Fall 2009 Journal of Computer Information Systems 25

SOCIAL INFLUENCE ON
KNOWLEDGE WORKERS ADOPTION
OF INNOVATIVE INFORMATION TECHNOLOGY
HEE-DONG YANG YUN JI MOON CHRIS ROWLEY
Ewha Womans University Ewha Womans University City University
Seoul, South Korea Seoul, South Korea London, UK
ABSTRACT
User perceptions toward information technology (IT) are
crucial to its successful implementation. The purpose of our study
is to improve the understanding of the impact of social inuences
on different types of users perceptions and adoption of IT. To do
this, the study renes and expands the operationalization of the
social inuence construct to include four components: subjective
norm, image, visibility, and voluntariness. This is used to examine
inuences by type of user (knowledge worker versus university
student) and IT (innovative versus mature). The key nding is that
when knowledge workers consider adopting innovative IT they
are sensitive to general perceptions of its usefulness. The results
have implications for management enquiry and practice.
Keywords: IT, users, social inuence, subjective norm,
knowledge workers
1. INtroductIoN
The purpose of this study is to improve understanding of the
impact of social inuence on different types of users perceptions
and adoption of information technology (IT). To do this, the study
renes and expands the operationalization of the social inuence
construct to include four components: subjective norm, image,
visibility, and voluntariness. This is used to examine inuences
by type of IT (innovative versus mature) and user (knowledge
worker versus university student). This is an important topic as IT
is crucial to business and user perceptions towards IT are crucial
to its successful implementation. Also, in recent years much
economic growth has occurred in elds in which knowledge
workers are the key factor of production (Drucker, 1997; Cohen &
Levinthal, 1990; Prahalad & Hamel, 1990; Amit & Schoemaker,
1993; Hall, 1992).
The paper is organized as follows. In the next section issues and
problems in the area are outlined and the importance of social
inuence is discussed in light of IT adoption. The denition of
knowledge workers and their characteristics in task execution
and innovative IT is also discussed. The third section presents the
research model and hypotheses. The next section introduces the
research methodology, including data sampling, collection and
analysis methods. Tests of the research hypotheses are given in
the subsequent section. The last section summarizes the research
ndings and discusses implications of the results and concludes
the paper.
2. PROBLEMS IN STUDIES ABOUT
SOCIAL INFLUENCE ON IT ADOPTION
Research on the adoption and implementation of organizational
IT shows user attitudes toward the innovation are important to
success (Lucas, 1981). According to Innovation-Diffusion Theory
the rate at which an innovation is adopted is highly dependent
not only on the users beliefs toward that innovation (Rogers,
1983), but also on social inuence (Fulk, Steineld, & Power,
1987). However, empirical tests of social inuence (the subjective
norm) on attitudes toward IT have produced mixed results. While
Svenning (1982) found positive inuences from the subjective
norm on user attitudes to use, Pease (1988) found no inuence
(using the same video conferencing system). These controversies
are noticeable in the Technology Acceptance model. Debates
continue on the effect of the subjective norm on intention to use
IT, nding it either postitive (e.g., Cheung, Chang & Lai, 2000;
Taylor & Todd, 1995; Thomson, Higgins & Howell, 1991) or
negative (e.g., Chau & Hu, 2002; Davis, Bagozzi, & Warshaw,
1989; Mathieson, 1991).
The reasons for these contradictory results include the
following. First, social inuence theories in IT research fail to
provide explicit and exact denitions of social inuence (Rice &
Aydin, 1991). For example, the subjective norm (the representative
concept of social inuence) can cover both the injunctive norm
(meaning what signicant others think the person ought to
do) and the descriptive norm (meaning what signicant others
themselves do) (Rivis & Sheeran, 2003), and studies are not
consistent in their choice.
Second, the referents of social inuence are not clearly
dened. Social inuence means that socially referent others can
inuence workers perceptions of, and reactions to, jobs (Shaw,
1980). However, in this denition it is not clear who the socially
referent others are.
Third, the confusing results about social inuence on intention
to use imply that various conditions or mechanisms are at work
(Davis et al, 1989). One of the possible conditions concerns user
characteristics such as demographics, job characteristics, IT
experiences, etc. Thus, this study looks at knowledge workers
to investigate whether they are sensitive to social inuence in
their internalization process of IT adoption. According to the
Technology Acceptance model the internalization process of
IT undergoes perceived usefulness (PU) and perceived ease-of-
use (PEU) that eventually lead to intention to adopt. Thus, to
investigate the internalization process of knowledge workers
IT adoption two comparative studies are conducted. First, the
internalization processes of knowledge and non-knowledge
workers are compared. Second, the adoption of innovative IT
versus mature IT are compared. With these comparisons it can be
identified more clearly how knowledge workers, who are anxious
to enhance the productivity of their ad hoc and unstructured tasks,
intend to adopt innovative IT for the sake of task productivity.
In summary, the objective of this study is to identify the
26 Journal of Computer Information Systems Fall 2009
role of social inuence and the internalization process of users
in adopting innovative IT. Specically, this paper answers three
questions based on this objective:
1) What components make up the construct of social
infuence?
2) Will social inuence have a signicant effect on intention
to use through PU and PEU?
3) Do user characteristics (i.e., knowledge workers versus
non-knowledge workers) have a moderation effect
among social infuence, PEU and PU?
4) Do IT characteristics (i.e., innovative IT versus mature
IT) have a moderation effect among social inuence,
PEU and PU?
The investigation of such moderation effects can identify the
effect of social inuence on knowledge workers perception and
behavior as well as different internalization processes by different
user characteristics in adopting innovative IT. The ndings will
contribute to the formation of IT strategies to align IT with
knowledge workers productivity.
3. THEORETICAL BACKGROUND
3.1 Social Inuence
Perceptions of IT are likely to be inuenced by the objective
characteristics of the system, individual differences (such as past
experiences with similar systems), extent of use of the system
and occupational demands (e.g., Lucas, 1981; Rice & Shook,
1990). However, social inuence theories argue that individual
perceptions are also likely to be inuenced by the opinions,
information and behaviors of salient others (Salancik & Pfeffer,
1978) and socially referent others (the people whose opinions can
inuence others opinions and behaviors). From this perspective
several structural contexts inuence an individuals perceptions,
actions and experiences. The literature review shows that
individual beliefs and intentions to use IT are vulnerable to the
following four kinds of social inuence: subjective norm, image,
visibility, and voluntariness.
