an exploratory investigation of the antecedents and impact of internet usage: an individual...

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An exploratory investigation of the antecedentsand impact of internet usage: An individualperspectiveMurugan Anandarajan , Claire Simmers & Magid IgbariaPublished online: 08 Nov 2010.

To cite this article: Murugan Anandarajan , Claire Simmers & Magid Igbaria (2000) An exploratory investigationof the antecedents and impact of internet usage: An individual perspective, Behaviour & Information Technology,19:1, 69-85, DOI: 10.1080/014492900118803

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An exploratory investigation of the antecedentsand impact of internet usage: an individualperspective

MURUGAN ANANDARAJAN ² , CLAIRE SIMMERS ³ and MAGID IGBARIA§

² Department of Management, Drexel University, Philadelphia, PA 19104, USA; e-mail: ma33@ drexel.edu

³ Department of Management and Information Systems, St. Joseph’s University, Philadelphia, PA 19131, USA;

e-mail: simmers@ sju.edu

§Programs in Information Science, Claremont Graduate University, Claremont, CA 91711, USA;e-mail: igbariam@ cgs.edu

Abstract. Internet usage in the US workplace is increasing at aphenomenal rate. This exploratory study examines factorsin¯ uencing employee internet usage and individual perceptionsof the consequences of such usage. Using the Theory ofReasoned Behaviour, a questionnaire was designed andcirculated to part time MBA students in north-east UnitedStates. This preliminary study suggests that the personal factorsof web skills and playfulness are associated with perceivedinternet usefulness, the degree of internet usage, and have bothpositive (enhanced job characteristics, job satisfaction) andnegative (increased ine� ciency) impacts. Neither the personalvariables of age and gender nor any of the organizationalvariables are important antecedent variables. To those whoperceive the internet as intimidating, there was, understand-ably, less internet usage. Perceived usefulness was positivelyrelated to increased time of use and internet impacts.

In general, the ® ndings indicate that extending the researchon microcomputers to internet usage is a promising researchfocus. On the basis of this study, the leadership challenge is toharness the tremendous potential of the internet, working tocontrol and improve ine� ciencies while not discouraginginternet usage.

1. Introduction

The internet, the universal network of networks, is

having a dramatic impact on the scope of businessapplications and has become the foundation for the

world’s new information infrastructure. It is helping

businesses reduce costs, shorten product cycle times, and

market products and services more eŒectively.

Realizing the enormous potential of the internet,

many businesses have embraced it as a tool to help them

achieve a competitive edge. The United States Depart-

ment of Commerce reports that the number of

businesses maintaining websites has quadrupled since

1996, and that over 54% of American businesses haveaccess to the internet. This rapid growth has prompted

growing concerns about the impact of internet usage on

productivity and information system security. For

instance, a study conducted in a manufacturing ® rmfound that in a typical eight-hour working day, over

250,000 internet sites were accessed by a work force of

386 employees. Of particular concern to the organiza-

tion was the discovery that approximately 90% of the

accessed sites were non-work related (LaPlante 1997).

On the positive side, internet use has been shown toimprove employee productivity in terms of customer

service and information gathering. Organizations are

now attempting to come to grips with internet-related

issues such as employee productivity, security, and

policy, and are examining a wide spectrum of strategies

for managing this technology.This study examines the phenomenon of internet

usage. Although internet usage and microcomputer

usage appear to be the same because of similar

technological media (i.e. microcomputers), internetusage intensi® es productivity issues for an organization

by creating an interactive, open system with direct

contact between the organization and its external

environment. Microcomputer usage, on the other hand,

BEHAVIOUR & INFORMATION TECHNOLOGY, 2000, VOL. 19, NO. 1, 69± 85

Behaviour & Information TechnologyISSN 0144-929 X print/ISSN 1362-3001 online Ó 2000 Taylor & Francis Ltd

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is a more closed system, with usage usually con® ned to

within each organization. While many studies have

focused on the antecedent factors of microcomputer

usage, for instance Igbaria (1990, 1992 ), Harrison andRainer (1992), Webster and Martocchio (1995 ), Igbaria,

Parasuraman, and Baroudi (1996 ), little is known on the

antecedent in¯ uences and impact of internet usage. This

study begins to address this gap.

2. Theoretical framework

This empirical study is one of the early attempts to

examine the factors which in¯ uence an employee to use

the internet and the impact of such usage. The studyinvestigates the antecedent factors leading to the use of

the Internet, its usage, as well as the impact of the

Internet. The theoretical basis for this study is the

research by Fishbein and Ajzen (1975 ). The Theory of

Reasoned Action (TRA) indicates that the behaviour, inthis instance internet usage, is shaped by individual

perceptions and attitudes as well as by social in¯ uences.

The Technology Acceptance Model (TAM) which

focuses on the perceived usefulness of computer

technology extended the TRA. In the TAM, anindividual’s motivating factors for using computer

technology can be categorized into two groups, namely,

extrinsic motivators, such as perceived bene® ts and

social pressure, and intrinsic motivators such as enjoy-

ment and fun. The TAM model has been applied to

variety of IT technologies, for instance e-mail andgraphics (Davis 1989, 1992), voice-mail and word

processing (Adams, Nelson, and Todd 1992), and

spreadsheets (Mathieson 1991).

The research model examined in this study is

illustrated in ® gure 1. The model is comprised of fourmulti-dimensional sets of variables. The antecedent

factors are grouped by individual and organizational

factors. Individual factors are: age, gender, internet

skills, and internet playfulness. Organizational factors

are: social pressure, organizational support, and taskcharacteristics. A second set of factors, representing

beliefs and attitudes about the internet are: internet self-

e� cacy, internet attitudes, and internet satisfaction. The

third factor set in the model is internet usage and thefourth set is the impact of internet usage. In summary,

the purpose of this preliminary study is to explore

associations between potential antecedent factors, inter-

net usage, and eŒects of internet usage.

3. Method

3.1. Sample and procedure

A research questionnaire (Appendix ) was distributed

to 95 part time MBA students at a university in thenorth-eastern United States. Of the 80 returned ques-

tionnaires, 40 of the respondents had internet access at

their place of employment and answered the entire

questionnaire. Participation was voluntary and con-

® dential. Thirty-two (32) of the respondents were menand eight (8) were women. The majority of the

respondents (82% ) were between the ages of 26 and 38

years. Finance/Insurance/Real Estate organizations had

the largest representation with 32% . Half of the

respondents were either professionals (27% ) or middle

level managers (23% ).

