More Web Proxy on the site http://driver.im/

article

An empirical study of mobile commerce in insurance industry: Task-technology fit and individual differences

Authors:

Ching-Chang Lee,

Hsing Kenneth Cheng,

Hui-Hsin ChengAuthors Info & Claims

Decision Support Systems, Volume 43, Issue 1

Pages 95 - 110

https://doi.org/10.1016/j.dss.2005.05.008

Published: 01 February 2007 Publication History

Abstract

Mobile commerce, generally defined as conducting information inquiries and/or business transactions by using mobile devices via wireless communications, is considered the next big wave of investment and application of information technology. Mobile commerce is estimated to account for a sizable portion of the US$6.9 trillion e-commerce revenue by 2004. Although abundant research on the technology side of mobile commerce has been published, there exists ''very limited research on the strategies and applications of M-commerce'' [A. Gunasekaran, E. Ngai, Call for papers: special issue on mobile commerce: strategies, technologies, and applications, Decision Support Systems 35 (2003) 187-188. [15]]. We propose and validate a modified task-technology fit model to explore the factors affecting the effective adoption of mobile commerce in the insurance industry. In particular, we study: (1) if the mobile commerce system based on personal digital assistant (PDA) technology is applicable for the insurance industry; (2) whether individual differences will impact the cognitive fit of insurance agents' use of PDA mobile commerce system; (3) which of the three major insurance tasks is better suited for the PDA technology; and (4) which PDA technology characteristic is best for which type of insurance tasks. Our research finds that the PDA mobile commerce system is indeed suitable for the insurance industry. In terms of the impact of individual differences, we discover that position experience, cognitive style, and computer self-efficacy are major factors that can predict the fit of applying PDA technology for insurance tasks. Counter conventional wisdom, other demographic variables such as gender and age are found to be non-significant. Among three major insurance tasks, we find that the PDA mobile technology provides the highest level of assistance in post-contract customer services, followed by recruiting new insurance contracts, and tax and legal information services.

References

[1]

Anderson, A.A., Predictors of computer anxiety and performance in information systems. Computers in Human Behavior. v12 i1. 61-77.

[2]

Barnes, S., The mobile commerce value chain: analysis and future developments. International Journal of Information Management. i22. 91-108.

Digital Library

[3]

Benbasat, I. and Taylor, R.N., The impact of cognitive styles on information system design. MIS Quarterly. v2 i2. 43-54.

Digital Library

[4]

Benbasat, I. and Taylor, R.N., Behavioral aspects of information processing for the design of management information systems. IEEE Transactions on Systems, Man, and Cybernetics. v12 i4. 439-450.

[5]

Chen, M., Gender and computers. Journal of Educational Computing Research. i16. 50-55.

[6]

Compeau, D.R. and Higgins, C.A., Computer self-efficacy: development of a measure and initial test. MIS Quarterly. v19 i2. 189-211.

Digital Library

[7]

Compeau, D.R., Higgins, C.A. and Huff, S., Social cognitive theory and individual reactions to computing technology: a longitudinal study. MIS Quarterly. v23 i2. 145-158.

Digital Library

[8]

Davis, F.D., Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly. v13 i3. 319-342.

[9]

Davis, F.D., Bagozzi, R.P. and Warsaw, P.R., User acceptance of computer technology: a comparison of two theoretical models. Management Science. v35 i8. 983-1003.

Digital Library

[10]

Delcourt, M.A.B. and Kinzie, M.B., Computer technologies in teacher education: the measurement of attitudes and self-efficacy. Journal of Research and Development in Education. v27 i1. 35-41.

[11]

Epstein, S., Pacini, R. and Denes, R.V., Individual differences in intuitive-experiential and analytical-rational thinking styles. Journal of Personality and Social Psychology. v71 i2. 390-405.

[12]

Gefen, D. and Straub, D.W., Gender differences in the perception and use of e-mail: an extension to the technology acceptance model. MIS Quarterly. v21 i4. 340-389.

Digital Library

[13]

Goodhue, D.L., Development and measurement validity of a task-technology fit instrument for user evaluations of information systems. Decision Sciences. v29 i1. 105-137.

[14]

Goodhue, D.L. and Thompson, R.L., Task-technology fit and individual performance. MIS Quarterly. v19 i2. 213-236.

Digital Library

[15]

Gunasekaran, A. and Ngai, E., Call for papers: special issue on mobile commerce: strategies, technologies, and applications. Decision Support Systems. v35. 187-188.

Digital Library

[16]

Hill, T., Smith, N.D. and Mann, M.F., Role of efficacy expectations in predicting the decision to use advanced technologies: the case for computers. Journal of Applied Psychology. v27 i2. 307-313.

[17]

Howard, J.S. and Smith, R.D., Computer anxiety in management: myth or reality. Communications of the ACM. v29 i7. 567-579.

Digital Library

[18]

Levine, T., Commitment to learning: effects of computer experience, confidence and attitudes. Journal of Research on Computing in Education. v16 i1. 83-105.

