Proceedings of the 43rd Hawaii International Conference on System Sciences - 2010
The Cognitive Selection Framework of Knowledge Acquisition Strategy in
Virtual Communities
Junghwan Kim
Texas Tech University
[email protected]
Jaeki Song
Texas Tech University
[email protected]
Abstract
As a significant source of knowledge, virtual
communities have stimulated interest in knowledge
management research. Nonetheless, very few studies
to date have examined the demand-side knowledge
perspective such as knowledge acquisition in virtual
communities. In order to explore the knowledge
acquisition process within virtual communities, this
study proposes the cognitive selection framework of
knowledge acquisition strategy in virtual communities.
The proposed framework identifies how knowledge
recipients select their strategy for acquiring
specialized
knowledge,
emphasizing
cognitive
perspectives such as cognitive goals (cognitive
replication and innovation) and cognitive motivators
(virtual community self-efficacy, heightened enjoyment,
and time resources) at the individual level. On
analyzing the preliminary data, we obtain findings that
suggest that knowledge recipients’ cognitive
motivators differentially influence their cognitive goals
for selecting knowledge acquisition strategy. However,
in order to empirically examine the hypotheses
identified in this study, further research is still needed.
1. Introduction
With the change from a production-based economy
to a knowledge-based economy, knowledge plays a
critical role in enhancing individual and organizational
effectiveness as the business environment evolves [19,
25]. According to a market intelligence and advisory
firm, AMR Research [3], it has been estimated that
business spending on knowledge management software
and services has grown from $73 billion in 2007 to $85
billion in 2008. The federal government also increased
knowledge management spending from $850 million in
FY 2004 to almost $1.1 billion in FY 2009 [21].
Reflecting these current business and government
endeavors, many studies have investigated the impact
Donald, R. Jones
Texas Tech University
[email protected]
of knowledge management in organizations over recent
years [2, 5].
In spite of the growth and development of
knowledge management, many organizations still have
experienced the lack or failure of knowledge transfer to
the detriment of their operations [7, 27]. Organizations
can be more successful when they facilitate the
conditions in which knowledge providers effectively
share their knowledge and knowledge recipients
effectively acquire and apply that knowledge [39, 4].
While there has been extensive research on knowledge
transfer from various perspectives, most studies to date
have neglected the knowledge recipient aspect, and
have instead targeted the knowledge provider aspect
such as knowledge contribution and sharing. Thus,
there is a critical need to examine how knowledge
recipients select their strategy for acquiring the
specialized knowledge needed to do their work.
A significant knowledge source is virtual
communities, which are defined as social networks of
individuals who share and acquire information and
knowledge using Internet technologies [11]. Virtual
communities provide unprecedented opportunities for
individuals to participate in interactions with others
even when no previous social ties exist [8]. Thus, a
number of individuals participate in virtual
communities for acquiring the knowledge required for
their work. Many organizations have also recognized
the importance of virtual communities as a valuable
system to meet their business objectives [20]. In this
sense, virtual communities can offer an ideal context
for studying knowledge transfer from the perspective
of knowledge acquisition.
The present study has the following research
questions: in the virtual communities, (1) “What are
the salient factors which influence knowledge
acquisition in terms of the knowledge recipient’s
perspective?”, and (2) “How do knowledge recipients
select their strategy for effectively acquiring the
knowledge they need?” In answering these research
questions, we focus on the knowledge recipients’
cognitive motivations, drawing on two theoretical
978-0-7695-3869-3/10 $26.00 © 2010 IEEE
1
Proceedings of the 43rd Hawaii International Conference on System Sciences - 2010
perspectives: social cognitive theory and goal-setting
theory. In doing so, this study aims to develop and test
the cognitive selection framework of knowledge
acquisition strategy in virtual communities, as
influenced by salient cognitive factors of the
knowledge recipients.
Emphasizing knowledge recipients’ intentions (e.g.,
the selection of knowledge acquisition strategy) for
optimally acquiring the knowledge in virtual
communities, this study theoretically contributes to the
elucidation of the knowledge transfer phenomenon
from the knowledge demand-side perspective. This
research also practically contributes for virtual
communities to effectively construct their knowledge
structure, repositories, web design, and management
systems in balance between knowledge demand- and
supply-side.
The rest of paper is organized as follows: In the
next sections we provide the theoretical background for
the conceptual framework, which is followed by the
conceptualization of the knowledge acquisition
strategy and individual cognitive perspectives, and
present our conceptual framework and hypotheses.
After introducing our hypotheses, we discuss our
research method, describe our data collection, and
present our preliminary data analysis. We conclude by
discussing the implications, limitations and future
direction, and conclusion of our study.
2. Theoretical background
2.1. Knowledge acquisition in virtual
communities
Among various knowledge management processes
such as knowledge creation, storage, and application,
knowledge transfer refers to the knowledge exchange
process between knowledge providers (i.e., source,
sender, or contributor) and knowledge recipients (i.e.,
receiver and seeker) [44, 29]. Knowledge resides in
multiple repositories such as individual members,
organizational structures, operating procedures,
organizational culture, and the actual physical
workplace [4, 28].
Thus, knowledge could be
transferred directly by communication between
individuals or indirectly from knowledge archive.
Moreover, knowledge transfer can occur explicitly, as
when a knowledge provider communicates with a
knowledge recipient about tasks or practices for
improving performance, or implicitly, as through
norms and routines which are integrated or coordinated
by organizational members.
