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ent 45 (2008) 75–87
Information & ManagemExploring the absorptive capacity to innovation/productivity link
for individual engineers engaged in IT enabled work§
Xiaodong Deng a,*, William J. Doll b,1, Mei Cao c,2
a Department of Decision and Information Sciences, Oakland University,
School of Business Administration, 2200 North Squirrel Road,
Rochester Hills, MI 48309, United Statesb Management Department, The University of Toledo, Toledo, OH, United States
c Department of Business & Economics, University of Wisconsin-Superior, Superior, WI, United States
Received 25 July 2006; received in revised form 26 September 2007; accepted 13 December 2007
Available online 20 February 2008
Abstract
The hypothesis that absorptive capacity leads to greater innovation/productivity has been supported at the country, inter-organizational,
organizational, and group levels. We adapted the absorptive capacity concept to individuals engaged in IT enabled engineering work, which is
a situated and emergent phenomenon that requires individuals to posses or develop ability to acquire new task and computer knowledge; use or
develop analytical and intuitive problem solving skills to assimilate and integrate these two types of knowledge; and apply them to their work.
A model was developed linking the absorptive capacity of individuals, through enhanced IT utilization for problem solving/decision
support, to task innovation and productivity. It was tested using a sample of 208 engineers using computers in their work. The results suggested
that using IT innovatively and productively in such a work environment requires a mix of task knowledge, computer knowledge, and problem
solving modalities.
# 2007 Elsevier B.V. All rights reserved.
Keywords: Absorptive capacity; Engineering work; IT enabled work; Systematic problem solving; Intuitive problem solving; Task innovation; Task
productivity
1. Introduction
Cohen and Levinthal [11] defined absorptive capacity as
a person’s ability to recognize the value of new information,
assimilate it, and apply it to commercial ends. They
contended that absorptive capacity lead to greater innovation
and productivity. In IT enabled engineering work, it is the
engineer’s ability to acquire new knowledge, synthesize it,
§ This research was partially supported by a 2001 School of Business
Administration of Oakland University spring/summer research grant to the
first author.
* Corresponding author. Tel.: +1 248 370 4089; fax: +1 248 370 4275.
E-mail addresses: [email protected] (X. Deng),
[email protected] (W.J. Doll), [email protected] (M. Cao).1 Tel.: +1 419 530 2850; fax: +1 419 530 2365.2 Tel.: +1 715 394 8281; fax: +1 715 394 8374.
0378-7206/$ – see front matter # 2007 Elsevier B.V. All rights reserved.
doi:10.1016/j.im.2007.12.001
and apply it to his/her emerging task requirements that help
make the work group or organization productive [18]. New
ideas are, of course, created by individuals, and then shared
across networks of individuals [33]. Organizations learn
through their individual members [49]. Individuals are the
main agents of learning and change. Insights and innovative
ideas occur to individuals—not organizations [43]. Knowl-
edge intensive organizations are not productive unless their
individual members have the absorptive capability to learn
and innovate, and proactively envision problems and/or
novel solutions.
IT enabled engineering is a type of knowledge work
because it requires individuals to use substantial cognitive
effort [15,25]. It is information intensive and highly
analytical and individuals must make substantial use of
the computer to obtain high productivity in their problem
solving and innovation.
X. Deng et al. / Information & Management 45 (2008) 75–8776
While absorptive capacity is typically viewed as a firm
level construct, it is based upon research on cognitive
structures and processes of individuals [8]. Studies of
absorptive capacity at the country [22], interorganizational
[23,38,40], organizational [5,9,12,35,53], and group [56]
levels supported this absorptive capacity—innovation/
productivity linkage.
Individual differences [61] in absorptive capacity might
be expected to influence an individual’s learning, innova-
tion, and productivity in IT enabled engineering work. Our
study was an initial exploration of the absorptive capacity—
innovation/productivity link in IT enabled engineering.
2. The absorptive capacity—innovation/productivity
link
In their seminal work, Cohen and Levinthal developed
the absorptive capacity construct and explored its relation-
ship with innovation and productivity. Absorptive capacity
improves the speed, frequency, and magnitude of innovation
[24,34] and enhances learning within an organization
[3,44,51]. In turn, this encourages innovations that improve
productivity or effectiveness.
This link has been explored in different contexts. Table 1
summarizes and illustrates how the definition of absorptive
capacity is adapted to the context. It shows that the construct
has been broadly applied to explain why some countries,
organizations, or groups are able to assimilate new
information successfully and apply it for commercial gain.
Firms with high R&D funding are assumed to have the in-
house knowledge structures and processes needed to
advance learning, innovation, and thus apply new external
knowledge.
A number of different ways of measuring absorptive
capacity have been developed. Only a few, however, have
measured absorptive capacity outside the R&D context. In
the inter-organizational alliance context, Lane and Lubatkin
considered the similarity of the knowledge base and research
capabilities of alliance partners. In the supply chain context,
Malhotra et al. measured the supply chain’s integrative
process mechanisms, partner interface-directed IS, and
information exchange. In the IT context, Boynton et al.
measured two related constructs—managerial IT knowledge
and the IT management process. In the context of
Fig. 1. Impact of absorptive capacity on IT enabled eng
international expansion, absorptive capacity has not been
defined and measured. Barkema and Vermeulen represented
the firm’s absorptive capacity by multinational and multi-
product diversity.