First, subjective norm is the most popularly measured
construct of social inuence in IT acceptance theories such as
the Technology Acceptance model, Reasoned Action theory
(Fishbein & Ajzen, 1975) and Planned Behavior theory (Ajzen,
1985). Individuals allow themselves to be inuenced by observing
others and/or seeking information from others, particularly for
uncertainty reduction. However, the actual source of greatest
inuence remains vague because there is no denitive way of
establishing the referent. Social Information Processing theory
postulates that the inuence of socially constructed meanings is
affected by factors such as the others credibility, status (Shaw,
1980) and perceived and/or informal power (Brass, 1984). From
these factors it can be inferred that the referent is a person who
has some power by virtue of some specic status and whose
trustworthiness has been proven through their past relationships.
Proximity is also a criterion for the referent and their signicance.
Proximity is the extent to which one could be exposed to social
information in a given social system and includes three elements:
relational, positional and spatial proximity (Rice & Aydin, 1991).
Hence, the referent of the subjective norm should be the person
with a professional reputation for trustworthiness and a history of
close relationships.
Second, image is the degree to which adoption of the
innovation is perceived to enhance ones image or status in
ones social system (Moore & Benbasat, 1991). The subjective
norm positively inuences image because if important members
of a persons social group at work believe they should perform
a behavior, execution of such performance can elevate their
standing within the group. Increased status within the group is
a basis of power and inuence, which in turn provides a general
basis for greater productivity. Thus, an individual may perceive
that using IT will lead to improved job performance, even though
benets result from image enhancement rather than the attributes
of the IT (Venkatesh & Davis, 2000; Pfeffer, 1982). Chau (1996)
also shows that the long-term usefulness of adopting IT socially
contributes to the elevation of individual status.
Third, visibility is the degree to which the innovation is visible
in the organization, so the more familiar a potential adopter is
with an innovation the more likely they are to adopt it (Moore
& Benbasat, 1991). Visibility is a closely related concept to
observability (Rogers, 1983) and critical mass (Markus, 1990).
These concepts denote that the dominant number of users in an
organization inuences a users perception and usage of IT.
Finally, voluntariness is the extent to which potential adopters
perceive the adoption decision to be non-mandatory (Rogers,
1983; Moore & Benbasat, 1991; Venkatesh & Davis, 2000).
Voluntariness makes the assumption that external pressure affects
IT adoption.
Further, this paper regards four social inuence components
as the formative indicators rather than reective indicators.
According to Jarvis, Mackenzie, & Podsakoff (2003), a construct
should be modeled as having formative indicators if the following
conditions prevail: (a) the indicators are viewed as dening
characteristics of the construct, (b) the indicators do not necessarily
share a common theme, (c) eliminating an indicator may alter the
conceptual domain of the construct and (d) a change in the value
of one of the indicators is not necessarily expected to be associated
with a change in all of the other indicators. Four social inuence
components subjective norm, image, visibility and voluntariness
are used in combination to dene the social inuence construct.
In other words, each indicator is needed to dene characteristics
of the social inuence construct, not to manifest social inuence.
Also, each indicator does not have the same/similar content and
dropping one of the indicators would alter the conceptual domain
of the social inuence construct. Moreover, a change in one of
the indicators would not be associated with changes in the other
three indicators. Considering theses conditions, therefore, the
social inuence construct should be modeled as having formative
indicators.
3.2 Knowledge Workers
Knowledge workers quickly identify the value of knowledge
and apply it in the interest of productivity (Nonaka, Toyama,
& Konno, 2000). A knowledge worker is a different kind of
employee, characterized by being paid not to create, produce or
manage a tangible product and/or service, but rather to gather,
develop, process and apply information that generates protability
to the enterprise (Smith & Rupp, 2004). As Amar (2002) suggests,
typical knowledge workers are complex individuals who bring
unique skills, intelligence and work methods to the workplace.
Knowledge work is also cognitive rather than physical and
constitutes a high mental activity with specic work characteristics
(Davis, 2002; Pradip & Sahu, 1989). On the one hand, knowledge
Fall 2009 Journal of Computer Information Systems 27
workers are typically assumed to require some kind of experiences
which reinforce new ways of working and to keep learning from
(Ribire & Sitar, 2003). Evers & Menkhoff (2004) propose that
knowledge workers tend to distance themselves from academics
as the producers of innovative knowledge, but stress their own
experience. Taken together, the scope of knowledge workers
are likely to be determined by their specic task characteristics
(e.g., cognitive, self-decisive etc.) and certain level of work
experiences.
As a contrast to knowledge workers with such characteristics
we used university students. While they are involved with
knowledge work and will eventually turn into knowledge workers
in enterprises (Drucker, 1997), the major difference lies in
different task characteristics and work experiences. University
students are required to complete assignments whose solutions
or answers are already dened in very structured ways whereas
knowledge workers are required to make improvised and ad hoc
decisions in competition for unstructured future objectives. The
tasks of university students lack creativity and nimble judgment
and require them to nd out the expected right solutions (Johnson
& Levenberg, 1994). Regarding work experience, most university
students aged between 20~23 in South Korea usually have
no employment experience because of various reasons such
as military service and education policy. Although university
students would not be adequate as a non-knowledge group in all
contexts, it seems to be valid in this study.
Compared to university students, knowledge workers
proactively look forward to innovative IT that help increase their
productivity, enhance the quality of their work lives and improve
decision-making skills (Farhoomand & Drury, 2000). Knowledge
workers also create and share knowledge about IT usage to
perform their cognitive work. Knowledge workers demand easy
and rapid access to critical information to cope with dynamic
changes of business environments. Davis (2002) uses wireless
internet service as an innovative IT that satises such demands
of knowledge workers and insisted that they could successfully
enhance productivity by access to real-time transaction data and
management information.
4. RESEARCH AND HYPOTHESES
Banduras (1986) Social Learning theory and Salancik &
Pheffers (1978) Social Processing theory propose that interactions
with social agents control the effects of IT and that diverse beliefs
about, and uses of, IT converge in social systems (Fulk, 1993).
Social Learning theory predicts that coordinated behaviors and
meanings arise through social processes. Observational learning
occurs when individuals acquire cognitive skills or technologies
by observing the behavior of others. The observers then experience
an emotional reaction by receiving stimuli in processes and then
elicit similar behavior from others. This behavioral pattern is
not simply imitation, but considerable cognitive processing of
stimuli. Similar behavior and attitudes can be acquired through
social learning and the complex interplay with others (Fulk,
1993). Based on Social Processing theory, the conceptual model
was developed (see Figure 1).