For our purposes focusing on knowledge
acquisition, knowledge consists of a state of mind, as
distinct from free-standing data or information. In this
sense, when an individual acquires knowledge, that
person’s knowledge set has expended or application of
knowledge has been enabled. Information technology
plays a role in facilitating the access to the knowledge
sources [2]. Therefore, in the knowledge demand-side
perspective, in contrast to the knowledge supply-side,
knowledge acquisition is regarded as the important
activity in knowledge management. Hence, it is
critical to investigate where and how people access and
acquire the knowledge they need.
Although
knowledge may be acquired in many ways such as
reading, sharing, observing, and experiencing, people
strive for an optimal investment of their effort and time
in each knowledge acquisition strategy as they attempt
to optimally obtain the specialized knowledge related
their own tasks.
2.1.1. The sources of knowledge acquisition. When
individuals obtain knowledge, the selection of
knowledge sources is the critical part of knowledge
acquisition process [48, 15, 16]. There are many
potential sources of knowledge available for
individuals to use such as culturally embedded
practices, documents, policies, knowledge repositories,
virtual communities, and with individuals themselves
[2, 48]. In the present study, we define knowledge
source as the source selected by the knowledge
recipient in order to look for or access other people’s
knowledge such as expertise, experience, insights, and
opinions [15]. Getting the right knowledge on a timely
basis is one of the major challenges of knowledge
acquisition. If appropriate knowledge sources are not
accessible, for example, knowledge recipients may
seldom find the person who possesses the knowledge
required to perform their tasks, and in such case the
best available knowledge can be of limited value.
Therefore, where individuals choose to look for
knowledge can be an important consideration in
knowledge acquisition processes. Especially regarding
decision making processes, the selection of knowledge
sources is important [48].
Based on the communication-based learning model
[18], knowledge sources are categorized into three
forms: dyadic knowledge sources, published
knowledge sources, and group knowledge sources [15,
16]. A dyadic knowledge source is defined as a
knowledge source where a knowledge recipient
engages in one-to-one dialogue with a knowledge
provider via direct communication. A published
knowledge source is based on many-to-one or one-tomany relationships between knowledge providers and
knowledge recipients. Examples of published sources
include documents stored in knowledge repositories,
printed in books, or posted in virtual communities [15].
2
Proceedings of the 43rd Hawaii International Conference on System Sciences - 2010
Finally, a group knowledge source refers to the
exchange of knowledge amongst multiple knowledge
recipients and multiple sources in an open venue.
Knowledge recipients can access and obtain the
knowledge through group knowledge sources such as
open or public conversation in the communities,
question-and-answer systems, or work teams [16].
2.1.2. Virtual communities as knowledge sources.
Virtual communities are online social networks in
which people with common interests, goals or practices
interact to share information and knowledge, and
engage in social interactions [17].
In virtual
communities, for instance, community members may
acquire knowledge through postings which include
information from other community members as well as
bundled information from the virtual community
providers such as wikis, blogs, and manuals.
Knowledge sources in virtual communities can be
distinguished as static knowledge sources or dynamic
knowledge sources [15, 48]. A static knowledge
source consists of web pages where knowledge flows
from the source to the knowledge recipient, and the
knowledge recipient does not give any responses to the
knowledge sources or providers on the site. On the
other hand, a dynamic knowledge source consists of
web pages where information can flow bidirectionally.
In dynamic knowledge sources (e.g., newsgroups,
bulletin boards, and online forums), the knowledge
recipients can directly post their problem or question,
and then some community members who know the
solution to the problem serve as knowledge providers
by answering the knowledge recipients’ posting.
Therefore, we regard the virtual community as a
significant knowledge source in which knowledge
recipients can access and acquire the knowledge.
2.1.3. Knowledge acquisition strategies in a virtual
community. According to activity theory, individual
activities and mental processes inevitably depend on
the specific situation the individual finds himself or
herself in, such as task structure, prior experience,
cognitive ability, work process, exchange relationships
with others, and so forth [38]. Similarly, knowledge
recipients can obtain specialized knowledge through
three learning processes: learning-by-self-investment,
learning-by-doing, and learning-from-others [40].
Among these learning processes, we focus solely on
the learning-from-others process since virtual
communities provide knowledge sources by
interconnectedness between community members. The
learning-from-others perspective can be a major
strategy for acquiring the specialized knowledge,
because knowledge transfer is mostly dyadic
exchanges between individuals, especially in the
“source-recipient” generic model [29], and because a
significant knowledge is embedded in individual
members [4].
When people acquire knowledge, they consider
which knowledge sources and learning processes will
be optimal. Based on knowledge sources and the
learning-from-others perspective, we employ the
concept of knowledge acquisition strategy in virtual
communities as constituting a static acquisition
strategy or a dynamic acquisition strategy. First of all,
in a static acquisition strategy knowledge recipients
unidirectionally acquire the knowledge from virtual
communities.
This strategy enables knowledge
recipients to seek and obtain the knowledge provided
by other people without any direct communication (e.g.,
traditional web pages, web-blogs, search engine, FAQs
etc.). On the other hand, in a dynamic acquisition
strategy knowledge recipients bidirectionally obtain
the knowledge through bulletin boards, questions-andanswer systems, or direct communications such as
email, message, and telephone with actual or potential
knowledge providers. This strategy has the potential to
provide the knowledge recipients with more in-depth
and customized knowledge than a static acquisition
strategy.
2.2. Cognitive perspectives
acquisition strategies
in knowledge
In this study, our concern is how each individual
selects the knowledge acquisition strategy in virtual
communities in order to effectively obtain specialized
knowledge. According to prior research, individuals
anticipate their desired future outcomes, and then
develop their strategies and plans which enable them to
achieve the desired outcomes [24, 9]. The desired
future outcomes are commonly called goals [10].
