEA = 0.052, x2/DF = 1.69, CFI = 0.091, IFI = 0.090). Except for
one factor loading for the construct ‘‘developing unique practices,’’
all factor loadings are greater than 0.50 (Table 3). The lone loading
below 0.50 is 0.38, still above the threshold of 0.30 (Hair et al., 2005).
We used pair-wise Chi-square (x2) difference tests to examine the
discriminant validity of the measurement scales (Bagozzi and
Phillips, 1982; Bagozzi et al., 1991). The x2 difference tests are all
significant at the 0.01 level, indicating acceptable discriminant
validity among the scale measures. We also compared the squared
correlation between each pair of variables measured by a multi-item
scale and variance explained for that pair of variables. No squared
correlation is found to exceed the corresponding values of variance
explained (Fornell and Larcker, 1981), again suggesting acceptable
discriminant validity. Next, we constructed the averaged scale
variables using simple arithmetic averages of the multiple item
scores related to a construct (MacDuffie, 1995).
The non-scale variables are mostly objective measures such as
plant annual sales, plant total number of employees, and plant
square footage. These variables are described in Tables 2 and 3.
When necessary, we normalized the variables using a natural log
transformation.
3.3. Empirical methodology
We test our propositions using multiple linear regression
models. We performed five sets of regressions. In each set of
regressions, SPC and the three dynamic intelligence variables are
entered as the predictor variables. For each set of regression
models, the dependent variables represent a specific category of
the expected outcomes implied by Jaikumar’s framework (i.e.,
Table 1): operations size, operations productivity, organizational
changes, experimentation and development practices, and technology focus. Operations size includes variables measuring plant
annual sales, total number of plant employees, and size of the
facility. Operations productivity includes variables measuring
labor productivity (plant sales per employee), capital productivity
(plant sales/facility size), orders processed per month, and number
of final product configurations produced. Variables related to
organization changes include centralization of decision making,
flatness of organization structure, cooperation between employees, employee suggestions, employee training, and problem
solving teams. Variables related to experimentation and development practices represent the development of unique practices,
willingness to introduce new products, new product configurations introduced last year, and an average product’s lifetime.
Finally, variables that capture technology focus are volume
flexibility, customization orientation of processes, integration
between functions, scrap rate, and customer satisfaction with
product and service.
153
We include control variables where warranted. Because the
macro-economic environments and the cultural and socioeconomic factors could contribute to cross-country variation in
firm size and productivity (Bernard and Jones, 1996; Dewan and
Kraemer, 2000; Kumar et al., 1997), for operations size and
productivity regressions, we control for the impact of country-tocountry differences by including indicator variables for the
nationality of each plant. The base country against which the
other country controls can be compared is Austria. We do not
include country as a control for the remaining regressions since the
dependent variables in these regressions capture within-plant
practices, which are mainly influenced by plant specific factors
rather than country effects (Naor et al., 2008). We also use
indicator variables to control for industry-to-industry differences
(Banker et al., 2006). The base industry for comparison is the
transportation component industry. Prior literature suggests that
larger firms tend to have larger plants (Bernard and Jensen, 2006).
Thus, for regressions on operations size, we control for sales of the
parent firm. Finally, because prior studies find that organization
size and productivity are correlated (Dhawan, 2001; Haltiwanger
et al., 1999), in productivity regressions we control for size of the
manufacturing plant, measured by the natural log of the total size
of the labor force (Banker et al., 2006; Ettlie et al., 1984).
Because of missing responses in the data set, we performed
Little’s MCAR test using the SPSS 13 missing value analysis
procedure to check if the variables are Missing Completely At
Random (MCAR). The resulting p-value is insignificant at the 0.10
level, indicating the variables are MCAR. Thus, in each regression
we list-wise delete the plants having missing values.
After running each regression, we examined relevant diagnostic
statistics to rule out potential violation of regression assumptions.
We visually checked the distribution of each variable. The variables
all seem to have a bell shaped distribution (after transformation for
some variables). For each regression, we examined variance inflation
factor to rule out multicollinearity problems. The VIFs of the main
effects and interaction effects are all below 2. Some of the control
variables, mostly country controls, have relatively higher VIFs.
However, none of the VIFs is above 5. Again, literature suggests that
VIF below 5 is acceptable (Haan, 2002; Hair et al., 2005). We checked
various residual plots. The residuals seem to have a bell-shaped
distribution and also appear to be constant across the predicted
dependent values, ruling out the presence of heteroskedasticity.
Thus, the data appear appropriate for regression analysis.
3.4. Results
For ease of comparison, we condense our findings into a
summary table. A summary of the regression results can be found
in Table 5. Detailed regression results are provided in Appendix A
(Tables A.5–A.9).
3.4.1. Proposition 1—SPC
Overall, we observe weak evidence indicating that SPC is
positively associated with operations size and no evidence linking
SPC to operations productivity measures. SPC has a marginally
positive association with the total number of plant employees
(b = 0.126, p = 0.080) and with plant sales when we control for
parent firm sales (b = 0.161, p = 0.074). The SPC variable is not
significantly related to any of the productivity measures at the 0.10
level.
In contrast, we observe fairly strong evidence of the positive
effect of SPC on organization change. SPC is significantly related to
seven of the eight outcome measures in this category, with all
estimated parameters in the expected direction. As expected, SPC
is positively associated with practices oriented toward process
improvement. SPC is negatively related to centralization of
G.R. Heim, D.X. Peng / Journal of Operations Management 28 (2010) 144–162
154
Table 5
Summary of regression results.
Proposition 1
Statistical
process control
Proposition 2a
Process
intelligence
Proposition 2b
Integration
intelligence
Proposition 2c
Collaboration
intelligence
Size trends
Size (Appendix A, Table A.5)
0.161(0.074)y
0.126(0.080)y
Dependent
variables
ln(Plant Sales) Control for Firm Sales
ln(Plant sales)
ln(Labor) Control for Firm Sales
ln(Labor)
ln(Facility Size) Control for Firm Sales
ln(Facility size)
0.031(0.005)**
0.040(0.000)***
0.033(0.026)*
0.037(0.009)**
Productivity (Appendix A, Table A.6)
0.072(0.085)y
y
0.057(0.067)
0.094(0.026)*
Discretionary work practices
Organizational change (Appendix A, Table A.7)
0.163(0.004)**
0.192(0.013)*
0.159(0.000)***
0.031(0.000)***
0.037(0.000)***
y
0.079(0.003)**
7.429(0.012)*
0.161(0.001)**
7.692(0.005)**
0.341(0.000)***
0.014(0.018)*
0.031(0.028)*
Experimentation and development practices (Appendix A, Table A.8)
0.225(0.000)***
0.012(0.077)y
0.393(0.000)***
0.026(0.055)y
0.091(0.000)***
0.181(0.005)***
Technology keys
Technology focus (Appendix A, Table A.9)
0.012(0.076)y
0.233(0.020)*
0.031(0.017)*
0.265(0.000)***
0.262(0.000)***
0.535(0.001)**
0.187(0.000)***
0.009(0.097)y
0.018(0.058)
0.171(0.008)**
0.037(0.007)
**
0.025(0.039)*
ln(Labor productivity)
ln(Capital productivity)
ln(Orders processed per month)
ln(Number of final product configurations produced)
Centralization
Flatness of organization structure
Cooperation
ln(Employee suggestions)
Percentage of workers cross trained
Multi-functional employees
Percent employees on problem solving teams
Small group problem solving
Knowledge level sqrt (% of workers with engineering degrees)
Developing unique practices
Willingness to introduce new products
ln(New product configurations introduced last year)
ln(Average lifetime of product configuration)
ln(Volume flexibility)
Customization orientation of process
Integration between functions
Inter-functional design efforts
ln(Scrap rate)
Customer satisfaction
VIF statistics range from 1.12 to 1.86 for main effects and interaction effects, and 1.99–4.84 for control variables. A block (representing regression coefficients from one
independent variable to a group of outcome variables) is shaded if there are three or more regression coefficients that have a p-value smaller than 0.10 within the block.