3.2. Measures

3.2.1. Internet impact: Five self-reported indicatorswere used to measure the impact of internet usage: (i)

M. Anandarajan et al.70

AgeGenderInternet skillsInternet playfulness

Social pressureOrganizational supportTask characteristics

Internet attitudes and beliefsInternet self-efficacyAttitude towards internetInternet satisfaction

TimeFrequencyBusiness activitiesTypes of web pages

Internet usage

Impact of internetJob characteristicsJob satisfactionOverall productivityInefficiencySecurity threats

Individual

Organizational

ANTECEDENT FACTORS

Figure 1. Research model.

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changes in current job characteristics, (ii) job satisfac-

tion, (iii) overall productivity, (iv) changes in e� ciency,

and (v) threats to organizational security. The questions

used to measure the ® rst four indicators can be found inpart 7 of the survey (Appendix 1), and the last indicator

was measured in part 11, questions 11.6 to 11.8.

Spreading indicators of constructs throughout the

survey is often done to reduce response bias (DeVellis1991 ).

Job characteristics were evaluated using a ® ve-point

scale ranging from 1 (greatly decreased) to 5 (greatly

increased). Respondents were asked to evaluate how the

use of the internet had changed job characteristics such

as: importance of job’, `opportunity for advancement’ ,

`autonomy in the job’, and `variety in the job’. The itemswere suggested by prior work of Hackman and Oldham(1980) and high scores indicated that internet usage had

changed job characteristics.

Job satisfaction and productivity were assessed with

single item questions. Respondents were asked toevaluate if the internet had changed their overall job

satisfaction (question 7.7) and their overall productivity(question 7.8) on a scale of 1 (greatly decreased) to 5(greatly increased). Job satisfaction re¯ ected an indivi-

dual’s positive feelings about the job. Productivityreferred to an assessment of job outputs relative to job

inputs.

Ine� ciency was assessed by asking six questions (7.11

to 7.16) developed for this study. Although e� ciency

and productivity are correlated, they are conceptually

distinct. Productivity is used to refer to output relativeto input, while e� ciency is used to refer to actual output

relative to possible output (Stevenson 1993). Respon-

dents were asked to evaluate items such as: time to

complete work’, `wasted time’, and `weeding through

extraneous material’ , on a scale of 1 (greatly decreased)

to 5 (greatly increased). Question 7.15 (`Number of

projects in progress’ ) and question 7.16 (`Number of

projects completed’ ) were reverse coded so that higher

scores indicated increasing ine� ciency.

Security threats are major concerns of informationsystem managers when the internet is used (Lawton

1996 ). The measure of security threats, developed for

this study, assesses perceptions of the impact of system

viruses (question 11.6), unsecured web sites (question

11.7), and competitive intelligence (question 11.8). High

scores represented opinions that internet usage wasincreasingly a danger to organizationa l security.

3.2.2. Internet usage: Four indicators of internet

usage were used in this study. They were: (i) duration

of daily use of the internet at work; (ii) frequency of use;(iii) the extent of using the internet for a variety of

business activities; and (iv) the likelihood of accessing

diŒerent types of web pages while at work. Duration of

usage and frequency of usage were adapted from

previous studies of microcomputer usage (Cheney and

Dickson 1982; Igbaria, 1992, 1993; Igbaria et al. 1996 )

and were in part 8 of the survey. Activities and accessing

diŒerent types of web pages were indicators of internet

usage created for this study, and suggested by Cronin(1995). They were in part 3 of the questionnaire.

Time on the internet (question 8.1) was ascertained by

asking individuals to indicate the amount of time spent

on the internet per day, using a six-point scale ranging

from 1 (almost never) to 6 (more than three hours per

day).

Frequency of use (question 8.2) was measured on a six-

point scale ranging from 1 (less than once a month) to 6(several times a day).

Business activity usage was used as an indicator of

overall internet usage. Respondents were asked to

indicate, on a scale of 1 (not at all) to 5 (very extensive),

the extent to which they used the internet to performeight diŒerent business activities (questions 3.1 through

3.8): marketing, sales, purchasing, customer service,

administrative, communicating, information gathering,

and recruiting. This construct was developed for this

study, but LaPlante (1997 ) suggested the variety ofactivities. The responses to each item were totalled over

the eight activities to get a composite score of the extent

of usage. The scores could range from a high score of 40,

representing very extensive use, to a low score of eight,

representing no usage for any of the listed activities.

Types of webpages accessed at work was used as afourth indicator of internet usage and was also

developed for this study based on LaPlante (1997).

Individuals were asked to indicate how likely they were

to access 14 diŒerent types of web pages (questions 3.9

to 3.22), on a scale ranging from 1 (very likely) to 5 (veryunlikely). All of the items were reverse coded so that

high scores re¯ ected high likelihood of access.

3.2.3. Beliefs and attitudes: Internet self-e� cacy was

de® ned as an individual’s beliefs about his/her ability tocompetently use the internet. It is derived from the self-

e� cacy literature (Bandura, Adams, and Beyer 1977), as

well as from more recent work on computer self-e� cacy(Gist, Schwoerer, and Rosen 1989, Compeau and

Higgins 1995). Computer self-e� cacy perceptions have

been found to be associated with performance insoftware training (Gist et al. 1989, Webster and

Martocchio 1992, 1995), adoption of computers (Hill

et al. 1987), and innovations (Burkhardt and Brass

1990). Internet self-e� cacy was measured using a six-

item scale adapted from Hollenbeck and Brief (1987).Responses were rated on a ® ve-point scale ranging from

1 (strongly agree) to 5 (strongly disagree) and were

Impact of internet usage 71

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reverse coded so that high scores indicated high self-

e� cacy. These questions (5.1 through 5.6) included

statements like: I expect to become very pro® cient in the

use of the internet’ , and I feel con® dent that I can usethe internet’ .

Internet attitudes assess a person’s general beliefs

about the internet (Zolton and Chapanis 1982). The

scale was modi® ed from a nineteen-item scale used tooperationalize computer attitudes (Nickell and Pinto

1986 ), by substituting the internet’ for `computer’ . The

questions were placed in part 4 (questions 4.1 to 4.19).