4.1 Social Inuence versus PU, PEU and Intention
Davis et al. (1989) focus on two beliefs (PU and PEU) because
they represent the process and mechanism of internalization of the
characteristics of IT. All external variables must inuence PU and
PEU before they can lead to intention to use. External variables
include both the technical and non-technical characteristics of IT,
such as social inuence (Davis et. al., 1989). The subjective norm
has an indirect effect on intention to use IT through PU and PEU
(Warshaw, 1980). The subjective norm affects internalization of
IT because when one perceives that an important referent thinks
one should use a system, one incorporates the referents belief into
ones own belief structure (Warshaw, 1980). From the perspective
of image one recognizes usefulness if IT usage is believed to
enhance social status (Venkatesh & Davis, 2000). The argument
that late adopters PU and PEU are inuenced by surrounding
early adopters indicates the inuence of visibility (Fisher & Price,
1992). Voluntariness has a direct effect on users beliefs of IT
(Agarwal & Prasad, 1997). Therefore, the following hypotheses
are made.
H1: Social inuence has a signicant impact on the PU
of both innovative and mature IT in both knowledge
and non-knowledge groups.
H2: Social inuence has a signicant impact on the PEU
of both innovative and mature IT in both knowledge
and non-knowledge groups.
Some Technology Acceptance model studies include the
social inuence construct as an exogenous variable in the model
even though the path structure around social inuence has not
been uniform (Lucas & Spitler, 1999; Venkatesh & Davis, 2000).
Technology Acceptance model, Reasoned Action theory and
FIGurE 1. research Model
28 Journal of Computer Information Systems Fall 2009
Planned Behavior theory propose a direct relationship between
the subjective norm and intention to use. The fundamental reason
for the subjective norm is compliance (Fishbein & Ajzen, 1975).
Though individuals have positive attitudes towards IT, they can
accept the referents perspective. Also, when the expected results
of using IT are uncertain, users use IT in compliance with their
referent. Visibility inuences the diffusion of innovations (Moore
& Benbasat, 1991). Harris (1992) proposes that the more that
organizational culture accepts individual voluntariness, the
greater the intention to use IT. In this context it seems that the
social inuence of the subjective norm, image, visibility and
voluntariness inuence the intention to use IT. However, university
students lack the internalization of IT qualities and characteristics
compared to that of knowledge workers. Knowledge workers are
mature enough to be less likely to follow others blindly in using
IT whereas university students are more likely to be more easily
inuenced in their IT-using behavior. Therefore:
H3: Social inuence will positively inuence the intention
to use innovative and mature IT only in the non-
knowledge group.
4.2 Moderators of Social Inuence:
Knowledge Workers and IT Maturity
When knowledge workers adopt IT, environmental and cultural
elements tend to exert substantial inuence (Sviokla, 1996). These
elements include training and education, organizational structure,
relationships with co-workers and co-workers and supervisors
perceived worth of the system and voluntariness of IT adoption
(Charan, 2001; Etillie, 1983). Beliefs about IT are inuenced
through informal, verbal communication and personal training,
rather than solely through direct experience or formal channels.
This fact is associated with the proximity of the referents
inuential power.
Gefen & Straub (2000) contend that the inuential factors
on intention to use IT can be different according to the usage
objective of IT. For instance, PEU is important in using the
Internet for the purpose of communication or entertainment
whereas PU is important for work (Etillie, 1983; Gefen & Straub,
2000). Knowledge workers must consider more of the relevancy
to their tasks in making the intention to use IT. Instead of promptly
forging use intention, they seriously consider the usefulness and
relevancy of IT to their tasks and also welcome opinions of others
in this regard. Meanwhile, university students are more sensitive
to ease-of-use of the IT characteristics and opinions of others in
this respect.
Wireless internet service is the service that transmits voice,
data and multi-media wirelessly and has recently added mobility
by mobile internet technology. The number of registered users
of this service had reached 1.5 billion people globally in 2005
(i.e., one-fourth of the total population of the world). In enterprise
operations and processes, eld-oriented functions, such as sales
and marketing, logistics, distribution and insurance businesses,
have been very proactive in adopting wireless internet service.
It can be regarded as an innovative IT. Indeed, wireless internet
service interviews with three consultants in the area agreed it tted
the prole of a less mature and innovative IT. Although wireless
internet service is in the immature phase and has an uncertain
future, the use of wireless internet service is a hot topic. Social
pressure may inuence non-users of wireless internet service
to believe that they have been left behind (Cheung et al., 2000).
Therefore, the following hypotheses specically in the context of
wireless internet service are developed.
H4: Social inuence will have a more signicant impact
on knowledge workers PU of innovative IT than
upon non-knowledge workers.
H5: Social inuence will have a more signicant impact
on a non-knowledge workers PEU of innovative IT
than upon knowledge workers.
In contrast, spreadsheets are used to represent a mature IT for
several reasons. According to the Technology Acceptance model
(McFarlan, McKenny, & Pyburn, 1983) there are four levels
of IT maturity: 1) technology identication and investment;
2) technology learning and adaptation; 3) rationalization
and management control; 4) widespread technology transfer.
Knowledge workers have considerable experiences with
spreadsheets. IT means the technology corresponds to the
Technology Acceptance Models fourth phase. Perceptions about,
as well as diffusion of, spreadsheets are higher than for wireless
internet service.
Technology innovation studies insist that technical utility
is the major concern in early stages of innovative technology,
whereas complementary features (mainly related to ease-of-
use) come afterwards (Anderson & Tushman, 1990; Schilling,
2005; Utterback & Abernathy, 1975). This argument is actually
opposite to that of the Technology Acceptance model where PEU
matters rst and leads to PU afterwards. However, it is naive
to insist that PEU always matters prior to PU in every context
because when relevancy to tasks is the major concern (as for
knowledge workers), the sequence of priority between PU and
PEU can be the reverse of the Technology Acceptance Model
argument. For instance, according to IDC and Delphi Group their
knowledge workers spend about 25% of their daily working hours
on knowledge or information retrieval (Business Wire, 2001).
Within such functions, knowledge workers would put up with
uncomfortable or inconvenient IT qualities only if it can enhance
task performances. Therefore:
H6: Social inuence will have a more signicant impact
on knowledge workers PU of immature IT than of
mature IT.