Furthermore, individuals modify and monitor their
behaviors to attain their goals. In our quest to better
understand the selection of knowledge acquisition
strategies, we will link two theoretical bases, social
cognitive theory and goal-setting theory, to develop the
cognitive selection framework of knowledge
acquisition strategy in virtual communities at the
individual level.
2.2.1. Social cognitive theory. In a number of studies
concerned with human motivation and action, social
cognitive theory (SCT) postulates that human
behaviors are determined by the continuous, reciprocal
interaction among behavioral, cognitive, and
environmental factors [34]. In particular, SCT is
concerned with how environmental and cognitive
factors interact to influence human behaviors such as
knowledge acquisition in the specific context.
3
Proceedings of the 43rd Hawaii International Conference on System Sciences - 2010
According to SCT, individuals can influence
changes in themselves and their situations through their
efforts, because judgment and actions are partly selfdetermined. In this regard, Bandura et al. (1989)
averred that “individuals are neither autonomous
agents nor simply mechanical conveyer of animating
environmental influences (pp. 1175).” That is, in order
to achieve higher performance, individuals make
causal contribution to their motivation and actions
through the reciprocal causation of behavioral,
cognitive, and environmental factors.
Reciprocally interacting with knowledge sources in
virtual communities, individuals’ cognitive motivations
regulate their actions and behaviors such as the
selection of knowledge acquisition strategy.
Therefore, SCT can be useful for understanding of the
cognitive perspectives in knowledge acquisition at the
individual level.
2.2.2. Goal-setting theory. According to goal-setting
theory, both motivation and subsequent performance
increase when individuals set specific, difficult goals
that have high valence, and individuals react or change
their behaviors when a discrepancy is perceived
between their goal and the current state of the
environment. Thus, the basic assumption of goalsetting theory is that much of human action is
purposeful and is directed by conscious goals [31].
Goal-setting theory is useful in predicting performance
and understanding the cognitive mechanisms involved
in setting performance goals [37], particularly
providing the motivational explanation of why some
people perform better than others on work tasks.
People have different performance goals, and their
different goals subsequently lead to their different
behaviors resulting in different qualities of
performance.
According to Locke and Latham (2002), goals
motivate individuals to achieve better performance
through four primary mechanisms: direction, effort,
persistence, and strategies. Among them, the last one
is especially critical when individuals face complex
problems. Goals affect action indirectly by leading to
the arousal, discovery, and/or use of task-relevant
knowledge and strategies from a variety of knowledge
sources [39, 31]. Thus, individuals’ actions are the
result of their cognitive motivation which can interact
in complex ways. Under different types of goals
depending on assigned tasks, individuals are most
likely to formulate effective strategies, and to
accurately predict the conditions that would be most
likely to facilitate the selection of suitable strategies
[31].
Therefore, we propose that individuals cognitively
set the goals depending on the salient factors which
facilitate individual cognitive motivation, and that they
select the proper strategies for effectively achieving
their cognitive goals.
3. Conceptual framework and hypotheses
Based on two theoretical bases, Figure 1 depicts our
conceptual framework. It elucidates that in virtual
communities the knowledge recipients use different
types of knowledge acquisition strategy, depending on
the cognitive goals they set. Additionally, their goals
can be influenced by salient cognitive motivators such
as virtual community self-efficacy, heightened
enjoyment, and time resources they recognize in virtual
communities.
The posited constructs and their
relationships shown in Figure 1 have been identified
because of their theoretical relevance and managerial
importance, as subsequently described.
Cognitive
Motivators
Virtual
community
self-efficacy
Heightened
enjoyment
Time
resources
Cognitive
Goals
Knowledge
Acquisition Strategy
Cognitive
replication
Static
acquisition
strategy
Cognitive
innovation
Dynamic
acquisition
strategy
Figure 1. A Conceptual framework
3.1. The influence of cognitive goals on
knowledge acquisition strategy
Cognition has traditionally been thought of as
individually created and structured [46], and cognitive
structures, which are mental representations of a
person’s knowledge, serve to guide both individual
perception and inference [6, 12]. Previous studies have
particularly dealt with the changes of individual
cognitive structure, regarding such change as the
outcome of knowledge acquisition [6, 15]. According
to this view, knowledge acquisition entails changes in
cognitive structures, and therefore the modifications of
cognitive structures play a critical role in the
knowledge acquisition.
In the present study, we employ the concept of
cognitive structures and changes [15] to understand
individuals’ cognitive goals. Individuals are more
likely to set their goals which result in cognitive
changes before they decide to acquire the knowledge
[32]. Therefore, goal-setting can be an important
4
Proceedings of the 43rd Hawaii International Conference on System Sciences - 2010
means of providing the necessary motivation to
actively seek out and acquire knowledge.
Two types of cognitive changes are relevant to our
research: cognitive replication and cognitive
innovation [15]. A third type of cognitive change,
cognitive adaption is not included in this research
because it is hard for people to distinguish from
cognitive innovation [13, 32] and because it may be a
result (rather than antecedent) of knowledge
acquisition.