*
p < 0.050.
**
p < 0.010.
***
p < 0.001.
y
p < 0.100.
authority (b = 0.163, p = 0.004), and positively related to flatness
of organization structure (b = 0.192, p = 0.013). SPC also has a
positive relationship with cooperation (b = 0.159, p = 0.000),
employee training (b = 7.429, p = 0.012), and problem solving
teams (b = 7.692, p = 0.005; b = 0.341, p = 0.000).
We observe mixed evidence with respect to our expectation
that SPC would be insignificant or negatively associated with
experimentation and development practices. SPC is negatively
related to willingness to introduce new products (b = 0.393,
p = 0.000) and insignificantly related to two other variables,
providing support for Proposition 1. However, SPC also relates
positively to developing unique practices (b = 0.225, p = 0.000) and
negatively to the average lifetime of a product configuration
(b = 0.181, p = 0.005).
Although we expected insignificant or negative associations, we
again observe mixed yet relatively strong evidence supporting an
association between SPC and changes in technology focus. As
expected, an increased level of SPC is related to lower level of
customization orientation in processes (b = 0.233, p = 0.021).
Contrary to Proposition 1, we observe strong associations of SPC
with improved scrap rates (b = 0.535, p = 0.001), inter-functional
integration (b = 0.265, p = 0.000), inter-functional design efforts
(b = 0.262, p = 0.000), and customer satisfaction (b = 0.187,
p = 0.000). Thus, SPC appears to support changes in technology focus.
3.4.2. Proposition 2a—process intelligence
Process intelligence exhibits positive associations with several
operations size variables and two productivity measures. However,
several of the parameter estimates go against expectations. Among
operations size variables, process intelligence positively relates to
the total number of employees (b = 0.031, p = 0.005; b = 0.040,
p = 0.000) and facility size (b = 0.033, p = 0.026; b = 0.037,
p = 0.009). Thus, the operations size variables appear to contradict
expected associations. In contrast, process intelligence appears to be
partially consistent with the expectation for operations productivity. Process intelligence exhibits a positive association with orders
processed per month (b = 0.057, p = 0.067) and number of final
product configurations produced (b = 0.094, p = 0.026) but it is
insignificantly related to other productivity measures at the 0.10
level. These findings provide partial support for Proposition 2a.
Process intelligence exhibits far fewer associations with the
remaining sets of practices. Process intelligence does not have a
significant effect on practices related to organizational changes or
technology focus. With respect to experimentation and develop-
G.R. Heim, D.X. Peng / Journal of Operations Management 28 (2010) 144–162
ment practices, process intelligence exhibits mixed evidence
including a positive effect on new product configurations
introduced last year (b = 0.091, p = 0.000) but a negative effect
on willingness to introduce new products (b = 0.026, p = 0.055).
In summary, we observe mostly positive associations between
process intelligence and operations size, which tends to contradict
Proposition 2a. We find evidence supporting a positive link
between process intelligence and operations productivity, supporting Proposition 2a. Finally, we do not find any significant
relationship between process intelligence and discretionary work
practices or changes in technology focus.
3.4.3. Proposition 2b—integration intelligence
In contrast to SPC and process intelligence, integration
intelligence does not relate to any operations size or productivity
measures at the 0.10 level. However, the effects of integration
intelligence on variables for discretionary work practices related to
organizational changes are mostly consistent with our predictions:
higher integration intelligence is related to a lower degree of
centralization of decision making (b = 0.031, p = 0.000), high
degree of horizontal organization structure (b = 0.037, p = 0.000),
and increased multi-functional training (b = 0.014, p = 0.018). The
only exception is with the average number of employee suggestions (b = 0.079, p = 0.003). Integration intelligence also exhibits
moderately positive relationships with three technology focus
variables: volume flexibility (b = 0.012, p = 0.076), customization
orientation of processes (b = 0.031, p = 0.017), and customer
satisfaction (b = 0.009, p = 0.097).
With respect to variables measuring experimentation and
development practices or technology focus, integration intelligence
only exhibits a weak positive relationship with developing unique
practices (b = 0.012, p = 0.077). Overall, there is moderate evidence
supporting the hypothesized links between integration intelligence
and the outcome variables, particularly discretionary work practices
related to organizational change or technology focus.
3.4.4. Proposition 2c—collaboration intelligence
While collaboration intelligence is related to only a small
number of outcome variables, the directions of the relationships
are mostly consistent with predictions. Collaboration intelligence
is positively related to capital productivity (b = 0.072, p = 0.085),
cooperation (b = 0.018, p = 0.058), average number of employee
suggestion (b = 0.171, p = 0.008), small group problem solving
(b = 0.031, p = 0.028), integration between functions (b = 0.037,
p = 0.007), and customer satisfaction (b = 0.025, p = 0.039). Thus,
there is weak support for Proposition P2c.
4. Discussion and conclusion
4.1. Discussion of findings
Over the past century, manufacturing process control has
changed profoundly due to the adoption of SPC and various forms
of information technology. Jaikumar’s theoretical framework of the
evolution toward dynamic intelligence brought these two factors
together. Yet, to date no one has empirically examined the impact
of IT use at the plant level after controlling for developments
related to SPC. We attempt to explore the extent to which IT use
impacts various aspects of plant operations drawing on Jaikumar’s
theoretical framework. As in related studies, we observe substantial impacts due to intelligence gathering via SPC yet limited
evidence related to the impact of adopting IT for process,
integration, and collaboration intelligence.
In Jaikumar’s original model, advances in process control are
described in terms of stages of adopting statistical process control,
numerically controlled (NC/CNC) systems, flexible manufacturing
155
systems, and computer-integrated manufacturing (CIM). In this
study, we broadly represent plant-level IT use with our dimension
of process intelligence, and break the movement toward intra-firm
and inter-firm IT integration into two separate dimensions
representing ERP-based integration intelligence and supplier/
customer collaboration intelligence. We examine the impact of
three dimensions of dynamic intelligence – process, integration,
and collaboration – on manufacturing plant outcomes representing plant structure, practices, and performance. While it clearly is
quite challenging to perform such a comprehensive examination of
the IT impact on manufacturing plants, we believe our exploratory
efforts in this paper provide a useful starting point.
As a foundation for building dynamic intelligence, SPC
repeatedly comes up significant in ways that lead to better control
and reduced process variability. Compared with each of the three
dynamic intelligence variables, SPC tends to be associated more
strongly with the outcome variables, particularly with variables
measuring organizational changes, experimentation and development practices, and technology focus. These findings are consistent
with prior literature that highlights the positive impact of SPC and
quality management practices in general. The impact of SPC and
quality management practices can be quite broad and transcend
multiple organization levels, from employee autonomy and job
satisfaction to relationships among functional areas, and to overall
organizational performance and productivity (e.g., Flynn et al.,
1995; Rungtusanatham, 2001).