Individuals were asked to evaluate how they felt about

the internet in general, using a ® ve-point scale ranging

from 1 (strongly agree) to 5 (strongly disagree).

Examples of the questions were: `Soon our lives willbe controlled by the internet’ , `Life will be easier and

faster with the internet’ , and I feel intimidated by the

internet’ .

Internet satisfaction was adapted from a scale

originally designed to measure microcomputer usersatisfaction (Doll and Torkzadeh 1988, Igbaria 1990,

1992 ). For this study the nine item scale was modi® ed to

provide a measure of the degree of satisfaction in the

way the internet met the user’s requirements for

information content, accuracy, output, format, ease ofuse, and timeliness. The questions to measure this

construct were in part 9, questions 9.1 to 9.9.

Respondents answered statements like: `The internet

provides the precise information I need’, `The internet is

accurate’ , and `The internet is easy to use’ , using a ® ve-

point Likert scale ranging from 1 (almost never) to 5(most often).

3.2.4. Organizational factors: Both social pressure

and organizational support were suggested by Fishbein

and Ajzen (1975 ) and were used in examining micro-computer usage (Igbaria et al. 1996 ). They were

modi® ed for this study by substituting internet for

microcomputer. They were both reverse coded so that

high scores indicated either strong organizational

support or strong social pressure.Social pressure was operationalized by a single item in

part 5, question 5.7. Individuals indicated their agree-

ment or disagreement with the following statement:

`Most people who are important to me in my job think I

should be using the internet regularly in my job’ . The

response options were anchored in a ® ve-point scale,ranging from 1 (strongly agree) to 5 (strongly disagree).

Organizational support assessed general support,

which included encouragement by top management

and allocation of adequate resources. Individuals were

asked to indicate the extent of agreement or disagree-ment with four statements (part 5, questions 5.8 through

5.11) on a ® ve-point scale ranging from 1 (strongly

agree) to 5 (strongly disagree). Examples of the

statements were: `Management has provided the neces-

sary help and resources to get me used to the internet

quickly’ , and I am always supported and encouraged bymy boss to use the internet in my job’.

Task characteristics refer to the amount of structure

and variety in the job itself and were operationalized

using an instrument from Withey, Daft, and Cooper(1983). The scale assessed the degree of structure and

variety of the work. In part 6 of the survey,

individuals were asked to indicate how well eight

statements described their jobs with questions like: `To

what extent is there a clearly known way to do the

major types of work you normally encounter?’ and

`To what extent is your work repetitious?’ A ® ve-pointLikert type scale ranging from 1 (very little extent) to

5 (to a very large extent) was used and high scores

indicated more structure and less variety in task

characteristics.

3.2.5. Individual factors: Age and gender were ascer-

tained using single-item questions. Gender was assessed

with a ® xed response item (1 = male; 2 = female). Age

consisted of seven levels from (1) under 25 to (7) over 66.

Demographic variables were included in part 1, thebackground section of the survey.

Internet playfulness was adopted from work on

microcomputer playfulness (Webster and Martocchio

1995 ). It represented a type of intellectual playfulness

and was de® ned as an individual characteristic that

described an individual’s tendency to interact sponta-neously, inventively, and imaginatively with the internet.

The measure was based on a seven-item scale developed

by Webster and Martocchio (1992). Individuals were

asked to characterize themselves when using the internet

for seven adjectives, such as: spontaneous’ , ` exible’ ,and `playful ’ . Respondents indicated their level of

agreement using ® ve-point scales ranging from 1(strongly disagree) to 5 (strongly agree). The items

formed part 10 of the survey. Four of them (questions

10.2, 10.6, and 10.7) were reverse coded such that highscores indicated high playfulness.

Internet skills were de® ned as a combination of users’

experience with the internet and the training they

obtained. Prior research on computer skills indicated a

diŒerentiation between experience and training (Igbaria

1992, 1993, Igbaria et al. 1996 ). In part 2, participantswere asked to indicate on a ® ve point scale from 1 (none )

to 5 (very extensive) the extent of their experience using

the internet for things such as: `accessing the internet’ ,

`creating web pages’ , and `programming in hypertext

based software’ . Individuals were also asked to report,on the same ® ve-point scale as above, the extent of

training in the internet they had received from the


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following sources: general courses, vendors, in-house

company courses, and self-study.

Self-report indicators are often used to operationalize

system use and impact, particularly where objectiveusage metrics are not readily available. Since partici-

pants in this study accessed the internet from a variety of

organizations , objective logs were not obtainable. Self-

reported usage and impact are not precise measures, butprior research suggests they are suitable as relative

measures (Blair and Burton 1987).

3.2. Data analysi s

The ® rst stage of data analysis consisted of factoranalyses to identify and con® rm factors and the

assessment of factor reliability. All the scales were

factor-analysed with the iterated principal components

method and varimax rotation (where applicable) using

the PC version of SPSS 8.0. The robustness of the scalewas assessed by internal consistency reliabilities as

indexed by Cronbach’s coe� cient alphas and were

within the guidelines recommended by Nunnally (1978).

Associations among the study variables depicted in

® gure 1 were examined by means of bivariate correla-tions to assess the degree of relationship between two

variables, with no inherent distinction between the

independent and dependent variables. A more complete

analysis of the model would have included path analysis

techniques such as hierarchical multiple regression, or

structural equation modelling. Both methods wouldhave identi® ed the network of relationships among the

model variables, but the small sample in this current

study precluded the use of these tests. Given the

exploratory nature of this study, coupled with the small

sample size, the focus of data analysis was on theoperationalization of the variables and the degree of

association among the study variables.

A total of nine (9) bivariate correlations were run.

Correlations of individual factors with internet beliefs

and attitudes, indicators of internet usage, and then theindicators of the impact of the internet were run. Next

the correlations of the organizational factors with

internet beliefs and attitudes, with the indicators of

internet usage, and then with the indicators of the

impact of the internet were performed. Third, correla-

tions were run with the internet attitudes and beliefs andinternet usage, and then the indicators of the impact of

the internet. Finally, correlations were run among the

indicators of internet usage and the indicators of the

impact of the internet. The series approach to the

correlations was taken because of the mediational modeland also due to the small sample size (n= 39) in relation

to the large number of variables. Additionally, a more

stringent p level was used to report signi® cant relation-

ships to counter the statistical distortion resulting from

a large number of variables and a small number of

respondents. Thus only those correlations greater than0.39, equivalent to p <0.01, were used to report

signi® cant results.