H7: Social inuence will have a more signicant impact
on knowledge workers PEU of mature IT than
immature IT.
5. RESEARCH METHOD
5.1 Data Collection
In recent empirical studies knowledge workers have been
dened and measured in two ways. First, by job category, and
then compared with people in non-knowledge working jobs (Tam,
Maret, & Stephen, 2002). However, this method lacks reliability
in job categories. For example, Elkajaer (2000) categorizes
medical doctors as general experts who do not need creativity
whereas Flood, Turner, Ramamoorthy, & Pearson (2001) include
doctors as knowledge workers. Second, Sahraoui (2001) suggests
selecting knowledge workers by self-perception (self-awareness)
because it is hard to dene the prole of the knowledge worker
population. There is no professional list that would ensure that
knowledge workers would be particularly targeted. Therefore,
Fall 2009 Journal of Computer Information Systems 29
convenience sampling through self-perception proved to be a
satisfactory alternative in this study.
In order to measure self-awareness as knowledge workers we
developed the following four measurement items were developed:
1) I am free from any interference in making decisions in my
job; 2) I have my own methodology or specic knowledge to
solve problems related to my work; 3) I perform more cognitive
and mental work than physical work; 4) My productivity makes
a large contribution to my organizations competitive advantage.
Workers were asked for their self-perception on these. Only the
samples of knowledge workers whose scores were beyond the
average were included.
The target population was any organization across various
industries using wireless internet service wireless internet service
at work in South Korea. The list of the Korea Information Society
Development Institute and IT Research and Consulting (A white
book on IT, 2004) were consulted rst. These two organizations
survey annual IT investment and usage of enterprises listed
on the Korean Stock Exchange. The survey contains a list of
organizations using wireless internet service at work in Korea and
shows that wireless internet service is predominantly used in sales
and logistics departments. For example, sales persons can gain
access to data stored on the server computers of headquarters and
employees can measure the payment or usage records of facilities
and automatically report or identify the current location of vehicle
drivers.
Before distributing questionnaires a pilot test was conducted
by randomly selecting ve rms representative of ve industries
(telecommunications, mobile, nance/insurance, logistics,
manufacturing) and contacting employees in their sales or
logistics departments by telephone. Employees were asked if they
used wireless internet service in such activities as mobile ofces,
telemetry and mobile tracking. This allowed the content validity
of the survey questionnaire to be veried.
From these ve industries 100 rms listed on the Korean
Stock Exchange were randomly selected. Questionnaires to
250 of their employees in sales and logistics departments were
randomly distributed by e-mail. Some 162 responses from
42 rms (a response rate of 64.8%) were received. Following
elimination of 9 responses that had below average scores in
four questions related to knowledge workers self-perception
on the degree of their work expertise, 154 replies were left
for statistical analysis. Of these 31.2% were in telecommuni-
cations, 22.1% in nance/insurance, 16.9% in logistics, 11.7%
in manufacturing, 9.7% in mobile, and 8.4% in others. The ma-
jority of respondents were assistant mangers (34.4%), followed
by managers (28.6%), IT technicians (20.1%), executives
(11.0%), CEOs (1.3%), with a few missing their titles (4.5%).
The majority of respondents job tenure fell within the range
of 6-10 years (46.8%), with a few under 5 years (N = 38,
24.7%).
Next the non-knowledge workers (university students) were
surveyed. The authors asked class students and students alumni
in college (Yonsei University and Ewha Womans University) in
Seoul, South Korea) to ll out the survey. The authors collected
the survey data. The faculty explained the purpose of the
study and gave instructions on how to ll out the question-
naire in their class. Some 197 questionnaires were returned
out of the 250 sample. Most of students were majoring in
MIS, engineering and other computer-related disciplines. The
students had already taken, or were taking, courses related to
computers, database and programming languages and were
quite familiar with wireless internet service. Most students
were between the ages of 20-23. Students who were older had
completed their military service. As is typical in South Korea, no
students in our sample had a job. In sum, the university student
sample is quite different from knowledge workers in terms of
employment status and age.
tABLE 1: demographic characteristics of Samples
30 Journal of Computer Information Systems Fall 2009
5.2 Questionnaires
Most of the measurement items relating to social inuence
(subjective norm, image, visibility, voluntariness) were taken from
relevant studies. First, criteria of the referent for the subjective
norm are via Shaw (1980), Brass (1984) and Rice & Aydin (1991).
These criteria included: credibility (i.e. trust), status (i.e., equal
or higher position), informal power, and relational proximity
(i.e. past experience). For each question respondents were asked
to indicate the extent of their agreement on a ve-point Likert
scale (1 = strongly disagree to 5 = strongly agree). The item for
credibility is: I trust the person who is signicant or inuential
to me. Status is measured by: The person who is signicant
or inuential to me has the position similar to or beyond mine.
Informal power is measured by: The person who is signicant
or inuential to me is acknowledged of authority and prestige in
my professional community and society. Relational proximity
is measured by the perceived frequency of ofcial task-related
meetings in a week with the person who is signicant or inuential
to the respondent.
The measurement items of the subjective norm were adopted
from Mathieson (1991) and Taylor & Todd (1995). These items
relate to the injunctive norm that has been popularly stressed in
studies. Measurement items of visibility, image and voluntariness
came from Moore & Benbasat (1991). The measurement items of
PU, PEU and intention to use were taken from Davis (1980) and
Venkatesh & Davis (2000).
1
6. RESULTS
In testing the hypotheses four different models were run by
PLS-Graph (version 3.0) and compared: two models (wireless
internet service and spreadsheet) of knowledge workers and
students. PLS was chosen because unlike other structural equation
modeling tools, such as EQS, AMOS and LISREL, PLS does
1. Measurement items are listed in the Appendix.
TABLE 2: Reliability and Correlation of Constructs
not require a large sample size (Barclay, Higgins, & Thomson,
1995; Chin, 1998). In order to ensure that the referent is indeed
the person who has power and specic status, only the surveys
whose average scores for the ve questions on the referent in
a questionnaire exceed 2.5 (the median of the 5 point scale)
were included. Finally, 23 invalid cases were excluded from the
knowledge worker samples, so 130 valid questionnaires were
analyzed. In the student samples 183 valid questionnaires out of
the 197 submitted were used.