The integration of knowledge structures and processes
had enhanced the organization’s ability to assimilate and
apply new knowledge. Important variables for measuring it
included total factor productivity of the country, alliance
learning, return on joint ventures, partner enabled market
knowledge creation in supply chains, success in using IT,
success in foreign start-ups, new product technical
performance, competitive advantage, innovation and busi-
ness performance. The implicit or explicit hypothesis is that
absorptive capacity enhances problem solving and learning,
leading to innovations that improve performance (i.e., the
absorptive capacity—innovation/productivity hypothesis).
The findings in each of these studies generally support this
absorptive capacity—innovation/productivity link.
3. The IT enabled engineering work context
IT enabled engineering work creates value by enabling
individual human agents to apply specialized knowledge and
integrate it within and between communities of knowing
[21,57]. It encourages workers to integrate task knowledge
and computer knowledge. In 1993, Kim describes these
knowledge domains as consisting of frameworks and mental
models that workers can use in their problem solving and
modify as they learn. This cognitive integration of task and
computer knowledge [50] is often developed on-the-job by
doing and learning.
IT enabled engineering work is typically divided into
projects or modules that can be assigned to a team or an
individual. The tasks in the projects often vary in their
degree of complexity, how well the tasks are defined, how
well the problems are structured, and what degree of
interpretation, judgment, and creativity the individual
engineers must exercise.
Engineering work varies from that which is well-
understood to uncertain and programmed or non-pro-
grammed. This was known from the earliest days of science,
evolving into design methodologies, and in the mid 20th
century leading to the idea of software engineering. For
routine engineering tasks, planning can be achieved ahead of
ineering work: rational and experiential strategies.
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Table 1
Absorptive capacity—innovation/productivity link
Level of analysis Researchers and year Context Definition and operationalization of
absorptive capacity
Dependent variable Findings related to
innovation and productivity
Country Griffith et al. (2003) [22] Economic development
of countries
Absorptive capacity is a country’s ability
to assimilate and understand the discoveries
of other countries. Measured by ratio
of R&D spending to output
Total factor productivity
growth of a country
Many studies may underestimate R&D’s
social rate of return if they neglect
absorptive capacity [absorptive
capacity improves productivity]
Inter-organizational Lane and Lubatkin (1998) [38] Inter-organizational R&D
learning alliances in
pharmaceutical-
biotechnology industry.
Absorptive capacity is the ability to value,
assimilate, and apply new knowledge from
a learning alliance partner. Measured by the
similarity of alliance members in scientific
knowledge-base and research capabilities
Firm’s success in inter-
organizational learning
within an alliance. Three
item survey scale
Alliance partners who were similar
in scientific knowledge-base and
research capabilities reported
greater inter-organizational
learning success [absorptive
capacity improves learning]
Grunfeld (2003) [23] Joint venture R&D spillovers
between firms.
Absorptive capacity is the ability of a
firm to learn from other firms. Measured
by level of R&D investment
Return on joint ventures Absorptive capacity effects of own
R&D do not necessarily drive
up the incentive to invest in R&D
[absorptive capacity improves
learning and return on joint ventures]
Malhotra et al. (2005) [40] Supply chain partnership
at RosettaNet Consortium
in IT industry
Absorptive capacity refers to the set of
organizational routines and processes by
which organizations acquire, assimilate,
transform, and exploit knowledge to
produce dynamic organizational capabilities.
Operationalized by three IT construct
groups (i.e., supply chain’s integrative process
mechanisms, partner interface-directed
information systems, and rich information exchange
Operational efficiency and
partner-enabled market
knowledge creation
Absorptive capacity of supply chain
partnership enhances operational
efficiency and partner-enabled
market knowledge creation
[absorptive capacity improves
productivity and knowledge creation]
Organizational Boynton et al. (1994) [9] Managerial practice and IT use
in large organizations
Absorptive capacity is not specifically defined
or measured; rather it is represented in this
context by two constructs—managerial IT
knowledge and IT management process
Success in using IT
in organization
Managerial IT knowledge and IT
management process effectiveness
are related to success in using IT
[absorptive capacity improves
technology adoption/innovation]
Barkema and Vermeulen (1998) [5] International expansion through
start-up or acquisition
Absorptive capacity is not specifically defined
or measured; rather a firm’s learning capability
is represented by two constructs – multi-national
diversity and multi-product diversity.
Foreign start-up
versus acquisitions
Multi-national diversity leads to foreign
start-ups rather than acquisitions
[absorptive capacity increases the
likelihood of new start-up]
Stock et al. (2001) [53] Product development
in modem industry
Ratio of R&D expenditures to sales New product performance
measured by the product’s
technical performance
The relationship of absorptive capacity
and product performance is non-linear.
An inverted U shape suggests diminishing
returns for absorptive capacity [absorptive
capacity improves product performance]
Zahra and George (2002) [60] Strategic management Absorptive capacity is defined as a dynamic
capability with two components (potential
capability and realized capability).
No measures are suggested
Competitive advantage
(flexibility, innovation,
performance)
This literature review paper emphasizes
the importance of the dynamic nature
of absorptive capacity in the strategic
context [absorptive capacity improves
innovation and performance]
Group Tsai (2001) [56] Companies in petrochemical
and food-manufacturing industries
Unit’s absorptive capacity is defined as the
ability to successfully replicate new knowledge.
