Measurement and Concepts of Individual Application Capability of

e-Business

Chui Young Yoon

and Sung Koo Hong

Industry-Academic Cooperation Foundation (Management Information System),

Korea National University of Transportation, 50 Daehak-ro, Chungju City, Chungbuk, 380-702, Republic of Korea

Department of International Business and Commerce, Korea National University of Transportation,

50 Daehak-ro, Chungju City, Chungbuk, 380-702, Republic of Korea

Keywords: e-Business, e-Business Competency, Measurement Tool, Measurement Item.

Abstract: Understanding measurement and concepts for an individual application capability of e-business is important

to manage and improve their work ability in an e-business environment. This study presents a 17-item tool

to measure an individual application capability of e-business with the measurement items, process, and

method based on the previous literature. The developed tool construct were verified by factor and reliability

analysis with the questionnaire survey. This tool has four measurement factors and seventeen items. The

utilization of the developed tool was confirmed by applying it to a case study.

1 INTRODUCTION

Nowadays, e-business is a paradigm in firm’s

business and is going to the advanced e-business (u-

business). For preoccupying and utilizing it,

enterprises have established the e-business systems

based on the construction of information

environment to promote their competitiveness and

performance. This endeavour improves their

productivity and effectiveness by applying the

advanced technology to their business. The efficient

utilization of the e-Business systems will contribute

to raising the organizational business performances

and to improving the firm's competitiveness.

In this environment, an individual who directly

performs his or her tasks needs the e-business

application capability to efficiently do his or her

works by applying e-Business systems to his or her

business tasks (Brancheau and Brown, 2002). As the

workers assume greater responsibility for e-Business

systems, it has become increasingly important to

develop a measure appropriate for their application

capability of e-business (Brancheau and Brown,

2002; Doll and Torkzadeh, 1989; Torkzadeh and

Lee, 2003). Because we can measure and manage an

individual application capability of e-business based

on a scientific and practical tool. Individual

application capability of e-Business (IACEB) means

the total capability of an individual to efficiently

apply e-Business resources to his or her business

tasks in this environment.

Therefore, this study presents a measurement

tool that can entirely gauge the individual

application capability of e-business to efficiently

improve his or her e-Business capability in an e-

Business environment.

2 RELATED LITERATURE

The studies for e-business have researched in many

ways. E-business was defined as an approach to

increase the competitiveness of organizations by

improving management activities through using IT

and the Internet (Yoon and Leem, 2006).

Competency is an entire application capability with

a total set of knowledge, skills, and attitudes which

function as the action characteristics of an

organizational member who can do his task

outstandingly in an organizational environment

(Spencer and Spencer, 1993).

In general competency, individual characteristics

such as motives, traits, self-concepts and knowledge

lead to skills, and the action of a person with skills

has an effect on the performance of his or her

business in an organizational environment (Spencer

and Spencer, 1993). The application capability of e-

Business can be conceptualized by transforming a

129

Yoon C. and Hong S..

Measurement and Concepts of Individual Application Capability of e-Business.

DOI: 10.5220/0004127101290134

In Proceedings of the International Conference on Data Communication Networking, e-Business and Optical Communication Systems (ICE-B-2012),

pages 129-134

ISBN: 978-989-8565-23-5

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

general competency into a type of competency in e-

Business perspective.

Based on the previous literature reviews, we

define the individual application capability of e-

Business (IACEB) as the entire capability that an

individual can efficiently apply e-Business

knowledge and ability to his or her tasks to execute

the given task in an e-Business environment. This

focuses on development of measurement items to

gauge IACEB in terms of a total application

capability of e-business. That is, IACEB explains the

total application capability that an individual can

effectively execute his or her tasks in an e-Business

environment.

Exploring literature reviews, we extracted four

major components of IACEB: awareness of e-

business application, knowledge of e-business

application, skills of e-business application, and

experience of e-Business application. They are the

potential measurement factors of IACEB in terms of

a total application capability of e-Business

(Torkzadeh and Lee, 2003; Mnro et al, 1997;

Etezadi-Amoli and Farhoomand, 1996; McCoy,

2001; Marcolin et al, 2000; Wu et al, 2005).