6.1 Test of Measurement Model
The measurement model for each sample (knowledge workers
and university students) was tested separately by examining (1)
internal consistency, (2) convergent validity, (3) discriminant
validity. Internal consistency is examined using the composite
scale reliability index developed by Fornell & Larcker (1981),
which is similar to Cronbachs alpha. Fornell & Larcker
recommend using a criterion cut-off of .7 or .6. An examination
of internal consistency shows that all items in both groups satisfy
this criterion.
Convergent validity was addressed by examining the loadings
of the measures on their corresponding construct. In this case the
estimates of loadings for our four indicators (subjective norm,
image, visibility, and voluntariness) are the regression weights (or
coefcients). In the formative model the corresponding constructs
are estimated by the linear aggregates of their observed indicators.
Thus, the regression weights (or coefcients) can be used for the
judgment of convergent validity, in contrast to the component
loadings in the reective model (Chin, 1998).
A rule of thumb is to accept items with regression weights of
.7 or more (Barclay et al., 1995; Chin, 1998; Carmines & Zeller,
1979). However, it is also important to retain as many original
items as possible to preserve the original research design and to
compare the results with other studies that used the same scales.
Six items in the knowledge worker group model show weights
below 0.7. However, such low weights may also be the result of
the small sample size (Barclay et al., 1995; Yoo & Alavi, 2001).
Because these items exhibited the acceptable factor loading
Fall 2009 Journal of Computer Information Systems 31
scores in the other studies, these six items were included in the
nal analysis.
In PLS the discriminant validity of items is assessed by
criteria similar to multi-trait/multi-method analysis (Barclay
et al., 1995). One criterion is that the construct should share
more variance with its measure than with other constructs in a
model. To assess discriminant validity Fornell & Larcker sug-
gest using the measure of Average Variance Extracted. Table 2
shows the correlation matrix of all the constructs. For adequate
discriminant validity the diagonals should be greater than the
off-diagonals in the corresponding rows and columns and ex-
ceed .5 (Chin, 1998). Table 2 indicates that both samples meet
these criteria.
Another criterion for discriminant validity is that no
measurement item should load more highly on other constructs
than the construct it intends to measure. An examination of
factor and cross-factor loadings (Table 3) shows that all the
items except voluntariness satisfy this criterion for both samples.
So, voluntariness was dropped because it failed to meet this
condition.
Tests for moderation using PLS require separating samples
into groups where membership is based on some level of the
hypothesized moderator variable. Separate analyses are run for
each group and path coefcients are generated for each sub-
sample. Problems occur when PLS derives new factor loadings and
weights in separate analyses conducted in each sub-sample. The
construct-level scores are subsequently estimated using different
item weights in each sub-sample (Carte & Russell, 2003). The
matching constructs between two sub-samples should consist
of identical item weights. So, Carte & Russell (2003) proposed
Boxs M test to verify this concern. If the two groups reected
in the dummy coded Z variable (e.g., knowledge worker and
university students in our study) are independent, investigators
should test the null hypothesis that inter-item covariance matrices
within scales are equal using Boxs M test of equal covariance
matrices. The analyses of Boxs M test on three independent
variables (social inuence, PU, and PEU) show that inter-item
covariance matrices between knowledge worker and university
students are equal for both wireless internet service (Boxs
M = 2.610, p = .459) and spreadsheet technology (Boxs M =
5.680, p = .131).
6.2 Test of Structural Model
Figure 2 presents the results of the structural model for both
samples (knowledge workers and university students). To assess
the statistical signicance of the loadings and the path coefcients,
which are the standardized bs, a bootstrap analysis was used
(Chin, 1998).
The results provide support for social inuence on user beliefs
on PU and PEU. For both groups it is hypothesized that social
inuence would have a positive impact on PU and PEU of both
knowledge and non-knowledge groups (H1 and H2). The results
of the PLS analysis show that social inuence signicantly
affects PU and PEU in both groups. The direct path from social
inuence to intention to use is signicant only for students (H3).
As such, H1, H2 and H3 are supported. These ndings suggest
that the indirect impact of social inuence on intention to use
through internalization processes (i.e., PU and PEU) is explicit
for knowledge workers whereas the direct impact on intention to
use is not signicant for knowledge workers.
It is hypothesized that social inuence affects knowledge
workers PU more strongly than that of students in regards to
innovative IT, whereas PEU demonstrates the opposite situation.
To test these hypotheses two unpaired t-tests, as in Keil, Tan,
Wei & Saarinen (2000), were conducted (Table 4). Hypotheses
on social inuence (H4, H5) could be tested by statistically
comparing the corresponding path coefcients in these structural
models. Hence, H4 and H5 were tested by statistically comparing
the path coefcients from social inuence to PU and PEU in the
structural model for knowledge workers with the corresponding
path coefcients in the structural model for students. This
statistical comparison was carried out using the following
procedure (suggested by Keil et al., 2000).
TABLE 3: Loading & Cross-loading of Measures
32 Journal of Computer Information Systems Fall 2009
Where S
pooled
= pooled estimator for the variance
t = t-statistic with N
1
+

N
1
2 degrees of
freedom
N
i
= sample size of dataset for culture i
SE
i
= standard error of path in structural
model of culture i
PC
i
= path coefcient in structural model of
culture i
Results showed that the path coefcient from social
inuence to PU for wireless internet service in the
structural model for knowledge workers was signicantly
stronger than the corresponding path coefcient in the
structural model for university students, supporting H4
(t = 31.96, p < 0.01). Also, as hypothesized, social
inuence of knowledge workers on PEU yielded a
signicantly stronger inverse relationship. The paths
from social inuence to PEU demonstrated that the path
coefcient from social inuence to PEU of wireless
internet service (t = -13.228, p < .001) for university
students was signicantly stronger than the corresponding
path coefcient for knowledge workers. Social inuence
has a more profound inuence on university students
PEU than as knowledge workers, supporting H5.
Moreover, to examine whether the effects of social
inuence on knowledge workers beliefs (PU and PEU)
are moderated by IT maturity, another round of unpaired
t-tests was conducted. The beta coefcients from social
inuence to PU (H6) and PEU (H7) between the two
ITs in the knowledge worker group were compared.
The results support H6. Social inuence has a more
signicant impact on the PU of wireless internet service
than spreadsheets for knowledge workers (t = 21.237,
p < 0.01). Hence, the social inuence of knowledge
workers on PEU yielded a signicantly stronger in-
verse relationship. Social inuence has a more pro-
found inuence on knowledge workers PEU about ma-
ture IT (t = -21.187, p < .001) than innovative IT,
supporting H7.