Measured by ratio of R&D expenditures to sales
Innovation and business
performance
Absorptive capacity improves innovation
and business performance
X. Deng et al. / Information & Management 45 (2008) 75–8778
time as a series of discrete steps and a compressed schedule
is possible.
An experiential or prototyping strategy is advocated for
situations where uncertainty is high. Product development
becomes an emergent process of deliberations characterized
by a changing task environment with unexpected opportu-
nities and problems, and sometimes an unknown or
incompletely understood end product. Engineers iteratively
try out ideas, reflect on what they have learned, exchange
ideas with colleagues, use their analytical or intuitive problem
solving skills, and apply new knowledge in their work.
4. The absorptive capacity-productivity model in IT
enabled engineering
Fig. 1 shows our model of the relationship between
absorptive capacity and task productivity in IT enabled
engineering. Absorptive capacity effects task productivity
through paths that reflect rational and experiential strategies
for engineering knowledge work [19]. The rational strategy
is the direct path from absorptive capacity to task
productivity. Here, the engineer has the existing task and
computer knowledge and understands the analytical
procedures necessary to complete the task. The experiential
strategy is shown as the indirect path from absorptive
capacity to problem solving/decision support IT usage, to
task innovation, and thus to task productivity. This path is a
static representation of an inherently dynamic (often
recursive) process of individual learning and innovation.
4.1. The absorptive capacity of individuals in IT enabled
engineering work
Cohen and Levinthal suggest that accumulated knowl-
edge increases both the ability to put new knowledge into
memory (acquisition) and to recall and use it. An
individual’s absorptive capacity is critical in IT enabled
engineering work where productivity requires substantial
computer problem solving and innovation.
Absorptive capacity here refers to an individual’s ability
to acquire new knowledge, assimilate it, and apply it in his or
Table 2
Components of absorptive capacity in IT enabled engineering work
Construct Definition/description
Absorptive capacity in IT enabled
engineering work
An individual’s ability to a
assimilate it, and apply it
Knowledge bases
Task knowledge An individual’s understand
Computer knowledge An individual’s understand
Reasoning Mechanisms
Systematic problem solving An individual’s ability to s
methods or procedures
Intuitive problem solving An individual’s ability to s
domains of knowledge sim
her work. Table 2 illustrates the definition of absorptive
capacity, descriptions of its four facets (task knowledge,
computer knowledge, systematic problem solving, intuitive
problem solving), and the related literatures.
Absorptive capacity can be viewed as having two parts—
knowledge bases and reasoning mechanisms. The latter may
include both systematic and intuitive facets of a knowledge
worker’s problem solving style. Existing task and computer
knowledge bases augment one’s ability to assimilate new
knowledge. Reasoning mechanisms represent problem
solving skills that allow users to integrate their knowledge,
create novel ideas, and apply them in their work. These skills
give rise to creativity [2], permitting associations and
linkages that may have never been considered before.
It is the integration of task and computer knowledge
within an individual knowledge worker that reflects the
individual’s knowledge base for problem solving and
innovation. When the individual does not understand the
work process, general computer expertise is of little value.
Absorptive capacity also depends on one’s reasoning
mechanism. Koestler’s [37] seminal work on the act of
creation provides the foundation for viewing problem
solving as consisting of both systematic and intuitive
thinking skills. Engineering knowledge work requires both
systematic and intuitive problem solving skills. Systematic
problem solving refers to an individual’s ability to solve a
problem using established methods or procedures. Intuitive
problem solving refers to an individual’s ability to solve a
problem by overlapping separate domains of knowledge
simultaneously (associative thinking).
Disciplined step by step procedures are ineffective in
creating new knowledge without some intuitive insights,
needing analytical discipline for their execution. Engineers
must use both their intuitive and analytical problem solving
skills to synthesize and execute new solutions.
4.2. Problem solving and decision support IT usage
Problem solving and decision IT usage is the extent that
an individual uses IT applications in analyzing cause and
effect relationships (e.g., making sense out of data) and
improving the decision making process (e.g., explaining the
Related literature
cquire new knowledge,
in engineering work
[9,11,35,38,60]
ing of the work process [11,28,30,42]
ing of computer technologies [11,28,30,42]
olve a problem using established [11,31,37,46]
olve a problem overlapping separate
ultaneously
[11,31,37,46]
X. Deng et al. / Information & Management 45 (2008) 75–87 79
Table 3
Dimensions of IT use, task innovation, and task productivity and related literature
Construct Definition Related literature
IT use for problem solving
and decision support
The extent that an individual uses IT applications in engineering
work for analyzing cause and effect relationships (e.g., making
sense out of data) and improving the decision making process
(e.g., explaining the reasons for decisions)
[17,26,27,59,62]
Task innovation The extent that an IT application helps the user create and try out
new ideas in their work
[27,54,62]
Task productivity The extent that an IT application improves the user’s output per unit of time [27,54,59,62]
reasons for decisions). Table 3 illustrates the definitions and
related literature for IT use by individuals in problem solving
and decision support.
This usage of IT is a part of the experiential strategy for
IT enabled engineering. A model may help solve a problem
but also facilitate the process of gaining organizational
support for the solution. The solution is considered to be an
objective, scientific, and a justified belief.
This problem solving and decision support use of IT
requires absorptive capacity. Engineers must acquire and
make sense out of the data provided by their IT systems.
Without task or process knowledge, the data provided by the
computer model makes little sense. The engineer must know
the discipline in order to use established methods or proce-
dures. However, for ill structured problems, simply following
existing procedures may not be sufficient or desirable.