Cognitive replication refers to the propagation of
existing cognitive structures. This means knowledge
exploitation that generates value through productive
repetition or recreation of knowledge that already
exists. Thus, knowledge recipients acquire and apply
the knowledge (e.g., usually explicit knowledge) from
knowledge sources, replicating the essential elements
of the sources’ knowledge [32, 45]. On the other hand,
cognitive innovation refers to radical and discontinuous
changes of cognitive structures [47]. For attaining the
innovative goals, knowledge recipients make an effort
to not only acquire the knowledge but also integrate the
knowledge with their existing knowledge. Transferred
knowledge for cognitive innovation is likely to be
ambiguous, complex or tacit knowledge [32]. Thus,
the cognitive innovation can often substitute for old
knowledge, encouraging radical improvement of
existing knowledge.
In our framework, an individual’s cognitive goal of
replication or innovation influences the selection of
how to create and apply the appropriate knowledge
acquisition strategy [39]. Therefore, cognitive goals
which people set can be the proximate causes of their
behaviors, including selection of knowledge
acquisition strategy in virtual communities.
We
hypothesize that goal and strategy will share similar
levels of (dis)continuity. In sum, we posit the
following hypotheses below:
H1a: The knowledge recipient who sets a cognitive
replication goal is more likely to select a static
knowledge acquisition strategy than a dynamic
knowledge acquisition strategy.
H1b: The knowledge recipient who sets a cognitive
innovation goal is more likely to select a dynamic
knowledge acquisition strategy than a static knowledge
acquisition strategy.
3.2. The influence of cognitive motivators on
cognitive goals
The present study emphasizes the salient cognitive
factors influencing the knowledge recipients’
knowledge acquisition. According to Payne et al.
(1993), cognitive factors such as prior knowledge,
experience, cognitive ability, and perceived benefits
affect the frequency and recall of available and
particular strategies, when people face the specific
problems or tasks. Different cognitive beliefs of
individuals have an impact on their reactions or
behaviors when people decide how to acquire the
specialized knowledge.
The knowledge recipients’ cognitive goals can be
influenced by the recipients’ motivation about how to
use available new knowledge for their task [39, 44].
As a result, the knowledge recipients set their goals to
be consistent with their motivations. Therefore, we
draw on virtual community self-efficacy, heightened
enjoyment, and time resources as the salient cognitive
factors which motivate the knowledge recipients’
cognitive goal-setting.
3.2.1. Virtual community self-efficacy. Self-efficacy
is defined as individuals’ personal beliefs in their own
ability to organize and execute courses of actions,
functioning as the critical determinant of human
motivation, judgments, and behaviors [9]. This plays a
pivotal role as socio-cognitive determinants
influencing human action, adaptation, and change [42].
In this study, we redefine self-efficacy as virtual
community self-efficacy. Virtual community selfefficacy is defined as individual judgments of a
person’s capabilities to perform tasks in virtual
communities.
Virtual community self-efficacy
encourages knowledge recipients to effectively acquire
the knowledge through the utilization of virtual
communities. Therefore, individuals who have higher
virtual community self-efficacy have a tendency to
mentally or physically invest the optimal efforts in
knowledge acquisition strategies, facilitating high
levels of learning goals.
This view is consistent with the argument that selfefficacy facilitates high levels of knowledge
acquisition. Gist et al. [14] maintained that selfefficacy has positive influences on the knowledge
acquisition, specifically during the selection processes
of knowledge acquisition [24]. That self-efficacy
influences individual intentions for effort allocations
may be applied to cognitive goal setting to pursue the
appropriate knowledge acquisition strategy, in
affecting the direction of effort and task persistence
[33]. Hence, individuals who have greater virtual
community self-efficacy are more likely to pursue the
higher level of cognitive goals with their higher selfconfidence.
H2a: The greater the virtual community selfefficacy, the more likely the knowledge recipient is to
5
Proceedings of the 43rd Hawaii International Conference on System Sciences - 2010
pursue a cognitive innovation goal rather than a
cognitive replication goal.
3.2.2. Heightened enjoyment.
To capture the
pleasurable aspects as cognitive motivators, we adopt
the concept of heightened enjoyment. The heightened
enjoyment means the perceived degree to which the
knowledge recipient is involved in his or her activity
(e.g., knowledge acquisition activity itself or virtual
community activities) for the pleasure and enjoyment it
provides [1]. Moreover, the heightened enjoyment is
closely related to cognitive absorption, and plays an
important role as a core component of flow such as the
knowledge acquisition process [1]. Therefore, we
hypothesize that individuals who find virtual
community activities or knowledge acquisition itself as
cognitively pleasurable have a tendency to set the
innovation-oriented goal rather than simple repetition
of the existing knowledge.
H2b: The greater the heightened enjoyment, the
more likely the knowledge recipient is to pursue a
cognitive innovation goal rather than a cognitive
replication goal.
3.2.3. Time resources. The amount of time available
to perform the task plays a critical role in decision
making process, and many decisions in reality are
made under time pressure. Individuals have some
feelings of stress for coping with the limited time, and
as a result, time pressure influences their judgments
and choices by restricting the cognitive capacity [43].
Under time pressure, individuals are more likely to
speed up executing their decision strategies or switch
to simpler strategies [35, 22].
The amount of time has been regarded as one of the
critical resources for knowledge acquisition [36],
providing enough cognitive resources available for
performing the task. Time resources enable knowledge
recipients to browse for the knowledge since seeking
and acquiring the appropriate knowledge is timeconsuming tasks.
Therefore, when knowledge
recipients have enough time resources, they are more
likely to expect to get more knowledge, as well as to
have the opportunities for applying innovative ideas to
the task. We thus propose that having greater time
resources can provide the knowledge recipient with
more chances to pursue innovative goals without
cognitive time pressure.