7. DISCUSSION
This study investigated the role of social inuence
on innovative IT adoption by knowledge workers with
reference to the Technology Acceptance model. Most
Technology Acceptance model studies use social
inuence interchangeably as the subjective norm.
However, the denition of social inuence is expanded
to include three more constructs: image, visibility,
and voluntariness. Rening the components of social
inuence is a prior step to identifying the genuine role
of social inuence on IT adoption. Besides, the criterion
of referent for the subjective norm is set so that only
inuential peoples opinions can be reected in the
assessment of the subjective norm. Also, a contingency
approach (Woodward, 1958; Fiedler, 1967; Lawrence &
Lorsh, 1967; Thompson, 1967) is taken regarding the role
FIGURE 2: Analysis Results
Fall 2009 Journal of Computer Information Systems 33
of social inuence, challenging the now traditional perspective
of whether social inuence makes an absolute impact on an
individuals IT adoption. So, whether social inuence is subject
to moderators (knowledge work and IT maturity) on the route to
impact on PU, PEU and intention to adopt IT was investigated.
It can be concluded that only the subjective norm, image and
visibility are appropriate operators of social inuence, whereas
voluntariness is not. The representative component of social
inuence, the subjective norm, can be measured more objectively
with four criteria of referent: credibility, status, informal power,
and relational proximity. It is also found that social inuence is
subject to user characteristics and IT maturity. User characteristics
can be further broken down to the purpose of IT usage (Gefen &
Straub, 2000) and to the users task characteristics.
Regarding task characteristics, knowledge workers are required
more to make nimble judgment, discern the value of overloaded
information and make decisions against unstructured business
problems. For example, the nancial consultants in insurance
companies in the sample acquire information on product options
by using mobile technology to access the organizations database,
compare those options, and make suggestions to t the clients
circumstances during the initial contacts with clients.
Contrary to this situation and characteristics of users, university
students tend to use spreadsheet software to solve the questions
assigned by lecturers. The right solutions and processes already
exist for such questions and students need to apply them to solve
other or similar questions after getting familiar with such software.
Students use wireless internet service only for simple retrieval of
information that helps in assignment or communication with team
mates to arrange meetings to conduct projects.
In short, these two groups of people are quite different in
terms of task characteristics, such as structuredness and creativity.
These differences may be related to some unique characteristics
of the national culture of our research context (South Korea).
However, due to such different task characteristics, knowledge
workers and university students have different requirements for
IT. Knowledge workers look to PU of innovative IT for the sake
of effective decision making, whereas students concerned with
PEU because they want to learn and use innovative IT more easily
because they know the right solutions already exist. As knowledge
TABLE 4: Analysis Results
34 Journal of Computer Information Systems Fall 2009
workers are concerned more about PU in adopting innovative ITs,
they are also more sensitive to social inuence in regards to PU
rather than to PEU.
Besides, as Gefen & Straub insist (2000) note, the purpose
of IT usage is found to be another critical context where social
inuence matters in IT adoption. In this study, knowledge workers
are likely to use wireless internet service for business purposes,
not for mere exchange of information or for entertainment, as
used for by university students. Therefore, knowledge workers
prudently consider the usefulness of IT for their own tasks rather
than blindly adopting and are open minded to others opinions
in regard to the usefulness of IT. IT maturity does matter for
knowledge workers adoption of IT. Thus, immature IT is not well
proven in terms of usefulness. Therefore, knowledge workers
must look for others opinions on the usefulness of immature IT
with the focus on whether such new IT can enhance current task
performance.
While social inuence is a notable factor in understanding
an individuals adoption of IT, intense discussion of the
operationalization of social inuence has been omitted from the
Technology Acceptance Model and related research. Specically,
no investigation has been conducted on the condition and
mechanism governing the impact of social inuence on usage
behavior (Venkatesh & Davis, 2000). The proposition here to
integrate image, visibility, subject norms and voluntariness for
social inuence addresses this gap in the literature.
Furthermore, the major mitigating factors on the effects
of social inuence on PU, PEU and intention to adopt IT were
investigated. As knowledge becomes a core asset in organizations,
rms should understand the effects of knowledge workers
decision to adopt innovative IT. Such understanding can help
improve the productivity of knowledge workers because IT should
be adopted prior to realizing its promised value. Indeed, such an
understanding may well be reected in IT training programs or
diffusion strategies of innovative IT in organizations. These all
have practical implications for management.
Several directions for future research emerge from this study
(which may be seen as current limitations i.e., its single location
context). According to OECD reports in 2004, South Korea
continues to take the lead in broadband network development and
is ranked very high among OECD countries in regard to high-
speed web access. Therefore, we assume that most knowledge
workers and university students in South Korea have a similar
familiarity with the Internet or wireless internet service. The
difference between the groups lied in their reasons for using
wireless internet service. However, given the different types of
IT infrastructure, culture and student characteristics, the results
may not be duplicated in other countries. Therefore, a comparison
among countries of varying social structures and cultures can
provide more robust insight regarding the interplay between IT
and various social inuences. For example, it would be interesting
to compare which component of social inuence is stronger
between Asian and Western cultures. Future studies also can
investigate whether IT maturity and knowledge workers (versus
university students) can moderate the impact of social inuence
on innovative IT adoption.
8. CONCLUSION
This study investigated the process of how users (especially
knowledge workers) are inuenced by others (not necessarily
by system functions) in innovative IT adoption. We found that
knowledge workers care about others opinions on usefulness
of innovative IT. This nding suggests that new IT service pro-
viders need to develop and make good reference to socially
inuential people and ask them to endorse the usefulness of
such innovative IT. Academically, it cautions scholars not to
take a binary view about social inuence: i.e., does it matter
or not. Instead, it argues that social inuence works in certain
cases and identifies when it does matter. People cannot avoid
others attitudes and opinions on their own decisions because
they voluntarily look for references, especially when faced with
uncertainties. IT service providers should be concerned about
general perceptions and opinions coming from user experiences
as well as the improvements in functions. However, people
(especially knowledge workers) do not listen to all of the others
voices. Identifying what are the sensitive opinions is the key
to taking advantage of peoples psychology to allude to social
inuence in their decision-making. In short, this research pro-
vides useful implications for audiences in the conceptual
(theoretical developments and empirical data) and practical
aspects (for a range of groups, from workers to their employers
and IT developers and providers).