4.3. Task innovation
Task innovation examines the extent to which a computer
application helps users create and try out new ideas. The
impact of information technology on task innovation has
gained increased recognition [42].
IT facilitates innovation through experiential learning.
Torkzadeh and Doll [54] reviewed the post-industrial
literature on the impact of technology on the work of
individuals and proposed four dimensions—task innovation,
task productivity, management control, and customer
satisfaction. Of these, task innovation was, possibly, the
most important because it is necessary to improve
productivity, control, and customer satisfaction.
4.4. Task productivity
Task productivity is the extent to which an IT application
improves the user’s output per unit of time. In engineering
work, productivity is an appropriate success measure. High
productivity means that projects are completed more quickly
with less manpower.
4.5. Hypotheses development
Drucker argued that productivity is a key indicator of
performance in knowledge work. Tullett [58] argued that
thinking style and ability make important contribution to the
effectiveness of managerial work. Griffith, Redding, and
Van Reenen and Malhotra et al. reported a relationship
between absorptive capacity and productivity in the
economic development and supply chain contexts. Absorp-
tive capacity could help individual knowledge workers
embed a priori best practices into IT. Thus, engineering
knowledge workers could enhance their productivity. Thus,
we hypothesized:
Hypothesis 1. The greater an individual’s absorptive capa-
city, the greater the impact of IT on task productivity.
Engineering knowledge workers need to learn and refresh
their knowledge to continue to make effective use of IT in
their problem solving and decision support. Seeley and
Targett [47] found that when users cannot keep up with
advances in process knowledge and IT, they experienced a
loss in their ability to use computers effectively.
Cognitive style is typically defined as consistent
individual differences in preferred ways of organizing and
processing information and experience [41]. When focusing
on decision making and problem solving, researchers have
often focused on single dimensional bi-polar measures such
as Allinson and Hayes’ [1] cognitive style index (CSI) or
Kirton’s [36] adaptation–innovation inventory (KAI). We
argue that the two-dimensional problem solving style
construct (systematic and intuitive) of Koestler, later
operationalized by Jabri [31], better reflects an individual’s
full reasoning mechanisms.
Problem solving methods and heuristics constitute prior
knowledge that permits individuals to acquire related problem
solving abilities. Existing domain knowledge improves one’s
ability to assimilate new domain knowledge while problem
solving skills present a capacity to use the computer creatively
in problem solving and decision support. We argue that
individuals’ absorptive capacity (as reflected by their
knowledge bases and reasoning mechanisms) will enable
them to use the computer more extensively for problem
solving/decision support. Thus, we hypothesized:
Hypothesis 2. The greater an individual’s absorptive capa-
city, the more extensively IT is used for problem solving/
decision support.
Without using IT, an individual cannot describe how it
shapes the nature of work and how it impacts task
X. Deng et al. / Information & Management 45 (2008) 75–8780
performance. Danziger [14] supports use-impact link
through his findings that the impacts of computing were
highly dependent upon the context of use. Empirical studies
in the field also supported the use-impact link [29,39,45].
In the system-to-value chain described by Doll and
Torkzadeh [16], IS-use was proposed as a causal agent that
predicted the impact of IT on individuals’ work (e.g., task
innovation and task productivity). In 1998, they also
developed a three-dimensional system-use measure [17]
that described an individual’s use of technology in an
organizational context for problem solving/decision support,
work integration, and customer service. IT use for problem
solving/decision support was found to be most appropriate
for the iterative and experiential knowledge work of
engineers.
Problem solving and decision support facilitates an
experiential strategy for dealing with uncertainty by
providing an information rich environment that helps
individuals generate new ideas (task innovations) and helps
them try out and assess the consequences of innovative ideas
rapidly. Thus, we proposed:
Hypothesis 3. The more extensively IT is used for problem
solving/decision support, the greater the task innovation.
We contend that new ideas will be generated and tried out
through iterative learning cycles. Within such experiential
cycles, many innovative ideas may be generated. Not all will
be adopted but some will provide a satisfactory solution.
Here, an innovative solution is the key to task productivity.
As more innovative ideas are incorporated into engineering
work, productivity is enhanced. Thus, we proposed:
Hypothesis 4. The greater the task innovation, the greater
the task productivity.
As engineers experientially build and refresh their
individual areas of process expertise, integrate these
knowledge domains, develop their capability for systematic
as well as intuitive problem solving, and thus use the
computer more extensively, the outcome will be enhanced
task innovation and productivity.
5. Research method
For an initial test of the productivity hypotheses in
knowledge work, we chose, as our sample population,
engineering professionals who did computer intensive,
complex, analytical work. The unit of analysis was an
individual who used a specific software package for a
specific work process.
5.1. The sample
The survey was administered in five firms performing
knowledge intensive engineering work. The firms were
asked to identify the engineers, software packages, and
processes that would be included in the survey. Management
asked 743 engineers doing highly analytical engineering
work to respond to questions about their use of a specific
package; 208 replies were received, representing a 28%
response rate. We thus captured assessment of the knowl-
edge bases and reasoning mechanisms for each engineer’s
specific work process and their software package. The
processes under study were: computer aided design,
computer-aided engineering, or computer aided manufac-
turing (79%); project management (17%); and manufactur-
ability analysis (4%).