The three dimensions of dynamic intelligence collectively are
related to a broad array of outcome variables, although the effects
are not all consistent with our expectation and many of the
relationships are somewhat weak. The three dimensions of dynamic
intelligence exhibit quite different effects on various groups of
outcome variables. The effects of process intelligence are concentrated on operations size and operations productivity. In contrast,
integration intelligence and collaboration intelligence mainly
impact organizational change and technology focus variables, with
integration intelligence exhibiting broader impact on these two
groups of outcome variables than collaboration intelligence. These
findings are perhaps due to the different objectives of the three
dimensions of dynamic intelligence. Integration intelligence and
collaboration intelligence are mainly used to gather information and
aid decision making, whereas process intelligence to a large extent is
used to automate manufacturing and administrative processes. The
findings may also occur because IT supporting collaboration
intelligence is the newest of the three dimensions of dynamic
intelligence, and therefore its impact on plant operations may not
yet be as pronounced.
Contrary to Jaikumar’s expectation that dynamic intelligence is
associated with smaller minimal scale of efficient operations and
smaller operations size, we found that process intelligence is
associated with larger operations size. IT oriented toward process
intelligence is the initial stage of IT-based intelligence and has been
used mainly for automating manufacturing processes and streamlining workflow. As such, process intelligence is likely to facilitate
process standardization and large scale operations. Another
potential reason we did not observe the expected relationship is
the wide adoption of a ‘‘plant-within-a-plant’’ structure that
allows multiple different production lines housed in the same
plant to share resources in order to achieve economies of scale and
scope. Due to the constraints of our data, we were not able to
examine the operations size at the production line level.
Overall, none of the three dimensions of dynamic intelligence
appears to have much impact on discretionary work practices
related to experimentation and development. Their effects are
mixed at best. Perhaps these practices are driven to a greater
extent by factors other than IT. Although IT may facilitate
innovation practices, its effect may not be discernable unless
156
G.R. Heim, D.X. Peng / Journal of Operations Management 28 (2010) 144–162
embedded in specific organization contexts. Also, product innovation often takes place at a level higher than manufacturing plants
(e.g., business unit level), thus the effects of plant-level IT on
experimentation and development practices may not be highly
significant.
4.2. Research contributions
Our study makes two contributions to the literature. First, our
findings help enrich the literature by studying IT impacts on a broad
set of organizational outcome variables, whereas prior literature has
focused on examining IT impact on a limited set of performance
measures such as productivity, quality, and time performance, and
generally at a higher level of aggregation (Brynjolfsson, 1993;
Brynjolfsson and Hitt, 1996; Black and Lynch, 2001; Banker et al.,
2006). Our study systematically investigates the impact of IT use on
plant structure, practice, and performance. The scope of the IT
application areas and the outcome variables examined, along with
the manufacturing plant-level analysis, differentiate our study from
the existing literature. Second, we draw on IS literature and an
insightful theoretical operations management framework to examine the IT impact on plant operations, whereas prior studies have
typically taken a theoretical lens borrowed from management or
economics theories. Our findings provide new insights about the
impact of IT on plant operations, as Jaikumar’s model did not envision
the impacts we find concerning integration intelligence built around
enterprise resource planning systems, and collaboration intelligence
related to Internet-based purchasing, supply chain, and marketing
activities. Our results demonstrate that the three different dimensions of dynamic intelligence gathering appear to impact different
sets of variables related to plant structure, practices, and performance. Yet, the findings also suggest that SPC-related intelligence
perhaps has had an even stronger effect upon discretionary work
practices than has IT-based dynamic intelligence. Thus, future
studies probably should make sure to account for both.
Our study also provides useful managerial insights about using
IT to achieve desired organizational outcomes. Because building ITbased competences to support manufacturing operations is an
ongoing process, managers must understand the contribution of
each dimension of IT toward specific organizational outcomes in
order to make the right IT investment decisions. The specificity of
our IT measures disentangles the effect of each dimension of IT use
upon a variety of organizational outcomes and performance, thus
informing managerial decisions about IT investments in manufacturing plants.
4.3. Limitations and future research
As an exploratory study, this paper exhibits certain limitations.
First, as a broad empirical analysis that includes a large number of
variables, it is difficult to disentangle the extent to which
individual variables are endogenous or exogenous in each of the
regressions. To the best of our ability, we attempt to use reasonable
modeling approaches to limit the extent to which endogeneity
problems might crop up, while maximizing the ability to detect the
impacts of technologies supporting dynamic intelligence. Second,
our measures of dynamic intelligence variables are adapted from
the existing HPM database and capture whether a plant adopted
information technology to support specific plant processes. While
this approach is consistent with the existing literature (Bardhan
et al., 2007b), it does not measure the extent to which each IT
application supports certain plant processes. Finally, it is difficult
to appropriately control for potentially confounding factors in such
models. Because plant size and information technology adoption
are often very different in different contexts, it leads to issues of
how to model these relationships in order to obtain appropriate
parameter estimates for the dynamic intelligence variables. Also,
while using a multi-country sample improves the generalizability
of the research findings, country differences could potentially
confound the research results. During the research design stage,
the research team ensured the translation accuracy to eliminate
country differences due to misinterpretation of survey questions
because of inaccurate translation. We also controlled for country
effects in the regression analysis. Overall, the consistency of
parameter estimates across our regressions leads us to have
confidence in the findings presented.
We view our effort as a promising first step in examining the
extent to which the implications of Jaikumar’s model hold for plant
structure, practices, and performance. Our findings provide limited
support for portions of the model. Future research should develop
more refined measures of dynamic intelligence variables and retest
the research propositions. It would also be very interesting if
researchers having time-series panel data on these inputs and
outcomes could perform a related analysis. Another direction for
future research is to examine the extent to which process,
integration, and collaboration intelligence drive performance in
other production domains, particularly service operations. Because
service operations have undergone similar IT-based transformations, we expect that Jaikumar’s model may apply to service
industries. In closing, we hope the present paper stimulates
researcher interest in further examining the impact of IT use on
plant operations, as there are perhaps many interesting findings
yet to be discovered.
Acknowledgements
We gratefully acknowledge the helpful feedback from an Editor,
an Associate Editor, and two anonymous reviewers, as well as three
anonymous reviewers for the 2008 Academy of Management
conference.
Appendix A
See Tables A.1–A.9.
Table A.1
Dynamic intelligence measures.
Please check application areas supported by software at the plant:
Master production schedule
Rough cut capacity planning
Material requirements planning
Capacity requirements planning
Finite capacity scheduling
Shop floor control
Inventory management
Purchasing
Forecasting
Process intelligence
Integration intelligence
Check if supported by software
Check if integrated with ERP
References
2
2
1, 2
2
2
1
1
2, 6, 10
6, 10
G.R. Heim, D.X. Peng / Journal of Operations Management 28 (2010) 144–162
157
Table A.1 (Continued )
Process intelligence
Integration intelligence
References
Demand planning
Order management
Catalog and price management
Distribution management
Transportation management
Service management (after the sale)
Design (CAD, CAE)
Product data management
General accounting
Cost accounting
Budgeting
Human resource management
Maintenance management
Quality documentation management
Quality control and improvement
Performance measurement system
Project management
Workflow management
Business intelligence (query and report, OLAP, data mining)
Simulation and optimization of production and logistics planning
Groupware tools (e.g., Lotus Notes)
7
6, 10
Collaboration Intelligence
References
For which of the following purchasing and supply activities does your plant use the Internet?