IACEB should have an effect on the execution

ability of individual tasks. We need a measurement

tool for researchers and practitioners to efficiently

manage and improve it. IACEB concepts and

constructs have rarely been researched in the

previous literature. Hence, this study develops a tool

to measure the IACEB based on its definition and

four potential components. Based on the definitions

and components of the IACEB, this research

generated the 29 items that can measure the IACEB.

3 METHODS

Many previous studies presented two methods of

model construct validation (Etezadi-Amoli and

Farhoodmand, 1996; Torkzadeh and Doll, 1989;

Torkzadeh and Lee, 2003): (1) correlations between

total scores and item scores (Torkzadeh and Doll,

1989; Torkzadeh and Lee, 2003), and (2) factor

analysis (Etezadi-Amoli and Farhoodmand, 1996;

Torkzadeh and Lee, 2003). We used factor analysis

and reliability analysis to verify the tool construct

and to extract adequate items for measuring an

IACEB. The measurement questionnaire used a five-

point Likert-type scale from 1 (not at all) to 5 (very

good). Our survey was gathered data from a variety

of industries and business departments. We collected

263 responses from 436 respondents. Two

incomplete or ambiguous questionnaires were

excluded. A sample of 261 usable responses was

obtained from a variety of industries and business

departments, and from various levels of management.

Respondents had college or university degrees in:

humanities and societies (15.1%), management and

economics (27.4%), engineering (46.1%), and

science (11.4%). The respondents had on average

8.7 years of experience (S.D.=1.12) in their field,

their average age was 34.7 (S.D.=6.17), and the

gender breakdown was: male (77.1%) and female

(22.9%).

4 ANALYSIS AND DISCUSSION

We analyzed the collected questionnaires by using

SPSS ver.12 software. The correlations with the

corrected item-total and the criterion item were

significant at p <= 0.01 and similar to those used by

others in previous literature (McCoy, 2001; Rifkin et

al., 1999; Torkzadeh and Lee, 2003).

Table 1: Factor analysis results.

0.716V05

0.828V27

0.708V03

0.659V25

1.311.693.286.89Eigen-value

0.739V19

0.702V28

64.9558.1344.9832.01

Cumulative

variance

0.783V23

0.686V18

0.787V14

0.835V13

0.887V16

0.723V10

0.748V07

0.799V06

0.894V08

0.725V01

0.793V04

Factor 4Factor 3Factor 2Factor 1

Factor Loadings

Variable

0.716V05

0.828V27

0.708V03

0.659V25

1.311.693.286.89Eigen-value

0.739V19

0.702V28

64.9558.1344.9832.01

Cumulative

variance

0.783V23

0.686V18

0.787V14

0.835V13

0.887V16

0.723V10

0.748V07

0.799V06

0.894V08

0.725V01

0.793V04

Factor 4Factor 3Factor 2Factor 1

Factor Loadings

Variable

* Significant at P  0.01

Based on analysis results, the first 29

measurement items were reduced to 17 items, with

12 items were deleted. The elimination was

sufficiently considered to ensure that the retained

items were adequate measures of IACEB. This

research verified the validity and reliability of the

tool by factor analysis and reliability analysis. We

used to identify the underlying factors or

components that include the IACEB construct.

ICE-B 2012 - International Conference on e-Business

130

These deletions resulted in a 17-item scale for

measuring IEC. Each of the 17 items had a factor

loading > 0.659. The reliability coefficients

(Cronbach’s alpha) of four potential factors had

values > 0.811, above the threshold recommended

for exploratory research (Rodriguez et al., 2002). To

examine the reliability and validity of the measures,

we calculated the corrected item-total correlations

between each variable and its corresponding factor.

These correlations along with alpha coefficients of

each factor are indicated in Table 2.

Table 2: Corrected item-total correlations and coefficient

alphas for 17-measrement items.