The work of the engineers consisted mostly of product
development processes that were helpful in generating ideas
for solutions to specific design or project management
problems. Systems were also used to make explicit the
reasons for the decisions, thus supporting a process intended
to aid supervisors or other engineers in accepting and
implementing the solution.
Most of the engineers were moderate to heavy computer
users who had known and utilized the application for some
time. Forty percent said they used the software ‘‘a great
deal’’; 27% ‘‘much’’; and 20% ‘‘moderately’’. By length of
use, 19% had used the software for more than 5 years; 52%
between 1 year and 5 years; 23% between 1 month and 1
year; and 6% for several weeks but less than a month.
The sample consisted of 12% top or middle level
engineering management, 14% first level engineering
supervisors, 62% professional engineers without super-
visory responsibility, and 12% technical personnel. The
sample was highly educated, with 13% having a Ph.D.
degree, 41% a master’s, 30% a bachelor’s degree, 6% an
associate degree, and 10% having a high school diploma.
The respondents were asked to rate their knowledge/skill
in using the software for their work processes compared to
other users who could make full use of the software in their
work. In indicating their level, 48% rated it as 80% or more
of that required for full use, 24% ass 60–79% of full use,
15% as 40–59% of full use, 8% as 20–39% of full use, and
5% as less than 20% of full use.
5.2. The instruments
The structure and measurement of the absorptive capacity
and its facets were consistent with the definitions and
descriptions in Table 2. Absorptive capacity was measured
by assessing the extent of an individual’s knowledge bases
and reasoning mechanisms. Items for knowledge bases were
generated from studies of Igbaria et al. [30], Igbaria and
Iivari [28], and Cohen and Levinthal. Items for reasoning
mechanisms (systematic and intuitive) were generated by
reviewing the studies of Scott and Bruce [46] and Jabri. The
items are given in Table 4. A five-point Likert type scale was
used where 1 = none or a little, 2 = to some extent, 3 = to a
moderate degree, 4 = to a great extent, and 5 = to a very
great extent.
X. Deng et al. / Information & Management 45 (2008) 75–87 81
Table 4
The measurement instrumentsa
Label Item description
Absorptive capacity (a = .74)
KB The aggregation of the following items:
Task knowledge facet TSK1 I have general knowledge of this process for which I am using the software
TSK2 I have expertise on this process
TSK3 I have a theoretical understanding of this process
TSK4 I have an understanding of what the output of this application should look like
Computer knowledge facet CIS1 I have used programming languages for information system development
CIS2 I have implemented computer information systems
CIS3 I have experience in designing computer information systems
CIS4 I have implemented a database application
RM The aggregation of the following items:
Systematic problem-solving facet When using the software for this process, I
SYS1 adhere to the commonly established rules of my area of work
SYS2 adhere to the well-known techniques, methods, and procedures of my area of work
SYS3 adhere to the standards of my area of work
SYS4 follow well-established ways for solving problems
Intuitive problem-solving facet When using the software for this process, I
INT1 spend time tracing relationships between disparate areas of work
INT2 make unusual connections about ideas even if they are trivial
INT3 deal with a maze of ideas which may, or may not, lead to somewhere
IT use for problem solving/decision support (a = .91)
TUPD1 I use this application to improve the efficiency of the decision process
TUPD2 I use this application to help me make explicit the reasons for my decisions
TUPD3 I use this application to make sense out of data
TUPD4 I use this application to analyze why problems occur
Task innovation (a = .90)
IPTKI1 This application helps me come up with new ideas
IPTKI2 This application helps me create new ideas
IPTKI3 This application helps me try out innovative ideas
Task productivity (a = .91)
IPTKP1 This application increases my productivity
IPTKP2 This application saves me time
IPTKP3 This application allows me to accomplish more work than would otherwise be possible
a The survey instructions ask each engineer to respond to these questions with respect to his/her use of a specific software package for a specific process/task.
Cohen and Levinthal suggested that absorptive capacity
was developed cumulatively but separately within both the
knowledge bases and reasoning mechanisms domains. It
was the integration of task and computer knowledge within
an individual engineer, rather than separate indicators of an
individual’s task or computer expertise that best reflected
the individual’s knowledge base for problem solving and
innovation in IT enabled work. Jabri argued that the
combined effects of systematic and intuitive thinking,
rather than the separate effects of problem solving styles
that best reflected the individual’s reasoning capacity.
Absorptive capacity was measured as a first-order variable
with two aggregate reflective indicators—an indicator of
knowledge bases (task and computer facets) and an
indicator of reasoning mechanisms (systematic and
intuitive facets).
Bagozzi and Heatherton [4] supported the appropriate-
ness of using aggregate indicators to measure multifaceted
constructs such as the self-concept or absorptive capacity.
Appendix A provides descriptive statistics, reliability,
convergent validity, and discriminant validity for each of
these facets.
IT use was measured using a scale developed by Doll and
Torkzadeh in 1998. The development of the task innovation
and task productivity scales was reported by Torkzadeh and
Doll and a later confirmatory study by Torkzadeh et al. [55]
supported the reliability and factorial validity of the
instruments used to measure IT impact on task innovation
and task productivity. All the IT use and impact scales used a
five point Likert scale ranging from 1 = ‘‘not at all’’ to
5 = ‘‘a great deal’’.