Scanning the marketplace for identification of potential sources
Receiving and comparing suppliers’ offers
Providing dynamic pricing (negotiations and sellers’ bids) for purchased items
Transmitting orders to suppliers
Tracking/tracing supply orders
Real-time integrated scheduling, shipping and warehouse management across the supplier network
Supporting collaborative product design/improvement with suppliers
Supporting collaborative process and technology design/improvement with suppliers
4, 6
10
4
4
2, 4
2, 4
3, 4
3, 4
6, 10
7, 10
1, 8
2, 6
6
1
1
6
8
5
10
6,7
For which of the following marketing and sales activities does your plant use the Internet?
Presenting information about your plant
Presenting your sales products catalog
Providing on-line customized customer service, where customers can configure the product within the constraints stated by the plant
Providing fixed pricing offers to potential buyers
Providing dynamic pricing offers to potential buyers
On-line order entry
Customers can check delivery status of their orders
9, 10
2, 10
2, 10
(1) Boyer et al. (1996); (2) Banker et al. (2006); (3) Banker et al. (2006b); (4) Huang et al. (2008); (5) Nissen (2002); (6) Powell and Dent-Micallef (1997); (7) Rai et al. (2006);
(8) Rangaswamy and Lilien (1997); (9) Stegmann et al. (2006); (10) Subramani (2004).
Table A.2
EFA with process intelligence items.
Master production schedule
Material requirements planning
Shop floor control
Inventory management
Purchasing
Order management
Catalog and price management
General accounting
Cost accounting
Budgeting
Human resource management
Maintenance management
Quality documentation management
Quality control and improvement
Performance measurement system
Workflow management
Forecasting
Demand planning
Business intelligence (query and
report, OLAP, data mining)
Simulation and optimization
of production and logistics planning
Distribution management
Transportation management
Service management (after the sale)
Product configuration
Manufacturing
and
administration
Quality
management
Forecasting
and
planning
Logistics and
customer
service
Capacity
management
Product
development
Other
application
0.7370
0.8967
0.5355
0.9417
0.9027
0.8642
0.6456
0.9401
0.946
0.6792
0.4522
0.0888
0.1241
0.1233
0.1108
0.0132
0.4166
0.5044
0.2756
0.0093
0.0025
0.308
0.0336
0.0054
0.0332
0.0995
0.0903
0.1087
0.3557
0.4214
0.7246
0.7711
0.848
0.4008
0.5282
0.0834
0.0399
0.2459
0.1452
0.1804
0.3975
0.1634
0.1682
0.0752
0.3222
0.032
0.054
0.1666
0.1404
0.2502
0.138
0.1995
0.593
0.059
0.8105
0.6336
0.4739
0.1772
0.1168
0.2436
0.1269
0.1061
0.1251
0.1397
0.0172
0.0127
0.0515
0.092
0.0121
0.1014
0.1774
0.0576
0.5531
0.1551
0.1489
0.1071
0.3363
0.2196
0.0841
0.1323
0.1953
0.165
0.0297
0.0671
0.0211
0.0802
0.0583
0.0555
0.1441
0.0152
0.247
0.2623
0.0933
0.1768
0.066
0.0386
0.1194
0.1236
0.116
0.1673
0.1639
0.2533
0.0228
0.1457
0.2659
0.2441
0.0445
0.2203
0.0312
0.3438
0.175
0.1178
0.0427
0.108
0.1588
0.0574
0.1483
0.0782
0.0215
0.2099
0.2279
0.0454
0.0876
0.2769
0.3822
0.2238
0.2531
0.0484
0.078
0.311
0.0264
0.3743
0.6405
0.1017
0.342
0.3515
0.1016
0.0361
0.0919
0.0959
0.1219
0.0406
0.076
0.0584
0.1749
0.4093
0.1258
0.0664
0.0729
0.0489
0.5476
0.1882
0.1844
0.2883
0.0662
0.2603
0.0349
0.0016
0.4779
0.0675
0.238
0.1776
0.0649
0.389
0.5529
0.7424
0.7620
0.6212
158
G.R. Heim, D.X. Peng / Journal of Operations Management 28 (2010) 144–162
Table A.2 (Continued )
Rough cut capacity planning
Capacity requirements planning
Finite capacity scheduling
Design (CAD, CAE)
Product data management
Project management
Groupware tools (e.g., Lotus Notes)
Manufacturing
and
administration
Quality
management
0.5542
0.5603
0.3425
0.3445
0.5738
0.0066
0.0157
0.1583
0.1814
0.0145
0.1109
0.1863
0.4511
0.1902
Forecasting
and
planning
Logistics and
customer
service
0.1131
0.0108
0.3857
0.1504
0.0478
0.4108
0.0163
0.0166
0.1457
0.154
0.1219
0.3081
0.123
0.1349
Capacity
management
Product
development
Other
application
0.7024
0.6477
0.7127
0.011
0.1322
0.2649
0.0943
0.2291
0.0045
0.0815
0.7274
0.3580
0.5584
0.1816
0.0508
0.1645
0.1172
0.123
0.0638
0.0888
0.7872
Tetrachoric correlation matrix was used as the input matrix; seven factors emerged from the EFA using the process intelligence items. Six of the factors represent different
manufacturing plant activities as shown in the title of each column. The seventh factor consists of a single item measuring e-mail groupware, which is a broader IT application.
Table A.3
EFA with integration intelligence items.
Master production schedule
Rough cut capacity planning
Material requirements planning
Capacity requirements planning
Finite capacity scheduling
Shop floor control
Inventory management
Purchasing
Forecasting
Demand planning
Order management
Catalog and price management
Distribution management
Transportation management
Service management (after the sale)
Product data management
General accounting
Cost accounting
Budgeting
Human resource management
Business intelligence (query and report, OLAP, data mining)
Simulation and optimization of production and logistics planning
Design (CAD, CAE)
Project management
Workflow management
Groupware tools (e.g., Lotus Notes)
Maintenance management
Quality documentation management
Quality control and improvement
Performance measurement system
Enterprise resource
planning
Product development and
quality management
0.8176
0.695
0.8644
0.7434
0.644
0.8304
0.8337
0.8018
0.8319
0.7623
0.8593
0.7233
0.6258
0.5471
0.5389
0.7133
0.7283
0.7707
0.5772
0.3501
0.5793
0.6512
0.2636
0.2329
0.3121
0.4194
0.2123
0.2773
0.4057
0.6236
0.2445
0.3847
0.3608
0.402
0.2743
0.1312
0.4457
0.4249
0.0162
0.1777
0.3493
0.0901
0.3769
0.4204
0.5357
0.3257
0.4844
0.4707
0.3301
0.7409
0.3754
0.1611
0.7379
0.7591
0.6924
0.5547
0.7060
0.6612
0.5939
0.2698
Tetrachoric correlation matrix was used as the input matrix; two factors emerged from the EFA using the integration intelligence items. Compared to process intelligence,
integration intelligence normally builds on enterprise resource planning system (ERP). ERP tends to be installed as a software package that supports many different
operational activities within a plant. This may explain why only two factors emerged from the integration intelligence items.
Table A.4
EFA with collaboration intelligence items.
Customer
facing
Presenting your sales products catalog
Providing on-line customized customer service, where customers
can configure the product within the constraints stated by the plant
Providing fixed pricing offers to potential buyers
Providing dynamic pricing offers to potential buyers
On-line order entry
Customers can check delivery status of their orders
Scanning the marketplace for identification of potential sources
Receiving and comparing suppliers’ offers
Providing dynamic pricing (negotiations and sellers’ bids) for purchased items
Supporting collaborative product design/improvement with suppliers
Supporting collaborative process and technology design/improvement with suppliers
Presenting information about your plant
Transmitting orders to suppliers
Tracking/tracing supply orders
Real-time integrated scheduling, shipping and warehouse management across the supplier network
Tetrachoric correlation matrix was used as the input matrix.