H2c: The greater the time resources, the more
likely the knowledge recipient is to pursue a cognitive
innovation goal rather than a cognitive replication
goal.
4. Methodology
4.1. Experimental design and procedure
The experimental design was as follows. All
participants actually visited a particular virtual
community website in which they participated in
acquiring knowledge. The research protocol we
provided helped participants become familiar with
using the virtual community where they could find the
knowledge or solutions for their tasks. Next, the postexperiment survey was provided after participants
completed the exercise.
The survey instrument had two parts. The first part
included items for the assessment of: (1) individual
cognitive motivators (virtual community self-efficacy,
heightened enjoyment, and time resources), (2) the
cognitive goals and knowledge acquisition strategies,
and (3) control variables (perceived task significance
and
complexity,
perceived
past
experience,
participants’ habit, propensity to trust). These items
used a one to seven Likert scale, anchored with
“strongly disagree” (1) and “strongly agree” (7). The
second part solicited participants’ general background
such as gender, age, education level, and, virtual
community usage.
The experimental sessions were conducted in a
computer laboratory in which every computer had
Internet access. Participants were seated at a computer
and given verbal instructions about the experimental
procedure, emphasizing the point that they were to
work through the material in sequence without
returning to a prior page. Following our research
protocol, participants actually experienced the
knowledge acquisition in the virtual community
through various knowledge sources and acquisition
strategies. In the research protocol, we presented a
specific task description to participants (e.g., iTunes
troubleshooting). In order to resolve the task, it was
required to acquire the knowledge from the task-related
virtual communities (e.g., Apple virtual discussion
community). Subjects spent 10.16 minutes on average
to complete the task. Then, the participants answered
survey questions according to a survey instrument.
The survey instrument was constructed for the purpose
of measuring the participants’ cognitive motivation,
preferences of cognitive goals and knowledge
acquisition strategies, while they acquired the
knowledge in virtual communities.
4.2. Participants
For this study, 93 participants were drawn from a
pool that comprised senior-undergraduate and MBA
6
Proceedings of the 43rd Hawaii International Conference on System Sciences - 2010
students in a large university in the U.S. Participation
was voluntary: participants received extra course
credits to increase the seriousness of participation and
to encourage their participation in the experiment.
Table 1 shows the profiles of the participants.
Table 1. Profiles of participants (N=93)
Gender
Age
Male
48
Female
45
Mean
Standard deviation
24
4.2
Of the 93 participants, 48 were male and 45 were
female. On average, they were on average 24 years old.
We provided participants with the opportunity of
having experience with virtual community activities,
even though participants were familiar with activities
on the virtual communities. Using students as study
subjects always generates the question of external
validity. Many studies have found that there are no
significant differences between student subjects and
non-student subjects in individual behavior,
organization psychology, and so forth [30]. In addition,
participants in this study have the characteristic that
defines the population being sampled, which is that
they are potential or actual users in virtual
communities. Hence, the type of subjects in this study
does not present a significant threat to external validity
and could be considered as representative of
knowledge recipients who seek and acquire knowledge
in virtual communities.
construct prevents the construct from losing
information in case of ordered categorical
measurement [41]. The measures included in this
study and sample items are outlined in Table 2. The
constructs have alpha values well above the cutoff
point of 0.70 [34], indicating the high reliability of the
items.
Since our study has ordered categorical
measurement, this is the appropriate approach for item
combination. Besides, other research variables such as
virtual community self-efficacy, heightened enjoyment
and time resources were also measured using the
average of the items for these constructs.
Table 2. Constructs and measures
# of
Items
Alpha
Cognitive
replication
3
.812
Cognitive
innovation
3
.776
Static
acquisition
strategy
4
.849
Dynamic
acquisition
strategy
4
.754
Virtual
community
self-efficacy
6
.874
4.3. Variables
Heightened
enjoyment
3
.939
To elucidate relationships identified in our
conceptual framework, we used the following
variables: cognitive goals and knowledge acquisition
strategy. The scales for measuring these constructs
were developed based on an extensive review of
literature to ensure their content validity.
Cognitive goals can be defined as the expected
changes of individual cognitive structures. In other
words, the knowledge recipient has cognitive goaloriented behaviors for knowledge acquisition outcomes.
This definition is in line with previous studies in terms
of knowledge acquisition outcomes [15]. In this study,
cognitive goals can be measured as two different types:
cognitive replication and cognitive innovation.
Knowledge acquisition strategy can be measured by
addressing which acquisition strategy the knowledge
recipient prefers to pursue in virtual communities [16].
In the present study, the constructs are measured as
the average of the items.
Despite the critical
importance of instrument reliability in IS research, the
averaging approach has been applied in many studies
and it has been argued that the averaging of items for a
Time
resources
4
.726
Construct
Sample item
I want to incorporate more
established knowledge into my
work.
I will try to obtain the knowledge
which can make a number of
substantial improvements in the
way I do my work.
I will prefer to obtain useful
knowledge by reading written
materials in the virtual
community.
I prefer to communicate one-onone with other people who may
have encountered similar
problems.
I am confident I could acquire the
knowledge I need by using a
virtual discussion community.
Using the virtual community will
provide me with a lot of
enjoyment.
I expect to have enough time to
conduct the task on a timely basis.
5. Preliminary data analysis and results
Our data collection and analysis is ongoing, so in
this paper we report the results of a preliminary test of
a subset of the relationships identified by the
framework.