REFERENCES
Agrawal, R., & Prasad, J. 1997. The role of innovation
characteristics and perceived voluntariness in the acceptance
of information technologies. Decision Science, 28, 557-
582.
Ajzen, I. 1985. From intentions to actions: A theory of planned
behavior. In J. Kuhl, & J. Beckman (Eds.), Action control:
From cognition to behavior (pp.11-39). New York: Springer-
Verlag.
Amar, A.D. 2002. Managing knowledge workers. New York:
Quorum.
Amit, R., & Schoemaker, P.J.H. 1993. Strategic assets and
organizational rent. Strategic Management Journal, 14, 33-
46.
Anderson, P., & Tushman, M. 1990. Technological discontinuities
and dominant designs: A cyclical model of technological
change. Administrative Science Quarterly, 35, 604-634.
Bandura, A. 1986. Social foundations of thought and action.
Englewood Cliffs, NJ: Prentice-Hall.
Barclay, D., Higgins, C., & Thomson, R. 1995. The partial least
squares (PLS) approach to causal modeling: Personal computer
adoption and use as an illustration. Technology Studies, 2, 2,
285-309.
Brass, D.J. 1984. Being in the right place: A structural analysis
of individual inuence in an organization. Administrative
Science Quarterly, 29, 518-539.
Business Wire. Feb 2001. Working council of CIOs.
Carmines, E.G., & Zeller, R.A. 1979. Reliability and validity
assessment. Sage university paper series on quantitative
applications in the social sciences, California: Sage, Beverley
Hills.
Carte, T., & Russell, C. 2003. In pursuit of moderation: Nine
common errors and their solutions. MIS Quarterly, 27, 3, 479-
501.
Charan, R. 2001. Conquering a culture of indecision. Harvard
Business Review, 74-82.
Chau, P.Y.K. 1996. An empirical assessment of a modied
technology acceptance model. Journal of Management
Information Systems , 13, 2, 185-204.
Fall 2009 Journal of Computer Information Systems 35
Chau, P.Y.K., & Hu, P.J-H. 2002. Investigating healthcare
professionals decision to accept telemedicine technology:
An empirical test of competing theories. Information &
Management, 39, 4, 297-311.
Cheung, W., Chang, M.K., & Lai, V.S. 2000. Prediction of Internet
and world wide web usage at work: A test of and extended
Triandis model. Decision Support Systems, 30, 83-100.
Chin, W.W. 1998. Issues and opinion on structural equation
modeling. MIS Quarterly, vii-xvi.
Cohen, W., & Levinthal, D. 1990. Absorptive capacity: A new
perspective on learning and innovation. Administrative
Science Quarterly, 35, 128-152.
Davis, F.D., Bagozzi, R., & Warshaw, P.R. 1989. User acceptance
of computer technology: A comparison of two theoretical
models. Management Science, 35, 8, 982-1003.
Davis, G.B. 2002. Anytime/anyplace computing and the future
of knowledge work. Communications of the ACM , 45, 12,
67-73.
Drucker, P.F. 1997. The future that has already happened. Harvard
Business Review, 75, 5, 20-24.
Elkjaer, B. 2000. Learning and getting to know: the case of
knowledge worker. Human Resource Development Inter-
national, 3, 3, 343~359.
Etillie, J.E. 1983. A note on the relationship between managerial
change sector rms. R&D Management, 13, 231-244.
Evers, H-D., & Menkhoff, T. 2004. Expert knowledge and the
role of consultants in an emerging knowledge-based economy.
Human Systems Management, 23, 123-135.
Farhoomand, A.F., & Drury, D.H. 2000. Managerial information
overload. Communication of ACM, 45, 10, 127-131.
Fiedler, F. 1967. A theory of leadership effectiveness, New York:
McGraw-Hill.
Fishbein, M., & Ajzen, I. 1975. Belief, attitude, intention, and
behavior: An introduction to theory and research. Reading,
MA: Addison-Wesley.
Fisher, R.J., & Price, L.L. 1992. An investigation into the social
context of early adoption behavior. Journal of Consumer
Research, 19, 3, 477-486.
Flood, P.C., Turner, T., Ramamoorthy, N., & Pearson, J. 2001.
Causes and consequences of psychological contracts among
knowledge workers in the high technology and nancial
services industries. International Journal of Human Resource
Management, 12, 7, 1152-1165.
Fornell, C., & Lacker, D. 1981. Evaluating structural equation
models with unobservable variables and measurement error.
Journal of Marketing Research, 18, 921-950.
Fulk, J. 1993. Social construction of communication technology.
Academy of Management Journal, 36, 5, 921-950.
Fulk, J., Steineld, J.S., & Power, G. 1987. A social information
processing model of media in organizations. Communication
Research, 14, 529-552.
Gefen, D., & Straub, D.W. 2000. The relative importance of
perceived ease of use in IS adoption: A study of e-commerce
adoption. Journal of the Association for Information Systems,
1, 8, 1-28.
Hall, R. 1992. The strategic analysis of intangible resources.
Strategic Management Review, 13, 135-144.
Harris, R.W. 1992. Attitudes toward end-user computing:
A structural equation model. Behavior & Information
Technology, 18, 2, 109-125.
Jarvis, C.B., Mackenzie, S.B., & Podsakoff, P.M. 2003. A critical
review of construct indicators and measurement model
misspecication in marketing and consumer research. Journal
of Consumer Research, 30, September, 199-218.
Johnson, G.S., & Levenburg, N.M. 1994. The work team is not
the college professor. Journal of Education for Business, 69,
5, 303-307.
Keil, M., Tan, B.C.Y., Wei, K.K., & Saarinen, T. 2000. A cross-
cultural study on escalation of commitment behavior in
software projects. MIS Quarterly, 24, 2, 299-325.
Lawrence, P., & Lorsch, J. 1967. Organizations an environments,
Boston: Harvard University Press.
Lucas, H.Jr. 1981. Implementation: The key to successful
information systems. New York: Columbia Press.
Lucas, H.Jr., & Spiller, V.K. 1999. Technology use and per-
formance: A eld study of broker workstations. Decision
Science, 30, 2, 291-311.
Markus, M.L. 1990. Toward a critical mass theory of interactive
media. In J. Fulk, & C. Steineld (Eds.), Organizations and
communication technology (pp.194-218). Newbury Park, CA:
Sage.
Mathieson, K. 1991. Predicting user intentions: Comparing
the technology of planned behavior. Information Systems
Research, 2, 3, 173-191.