Sourcing and supplier
collaboration
Logistics
management
0.6098
0.8768
0.4181
0.1553
0.4642
0.0599
0.7399
0.7689
0.8649
0.8186
0.2028
0.1072
0.2987
0.4213
0.3324
0.2575
0.2096
0.0987
0.1978
0.2349
0.1724
0.0042
0.1073
0.6968
0.8629
0.5213
0.6400
0.7587
0.5272
0.0962
0.068
0.2572
0.2738
0.0615
0.1109
0.1223
0.0287
0.0682
0.2982
0.2679
0.1427
0.392
0.7405
0.8410
0.6457
G.R. Heim, D.X. Peng / Journal of Operations Management 28 (2010) 144–162
159
Table A.5
Regression results for operations size.
ln(Plant sales)
ln(Plant sales)
**
Constant
Statistical process control
Process intelligence
Integration intelligence
Collaboration intelligence
ln(Parent firm sales)
ln(Number of employees)
ln(Facility size)
Electronics
Machinery
Finland
Germany
Italy
Korea
Japan
Sweden
USA
3.130(0.001)
0.161(0.074)y
0.011(0.392)
0.008(0.545)
0.037(0.186)
0.072(0.014)*
1.010(0.000)***
0.027(0.807)
0.033(0.864)
0.119(0.566)
0.870(0.008)**
0.566(0.070)y
0.207(0.664)
–
1.721(0.000)***
2.446(0.000)***
0.603(0.300)
R2
Adjusted R2
F (p-Value)
N
ln(Labor)
***
4.833(0.000)
0.011(0.934)
0.003(0.867)
0.014(0.482)
0.045(0.307)
–
0.799(0.000)***
0.077(0.645)
0.302(0.313)
0.030(0.925)
0.577(0.234)
0.399(0.411)
0.408(0.429)
–
1.682(0.004)**
2.745(0.000)***
0.984(0.127)
0.745
0.713
23.203(0.000)
134
ln(Labor)
**
3.917(0.000)
0.097(0.198)
0.031(0.005)**
0.002(0.875)
0.008(0.746)
0.104(0.000)***
–
–
0.050(0.753)
0.074(0.668)
0.116(0.673)
0.222(0.398)
1.517(0.000)***
0.878(0.002)**
0.650(0.177)
0.711(0.059)
0.808(0.073)
0.505
0.458
10.652(0.000)
160
0.413
0.353
6.835(0.000)
150
ln(Facility size)
***
***
4.238(0.000)
0.126(0.080)y
0.040(0.000)***
0.002(0.836)
0.003(0.885)
–
–
–
0.051(0.738)
0.012(0.943)
0.007(0.979)
0.244(0.344)
0.288(0.293)
1.050(0.000)***
1.538(0.000)***
0.292(0.305)
0.634(0.022)*
8.228(0.000)
0.010(0.922)
0.033(0.026)*
0.005(0.711)
0.008(0.792)
0.110(0.001)**
–
–
0.437(0.041)*
0.067(0.774)
0.204(0.578)
0.324(0.356)
1.293(0.014)*
1.907(0.000)**
0.642(0.194)
1.628(0.008)**
0.307
0.254
5.754(0.000)
182
0.615
0.576
15.838(0.000)
142
ln(Facility size)
8.470(0.000)***
0.048(0.617)
0.037(0.009)**
0.002(0.877)
0.010(0.744)
–
–
–
0.391(0.061) y
0.138(0.535)
0.075(0.828)
0.440(0.197)
0.213(0.557)
–
2.209(0.000)***
0.410(0.275)
2.927(0.000)***
0.537
0.501
15.244(0.000)
170
Note: Korean plants were excluded above due to too many missing values for plant size data.
***
p < 0.001.
**
p < 0.010.
*
p < 0.050.
y
p < 0.100.
Table A.6
Regression results for operations productivity.
ln(Labor
productivity)
Constant
Statistical process control
Process intelligence
Integration intelligence
Collaboration intelligence
ln(Parent firm sales)
ln(Number of employees)
Electronics
Machinery
Finland
Germany
Italy
Korea
Japan
Sweden
USA
4.956
0.034
0.007
0.004
0.053
–
–
0.354
0.149
0.668
0.296
0.416
1.704
0.378
2.628
0.919
R2
Adjusted R2
F (p-Value)
N
***
**
*
y
ln(Capital
productivity)
(0.000)***
(0.778)
(0.683)
(0.829)
(0.177)
(0.172)
(0.593)
(0.134)
(0.496)
(0.360)
(0.000)***
(0.484)
(0.000)***
(0.051)y
0.361
0.331
7.332 (0.000)
182
0.095
0.112
0.005
0.010
0.072
–
–
0.140
0.279
0.394
0.080
0.036
0.667
2.791
2.614
1.169
ln(Orders processed
per month)
(0.924)
(0.428)
(0.791)
(0.598)
(0.085)y
3.857
0.175
0.057
0.009
0.050
–
0.166
0.640
1.782
0.355
0.869
0.247
2.335
1.281
0.743
0.833
(0.631)
(0.377)
(0.449)
(0.874)
(0.946)
(0.225)
(0.000)***
(0.000)***
(0.039)*
0.418
0.374
9.518 (0.000)
158
(0.018)*
(0.382)
(0.067)y
(0.759)
(0.467)
ln(Number of final product
configurations)
5.158
0.353
0.094
0.001
0.059
–
0.247
0.683
0.617
0.237
3.378
0.067
0.283
1.273
0.972
0.676
(0.443)
(0.139)
(0.000)***
(0.610)
(0.219)
(0.744)
(0.004)**
(0.305)
(0.341)
(0.294)
0.313
0.244
4.53 (0.000)
153
(0.019)*
(0.177)
(0.026)*
(0.970)
(0.497)
(0.412)
(0.222)
(0.314)
(0.802)
(0.000)
(0.946)
(0.851)
(0.219)
(0.339)
(0.522)
0.258
0.177
3.161 (0.000)
141
p < 0.001.
p < 0.010.
p < 0.050.
p < 0.100.
Table A.7
Regression results for discretionary work practices—organizational change.