0.6370.719V05

0.7480.829V27

0.6240.691V19

Coefficient alpha for the above 4 items as a composite measure of

Factor =0.811

0.6220.642V25

0.7130.719V28

0.7190.813V23

Coefficient alpha for the above 5 items as a composite measure of

Factor =0.838

0.7760.713V18

0.7990.837V14

0.7350.732V13

0.8490.821V16

Coefficient alpha for the above 4 items as a composite measure of

Factor =0.849

0.7270.793V10

0.7380.849V07

0.8610.781V06

0.8250.773V08

Coefficient alpha for the above 4 items as a composite measure of

Factor =0.871

0.6840.641V03

0.7590.748V01

0.7380.681V04

Alpha if item deleted

Corrected item-total

correlation

Variable

0.6370.719V05

0.7480.829V27

0.6240.691V19

Coefficient alpha for the above 4 items as a composite measure of

Factor =0.811

0.6220.642V25

0.7130.719V28

0.7190.813V23

Coefficient alpha for the above 5 items as a composite measure of

Factor =0.838

0.7760.713V18

0.7990.837V14

0.7350.732V13

0.8490.821V16

Coefficient alpha for the above 4 items as a composite measure of

Factor =0.849

0.7270.793V10

0.7380.849V07

0.8610.781V06

0.8250.773V08

Coefficient alpha for the above 4 items as a composite measure of

Factor =0.871

0.6840.641V03

0.7590.748V01

0.7380.681V04

Alpha if item deleted

Corrected item-total

correlation

Variable

This also explains the alpha coefficients for the

measurement of factors if a measure was deleted

from the scale. These coefficients present the

relative contribution of a measure to the construction

of a scale for measuring a particular factor. They are

all in the acceptable range. Most corrected item-total

correlations were greater than 0.600, showing that

the individual measures are good indicators of their

corresponding factors. The items were grouped by

their high factor loadings. Each of the 17 items had a

corrected item-total correlation > 0.641. The

correlation for each of the 17 items was positive and

significant (p <= 0.01). Hence, this study developed

the measurement items, with a validity and

reliability, by conducting two analyses as shown in

Table 1 and Table 2.

5 MEASUREMENT TOOL

This research classified into four factor groups based

on the factor analysis. The factor groups mean the

potential factors as major components to measure

IACEB. By exploring the measurement items of

each factor group based on previous studies, we

identified the following four potential factors: factor

1: awareness of e-business application; factor 2:

knowledge of e-business application; factor 3: skills

of e-business application; and factor 4: experience of

e-Business application. These extracted factors

comprise the overall measurement content for

IACEB from awareness of e-business application to

experience of e-Business application. Namely, this

means a tool that measure IACEB in terms of a total

IT capability.

The construct of the developed tool shows in

Figure 1. The tool has four measurement factors and

17 items. It is a crucial theoretical construct to

measure the IACEB that can efficiently execute his

or her tasks in an e-Business environment. Major

factors of this tool construct have the meanings and

measurement elements as follows. The awareness of

e-Business application examines acknowledge,

understanding, and ethic consciousness about e-

Business.

Measurement Tool of IACEB

Awareness of e-Business application (AEBA)

-V01: Acknowledge of the Internet and IT technology

-V03: Understanding of e-Business progress trends

-V04: Ethics and morality in e-Business execution

-V05: Consciousness for e-Business security

Knowledge of e-Business application (KEBA)

-V06: Knowledge of hardware, software, network, and database

-V07: Knowledge of solutions (ERP, SCM, and CRM etc.)

-V08: Knowledge of operating e-Business systems

-V10: Knowledge of e-Business security measures

Skills of e-Business application (SEBA)

-V13: Skills using word processing and presentation etc.

-V14: Skills using the solutions of ERP, SCM, and CRM etc.

-V16: Skills utilizing e-Business systems for e-Business tasks

(B to E, B to C, and B to B etc.)

-V18: Skills sharing and integrating business data

-V19: Skills establishing and managing security measures

Experience of e-Business application (EEBA)

-V23: Possession of degrees and certificates related to e-Business

-V25: Experience working in e-Business departments

-V28: Completion of education and training related to e-Business

-V29: Presentation of articles and ideas for e-Business works

Figure 1: Structure of the developed tool.

The awareness of e-Business application

examines acknowledge, understanding, and ethic

consciousness about e-Business. The knowledge of

e-Business application presents the knowledge that

an individual have to know to efficiently apply e-

Business solutions and systems to his or her tasks.

Namely, this represents e-Business knowledge that

Measurement and Concepts of Individual Application Capability of e-Business

131

needs to effectively perform the given tasks. The

skills of e-Business application indicate the ability

that an individual can effectively apply e-Business

knowledge, solutions, and systems to his or her tasks.