4. Results

4.1. Factor analyses and scale reliabilities

Table 1 presents the speci® c question numbers, the

factor structure, and reliability coe� cients for the multi-

item variables used in the model. Job satisfaction,productivity, time, frequency, business activities, social

pressure, gender, and age were single item measures.

4.1.1. Internet impact: Job characteristics was con-

® rmed as a single component with an eigenvalue of5.134 and with 73.3% of the variance explained. The

factor loadings ranged from 0.745 to 0.929. The eight

item scale was reduced to a seven item scale, with

question 7.3 (`Freedom in how to do the job’ ) deleted

before analysis because of a clerical error in the designof the question. The mean of the seven items was used as

a measure of changes in job characteristics and

Cronbach’ s reliability coe� cient was 0.94.

Ine� ciency was a single factor with an eigenvalue of

3.374 and with 56.2% of the variance explained. The

factor loadings ranged from 0.670 to 0.819. Given theseresults, the mean of answers to questions 7.11 to 7.16

was used to create a variable labelled ine� ciency’ .

Cronbach’ s reliability coe� cient was 0.84.

Security threats was validated as a single factor with

three items. The eigenvalue was 2.230 with 68.9% ofvariance explained. The mean was used for the variable

labelled security threats’ and the alpha was 0.74.

4.1.2. Internet usage: Types of web pages accessed

formed three distinct factors. The ® rst factor contained® ve types of web sites: (a) general interest (question

3.13), (b) arts and entertainment (question 3.16), (c)

travel and leisure (question 3.17), (d) living/consumer(question 3.19) and (e) sports (question 3.21). The

eigenvalue was 4.452 and explained 31.8% of the

variation. The mean score was used to create a scalelabelled `personal pages’ and the reliability coe� cient

was 0.90. The second factor contained four types of web

pages: competitors (question 3.9), government/research(question 3.10), business/ ® nancial (question 3.20) and

magazine or news (question 3.22). The eigenvalue was2.782, and the variance explained was 19.87% . The

mean of these items was used to create a scale re¯ ecting


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accessing work-related pages, and was labelled `work

pages’ and the reliability coe� cient was 0.78. The thirdfactor contained two items suppliers’ pages (question

3.14) and customers’ pages (question 3.15). This factor’ s

eigenvalue was 1.991 and explained 14.2% of the

variation. The mean of the two items created a variable

named `value chain pages’ and the reliability coe� cientwas the lowest in the study at 0.63. The total cumulative

variance explained by the three factors was 65.9% .

Accessing free software web pages (question 3.11),

educational web pages (question 3.12), and computer

web pages (question 3.18) did not clearly load on any

factor and were therefore deleted.

4.1.3. Beliefs and attitudes: Internet self-e� cacy was

also found to be one factor with an eigenvalue of 4.285

and variance explained of 71.4% . The factor loadings

ranged from 0.515 to 0.957. The mean of the six itemswas used to compose the variable named self-e� cacy’

and Cronbach’s alpha was 0.80.

Internet attitudes in this study had three embedded

factors, consistent with a computer attitudes scaledeveloped by Harrison and Rainer (1992 ). These factors

were labelled: `pessimism’ (belief that computers are

dominating and controlling humans), intimidation’(belief that computers are threatening), and `optimism’(belief that computers are helpful and useful). Factorone, called `pessimism’, consisted of ® ve items (questions

4.1, 4.2, 4.4, 4.6, and 4.7), the mean of which was used to

create a pessimism score. The eigenvalue was 3.379, the

percent of variation explained was 30.7, and the factor

loadings ranged from 0.599 to 0.884. The reliability

coe� cient was 0.84. Intimidation, consisting of threeitems (questions 4.174.19) had an eigenvalue of 2.047

with 18.6% of the variation explained, and with factor

loadings from 0.703 to 0.825. The scores were averaged

for an intimidation indicator, with an alpha of 0.74.

Factor three, labelled `optimism’, comprised three items(questions 4.94.11) which were averaged to produce an

optimism index (alpha = 0.76). The eigenvalue was


Table 1. Speci® c Question Numbers, Factor Structures, and Reliability Coe� cients Multi-Item Measures.

Variable Question Numbers Eigenvalues Percent ofVariance

ReliabilityCoe� cients

Internet impact1. Job characteristics 7.1, 7.2, 7.4 ± 7.6, 7.9, 7.10 5.134 73.3% 0.942. Ine� ciency 7.11 ± 7.14; 7.15 and 7.16

reverse coded3.374 56.2% 0.84

3. Security threats 11.6 ± 11.8 2.230 68.9% 0.74Internet usageTypes of web pagesa. Personal pages 3.13, 3.16, 3.17, 3.19, 3.21

(all reverse coded)4.452 31.8% 0.91

b. Work pages 3.9, 3.10, 3.20, 3.22 (allreverse coded)

2.782 19.8% 0.78

c. Value chain pages 3.14, 3.15 (reverse coded) 1.991 14.2% 0.63Beliefs and attitudes1. Self-e� cacy 5.1 ± 5.6 4.285 71.4% 0.802. Attitudes:

a. Pessimism 4.1, 4.2, 4.4, 4.6, 4.7 (reverse coded) 3.379 30.7% 0.84b. Intimidation 4.17 ± 4.19 (reverse coded) 2.047 18.6% 0.74c. Optimism 4.9 ± 4.11 (reverse coded) 2.250 20.5% 0.76

3. Internet satisfaction:a. Usefulness 9.1 ± 9.6, 9.9 4.368 48.5% 0.91b. Ease of use 9.7, 9.8 2.171 24.1% 0.94

Organizational factors1. Organizational support 5.8 ± 5.11 2.835 70.9% 0.862.Task characteristics:

a. Structure 6.1 ± 6.4 3.585 44.8% 0.81b. Less variety 6.5 ± 6.8 1.889 23.6% 0.85

Individual factors1. Playfulness 10.1 ± 10.7 4.608 65.8% 0.912. Internet skills:

a. Web page skills 2.4 ± 2.6 2.813 28.1% 0.96b. General internet skills 2.1 ± 2.3, 2.10 2.596 25.9% 0.80c. Formal training 2.7 ± 2.9 2.223 22.2% 0.79

Note: Job satisfaction, productivity, time, frequency, business activities, social pressure, gender, and age were single item measures.