Centralization
Constant
Statistical
process
control
Process
intelligence
Integration
intelligence
Collaboration
intelligence
Electronics
Cooperation
Flatness of
organization
structure
ln(Employee
suggestions)
Percent cross
trained
4.500 (0.000)***
0.163 (0.004)
3.251 (0.000)***
0.192 (0.013)*
4.948 (0.000)***
0.833 (0.465)
0.159 (0.000)*** 0.037 (0.861)
0.003 (0.660)
0.003 (0.719)
0.002 (0.671)
***
***
0.031 (0.000)
0.037 (0.000)
0.018 (0.308)
0.018 (0.453)
0.105 (0.383)
0.007 (0.967)
0.004 (0.265)
0.018 (0.058)y
0.028 (0.661)
0.009 (0.744)
0.079 (0.003)
**
16.575 (0.312)
7.429 (0.012)*
0.143 (0.694)
Small group
Percentage of
problem
employees on
problem solving teams solving
Multifunctional
employees
4.165 (0.000)*** 16.025 (0.280)
0.161 (0.001)**
7.692 (0.005)**
0.005 (0.337)
*
0.347 (0.286)
0.003 (0.574)
0.001 (0.904)
0.223 (0.575)
0.014 (0.018)
0.133 (0.706)
0.171 (0.008)** 0.389 (0.690)
0.012 (0.424)
0.765 (0.388)
0.103 (0.297)
0.988 (0.860)
0.445 (0.292)
0.882 (0.888)
3.445 (0.000)***
0.341 (0.000)***
0.031 (0.028)*
0.028 (0.771)
G.R. Heim, D.X. Peng / Journal of Operations Management 28 (2010) 144–162
160
Table A.7 (Continued )
Centralization
Flatness of
organization
structure
Cooperation
ln(Employee
suggestions)
Percent cross
trained
Machinery
0.162 (0.210)
0.045 (0.796)
0.045 (0.514)
0.644 (0.176)
R2
Adjusted R2
F (p-Value)
N
0.119
0.094
4.656 (0.000)
212
0.097
0.071
3.685 (0.002)
212
0.175
0.151
7.304 (0.000)
212
0.101
0.062
2.560 (0.022)
142
Small group
Percentage of
problem
employees on
problem solving teams solving
Multifunctional
employees
2.604 (0.701)
0.129 (0.225)
7.722 (0.203)
0.059 (0.560)
0.059
0.105
0.023
0.079
1.648 (0.137)
4.037 (0.001)
163
212
0.064
0.033
2.075 (0.058)
0.106
0.283
0.262
13.550 (0.000)
212
*
p < 0.050.
p < 0.010.
***
p < 0.001.
y
p < 0.100.
**
Table A.8
Regression results for discretionary work practices—experimentation and development.
Knowledge level sqrt
(percent engineering
degrees)
Constant
Statistical process control
Process intelligence
Integration intelligence
Collaboration intelligence
Electronics
Machinery
R2
Adjusted R2
F (p-Value)
N
*
**
***
y
6.379
0.157
0.017
0.011
0.060
0.143
0.243
(0.000)***
(0.447)
(0.514)
(0.689)
(0.373)
(0.749)
(0.612)
Developing
unique
practices
3.069
0.225
0.007
0.012
0.017
0.090
0.228
0.018
0.016
0.525 (0.789)
180
Willingness to
introduce new
products
(0.000)***
(0.000)***
(0.264)
(0.077)y
(0.301)
(0.400)
(0.049)*
4.758
0.393
0.026
0.006
0.010
0.011
0.384
0.129
0.104
5.106 (0.000)
212
ln(New product
configurations
last year)
(0.000)***
(0.000)***
(0.055)y
(0.704)
(0.779)
(0.962)
(0.121)
0.488
0.019
0.091
0.015
0.014
0.138
0.375
0.097
0.068
3.303 (0.004)
190
(0.637)
(0.917)
(0.000)***
(0.542)
(0.814)
(0.731)
(0.380)
0.114
0.079
3.230 (0.005)
157
ln(Average lifetime
of product
configuration)
2.397
0.181
0.011
0.006
0.028
0.161
0.123
(0.000)***
(0.005)**
(0.203)
(0.472)
(0.184)
(0.248)
(0.409)
0.116
0.083
3.504 (0.003)
166
p < 0.050.
p < 0.010.
p < 0.001.
p < 0.100.
Table A.9
Regression results for technology focus.
ln(Volume
flexibility)
Constant
Statistical process control
Process intelligence
Integration intelligence
Collaboration intelligence
Electronics
Machinery
R2
Adjusted R2
F (p-Value)
N
*
**
***
y
0.911
0.074
0.003
0.011
0.006
0.036
0.095
(0.001)**
(0.108)
(0.598)
(0.097)y
(0.689)
(0.714)
(0.383)
0.044
0.013
1.401 (0.216)
188
Customization
orientation of process
4.046
0.233
0.015
0.031
0.044
0.924
0.662
Integration
between functions
(0.000)***
(0.020)*
(0.221)
(0.017)*
(0.142)
(0.000)***
(0.004)**
0.175
0.148
6.312 (0.000)
184
3.625
0.265
0.005
0.006
0.037
0.066
0.132
(0.000)***
(0.000)***
(0.362)
(0.295)
(0.007)**
(0.466)
(0.175)
0.217
0.194
9.458 (0.000)
211
Inter-functional
design efforts
3.420
0.262
0.010
0.003
0.000
0.142
0.126
(0.000)***
(0.000)***
(0.161)
(0.718)
(0.997)
(0.238)
(0.331)
0.126
0.101
4.954 (0.000)
212
ln(Scrap
rate)
3.082
0.535
0.029
0.002
0.062
0.164
0.769
Customer
satisfaction
(0.001)**
(0.001)**
(0.123)
(0.907)
(0.226)
(0.614)
(0.032)**
0.098
0.068
3.249 (0.005)
165
4.196
0.187
0.000
0.009
0.025
0.060
0.103
(0.000)***
(0.000)***
(0.998)
(0.097)y
(0.039)*
(0.460)
(0.241)
0.145
0.120
5.801 (0.000)
212
p < 0.050.
p < 0.010.
p < 0.001.
p < 0.100.
References
Ahmad, S., Schroeder, R.G., 2003. The impact of human resource management
practices on operational performance: recognizing country and industry differences. Journal of Operations Management 21 (1), 19–43.
Aiken, M., Hage, J., 1966. Organizational alienation: a comparative analysis. American Sociological Review 31, 497–507.
Akkermans, H.A., Bogerd, P., Yűcesan, E., Van Wassenhove, L.N., 2003. The impact of
ERP on supply chain management: exploratory findings from a European Delphi
study. European Journal of Operational Research 146, 284–301.
Allen, B.R., Boynton, A.C., 1991. Information architecture: in search of efficient
flexibility. MIS Quarterly 15 (4), 435–445.
Anderson, J.C., Rungtusanatham, M., Schroeder, R.O., Devaraj, S., 1995. A path
analytic model of a theory of quality management underlying the Deming
management method: preliminary empirical findings. Decision Sciences 26 (5),
637–658.
Bagozzi, R.P., Phillips, L.W., 1982. Representing and testing organizational theories:
a holistic construal. Administrative Science Quarterly 27 (3), 459–489.
Bagozzi, R.P., Yi, Y., Phillips, L.W., 1991. Assessing construct validity in organizational research. Administrative Science Quarterly 36 (3), 421–458.
Banker, R.D., Bardhan, I.R., Hsihui, C., Shu, L., 2006. Plant information systems,
manufacturing capabilities, and plant performance. MIS Quarterly 30 (2), 315–
337.
Banker, R.D., Bardhan, I., Asdemir, O., 2006b. Understanding the impact of collaboration software on product design and development. Information Systems
Research 17 (4), 352–373.
Barczak, G., Sultan, F., Hultink, E.J., 2007. Determinants of it usage and new
product performance. Journal of Product Innovation Management 24, 600–
613.
Bardhan, I.R., Krishnan, V.V., Lin, S., 2007. Project performance and the enabling role
of information technology: an exploratory study on the role of alignment.
Manufacturing & Service Operations Management 9 (4), 579–595.
Bardhan, I., Mithas, S., Lin, S., 2007b. Performance impacts of strategy, information technology applications, and business process outsourcing in U.S.
manufacturing plants. Production and Operations Management 16 (6),
747–762.
G.R. Heim, D.X. Peng / Journal of Operations Management 28 (2010) 144–162
Barua, A., Konana, P., Whinston, A.B., 2004. An empirical investigation of netenabled business value. MIS Quarterly 28 (4), 585–620.
Benner, M.J., Tushman, M.L., 2003. Exploitation, exploration, and process management: the productivity dilemma revisited. Academy of Management Review 28
(2), 238–256.