The experience of e-Business application refers to

the potential ability such as certificates, job

experiences, and participations of education and

training related to e-Business. This factor provides

the potential ability to efficiently improve the

individual application capability of e-Business. This

is the important factor for the extension of the e-

Business application capability in terms of the

breadth and depth of the IACEB.

Hence, this tool is a crucial theoretical construct

to measure an individual's total application

capability of e-business that can efficiently perform

his or her tasks in an e-business environment.

6 MEASUREMENT SYSTEM

This measurement system presents measurement

stages and procedures. It has the measurement and

presentation stage as shown in Figure 2.

Start

KEBAAEBA SEBA EEBA

Measurement

Problem Database

Measurement Execution

Analysis of Measurement Results

(Extraction of Measurement Indices)

Application of

Weight Values

Measurement Tool

(4 Measurement Factors)

AEBA

(4 Items)

KEBA

(4 Items)

SEBA

(5 Items)

EEBA

(4 Items)

Extraction of Measurement Problems

(4 Measurement Factors)

Interpretation/Presentation of

Measurement Results

End

Figure 2: Framework of the measurement system.

The measurement results are analyzed by

extracting the measurement values of each factor

with applying each weight value to the measurement

values of each factor.

The presentation stage provides the measurement

results based on each factor. The results are

explained by each measurement index extracted

from each factor. The interpretation of the results

explains the present states and problems of the

IACEB, and the directions and methods to

efficiently improve the IACEB based on the

extracted measurement results.

6.1 Measurement Method

This research used the weight values for each

measurement factor in order to develop an efficient

tool that reflects the relative importance of each

factor in measuring the IACEB. The weight values,

as indicated in Table 3, were extracted from the

analysis results of the questionnaire survey (AHP) of

about 40 experts working in IT or e-Business

departments. The extraction method of the

measurement index (MI) first calculates the

measurement values of each factor, and figures out

the MI of each factor by multiplying each weight

value by each measurement value of each factor.

The MI means the value extracted by multiplying

the weight value by the measurement value. And,

the sum of the measurement indices of each factor

results in the total MI of the IACEB. In this way,

this tool presents the measurement results of the

IACEB based on the total measurement index and

the indices of each factor.

Table 3: Weight value of each measurement factor.

0.22Experience of e-Business application

0.31Skills of e-Business application

0.26Knowledge of e-Business application

0.21Awareness of e-Business application

Weight ValueMeasurement Factor

0.22Experience of e-Business application

0.31Skills of e-Business application

0.26Knowledge of e-Business application

0.21Awareness of e-Business application

Weight ValueMeasurement Factor

Hence, the total MI can be defined as Equation (1):

Total MI = ∑ MV

MFi

x WV

MFi

i=1

(1)

Where, Total MI: Total Measurement Index (MI)

of an individual application capability of e-business.

MFi

: Measurement Value (EV) of the i th

Measurement Factor.

MFi

: Weight Value (WV) of the i th

Measurement Factor.

Here, the sum of the weight values of each factor

is 1.00 and i = 1, 2, 3 and 4 indicate the four

measurement factors.

Hence, we extract the total measurement index of

an IACEB by the equation (1).

ICE-B 2012 - International Conference on e-Business

132

7 CASE STUDY AND

DISCUSSION

In this case study, we applied the developed tool to

204 persons working in "A" company, Republic of

Korea. The business departments of respondents

were identified as follows: strategy plan department

(SPD): 23.5%; development and maintenance

department (DMD): 21.6%; business application

department (BAD): 29.9% and administration

support department (ASD): 25.0% and so on. The

respondents had on average 7.9 years of experience.

We presented the measurement results obtained

from the organizational level and an individual level.

First, this research analyzed the measurement

results on the overall organization and each business

department. Based on the analysis results, the total

MI of the overall organization was 62.89. The MI of

the BAD was 69.16, the highest level among the

entire business departments.

This is due to the ability to effectively

accomplish their tasks by frequently utilizing e-

Business knowledge and systems for e-Business of

the form B to E, B to C and B to B, and the

knowledge and skills to use the various solutions

such as ERP, SCM, and CRM in order to do their

given tasks in an e-Business environment.