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2.250, the variation explained was 20.5% and the factor

loadings ranged from 0.791 to 0.841. The three factors

accounted for 69.8% of the variance. In this study, the

nineteen items reduced to eleven. Items 4.3, 4.5, 4.8,4.124.16 did not clearly load on any factor and did not

add to the variance explained, therefore, in the interest

of parsimony, they were deleted.

Internet satisfaction was designed to measure satisfac-tion as a single construct, but the factor analysis

revealed two independent factors with eigenvalues in

excess of one. Factor one, labelled ùsefulness’ , con-

tained seven items (questions 9.19.6; 9.9) and the factor

loadings were 0.718 to 0.833. The second factor

embedded in internet satisfaction consisted of two items,

which were averaged as an indicator of ease of use,labelled èase of use’ and contained questions 9.7 and 9.8(factor loadings of 0.927 and 0.963). The eigenvalues

were 4.368 and 2.171 and percentages of variation

explained were 48.5% and 24.1% , with a cumulative

variance of 72.6% . The reliability coe� cient forùsefulness’ was 0.91 and for èase of use’ was 0.94.

4.1.4. Organizational factors: Organizational sup-

port was con® rmed as a single factor with factor

loadings ranging from 0.789 to 0.870, an eigenvalue of

2.835, and percent of variance explained at 70.9% . Thefour items were averaged to produce a scale termed

òrganizational support’ (alpha= 0.86).

Task characteristics were composed of two factors.

Responses on the four items in factor one (6.16.4 ) were

averaged to produce a measure of task structure, so that

a high score represented a highly structured job. Theeigenvalue was 3.585, the percent of variance was

44.8% , and the factor loadings ranged from 0.677 to

0.873. This factor was called structure’ and the

reliability coe� cient was 0.81. Responses to the four

questions, 6.5 ± 6.8, were averaged to produce a measureof task variety such that high scores measured less

variety in the job (alpha = 0.81) and was named less

variety’ .

4.1.5. Individual factors: Internet playfulness wasfound to be one factor, with an eigenvalue of 4.608,

percent of variance equalling 65.8% , and factor loadings

ranging from 0.699 to 0.853. The reliability coe� cient

was 0.91.Internet skills, in this study, were divided into three

indicators: (1) webpage skills, (2) general internet skills,

and (3) formal training. Factor one, labelled `webpage’ ,

contained three items: creating webpages, programming

in hypertext, and maintaining webpages. The mean of

the three items was used to create an index of web page

skills (alpha = 0.96). The eigenvalue was 2.813, thepercent of variance explained was 28.1% , and the factor

loadings were from 0.928 to 0.951. The second factor

embedded in the internet skills scale, labelled `general

internet skills’ , consisted of four items: accessing the

internet, using internet search engines, downloading ® lesfrom the internet, and self-taught training. The mean

was used as a measure of general internet skills and self-

training and the reliability coe� cient was 0.80. The

eigenvalue was 2.596, the variance explained was 25.9% ,

and the factor loadings were 0.653 to 0.873. The thirdfactor related to formal training from college courses,

vendors or outside consultants, and in-house company

courses, and was labelled, formal training’ (eigenva-

lue= 2.223; 22.2% of variance, factor loadings 0.762 to

0.900, and the alpha= 0.79). Cumulative percent of

variance explained was 76.3% .

4.2. Bivariate correlations

Among the individual factors, web skills were posi-tively associated with ease of use (r= 0.44, p < 0.01), time


Table 2.1. Bivariate correlations ± individual factors and internet attitudes and beliefs.

Mean Std.Dev

1 2 3 4 5 6 7 8 9 10 11 12

1. Age2. Gender3. Web skill4. Internet skill5. Formal training6. Internet playfulness7. Self-e� cacy8. Pessimism9. Intimidation10. Optimism11. Usefulness12. Ease of use

2.581.201.356.631.614.762.672.391.713.743.273.54

0.840.410.960.750.891.310.940.870.560.660.690.78

1.00Ð 0.12

0.13Ð 0.11

0.290.06

Ð 0.17Ð 0.28Ð 0.03Ð 0.13Ð 0.11Ð 0.17

1.00Ð 0.17

0.020.030.07

Ð 0.03Ð 0.03

0.110.10

Ð 0.14Ð 0.36

1.000.450.110.290.30

Ð 0.06Ð 0.21

0.080.260.44

1.000.160.28

Ð 0.390.12

Ð 0.180.070.270.38

1.000.25

Ð 0.03Ð 0.11

0.070.010.20

Ð 0.09

1.000.290.02

Ð 0.250.220.490.16

1.00Ð 0.35Ð 0.21

0.030.270.24

1.000.36

Ð 0.080.150.10

1.00Ð 0.14Ð 0.19Ð 0.24

1.00Ð 0.06Ð 0.04

1.000.39 1.00

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usage (r= 0.51, p < 0.001 ), and frequency of use (r= 0.41,

p <0.01). Internet playfulness had a positive relationship

with usefulness (r= 0.49, p < 0.001 ), and four of the ® ve

indicators of the impact of the internet: (1) jobcharacteristics (r= 0.64, p < 0.001 ), (2) ine� ciency(r= 0.45, p <0.01), (3) job satisfaction (r= 0.58,

p < 0.001), and (4) productivity (r= 0.64, p <0.001).

There was a negative association between gender and

likelihood of accessing work pages (r= Ð 0.62,

p < 0.001), i.e. women were less likely to access workpages. There was also a negative relationship between

internet skills and self-e� cacy (r= Ð 0.39, p < 0.01).


Table 2.2. Bivariate correlations ± individual factors and internet attitudes and beliefs.

Mean Std.Dev

1 2 3 4 5 6 7 8 9 10 11 12

1. Age2. Gender3. Web skill4. Internet skill5. Formal training6. Internet playfulness7. Time8. Frequency9. Business activities10. Personal pages11. Work pages12. Value chain pages

2.581.201.353.631.614.762.794.1316.923.033.133.08

0.840.410.930.750.891.311.001.385.121.261.121.33

1.00Ð 0.12

0.13Ð 0.11

0.290.060.080.230.00

Ð 0.110.310.01

1.00Ð 0.17

0.020.03

Ð 0.07Ð 0.22Ð 0.33Ð 0.28

0.14Ð 0.62Ð 0.01

1.000.450.110.290.510.410.280.000.160.11

1.000.160.280.270.320.230.04

Ð 0.17Ð 0.23

1.000.250.000.020.290.180.250.17

1.000.310.280.220.08

Ð 0.13Ð 0.13

1.000.630.360.000.13

Ð 0.01

1.000.430.120.170.12

1.000.340.130.28

1.000.280.30

1.000.48 1.00

Table 2.3. Bivariate correlations ± individual factors and impact of internet.