A pilot study of 45 responses was gathered and analyzed
to assess the measures. The large scale study instrument is
shown in Table 4. The absorptive capacity measures were
modified based upon the initial results. The seven reasoning
mechanisms items were averaged to serve as another
aggregate indicator of absorptive capacity. The problem
solving/decision support, task innovation, and task produc-
tivity scales had good reliability (a > 0.90) and were not
modified after the pilot study.
X. Deng et al. / Information & Management 45 (2008) 75–8782
Fig. 2. The measurement model. Note: x2 = 76.16, df = 48, x2/df = 1.6, NNFI = 0.98, CFI = 0.98 and RMSEA = 0.053.
5.3. Data analysis methods
The measurement and the structural models were both
tested using SEM (LISREL) software. The adequacy of the
models for representing the variance in the co-variance
matrix was evaluated based on model-data fit, item-factor
loadings, and the significance of the corresponding
structural coefficients. The model-data fit was evaluated
by chi-square, degrees of freedom, p-value, Steiger and
Lind’s [52] RMSEA, Bentler and Bonnett’s [6] non-normed
fit index (NNFI), and Bentler’s [7] comparative fit index
(CFI). RMSEA less than 0.050 suggests good model-data fit;
RMSEA between 0.050 and 0.080 suggests acceptable
model-data fit. NNFI and CFI indices greater than 0.90
suggest adequate model-data fit. NNFI and CFI indices
greater than 0.95 suggest good model-data fit.
Measurement instruments were assessed for reliability,
convergent validity, and discriminant validity. Reliabilities
were assessed using Cronbach [13] alpha with 0.7 or above
being considered acceptable. Using SEM [32], convergent
validity was assessed by how well the items load on their
respective latent variable. Item-factor loadings above 0.60
were considered acceptable. Average variance extracted
(AVE) was used to assess the amount of variance that was
captured by each construct in relation to the amount of
variance due to measurement error [20]. For convergent
validity, the AVE of each factor should be greater than 0.50.
A chi-square test described by Segars [48] was used to
assess discriminant validity between pairs of latent factors.
The chi-square value indicated whether a unidimensional
rather than a two-dimensional model could account for the
correlations among the observed items in each pair. The chi-
square value indicated that a unidimensional rather than a
Table 5
Descriptive statistics
Absorptive capacity IT use for PS
Mean 2.91 3.30
Variance 0.39 1.30
Skewness 0.15 �0.56
Kurtosis �0.25 �0.61
two-dimensional model could account for the inter-
correlations among the observed items in each pair. As
further evidence of discriminant validity, the value of AVE
for each variable was higher than the squared correlation
between the focal factor and other factors.
6. Results
The data were analyzed in a two-step approach—first the
measurement model and then the structural model. The first
focused on evaluating the reliability, convergent validity,
and discriminant validity of the factors. The second reported
on the testing of the hypotheses.
6.1. Results for the measurement model
Fig. 2 illustrates the measurement model used. It depicts
the measurement indicators (in boxes) and the correspond-
ing latent variables (in ovals). As indicated in the legend, the
knowledge base (KB) indicator was an aggregate scale
consisting of the average of the four task and the four
computer knowledge items. The reasoning mechanism (RM)
indicator was also an aggregate scale consisting of the
average of the four systematic problem solving style items
and the three intuitive problem solving style items.
Our measurement model had good model-data fit with a
chi-square of 76.2 for 48 degrees of freedom,
RMSEA = 0.053, CFI = 0.98, and NNFI = 0.98. The item-
factor loadings for all 12 indicators was adequate—ranging
from 0.92 to 0.77. This indicated good convergent validity
for each latent variable. An examination of modification
indexes suggested only one pair of items (TUPD3 and
/DS Task innovation Task productivity
3.16 3.71
1.32 1.15
�0.34 �0.91
�0.75 0.33
X. Deng et al. / Information & Management 45 (2008) 75–87 83
Table 6
Reliability, average variance extracted, and correlations
Absorptive capacity IT use for problem
solving/decision support
Task innovation Task productivity
Absorptive Capacity a = 0.74, AVE = 0.61
IT use for problem solving/decision support 0.46 a = 0.91, AVE = 0.72
Task innovation 0.34 0.69 a = 0.90, AVE = 0.75
Task productivity 0.41 0.54 0.63 a = 0.91, AVE = 0.76
Table 7
Chi-square test of discriminant validity
Construct pair D Chi-square D Degree of freedom Discriminant validity*
Absorptive capacity–IT use for PS/DS 54.8 1 Yes
Absorptive capacity–task innovation 65.1 1 Yes
Absorptive capacity–task productivity 59.9 1 Yes
IT use for PS/DS–task innovation 191.0 1 Yes
IT use for PS/DS–task productivity 314.9 1 Yes
Task innovation–task productivity 239.8 1 Yes
* Indicates significance at p < 0.01, based on the D Chi-square greater than 9.88 for the six comparisons conducted.
IPTKI3) that may have had a correlated error term
(modification index = 9.61). We concluded that no mod-
ification of the measurement model was necessary.
Table 5 reports the mean, variance, skewness, and
kurtosis for each of the latent variables. The means for the
five point scales ranged between 2.91 for absorptive capacity
and 3.71 for task productivity. Each variable had adequate
variance ranging from 0.39 for absorptive capacity to 1.32
for task innovation. Each variable had reasonable skewness
(less than 2) and kurtosis (less than 5).