Business

Application

Administration

Support

Development/

Maintenance

Strategy Plan

Business

Department

Total Measurement Index

Range of Measurement

Index

Measurement IndicesDivision

Business

Application

Administration

Support

Development/

Maintenance

Strategy Plan

Business

Department

Total Measurement Index

Range of Measurement

Index

Measurement IndicesDivision

0 40 60 80 100

62.89

69.16

63.97

62.18

56.23

Figure 3: Measurement indices of each business depart-

ment and overall organization.

Second, the measurement results of an individual

working in the business application department

(BAD) were presented in an individual perspective.

The MI of each measurement factor was generated

by multiplying each weight value by the

measurement value of each factor. The total MI is

the sum of the measurement indices of each factor as

shown in Table 4. The total MI of the IACEB was

70.25, and the MI of the skills of e-Business

application (SEBA), 78.86, was quite high. It means

the outstanding ability to utilize the e-Business

knowledge, solutions and systems for his or her

tasks on an e-Business system.

Table 4: Extraction process of the total measurement index

for an individual.

70.2514.1424.4517.9713.69

Calculation of

Total MI

1.000.220.310.260.21

Weight Values

of Each Factor

-64.2978.8669.1365.19

MI of Each

Factor

Total MIEEBASEBAKEBAAEBADivision

70.2514.1424.4517.9713.69

Calculation of

Total MI

1.000.220.310.260.21

Weight Values

of Each Factor

-64.2978.8669.1365.19

MI of Each

Factor

Total MIEEBASEBAKEBAAEBADivision

The MI of the experience of e-Business

application (EEBA) was a little lower than the MI of

the other factors.

6 5 .1 9

6 9 .1 3

6 4 .2 9

7 0 .2 5

7 8 .8 6

Total

Measurement Index (MI)

Awareness of

e-Business

application (AEBA)

Knowledge of

e-Business

Application (KEBA)

Skills of

e-Business

Application (SEBA)

Experience of

e-Business

Application (EEBA)

6 5 .1 9

6 9 .1 3

6 4 .2 9

7 0 .2 5

7 8 .8 6

Total

Measurement Index (MI)

Awareness of

e-Business

application (AEBA)

Knowledge of

e-Business

Application (KEBA)

Skills of

e-Business

Application (SEBA)

Experience of

e-Business

Application (EEBA)

Total

Measurement Index (MI)

Awareness of

e-Business

application (AEBA)

Knowledge of

e-Business

Application (KEBA)

Skills of

e-Business

Application (SEBA)

Experience of

e-Business

Application (EEBA)

Figure 4: Measurement indices of an individual in the

BAD.

Hence, the individual should make an effort to

improve e-Business departments on certificates and

experiences, education and training, and presentation

of articles and ideas related to e-Business in order to

effectively improve his or her application capability

of e-Business in general.

8 CONCLUSIONS

This study presented the concepts of IACEB and a

measurement tool that can efficiently gauge an

IACEB in an e-Business environment. We provide

the concrete measurement items, measurement

process, method, and tool construct. Although it has

a little of limitation in a special perspective, our

research developed an alternative measure of

IACEB and presented an IACEB tool that is

applicable across industries and business

departments.

Therefore, this study will contribute to the

development of an IACEB tool construct and

improving IACEB that can efficiently execute an

Measurement and Concepts of Individual Application Capability of e-Business

133

individual's given tasks in an e-Business

environment.

ACKNOWLEDGEMENTS

The research was supported by a grant from the

Academic Research Program of Korea National

University of Transportation in 2012.

REFERENCES

Aladwani, A. M., Palvia, P. C., 2002. Developing and

validating an instrument for measuring user web

quality, Information and Management, vol.39, no.6,

pp.467-476.

Ascher-Svanum, H., 1999. Development and validation of

measure of patient's knowledge about Schizophrenia,

Psychiatric Services, vol.50, no.4, pp.561-563.

Brancheau, C. and Brown, V., 2002. The management of

end-user computing: Status and Directions, ACM

Computing Surveys, vol.25, no.4, pp.437-482.