Using one-way ANOVA analyses, parts of
hypotheses were examined in this study. Table 3
shows descriptive statistics of variables used in this
study. Table 4 reports the result of one-way ANOVA
among three cognitive motivators in terms of cognitive
goals – cognitive replication and cognitive innovation.
Hypothesis 2 posited differences between high and
low cognitive motivators on cognitive goals. The
mean value of cognitive replication with three low
cognitive motivators (VSE, mean=5.26; HE,
mean=5.36; TR, mean=5.10) and high cognitive
motivators (VSE, mean=5.95; HE, mean=5.83; TR,
mean=6.19) are different as shown in Table 3. Except
7
Proceedings of the 43rd Hawaii International Conference on System Sciences - 2010
for heightened enjoyment (F=3.45, p=.066), the
differences between high and low cognitive motivators
are significant since each F-value (p-value) is 8.29
(.005) for virtual community self-efficacy and 23.5
(.000) for time resources.
Table 3. Means and (standard deviation)
scores for cognitive goals for cognitive
motivators
Dependent variables
Cognitive
Cognitive
replication
innovation
5.26 (1.27)
4.99 (1.26)
5.95 (1.05)
5.65 (0.96)
5.36 (1.24)
4.98(1.22)
5.83 (1.15)
5.61 (1.04)
5.10 (1.29)
4.84 (1.21)
6.19 (0.80)
5.86 (0.83)
Cognitive motivators
Virtual community
self-efficacy (VSE)
Heightened
enjoyment (HE)
Time resources
(TR)
Low
High
Low
High
Low
High
For cognitive innovation, the mean value between
low cognitive motivators (VSE, mean=4.99; HE,
mean=4.98; TR, mean=4.84) and high cognitive
motivators (VSE, mean=5.65; HE, mean=5.61; TR,
mean=5.86) are different as shown in Table 3. In
addition, the differences between low and high
cognitive motivators are significant since each F-value
(p-value) is 7.89 (.006) for virtual community selfefficacy, 7.23 (.008) for heightened enjoyment, and
22.32 (.000) for time resources.
Table 4. Results of the one-way ANOVA
analyses: between subject effects
Source
Cognitive replication
VSE
HE
TR
Cognitive innovation
VSE
HE
TR
SS
MS
F
p-value
11.28
4.94
27.75
11.28
4.94
27.75
8.29
3.45
23.5
.005
.066
.000
9.90
9.14
24.45
9.90
9.14
24.45
7.89
7.23
22.32
.006
.008
.000
These results support that the knowledge recipients
differently set and pursue their cognitive goals
depending on the extent to which they recognize the
salient factors such as virtual community self-efficacy,
heightened enjoyment, and time resources. However,
in order to empirically support H2a, H2b, and H2c,
which posited that cognitive motivators such as virtual
community self-efficacy, heightened enjoyment, and
time resources differently influence knowledge
recipient’s cognitive goal setting whether cognitive
replication or innovation, we need further data
collection and additional data analysis. Moreover, in
order to empirically support the hypotheses (H1a and
H1b) about the relationships between cognitive goal
and knowledge acquisition strategy, we need further
study as well.
6. Discussion
This study addressed the importance of the
knowledge recipient aspect by deepening the
understanding of knowledge acquisition. For this
purpose, we hypothesized that individuals would select
appropriate knowledge acquisition strategy – static or
dynamic acquisition strategy, depending on their
cognitive goals, and that these cognitive goals are
motivated by their salient cognitive factors – virtualcommunity self-efficacy, heightened enjoyment, and
time resources. The findings of this study partially
provided empirical support for the proposed conceptual
framework. In this view, the knowledge recipients’
cognitive factors play a key role in setting their
cognitive goals. These results can be important in that
knowledge recipients’ acquisition strategy can be
influenced by their cognitive goals, as described in the
conceptual framework.
6.1. Theoretical and practical implications
This study has contributed to both theory and
practice.
The present study extends the extant
knowledge management literatures, emphasizing the
knowledge recipients’ demand-side perspective. Prior
research has addressed the knowledge supply-side
question of how to encourage people to contribute or
share their knowledge with other people, whereas this
research gives an explication of the knowledge transfer
phenomenon from the knowledge recipients’
perspective. To accomplish this goal, this study
integrates two cognitive perspectives, social cognitive
theory and goal-setting theory, to develop insights into
the cognitive framework in which knowledge
recipients select their knowledge acquisition strategy.
We found that knowledge recipients’ cognitive factors
were more likely to motivate their cognitive goals
which have an impact on selecting how to acquire the
knowledge. Particularly, our framework explains that,
for optimally obtaining the knowledge needed for their
work, knowledge recipients set goals influenced by
their cognitions, and then they select their acquisition
strategy to attain their goals.
More specifically, the concept of knowledge
acquisition strategy used in this study contributes to
previous research on knowledge sourcing [15, 16] and
information seeking behavior [23]. That is because the
knowledge acquisition strategy highlights the
knowledge recipients’ intention for optimally acquiring
the knowledge required in their work, as previously
8
Proceedings of the 43rd Hawaii International Conference on System Sciences - 2010
determined by knowledge recipients’ goals before
actual knowledge acquisition occurs.
This study also has several practical implications.
First, virtual communities try to facilitate a social
network of knowledge providers and knowledge
recipients via Internet technologies, supporting various
knowledge sources such as wikis, blogs, bulletin
boards, search engine, and so forth. Most communities
to date have focused on how to stimulate knowledge
contribution in the communities.
However, our
conceptual framework shows the significance of
knowledge demand-side, highlighting the knowledge
recipients’ cognitive factors such as cognitive
motivators and goals. Thus, this research imply that
virtual communities should construct effective
knowledge structure, repositories, web design, and
management systems, complemented by the
knowledge demand-side.