Table 6 indicates that all correlations were significant at
p < 0.01. Reliability (Cronbach’s alpha) scores were on the
diagonals. The three previously developed scales had high
reliability (>0.90) in this sample. The reliability of the
absorptive capacity scale was acceptable. AVE values for all
four variables were all greater than 0.61, indicating adequate
convergent validity. All item-factor loadings were above
0.77, providing additional evidence of convergent validity
for each variable.
For six comparisons, the chi-square value for the test of
discriminant validity between pairs of constructs must be
equal to or greater than 9.88 for significance at p < 0.01
[10]. Findings reported in Table 7 indicated that all chi-
Fig. 3. Hypotheses testing using structural model. Note: x2 = 76.16, df
square differences were significant at the 0.01 level,
indicating discriminant validity between each pair of
constructs. The fact that the AVE value of each variable
was greater than the squared correlations between the focal
variable and the three other variables (see Table 6) provided
further evidence of the discriminant validity between each of
the six pairs of variables.
6.2. Results for the structural model
Fig. 3 shows the results for the structural model. Overall,
the model demonstrated good model-data fit with chi-
square = 76.2, degrees of freedom = 50, RMSEA = 0.050,
NNFI = 0.98, and CFI = 0.99. An examination of modifica-
tion indexes revealed no suggested modifications for the
structural model. The results supported all four hypotheses.
The on-the-job virtual problem solving/decision support
behavior consisted of iterative experiential learning episodes
that may stimulate novel ways of getting work done. IT
enabled problem solving shortened learning cycles by
compressing the time required for observation, assessment,
design, and implementation. Shorter experiential learning
cycles rapidly built knowledge bases and problem solving
= 50, x2/df = 1.5, NNFI = 0.98, CFI = 0.99 and RMSEA = 0.050.
X. Deng et al. / Information & Management 45 (2008) 75–8784
skills, enabling the individual to generate and try out more
ideas.
The results suggested that IT enabled engineering work
productivity could be enhanced by an individual’s innova-
tive ideas. These may emerge from an iterative and
experiential problem solving process where learning and
innovation co-evolve.
7. Discussion
The findings suggested that absorptive capacity was
important in IT enabled engineering work. It represented a
state of readiness that facilitated subsequent behavior and
down-stream impact on task innovation and task productiv-
ity.
In knowledge work, we used the individual as the unit of
analysis. This means that absorptive capacity was defined, in
part, as the integration or sharing of task and computer
knowledge within a single individual or entity rather than
between entities. This adaptation of the absorptive capacity
concept was consistent with the concept’s origins.
7.1. Theoretical implications
When individual knowledge workers are responsible for
crafting their own work processes, there may be substantial
individual differences in innovation and productivity. Its use
for problem solving/decision support was a key mediating
mechanism by which the absorptive capacity of an
individual co-evolved with learning and innovation to
enhance productivity. It focused research attention on the
iterative and experiential nature of problem solving and
decision support in knowledge work. The workers’
innovative skills depended on how extensively they used
the computer for problem solving and solution justification.
Our measurement strategy was adapted to the individual
knowledge worker context. An individual’s absorptive
capacity was treated as a single capability with two
reflective indicators. This method provided a direct measure
of the absorptive capacity of an individual rather than a
surrogate measure. This provided new opportunities for us to
determine the impact of individuals on work teams.
7.2. Practical implications
Knowledge work is important for the survival and
renewal of organizations. Understanding how individual
Table A1
Descriptive statistics
Task knowledge Computer knowledge
Mean 3.53 1.95
Variance 0.85 0.93
Skewness �0.38 1.14
Kurtosis �0.54 1.10
differences affect IT use for problem solving is essential in
enhancing the innovativeness and productivity of knowledge
work. Since ideas are generated first by individuals,
enhancing their absorptive capacity is essential.
Absorptive capacity supports problem solving that, by
facilitating the generation of innovative ideas, improves
productivity. Managers should encourage computer-based
problem solving and decision support because it shortens the
learning cycle and enhances substantive feedback. This
facilitates the co-evolution of learning and innovation.
Appendix A. Facet analysis
The four facets of absorptive capacity were examined to
determine whether they are distinguishable. The statistical
evidence suggested that there are four facets, some
correlated and some statistically independent. While the
task knowledge and computer knowledge are correlated and
might be modeled as a single second-order knowledge bases
factor/facet, they are, instead, aggregated because of the
theoretical argument underlying the absorptive capacity
construct.
The four items for systematic problem solving and the
three for intuitive problem solving are also aggregated for
theoretical/logical reasons underlying the absorptive capa-
city construct itself, not because of statistical evidence that
they are correlated and therefore might comprise one higher
order reasoning mechanism construct. Indeed, as suggested
by Jabri, these two problem solving style scales were
developed and validated as uncorrelated. The theoretical/
logical argument for aggregation is that these two types of
problem solving styles when combined, not in separation,
better reflect an individual’s absorptive capacity.
Descriptive statistics for the four facets of absorptive
capacity are described in Table A1. The respondent’s mean
task knowledge (3.53) is considerably higher than their
mean computer knowledge (1.95). These engineers have
higher scores for systematic problem solving (3.79) than for
intuitive problem solving (2.36). All four facets have
adequate variance and moderate skewness and kurtosis
scores, suggesting a normal distribution.