Bernard, A.B., Jones, C.I., 1996. Comparing apples to oranges: productivity convergence and measurement across industries and countries. The American Economic Review 86 (5), 1216–1238.
Bernard, A.B., Jensen, J.B., 2006. Firm structure, multinationals, and manufacturing
plant deaths. Institute for International Economics Working Paper No. 06-7.
Bessen, J., 2002. Technology adoption costs and productivity growth: the transition
to information technology. Review of Economic Dynamics 5, 443–469.
Bharadwaj, A.S., Bharadwaj, S.G., Konsynski, B.R., 1999. Information technology
effects on firm performance as measured by Tobin’s q. Management Science 45
(7), 1008–1024.
Black, S.E., Lynch, L.M., 2001. How to compete: the impact of workplace practices
and information technology on productivity. The Review of Economics and
Statistics 83 (3), 434–445.
Boyer, K.K., Ward, P.T., Leong, G.K., 1996. Approaches to the factory of the future: an
empirical taxonomy. Journal of Operations Management 14 (4), 297–313.
Boyer, K.K., Verma, R., 2000. Multiple raters in survey-based operations management research: a review and tutorial. Production and Operations Management 9
(2), 128–140.
Bozarth, C.C., Warsing, D.P., Flynn, B.B., Flynn, E.J., 2009. The impact of supply chain
complexity on manufacturing plant performance. Journal of Operations Management 27 (1), 78–93.
Bresnahan, T.F., Brynjolfsson, E., Hitt, L.M., 2002. Information technology, workplace
organization, and the demand for skilled labor: firm-level evidence. Quarterly
Journal of Economics 117 (1), 339–376.
Brooking, S.A., Hailey, W.A., Parker, H.J., Woodruff, C.K., 1995. Evolving production
technologies: implications for inventory ordering formulations. International
Journal of Operations & Production Management 15 (10), 30–42.
Brynjolfsson, E., 1993. The productivity paradox of information technology. Communications of the ACM 36 (12), 66–77.
Brynjolfsson, E., Malone, T.W., Gurbaxani, V., Kambil, A., 1994. Does information
technology lead to smaller firms? Management Science 40 (12), 1628–1644.
Brynjolfsson, E., Hitt, L., 1996. Paradox lost? Firm-level evidence on the returns to
information systems spending. Management Science 42 (4), 541–558.
Caldeira, M.M., Ward, J.M., 2002. Understanding the successful adoption and use of
IS/IT in SMEs: an explanation from Portuguese manufacturing industries.
Information Systems Journal 12 (2), 121–152.
Cao, Q., Dowlatshahi, S., 2005. The impact of alignment between virtual enterprise
and information technology on business performance in an agile manufacturing
environment. Journal of Operations Management 23 (5), 531–550.
Choi, T.Y., Eboch, K., 1998. The TQM paradox: relations among TQM practices, plant
performance, and customer satisfaction. Journal of Operations Management 17
(1), 59–75.
Comin, D, 2002. Comments on James Bessen’s technology adoption costs and
productivity growth: the 70s as a technological transition. Review of Economic
Dynamics 5, 470–476.
Cua, K.O., McKone, K.E., Schroeder, R.G., 2001. Relationships between implementation of TQM, JIT, and TPM and manufacturing performance. Journal of Operations Management 19 (6), 675–694.
Dehning, B, Richardson, V.J., Zmud, R.W., 2007. The financial performance effects of
it-based supply chain management systems in manufacturing firms. Journal of
Operations Management 25 (4), 806–824.
Devaraj, S., Hollingworth, D.G., Schroeder, R.G., 2004. Generic manufacturing strategies and plant performance. Journal of Operations Management 22 (3), 313–
333.
Devaraj, S., Krajewski, L., Wei, J.C., 2007. Impact of ebusiness technologies on
operational performance: the role of production information integration in
the supply chain. Journal of Operations Management 25 (6), 1199–1216.
Dewan, S., Kraemer, K.L., 2000. Information technology and productivity: evidence
from country-level data. Management Science 46 (4), 548–562.
Dewan, S., Shi, C., Gurbaxani, V., 2007. Investigating the risk return relationship of
information technology investment: firm-level empirical analysis. Management Science 53 (12), 1829–1842.
Dhawan, R., 2001. Firm size and productivity differential: theory and evidence from a
panel of us firms. Journal of Economic Behavior & Organization 44 (3), 269–293.
Diamantopoulos, A., Winklhofer, H.M., 2001. Index construction with formative
indicators: an alternative to scale development. Journal of Marketing Research
(JMR) 38 (2), 269–277.
Ettlie, J.E., Bridges, W.P., O’Keefe, R.D., 1984. Organization strategy and structural
differences for radical versus incremental innovation. Management Science 30
(6), 682–695.
Flynn, B.B., Flynn, E.J., 2004. An exploratory study of the nature of cumulative
capabilities. Journal of Operations Management 22 (5), 439–457.
Flynn, B.B., Schroeder, R.O., Sakakibara, S., 1995. The impact of quality management
practices on performance and competitive advantage. Decision Sciences 26 (5),
659–691.
Fornell, C., Larcker, D.F., 1981. Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research 18
(1), 39–50.
Frohlich, M.T., Westbrook, R., 2002. Demand chain management in manufacturing
and services: web-based integration, drivers and performance. Journal of
Operations Management 20 (6), 729–745.
161
Frohlich, M, 2002. E-integration in the supply chain: barriers and performance.
Decision Sciences 33 (4), 537–556.
Griffin, A., Page, A.L., 1993. An interim report on measuring product development
success and failure. Journal of Product Innovation Management 10, 291–
308.
Haan, C.T., 2002. Statistical Methods in Hydrology. State University Press, Ames,
Iowa.
Hair, J.J., Anderson, R.E., Tatham, R.L., Black, W.C., 2005. Multivariate Data Analysis.
Englewood Cliffs, New Jersey.
Haltiwanger, J.C., Lane, J.I., Spletzer, J.R., 1999. Productivity differences across
employers: the roles of employer size, age, and human capital. American
Economic Review 89 (2), 94–98.
Handfield, R., Jayaram, J., Ghosh, S., 1999. An empirical examination of quality tool
deployment patterns and their impact on performance. International Journal of
Production Research 37 (6), 1403–1426.
Harris, S.E., Katz, J.L., 1991. Organizational performance and information technology
investment intensity in the insurance industry. Organization Science 2 (3), 263–
295.
Henderson, J.C., Venkatraman, N., 1999. Strategic alignment: leveraging information technology for transforming organizations. IBM Systems Journal 38 (2–3),
472–484.
Hendricks, K.B., Singhal, V.R., Stratman, J.K., 2007. The impact of enterprise systems
on corporate performance: a study of ERP, SCM, and CRM system implementations. Journal of Operations Management 25 (1), 65–82.
Hill, C.A, Scudder, G.D., 2002. The use of electronic data interchange for supply chain
coordination in the food industry. Journal of Operations Management 20 (4),
375–387.
Hitt, L.M., Wu, D.J., Zhou, X., 2002. Investment in enterprise resource planning:
business impact and productivity measures. Journal of Management Information Systems 19 (1), 71–98.
Huang, X., Gattiker, T.F., Schroeder, R.G., 2008. Structure-infrastructure alignment:
the relationship between TQM orientation and the adoption of supplier-facing
electronic commerce among manufacturers. Journal of Supply Chain Management 44 (1), 44–54.