Mean Std.Dev

1 2 3 4 5 6 7 8 9 10 11

1. Age2. Gender3. Web skill4. Internet skill5. Formal training6. Internet playfulness7. Job characteristics8. Ine� ciency9. Security threats10. Job satisfaction11. Productivity

2.581.201.353.631.614.763.182.972.103.313.51

0.840.410.930.750.891.310.640.551.110.770.82

1.00Ð 0.12

0.13Ð 0.11

0.290.060.010.050.020.050.33

1.00Ð 0.17

0.020.03

Ð 0.07Ð 0.06

0.170.00

Ð 0.04Ð 0.16

1.000.450.110.290.350.02

Ð 0.220.130.21

1.000.160.280.340.240.140.100.18

1.000.250.130.110.01

Ð 0.050.19

1.000.640.45

Ð 0.020.580.64

1.000.70

Ð 0.130.750.70

1.000.170.560.56

1.00Ð 0.02Ð 0.16

1.000.54 1.00

Table 2.4. Bivariate correlations ± organizational factors and internet attitudues and beliefs.

Mean Std.Dev

1 2 3 4 5 6 7 8 9 10

1. Social pressure2. Organizational support3. Strucure4. Less variety5. Self-e� cacy6. Pessimism7. Intimidation8. Optimism9. Usefulness10. Ease of use

2.952.843.302.562.672.391.713.743.273.54

1.231.070.820.880.940.870.560.660.690.78

1.000.690.120.01

Ð 0.02Ð 0.32

0.230.17

Ð 0.21Ð 0.37

1.000.15

Ð 0.16Ð 0.06Ð 0.23

0.220.06

Ð 0.15Ð 0.31

1.000.370.25

Ð 0.28Ð 0.10Ð 0.06

0.100.08

1.000.02

Ð 0.31Ð 0.02Ð 0.04Ð 0.18

0.20

1.00Ð 0.35Ð 0.21

0.030.270.24

1.000.36

Ð 0.080.150.10

1.00Ð 0.14Ð 0.19Ð 0.24

1.00Ð 0.06Ð 0.04

1.000.39 1.00

n= 39r> 0.39, p< 0.01r> 0.49, p< 0.001

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At the more restrictive signi® cance levels, there was

only one organizational variable with a signi® cantrelationship with either beliefs/attitudes, or internet

usage, or the impact of the internet. Structured task

characteristics were negatively correlated with accessing

personal webpages (r= Ð 0.51, p < 0.001 ).

Pessimism about the internet was positively relatedwith accessing personal-type webpages (r= 0.44,

p <0.001). As intimidation increased, time on the

Internet (r= Ð 0.45, p < 0.01) and frequency of usage(r= Ð 0.45, p < 0.01) decreased. Perceived usefulness ofthe internet was positively related to time of use (r= 41,

p <0.01), enhanced job characteristics (r= 0.75,

p <0.001), job satisfaction (r= 0.73, p <0.001), and

overall productivity (r= 0.53, p < 0.001 ). However, it

was also strongly related to ine� ciency (r= 0.73,p <0.001). Ease of use was associated with more

extensive internet usage to perform business activities


Table 2.5. Bivariate correlations ± organizational factors and internet usage.

Mean Std.Dev

1 2 3 4 5 6 7 8 9 10

1. Social pressure2. Organizational support3. Structure4. Less variety5. Time6. Frequency7. Business activity8. Personal pages9. Work pages10. Value chain pages

2.952.843.302.562.794.1316.923.033.133.08

1.231.070.820.881.001.385.121.261.121.33

1.000.690.120.01

Ð 0.18Ð 0.14Ð 0.33Ð 0.06

0.250.08

1.000.15

Ð 0.16Ð 0.30Ð 0.20Ð 0.31Ð 0.01Ð 0.08Ð 0.18

1.000.370.07

Ð 0.04Ð 0.27Ð 0.51Ð 0.24Ð 0.13

1.00Ð 0.19Ð 0.13Ð 0.23Ð 0.27Ð 0.15Ð 0.10

1.000.630.360.000.13

Ð 0.01

1.000.430.120.170.12

1.000.340.130.28

1.000.280.30

1.000.48 1.00

Table 2.6. Bivariate correlations ± Organizational factors and impact of internet.

Mean Std.Dev

1 2 3 4 5 6 7 8 9

1. Social Pressure2. Organizational support3. Structure4. Less variety5. Job characteristics6. Ine� ciency7. Security threats8. Job satisfaction9. Productivity

2.952.843.302.563.182.972.103.313.51

1.231.070.820.880.640.551.110.770.82

1.000.690.120.01

Ð 0.070.04

Ð 0.22Ð 0.26Ð 0.03

1.00Ð 0.15Ð 0.16Ð 0.04Ð 0.11Ð 0.09Ð 0.19

0.03

1.000.370.180.10

Ð 0.040.060.25

1.00Ð 0.08Ð 0.15Ð 0.03Ð 0.20Ð 0.22

1.000.70

Ð 0.130.750.70

1.000.170.560.56

1.00Ð 0.02Ð 0.16

1.000.54 1.00

Table 2.7. Bivariate correlations ± internet attitudes and beliefs and internet usage.