Another practical implication is based on our
findings that the knowledge recipients’ cognitive
factors motivate individuals’ innovative goal-setting.
This implies that it is important for senior managers to
encourage individuals to facilitate more innovative
goals which lead to acquiring enough knowledge and
utilizing it well. That is because innovative goals
enable individuals to generate new combinations of
existing knowledge as combinative capability [26],
rather than to just accept only pre-existing knowledge.
As a result, such capability is used as strategic, useful
resource in a competitive environment.
6.2. Limitations and future research
This study is subject to several important
limitations. First, it is possible that there are other
factors to affect knowledge recipients’ goal-setting and
the selection of knowledge acquisition strategy. For
example, the absorptive capacity and task are nonnegligible motivating factors at the knowledge
demand-side.
Therefore, we encourage further
research that includes other possible factors
influencing the selection framework of knowledge
acquisition strategy. Second, since this study reported
only preliminary data analysis, additional analysis is
needed in order to deeply understand the selection
framework of knowledge acquisition strategy.
Furthermore, although a virtual community was
presented as an IS-related context, the proposed
framework does not contain IT artifacts in the concrete.
Therefore, we suggest that future research pays
attention to IT artifacts as differentiated from other
contexts.
7. Conclusion
Virtual communities provide individuals with
unprecedented sources for knowledge acquisition, even
though there are not previous social ties or direct
interactions with other people in the communities. In
this context, the current study presents a conceptual
framework that investigates how the knowledge
recipients select their acquisition strategy for
effectively acquiring the knowledge. Analyzing the
preliminary data, our findings suggest that the
knowledge recipients’ cognitive goals can be
differently determined by their cognitive motivators
such as virtual community self-efficacy, heightened
enjoyment, and time resources. However, in order to
exactly examine the hypotheses presented in this study,
further study is still needed.
8. References
[1] R. Agarwal and E. Karahanna, "Time flies when you're
having fun: Cognitive absorption and beliefs about
information technology usage", MIS Quarterly, 24 (2000), pp.
665-694.
[2] M. Alavi and D. E. Leidner, "Review: Knowledge
Management and Knowledge Management Systems:
Conceptual Foundations and Research Issues", MIS
Quarterly, 25 (2001), pp. 107-136.
[3] AMR, The Knowledge Management Spending Report
2007-2008, AMR Research Report, 2007.
[4] L. Argote and P. Ingram, "Knowledge Transfer: A Basis
for Competitive Advantage in Firms", Organizational
Behavior and Human Decision Processes, 82 (2000), pp.
150-169.
[5] L. Argote, B. McEvily and R. Reagans, "Managing
Knowledge in Organizations: An Integrative Framework and
Review of Emerging Themes", Management Science, 49
(2003), pp. 571-582.
[6] D. J. Armstrong and B. C. Hardgrave, "Understanding
mindshift learning: The transition to object-oriented
development", MIS Quarterly, 31 (2007), pp. 453-474.
[7] P. Babcock, "Shedding Light on Knowledge
Management", HR Magazine, 49 (2004), pp. 46-50.
[8] R. Bagozzi and U. M. Dholakia, "Intentional social action
in virtual communities", Interactive Marketing, 16 (2002), pp.
2-21.
[9] A. Bandura, "Human Agency in Social Cognitive
Theory", American Psychologist, 44 (1989), pp. 1175-1184.
[10] A. Bandura, "Social cognitive theory of self-regulation",
Organizational Behavior and Human Decision Processes, 50
(1991), pp. 248-287.
[11] C. M. Chiu, M. H. Hsu and E. T. G. Wang,
"Understanding knowledge sharing in virtual communities:
An integration of social capital and social cognitive theories",
Decision Support Systems, 42 (2006), pp. 1872-1888.
[12] D. W. Dorsey, G. E. Campbell, L. L. Foster and D. E.
Miles, "Assessing knowledge structures: Relations with
experience and post-training performance", Human
Performance, 12 (1999), pp. 31-57.
[13] J. E. Ettlie, W. P. Bridges and R. D. O'Keefe,
"Organization Strategy and Structural Differences for
9
Proceedings of the 43rd Hawaii International Conference on System Sciences - 2010
Radical versus Incremental Innovation", Management
Science, 30 (1984), pp. 682-695.
[14] M. E. Gist, C. Schwoerer and B. Rosen, "Effects of
alternative training methods on self-efficacy and performance
in computer software training", Journal of Applied
Psychology, 74 (1989), pp. 884-891.
[15] P. H. Gray and D. B. Meister, "Knowledge sourcing
effectiveness", Management Science, 50 (2004), pp. 821-834.
[16] P. H. Gray and D. B. Meister, "Knowledge sourcing
methods", Information & Management, 43 (2006), pp. 142156.
[17] B. Gu, P. Konana, B. Rajagopalan and H. M. Chen,
"Competition among virtual communities and user valuation:
The case of investing-related communities", Information
Systems Research, 18 (2007), pp. 68-85.
[18] L. Harasim, Online education: A new domain, in R.
Mason and A. Kaye, eds., Mindwave: Communication,
computers, and distance education, Pergamon Press, Oxford,
1989, pp. 50-62.
[19] R. G. Harris, "The Knowledge-Based Economy:
Intellectual Origins and New Economic Perspectives",
International Journal of Management Reviews, 3 (2001), pp.
21-40.