Hunton, J.E., Lippincott, B., Reck, J.L., 2003. Enterprise resource planning systems:
comparing firm performance of adopters and nonadopters. International Journal of Accounting Information Systems 4 (3), 165–184.
Itami, H., Numagami, T., 1992. Dynamic interaction between strategy and technology. Strategic Management Journal 13, 119–135.
Jaikumar, R., 1988. From filing and fitting to flexible manufacturing: a study in the
evolution of process control. Working Paper. Harvard Business School.
Jaikumar, R., 2005. From filing and fitting to flexible manufacturing: a study in the
evolution of process control. Foundations and Trends in Technology, Information and Operations Management 1 (1), 1–120.
James, L.R., Demaree, R.G., Wolf, G., 1984. Estimating within-group interrater
reliability with and without response bias. Journal of Applied Psychology 69
(1), 85–98.
Karmarkar, U.S., Apte, U.M., 2007. Operations management in the information
economy: information products, processes, and chains. Journal of Operations
Management 25 (2), 438–453.
Kathuria, R., Anandarajan, M., Igbaria, M., 1999. Linking IT applications with
manufacturing strategy: an intelligent decision support system approach.
Decision Sciences 30 (4), 959–991.
Kelley, M.R., 1994. Productivity and information technology: the elusive connection. Management Science 40 (11), 1406–1425.
Koufteros, X., Vonderembse, M., Jayaram, J., 2005. Internal and external integration
for product development: the contingency effects of uncertainty, equivocality,
and platform strategy. Decision Sciences 36 (1), 97–133.
Kumar, K.B., Rajan, R.G., Zingales, L., 1997. What determines firm size? National
Bureau of Economic Research (NBER) working paper.
Leonard-Barton, D., 1988. Implementation as mutual adaptation of technology and
organization. Research Policy 17 (5), 251–267.
Mabert, V.A., Soni, A., Venkataramanan, M.A., 2003. The impact of organization size
on enterprise resource planning (ERP) implementations in the US manufacturing sector. Omega 31 (3), 235–246.
MacDuffie, J.P., 1995. Human resource bundles and manufacturing performance:
organizational logic and flexible production systems in the world auto industry.
Industrial & Labor Relations Review 48 (2), 197–221.
Malhotra, M.K., Heine, M.L., Grover, V., 2001. An evaluation of the relationship
between management practices and computer aided design technology. Journal
of Operations Management 19 (3), 307–333.
Malhotra, A., Gosain, S., El Sawy, O.A., 2005. Absorptive capacity configurations in
supply chains: gearing for partner-enabled market knowledge creation. MIS
Quarterly 29 (1), 145–187.
Mallick, D.N., Schroeder, R.G., 2005. An integrated framework for measuring product development performance in high technology industries. Production and
Operations Management 14 (2), 142–158.
Masini, A., Van Wassenhove, L.N., 2009. ERP competence-building mechanisms: an
exploratory investigation of configurations of ERP adopters in the European and
U.S. manufacturing sectors. Manufacturing & Service Operations Management
11 (2), 274–298.
Melcher, A.J., Khouja, M., Booth, D.E., 2002. Toward a production classification
system. Business Process Management 8 (1), 53–79.
Mishra, A.A., Shah, R., 2009. In union lies strength: collaborative competence in
new product development, its performance effects. Journal of Operations
Management 27 (4), 324–338.
162
G.R. Heim, D.X. Peng / Journal of Operations Management 28 (2010) 144–162
Mitchell, M.F, 2002. Technological change and the scale of production. Review of
Economic Dynamics 5, 477–488.
Mukhopadhyay, T., Rajiv, S., Srinivasan, K., 1997. Information technology impact on
process output and quality. Management Science 43 (12), 1645–1659.
Nahm, A.Y., Vonderembse, M.A., Koufteros, X.A., 2003. The impact of organizational
structure on time-based manufacturing and plant performance. Journal of
Operations Management 21 (3), 281–306.
Naor, M., Goldstein, S.M., Linderman, K.W., Schroeder, R.G., 2008. The role of culture
as driver of quality management and performance: infrastructure versus core
quality practices. Decision Sciences 39 (4), 671–702.
Nissen, M.E., 2002. An extended model of knowledge-flow dynamics. Communications of the Association for Information Systems 8, 251–266.
Parthasarthy, R., Sethi, S.P., 1992. The impact of flexible automation on business
strategy and organizational structure. Academy of Management Review 17 (1),
86.
Parthasarthy, R., Sethi, S.P., 1993. Relating strategy and structure to flexible automation: a test of fit and performance implications. Strategic Management
Journal 14 (7), 529–549.
Peng, D.X., Schroeder, R., Shah, R., 2008. Linking routines to operations capabilities:
a new perspective. Journal of Operations Management 26 (6), 730–748.
Petter, S., Straub, D., Rai, A., 2007. Specifying formative constructs in information
systems research. MIS Quarterly 31 (4), 623–656.
Podsakoff, P.M., MacKenzie, S.B., Jeong-Yeon, L., Podsakoff, N.P., 2003. Common
method biases in behavioral research: a critical review of the literature and
recommended remedies. Journal of Applied Psychology 88 (5), 879.
Powell, T.C., 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., Seth, N., 2006. Firm performance impacts of digitally
enabled supply chain integration capabilities. MIS Quarterly 30 (2), 225–246.
Rangaswamy, A., Lilien, G.L., 1997. Software tools for new product development.
Journal of Marketing Research 34 (1), 177–184.
Ray, G., Barney, J.B., Muhanna, W.A., 2004. Capabilities, business processes, and
competitive advantage: choosing the dependent variable in empirical tests of
resource-based view. Strategic Management Journal 25 (1), 23–38.
Rungtusanatham, M., 2001. Beyond improved quality: the motivational effects of
statistical process control. Journal of Operations Management 19 (6), 653–673.
Rungtusanatham, M., Forza, C., Koka, B.R., Salvador, F., Nie, W., 2005. TQM across
multiple countries: convergence hypothesis versus national specificity arguments. Journal of Operations Management 23 (1), 43–63.
Saeed, K.A., Malhotra, M.K., Grover, V., 2005. Examining the impact of interorganizational systems on process efficiency and sourcing leverage in buyer-supplier dyads. Decision Sciences 36 (3), 365–396.
Schroeder, R.G., Flynn, B.B., 2001. High Performance Manufacturing: Global Perspectives. Wiley, New York.
Shah, R., Shin, H., 2007. Relationships among information technology, inventory,
and profitability: an investigation of level invariance using sector level data.
Journal of Operations Management 25 (4), 768–784.
Stegmann, R, Leckner, T., Koch, M., Schlichter, J., 2006. Customer support for the
web-based configuration of individualized products. International Journal of
Mass Customization 1 (2–3), 195–207.
Subramani, M., 2004. How do suppliers benefit from information technology use in
supply chain relationships? MIS Quarterly 28 (1), 45–73.
Swink, M., Song, M., 2007. Effects of marketing-manufacturing integration on new
product development time and competitive advantage. Journal of Operations
Management 25 (1), 203–217.
Tippins, M.J., Sohi, R.S., 2003. IT competency and firm performance: is organizational learning a missing link? Strategic Management Journal 24 (8), 745–761.
Vickery, S., Droge, C., Germain, R., 1999. The relationship between product customization and organizational structure. Journal of Operations Management 17
(4), 377–391.
Wu, F., Mahajan, V., Balasubramanian, S., 2003. An analysis of e-business adoption
and its impact on business performance. Journal of the Academy of Marketing
Science 31 (4), 425–447.