Mean Std.Dev

1 2 3 4 5 6 7 8 9 10 11 12

1. Self e� cacy2. Pessimism3. Intimidation4. Optimism5. Usefulness6. Ease of use7. Time8. Frequency9. Business activity10. Personal pages11. Work pages12. Value chain pages

2.672.391.713.743.273.542.794.1316.923.033.133.08

0.940.870.560.660.690.781.001.385.121.261.121.33

1.00Ð 0.35Ð 0.21

0.030.270.240.230.14

Ð 0.03Ð 0.23Ð 0.08Ð 0.26

1.000.37

Ð 0.080.150.100.150.160.080.440.130.03

1.00Ð 0.14Ð 0.19Ð 0.24Ð 0.45Ð 0.45Ð 0.26

0.290.220.06

1.00Ð 0.06Ð 0.04Ð 0.15

0.100.110.04

Ð 0.160.09

1.000.390.410.380.15

Ð 0.16Ð 0.07Ð 0.22

1.000.300.360.40

Ð 0.03Ð 0.15Ð 0.05

1.000.630.36

Ð 0.00013

Ð 0.01

1.000.430.120.170.12

1.000.340.130.28

1.000.280.30

1.000.48 1.00

n= 39r> 0.39, p< 0.01r> 0.49, p< 0.001

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(r= 0.40, p <0.01). With the more stringent signi® cance

levels, internet usage had no statistically signi® cant

relationships with the indicators measuring internet

impact.

5. Discussion

The use of the internet is growing at a phenomenal

rate. An examination of the information systems

literature shows a dearth of empirical research on

increasing our understanding of the individual and

organizational factors in¯ uencing employee internet

usage and the perceptions of individual consequences

of such usage. This exploratory study attempts toempirically examine some of these issues.

The results suggest that several relationships among

individual factors and attitudes/beliefs, internet usage,

and internet impact are important. As web skills

increase, so do time and frequency on the internet.

Additionally, an individual with a natural tendency to

interact spontaneously and imaginatively with the

internet (internet playfulness) perceives the internet asuseful and reports enhanced perceptions of job char-

acteristics, higher job satisfaction, and more overall

productivity. However, internet playfulness can have

negative consequences in terms of increased wastedtime, need for re-work, and longer time for task

completion as supported by a positive correlation

between internet playfulness and ine� ciency. Perhaps

this ine� ciency is the `cost’ of doing business because

the internet is in a growth stage. Gender and age do not

have any signi® cant associations with internet related

factors, except that women are less likely to access workwebpages. This seeming unimportance of age and

gender is contrary to prior research on computer usage,

and suggests that further work is needed to see if

internet usage is less in¯ uenced by age and gender than


Table 2.9. Bivariate correlations ± internet usage and impact of internet.

Mean Std.Dev

1 2 3 4 5 6 7 8 9 10 11

1. Time2. Frequency3. Business activity4. Personal pages5. Work pages6. Value chain pages7. Job characteristics8. Ine� ciency9. Security threats10. Job satisfaction11. Productivity

2.794.13

16.923.033.133.083.182.972.103.313.51

1.001.385.121.261.121.330.640.551.110.770.82

1.000.630.360.000.13

Ð 0.010.340.300.080.360.32

1.000.430.120.170.120.340.160.220.360.33

1.000.340.130.280.19

Ð 0.04Ð 0.20

0.220.27

1.000.280.30

Ð 0.10Ð 0.15Ð 0.11

0.020.11

1.000.48

Ð 0.20Ð 0.05Ð 0.15Ð 0.22Ð 0.03

1.000.15

Ð 0.21Ð 0.33Ð 0.23Ð 0.13

1.000.70

Ð 0.130.750.70

1.000.170.560.56

1.00Ð 0.02Ð 0.16

1.000.54 1.00

n= 39r> 0.39, p< 0.01r> 0.49, p< 0.01

Table 2.8. Bivariate correlations ± internet attitudes and beliefs and impact of internet.

Mean Std.Dev

1 2 3 4 5 6 7 8 9 10 11

1. Self-e� cacy2. Pessimism3. Intimidation4. Optimism5. Usefulness6. Ease of use7. Job characteristics8. Ine� ciency9. Security threats10. Job satisfaction11. Productivity

2.672.391.713.743.273.543.182.972.103.313.51

0.940.870.560.660.690.780.640.551.110.770.82

1.00Ð 0.35Ð 0.21

0.030.270.240.200.140.250.360.22

1.000.37

Ð 0.080.150.100.060.120.050.14

Ð 0.04

1.00Ð 0.14Ð 0.19Ð 0.24Ð 0.26Ð 0.01Ð 0.19Ð 0.26Ð 0.26

1.00Ð 0.06Ð 0.04Ð 0.10

0.100.060.02

Ð 0.03

1.000.360.750.730.200.630.53

1.000.27

Ð 0.04Ð 0.01

0.220.03

1.000.700.130.750.70

1.000.170.560.56

1.00Ð 0.02Ð 0.16

1.000.54 1.00

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computer usage. Neither internet skills nor formal

training are important antecedent variables.

With the organization-related antecedents of internet

usage, there is only one signi® cant relationship: as taskstructure increases, accessing personal webpages de-

creases. This is understandabl e as there is less likely to

be free time to spend sur® ng the web. Organizational

factors may play a strong inhibiting role in internetusage because, surprisingly, many of the correlation

coe� cients, although not statistically signi® cant, are

negative. Consequently, while management involvement

and support in internet usage could dissuade employees

from misusing the internet during working hours, it may

also inhibit legitimate usage.

Beliefs and attitudes are also important in internetusage and impact. Intimidation is related to decreased

time spent on the internet, as well as less frequency, a

relationship also found in prior research on microcom-

puter usage. Interestingly, pessimism about the internet

is positively associated with accessing personal pages, sofeelings that the internet might control lives’ , and turn

people into just another number’ do not discourage

accessing personal webpages at work. Perceived useful-

ness is positively related to increased time of use, but is

even more strongly associated with the impacts ofinternet usage. As perceived usefulness increases, so do

reported enhancements in job characteristics, job

satisfaction, and overall productivity. However, the

more individuals report the internet as useful, the more

they also report increasing ine� ciency in terms such as

greater rework, greater time for work completion, andmore wading through extraneous material. Therefore,

both playfulness and perceived usefulness have con-

sequences that are both advantageous and disadvanta-

geous, suggesting a need to look for ways to control

without sti¯ ing.The most obvious limitation of this study is its sample

size. The small number of responses (n= 39) restricted

the possible types of data analyses and required a

stringent level for reporting signi® cant relationships.

Additional research is needed, and a few extensions ofthis work include directly surveying individuals from

work environments, conducting in-depth interviews, and

investigating internet usage around the globe. It should

be noted that the internet is still a `new frontier in

business’ (Cronin 1996 ) and that research into the

antecedents, usage, and impact of the internet is in itsinfancy. While cross-sectional studies, such as the

present one, are useful for identifying patterns of

relationships, a longitudinal research design is essential

to con® rm causal linkages. The ® ndings would also be

enhanced by the use of objective measures for internetusage and impact, as common method variance con-

tributing to the results cannot be ruled out. Despite the

limitations, the results of this exploratory study were

encouraging enough to launch a more extensive project

that will allow a robust testing of the research model.