Table A2 presents reliability (alpha), average variance
extracted, and the correlations among task knowledge,
computer knowledge, systematic problem-solving, and
intuitive problem-solving. The alpha for the first three
facets are adequate (0.85 or greater), but reliability of the
intuitive problem-solving facet (a = 0.66) is less than
Systematic problem-solving Intuitive problem-solving
3.79 2.36
0.60 0.94
�0.62 0.26
0.16 �0.45
X. Deng et al. / Information & Management 45 (2008) 75–87 85
Table A2
Reliability, average variance extracted, and correlations
Task knowledge Computer knowledge Systematic problem-solving Intuitive problem-solving
Task knowledge a = 0.86, AVE = 0.62
Computer knowledge 0.39** a = 0.85, AVE = 0.59
Systematic problem-solving 0.50** 0.12 a = 0.87, AVE = 0.62
Intuitive problem-solving 0.43** 0.44** 0.11 a = 0.66, AVE = 0.40
** Significant at p = 0.01.
Fig. A1. The measurement model. Note: x2 = 146.49, df = 84, x2/df = 1.7, NNFI = 0.94, CFI = 0.95 and RMSEA = 0.060.
desirable. Task knowledge, computer knowledge, and
systematic problem-solving facets have acceptable (greater
than 0.50) average variance extracted, indicating convergent
validity. Intuitive problem-solving has an average variance
extracted of 0.40, less than the 0.50 criterion. Task
knowledge has a moderate but significant ( p = 0.01)
correlation with computer knowledge (0.39), systematic
problem-solving (0.50), and intuitive problem solving (0.43)
facets. Intuitive problem-solving has a significant ( p = 0.01)
correlation (0.44) with computer knowledge. The results
from this sample confirm Jabri’s assertion that the
correlation (0.11) between the systematic and intuitive
problem-solving styles is not significant. In this sample,
computer knowledge has a non-significant correlation (0.12)
with systematic problem-solving.
Fig. A1 reports on a model of four first-order correlated
factors. The model-data fit is adequate with a x2 = 146.49 for
84 degrees of freedom, RMSEA below 0.080, and NNFI and
CFI scores above 0.90. All item-factor loadings or true
scores are acceptable (greater than 0.60) except item INT3
Table A3
Chi-square test of discriminant validity
Facet Pair D Chi-squa
Task knowledge–computer knowledge 362.7
Task knowledge–systematic problem-solving 329.2
Task knowledge–intuitive problem-solving 51.5
Computer knowledge–systematic problem-solving 458.3
Computer knowledge–intuitive problem-solving 50.5
Systematic problem-solving–intuitive problem-solving 88.3
* Indicates significance at p < 0.01, based on the D Chi-square greater than 9
measuring intuitive problem-solving. The correlations
between facets in Fig. A1 are adjusted for attenuation
and, thus, sometimes higher than those reported in Table A2.
The discriminant validity between pairs of facets is tested
by examining the change in chi-square between a model of
the two facets with the correlation set at 1.0 and a model with
the correlation between the pair of facets set free. The results
summarized in Table A3 indicate that each of the six pairs of
facets has discriminant validity at p < 0.01. Further support
for discriminant validity is provided by examining whether
the facet’s average variance extracted is greater than the
square of the correlation between the focal facet and other
facets. In all pair-wise comparisons, the facet’s average
variance extracted is greater than the square of its correlation
with other facets (see Table A2).
In summary, the results support the ability to distinguish
among the four facets. They reveal that one of the four facets
(intuitive problem solving style) has less than adequate
reliability and an average variance extracted below 0.50 if it
was going to be treated as a factor rather than a facet. An
re D Degree of freedom Discriminant validity*
1 Yes
1 Yes
1 Yes
1 Yes
1 Yes
1 Yes
.88 for the six comparisons conducted.
X. Deng et al. / Information & Management 45 (2008) 75–8786
examination of item-factor loadings reveals that only one of
the three items (INT3) has a loading (0.54) slightly below
the 0.60 criterion. However, intuitive problem solving style
is only one facet of absorptive capacity. Absorptive capacity
has adequate reliability (0.74) and an average variance
extracted (AVE = 0.61) that exceed the 0.50 criterion. In
general, the additional data analysis in this appendix
improves confidence that the facets are distinguishable
and supports this theory-based multi-faceted method of
measuring absorptive capacity.
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Xiaodong Deng is an associate professor of management information
systems at Oakland University. He received his PhD in manufacturing
management and engineering from The University of Toledo. His research
has appeared in Journal of Management Information Systems, Decision
Sciences, Information and Management, Information Resources Manage-
ment Journal, and Journal of Intelligent Manufacturing. His research
interests are in post-implementation information technology learning,
information systems benchmarking, and information technology acceptance
and diffusion.
William J. Doll is a professor of MIS and strategic management at the
University of Toledo. Dr. Doll holds a doctoral degree in business admin-
istration from Kent State University and has published extensively on
information system and manufacturing issues in academic and professional
journals including Management Science, Communications of the ACM,
MIS Quarterly, Academy of Management Journal, Decision Sciences,
Journal of Operations Management, Information Systems Research, Journal
of Management Information Systems, Omega, and Information & Manage-
ment.
Mei Cao is an assistant professor in the department of business &
economics at the University of Wisconsin-Superior. She holds a PhD in
manufacturing management and engineering from the University of Toledo.
She has publications in European Journal of Operational Research, Inter-
national Journal of Operations and Production Management, Industrial
Management & Data Systems, International Journal of Product Develop-
ment, and International Journal of Services Technology and Management.
Her research interests are e-commerce, IOS, and logistics and supply chain
management.