McFarlan, F.W., McKenny, J.L., & Pyburn, P. 1983. Information
archipelago-plotting a course. Harvard Business Review, 61,
145-156.
Moore, G.C., & Benbasat, I. 1991. Development of an instrument
to measure the perceptions of adopting an information
technology innovation. Information Systems Research, 2, 3,
192-222.
Nonaka, I., Toyama, R., & Konno, N. 2000. SECI, ba and
leadership: A unied model of dynamic knowledge creation.
Long Range Planning, 33, 5-34.
Pease, P. 1988. Factors inuencing the actual use of video
conferencing by managers for organizational communication.
Unpulished Ph.D dissertation, University of Southern
California.
Pradip, K.R., & Sahu, S. 1989. The measurement and evaluation of
white-collar productivity. Journal of Operations & Production
Management, 9, 4, 28-48.
Prahalad, C.K., & Hamel, G. 1990. The core competence of the
corporation. Harvard Business Review, 79-91.
Pfeffer, J. 1982. Organizations and organization theory.
Marsheld, MA: Pitman.
Ribire, V.M., & Sitar, A.S. 2003. Critical role of leadership
in nurturing a knowledge-supporting culture. Knowledge
Management Research and Practice, 1, 1, 39-48.
Rice, R.E., & Aydin, C. 1991. Attitudes toward new organizational
technology: Network proximity as a mechanism for social
information processing. Administrative Science Quarterly, 35,
219-244.
Rice, R.E., & Shook, D. 1990. Relationships of job categories
and organizational levels to use of communication channels,
including electronic mail: A meta-analysis and extension.
Journal of Management Studies, 27, 195-229.
Rivis, A., & Sheeran, P. 2003. Descriptive norms as an additional
predictor in the theory of planned behavior: A meta-analysis.
Current Psychology, 22, 3, 218-233.
Rogers, E.M. 1983. Diffusion of innovations (3
rd
edition), New
York: Free Pass, Macmillan publishing.
Sahraoui, S. 2001. Harnessing knowledge workers participation
for IT planning effectiveness. Behavior & Information
Technology, 20, 1, 69~77.
36 Journal of Computer Information Systems Fall 2009
APPENDIX 1: Questionnaire Items
Social inuence:1
1 Subject norm
1. People who inuence my behavior would think that I should use WIS (or Spreadsheet software).
2. People who are important to me think that I should use WIS (or Spreadsheet software).
2 Visibility
a. In my organization, I see WIS (or Spreadsheet software) on many computers.
b. WIS (or Spreadsheet software) is very commonly used in my organization.
c. It is easy for me to observe others using WIS (or Spreadsheet software my organization.
3 Image
a. People in my organization who use WIS (or Spreadsheet software) are more desirable than those who do not.
b. People in my organization who use WIS (or Spreadsheet software) have a high capability than those who do not.
c. Using WIS (or Spreadsheet software) is an indicator of advanced level in MIS.
d. Because of my use of WIS (or Spreadsheet software), others in my organization see me a more valuable man than those of others.
4 Voluntaries
a. My use of WIS (or Spreadsheet software) is voluntary.
b. My supervisor does not require to me to use WIS (or Spreadsheet software).
c. Although it might be helpful, using WIS (or Spreadsheet software) is certainly not compulsory in my job.

Perceived Usefulness: 1
y 1 Using WIS (or Spreadsheet software) would increase my productivity in my job.
y 2 Using WIS (or Spreadsheet software) would improve my performance in my job.
y 3 Using WIS (or Spreadsheet software) would enhance my effectiveness in my job.
y 4 I would nd WIS (or Spreadsheet software) useful in my job.

Perceived Ease of use: 2
y 5 Learning to operate WIS (or Spreadsheet software) is easy for me.
y 6 I nd it easy to get WIS (or Spreadsheet software) to do what I want to do.
y 7 It would be easy for me to become skillful at using WIS (or Spreadsheet software).
y 8 I would nd WIS (or Spreadsheet software) easy to use.

Intention to Use: 3
y 9 Assuming I have access to WIS (or Spreadsheet software), I predict that I would use it.
Note: All items were measured on a 5-point Likert scale, where 1 = strongly disagree, 2 = somewhat disagree,
3 = neutral (neither disagree nor agree), 4 = somewhat agree, and 5 = strongly agree.
Salancik, G.R., & Pfeffer, J. 1978. A social information processing
approach to job attitudes and task design. Administrative
Science Quarterly, 23, 224-252.
Schilling, M. 2005. Strategic management of technological
innovation. Boston: McGraw-Hill.
Shaw, J.B. 1980. An information-processing approach to the
study of job design. Academy of Management Review, 5, 41-
48.
Smith A.D., & Rupp, W.T. 2004. Knowledge workers perceptions
of performance ratings. Journal of Workplace Learning, 16, 3,
146-166.
Svenning, L. 1982. Predispositions toward a telecommunication
innovation. Unpulished Ph.D dissertation, University of
Southern California.
Sviokla, J.J. 1996. Knowledge workers and radically new
technology. Sloan Management Review, 37, 4, 25-40.
Tam, Y.M., Maret, K., & Stephen, J.F. 2002. Organizational
& occupational commitment: Knowledge workers in
large corporations. Journal of Management Studies, 39, 6,
775~801.
Taylor, S., & Todd, P.A. 1995. Understanding information
technology usage: A test of competing models. Information
Systems Research, 42, 1, 85-92.
Thompson, J.D. 1967. Organizations and actions, New York:
McGraw-Hill.
Thompson, R.L., Higgins, C.A., & Howell, J.M. 1991. Personal
computing: Toward a conceptual model of utilization. MIS
Quarterly, 125-143.
Utterback, J.M., & Abernathy, W.J. 1975. A dynamic model of
process and product innovation. Omega, the International
Journal of Management Science, 3, 639-656.
Venkatesh, V., & Davis, F.D. 2000. A theoretical extension of the
technology acceptance model: Four longitudinal eld studies.
Management Science, 24, 2, 451-481.
Warshaw, P.R. 1980. A new model for predicting behavioral
intentions: An alternative to Fishbein. Journal of Marketing
Research, 17, 2, 153-172
Woodward, J. 1958. Management and technology, London:
HMSO.
Yoo, Y., & Alavi, M. 2001. Media and group cohesion: Relative
inuences on social presence, task, participation, and group
consensus. MIS Quarterly, 25, 3, 371-390.