The ® ndings of this study contribute to a betterunderstanding of the construct structure of the model

variables and of the antecedent factors that can facilitate

internet usage. As the internet becomes an almost

indispensable part of work, it is important for allmanagers, especially information technology managers,

to be cognisant of both individual and organizational

factors associated with internet usage. It appears that

internet usage could have positive results in terms of

enhanced job characteristics, overall job satisfaction,

and productivity, though perhaps at the cost of

increasing ine� ciency. This especially challenges infor-mation technology managers to formulate policies and

procedures that control, but do not discourage, internet

work usage by all employees. The ultimate goal is

legitimate usage of the internet while at work, but

organizations may need to encourage any usage of theinternet for a time if its full potential is to be used for

competitive advantage.

References

ADAMS, D. A., NELSON, R. R. and TODD, P. A. 1992, Perceivedusefulness, ease of use, and usage of information technology:a replication, MIS Quarterly, 16, 227± 247.

BANDURA, A., ADAMS, N. E. and BEYER, J. 1977, Cognitiveprocesses mediating behavioral change, Journal of Person-ality and Social Psychology, 35, 125± 139.

BLAIR, E. and BURTON, S. 1987, Cognitive process used bysurvey respondents to answer behavioral frequency ques-tions, Journal of Consumer Research, 14, 280± 288.

BURKHARDT, M. E. and BRASS, D. J. 1990, Changing patterns orpatterns of change: the eŒects of a change in technology onsocial network stricture and power, Administrative ScienceQuarterly, 35, 104± 127.

CHENEY, P. and DICKSON, G. B. 1982, Organizational char-acteristics and information systems success: an extrapolationinvestigation, Academy of Management Journal, 25, 170±184.

COMPEAU, D. R. and HIGGINS, C. A. 1995, Computer self-e� cacy: development of a measure and initial test, MISQuarterly, 3, 189± 211.

CRONIN, M., 1995, Doing More Business on the Internet (2ndedn), (New York: International Thompson Publishing Inc).

CRONIN, M. 1996, The Internet Strategy Handbook, (Boston,MA: Harvard Business School Press).

DAVIS, F. D. 1989, Perceived usefulness, perceived ease of use,and user acceptance of information technology, MISQuarterly, 13, 983± 1003.

DAVIS, F. D. 1992, Extrinsic and intrinsic motivation to usecomputers in the workplace, Journal of Applied SocialPsychology, 22, 1111± 1132.

DEVELLIS, R. F. 1991, Scale development: theory andapplications, (Newbury Park, CA: Sage).


Dow

nloa

ded

by [

Nor

th W

est U

nive

rsity

] at

16:

26 2

1 D

ecem

ber

2014

DOLL, W. J. and TORKZADEH, G. 1988, The measurement ofend-user computing: an exploratory study, InformationResources Management Journal, 1, 39± 46.

FISHBEIN, M. and AJZEN, I. 1975, Belief, Attitude, Intentions,and Behaviour: An Introduction to Theory and Research,(Boston: Addison-Wesley).

GIST, M. E., SCHWOERER, C. E. and ROSEN, B. 1989, EŒects ofalternative training methods on self-e� cacy and perfor-mance in computer software training, Journal of AppliedPsychology, 74, 884± 891.

HACKMAN, J. R. and OLDHAM, G. R. 1980, Work Redesign.(Reading, MA: Addison-Wesley).

HARRISON, A. W. and RAINER, R. K. 1992, The in¯ uence ofindividual diŒerences on skill in end-user computing,Journal of Management Information Systems, 9, 93± 11.

HILL, T., SMITH, N. D. and MANN, M. F. 1987, Role of e� cacyexpectations in predicting the decision to use advancetechnologies: the case of computers, Journal of AppliedPsychology, 72, 307313.

HOLLENBECK, J. R. and BRIEF, A. P. 1987, The eŒects ofindividual diŒerences and goal origins on goal setting andperformance, Organizational Behaviour and Human DecisionPerformance, 40, 392± 414.

IGBARIA, M. 1990, End-user computing eŒectiveness: astructural equation model, OMEGA, International Journalof Management Science, 18, 637± 652.

IGBARIA, M. 1992, An examination of microcomputer usage inTaiwan, Information and Management, 22, 19± 28.

IGBARIA, M. 1993, User acceptance of microcomputer technol-ogy: an empirical test, OMEGA International Journal ofManagement Science, 21, 73± 90.

IGBARIA, M., PARASURAMAN , S. and BAROUDI, J. 1996, Amotivational model of microcomputer usage, Journal ofManagement Information Systems, 13, 127± 143.

LAPLANTE, A. 1997, Start small think in® nite, Computerworld,24± 30.

LAWTON, G. 1996, The Internet: taking a hard look atperformance, Telecommunication, 4, 29± 35.

MATHIESON, K. 1991, Predicting user intentions: comparing thetechnology acceptance model with the theory of plannedbehavior, Information Systems Research, 3, 173± 191.

NICKELL, G. S. and PINTO, J. N. 1986, The computer attitudescale, Computers in Human Behaviour, 2, 547± 559.

NUNNALLY, J. C. 1978, Psychometric Theory (2nd edn), (NewYork: McGraw-Hill).

STEVENSON, W. J. 1993, Production/Operations Management(4th edn), (Homewood, IL: Irwin).

WEBSTER, J. and MARTOCCHIO, J. J. 1992, Microcomputerplayfulness: development of a measure of workplaceimplications, MIS Quarterly, 16, 201± 226.

WEBSTER, J. and MARTOCCHIO, J. J. 1995, The diŒerential eŒectsof software training previews on training outcomes, Journalof Management , 21, 757± 787.

WITHEY, M., DAFT, R. L. and COOPER, W. H. 1983, Measures ofPerrow’s work unit technology: an empirical assessment anda new scale, Academy of Management Journal, 26, 45± 63.

ZOLTAN, E. and CHAPANIS, A. 1982, What do professionalpersons think about computers? Behaviour and InformationTechnology, 1, 55± 68.


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an exploratory investigation of the antecedents and impact of internet usage: an individual...

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