[20] M. Hsu, T. L. Ju, C. Yen and C. Chang, "Knowledge
sharing behavior in virtual communities: The relationship
between trust, self-efficacy, and outcome expectations",
International Journal of Human-Computer Studies, 65 (2007),
pp. 153-169.
[21] INPUT, Federal Knowledge Management MarketView
Report, INPUT, 2008.
[22] E. J. Johnson, J. W. Payne and J. R. Bettman, Adapting
to time constraints, in O. Svenson and A. J. Maule, eds.,
Time pressure and stress in human judgment and decision
making, Plenum, New York, 1993.
[23] J. D. Johnson, Information seeking: An organizational
dilemma, Quorum Books, Westport, CT, 1996.
[24] R. Kanfer and P. L. Ackerman, "Motivation and
cognitive abilities: An integrative/aptitude-treatment
interaction approach to skill acquisition", Journal of Applied
Psychology, 74 (1989), pp. 657-690.
[25] A. Kankanhalli, B. C. Y. Tan and K. Wei, "Contributing
Knowledge to Electronic Knowledge Repositories: An
Empirical Investigation", MIS Quarterly, 29 (2005), pp. 113143.
[26] B. Kogut and U. Zander, "Knowledge of the firm,
combinative capabilities, and the replication of technology",
Organization Science, 3 (1992), pp. 383-397.
[27] KPMG, Knowledge Management Research Report 2004
in Poland, KPMG Consulting Report, 2004.
[28] B. Levitt and J. G. March, "Organizational Learning",
Annual Review of Sociology, 14 (1988), pp. 319-340.
[29] L. Lin, X. Geng and A. B. Whinston, "A SenderReceiver Framework for Knowledge Transfer", MIS
Quarterly, 29 (2005), pp. 1-23.
[30] E. A. Locke, Generalizing from laboratory to field:
Ecological validity or abstraction of essential elements?, in E.
Locke, ed., Generalizing from laboratory to field settings,
Lexington Books, Heath and Company, Lexington: MA,
1986, pp. 1-9.
[31] E. A. Locke and G. P. Latham, Goal Setting Theory, in
H. F. O'Neil and M. Drillings, eds., Motivation: Theory and
Research, Taylor & Francis, Inc., 1994, pp. 13-29.
[32] A. Majchrzak, L. P. Cooper and O. E. Neece,
"Knowledge Reuse for Innovation", Management Science, 50
(2004), pp. 174-188.
[33] T. R. Mitchell, H. Hopper, D. Daniels, J. George-Falvy
and L. R. James, "Predicting self-efficacy and performance
during skill acquisition", Journal of Applied Psychology, 79
(1994), pp. 506-517.
[34] J. Nunally, Psychometric Theory, McGraw-Hill, New
York, NY, 1978.
[35] L. Ordonez and L. Benson, "Decisions under time
pressure: How time constraint affects risky decision making",
Organizational Behavior and Human Decision Processes, 71
(1997), pp. 121-140.
[36] P. A. Pavlou and M. Fygenson, "Understanding and
predicting electronic commerce adoption: An extension of
the theory of planned behavior", MIS Quarterly, 30 (2006),
pp. 115-143.
[37] C. C. Pinder, Work Motivation: Theory, Issues, and
Applications, Scott, Glenville, IL, 1984.
[38] F. Prenkert, "A Theory of Organizing Informed by
Activity Theory: The Locus of Paradox, Sources of Change,
and Challenge to Management", Journal of Organizational
Change, 19 (2006), pp. 471-490.
[39] N. R. Quigley, P. E. Tesluk, E. A. Locke and K. M.
Bartol, "A Multilevel Investigation of the Motivational
Mechanisms Underlying Knowledge Sharing and
Performance", Organization Science, 18 (2007), pp. 71-88.
[40] C. Ryu, Y. J. Kim, A. Chaudhury and H. R. Rao,
"Knowledge Acquisition via Three Learning Processes in
Enterprise Information Portals: Learning-by-investment,
Learning-by-doing, and Learning-from-others", MIS
Quarterly, 29 (2005), pp. 245-278.
[41] V. Srinivasan and A. K. Basu, "The metric quality of
ordered categorical data", Management Science, 8 (1989), pp.
205-230.
[42] A. D. Stajkovic and F. Luthans, "Social Cognitive
Theory and Self-Efficacy: Going Beyond Traditional
Motivational and Behavioral Approach", Organizational
Dynamics, 26 (1998), pp. 62-74.
[43] R. Suri and K. B. Monroe, "The effects of time
constraints on consumers' judgments of prices and products",
Journal of Consumer Research, 30 (2003), pp. 92-104.
[44] G. Szulanski, "Exploring Internal Stickness:
Impediments to the Transfer of Best Practice within the
Firm", Strategic Management Journal, 17 (1996), pp. 27-43.
[45] G. Szulanski, "The Process of Knowledge Transfer: A
Diachronic Analysis of Stickiness", Organizational Behavior
and Human Decision Processes, 82 (2000), pp. 9-27.
[46] F. B. Tan and M. G. Hunter, "The repertory grid
technique: A method for the study of cognition in
information systems", MIS Quarterly, 26 (2002), pp. 39-57.
[47] M. L. Tushman and P. Anderson, "Technological
discontinuities
and
organizational
environments",
Administrative Science Quarterly, 31 (1986), pp. 439-465.
[48] J. C. Zimmer, R. M. Henry and B. S. Butler,
"Determinants of the use of relational and nonrelational
information sources", Journal of Management Information
Systems, 24 (2008), pp. 297-331.
10