Campbell, D. T. and Fiske, D. W., 1959. Convergent and

discriminant validation by multitrait-multimethod

matrix, psychological Bulletin, vol.56, no.2, pp.81-

105.

Churchill, G. A., 1979. A paradigm for developing better

measures of marketing constructs, Journal of

Marketing Research, vol.16, no.1, pp.64-73.

Doll, W. J. and Torkzadeh, G., 1989. The measurement of

end-user computing involvement, Management

Science, Vol.35, No.10, pp.1151-1171.

Dubois, D. D., 1993. Competency-Based Performance

Improvement: A Strategy for Organizational Change,

Amherst, MA: HRD Press Inc.

Etezadi-Amoli, J. and Farhoomand, A. F., 1996. A

Structural Model of End User Computing Satisfaction

and User Performance, Information & Management,

vol.30, pp. 65-73.

Hu, P. J., Chau, P. Y. K., Liu Sheng, O. R., and Tam, K.

Y., 1999. Examining the technology acceptance model

using physician acceptance of telemedicine

technology, Journal of Management Information

Systems, vol.16, no.2, pp.91-112.

Jacobs, R. L., 2002. System Theory Applied to Human

Resource Development, in Theory-to-Practice

Monography, Alexandra, ed. D.B. Gardous, VA:

ASTD.

Kerlinger, F. N., 1978. Foundations of Behavioral

Research, McGraw-Hill, New York.

Klenke, K., 1992. Construct measurement in management

information systems: A review and critique of user

satisfaction and user involvement instruments, INFOR,

vo.30, no.4, pp.325-348.

Marcolin, B.L., Compeau, D.R., Munro, M.C., Huff, S.L.,

2000. Assessing user competence: Conceptualization

and measurement, Information Systems Research,

vo.11, no.1, pp.37-60.

McClelland, D., 1973. Testing for Competence Rather

than for Intelligence, American Psychologist, vol.28,

pp.1-14.

McCoy, R. W., 2001. Computer Competencies for The

21st Century Information Systems Educator,

Information Technology, Learning, and Performance

Journal, vo.19, no.2, pp.101-123.

McHaney, R., Hightower, R., and Pearson, J., 2002. A

validation of the end-user computing satisfaction

instrument in Taiwan, Information and Management,

vol.39, pp.503-511.

Munro, M. C., Huff, S. L., Marcolin, B. L., and Compeau,

D. R., 1997. Understanding and Measuring User

Competence, Information & Management, vol.33,

pp.45-57.

Palvia, P. C., 1996. A model and instrument for measuring

small business user satisfaction with information

technology, Information and Management, vo.31, no.3,

pp.151-163.

Rifkin, K. I., Fineman, M., and Ruhnke, C. H., 1999.

Developing Technical Managers First You Need a

Competency Model, Research & Technology

Management, vol.42, no.2, pp.53-57.

Rodriguez, D., Patel, R., Bright, A., Gregory, D., and

Gowing, M. K., 2002. Developing Competency

Models to Promote Integrated Human Resource

Practices, Human Resource Management, vol.41, no.3,

pp.309-324.

Spencer, L. M. and Spencer, S. M., 1993. Competence at

Work: Models for Superior Performance, John Willy

& Son Inc.

Torkzadeh, G. and Doll, W. J., 1991. Test-retest reliability

of the end-user computing satisfaction instrument,

Decision Sciences, vo.22, no.1, pp.26-37.

Torkzadeh. G. and Lee, J. W., 2003, Measures of

Perceived End-user's Computing Skills, Information &

Management, vol.20, no.18, pp.1-9.

Wu, J. H., Chen, Y. C., and Chang, J., 2005. The IS

Manager: A Study of Critical Professional Activities,

and Skills and Knowledge, Proceedings of the 38th

Hawaii International Conference on System Sciences,

pp.1-9.

Wu, J. H., Chen, Y. C., and Lin, L. M., 2007. Empirical

evaluation of the revised end-user computing

acceptance model, Computers in Human Behavior,

vol.23, pp.162-174.

Yoon, C. Y. and Leem, C. S., 2006. A Classification of e-

Business Human Resources and Its Applications,

Study of Internet and Electronic Commerce, vol.6,

no.2, pp.1-22.

ICE-B 2012 - International Conference on e-Business

134