[19]

Liang, T.P., Critical success factors of decision support systems: an experimental study. Data Base. 3-16.

Digital Library

[20]

Liang, T.P. and Doong, H.S., Effect of bargaining in electronic commerce. International Journal of Electronic Commerce. v4 i3. 23-48.

Digital Library

[21]

Messick, S., Individuality in Learning. Jossey-Bass, San Francisco.

[22]

Mikkelsen, A., Øgaard, T., Lindøe, P.H. and Einar Olsen, O., Job characteristics and computer anxiety in the production industry. Computers in Human Behavior. v18 i3. 223-239.

[23]

Murphy, C.A., Coover, D. and Owen, V., Development and validation of the computer self-efficacy scale. Educational and Psychological Measurement. v49. 893-899.

[24]

Nunnally, J.C. and Bernstein, I.H., Psychometric Theory. 3rd ed. McGraw Hill Book Co, New York.

[25]

B.T. Pentland, Use and productivity in personal computers: an empirical test, Proceedings of the Tenth International Conference on Information Systems, Boston, MA, 1989, 211-222.

Digital Library

[26]

Rowand, C., Teacher use of computer and the internet in public schools. National Center for Education Statistics.

[27]

Shashaani, L., Gender differences in computer experience and its influence on computer attitudes. Journal of Educational Computing Research. v11 i4. 347-367.

[28]

Thompson, R.L., Higgins, C.A. and Howell, J.M., Towards a conceptual model of utilization. MIS Quarterly. v15 i1. 125-143.

Digital Library

[29]

Todmand, J. and Lawrenson, H., Computer anxiety in primary school children and university students. British Educational Research Journal. v18 i1. 63-72.

[30]

Vessey, I., Cognitive fit: a theory-based analysis of the graphs versus tables literature. Decision Sciences. v22. 219-240.

[31]

Witkin, H.M., Goodenough, D. and Cox, P., Field-dependent and field-independent: cognitive styles and their educational implications. Review of Educational Research. v47 i1. 1-64.

[32]

Zmud, R.W., Individual differences and MIS success: a review of the empirical literature. Management Science. v25 i10. 966-979.

Cited By

Moreno DJavier MFrancia MHarina E(2023)The Mediating Effect of Replenishment Decisions on Cloud-Based Inventory Management and Record Keeping Performance: Evidence from Micro Businesses in Laguna, PhilippinesProceedings of the 2023 7th International Conference on E-Business and Internet10.1145/3633586.3633590(59-65)Online publication date: 20-Oct-2023
https://dl.acm.org/doi/10.1145/3633586.3633590
Ghods ACook D(2022)PIP: Pictorial Interpretable Prototype Learning for Time Series ClassificationIEEE Computational Intelligence Magazine10.1109/MCI.2021.312995717:1(34-45)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1109/MCI.2021.3129957
Hwang YMalaquias RMalaquias FHa Y(2021)A Cross-Country Study on Intention to Use Mobile BankingJournal of Global Information Management10.4018/JGIM.202103010629:2(102-117)Online publication date: 1-Mar-2021
https://dl.acm.org/doi/10.4018/JGIM.2021030106
Show More Cited By

Index Terms

An empirical study of mobile commerce in insurance industry: Task-technology fit and individual differences

Recommendations

Mobile commerce: its market analyses

As mobile telephones begin to incorporate web browser functionality, consumers are poised to take advantage of "virtual" storefronts. These web-enabled mobile devices allow m-commerce to be conducted anytime and anywhere. Mobile commerce offers a ...
The critical role of consumer behaviour research in mobile commerce

The changing role of mobile communications is reflected in the increasing use of mobile devices for e-commerce purposes. Supported by the penetration of mobile devices and the evolution of mobile technologies, mobile commerce promises to change the way ...
Exploring the Key Success Factors of Mobile Commerce in Taiwan
WAINA '12: Proceedings of the 2012 26th International Conference on Advanced Information Networking and Applications Workshops

The increasingly high penetration rate of mobile phones indicates that mobile commerce has become the hottest issue around the world. However, in Taiwan, the adoption rate of mobile commerce still is low. Very few previous studies have explored how to ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Decision Support Systems

Decision Support Systems Volume 43, Issue 1

February, 2007

301 pages

ISSN:0167-9236

Issue’s Table of Contents

Copyright © Elsevier B.V. © 2005.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 February 2007

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

45
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 22 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Moreno DJavier MFrancia MHarina E(2023)The Mediating Effect of Replenishment Decisions on Cloud-Based Inventory Management and Record Keeping Performance: Evidence from Micro Businesses in Laguna, PhilippinesProceedings of the 2023 7th International Conference on E-Business and Internet10.1145/3633586.3633590(59-65)Online publication date: 20-Oct-2023
https://dl.acm.org/doi/10.1145/3633586.3633590
Ghods ACook D(2022)PIP: Pictorial Interpretable Prototype Learning for Time Series ClassificationIEEE Computational Intelligence Magazine10.1109/MCI.2021.312995717:1(34-45)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1109/MCI.2021.3129957
Hwang YMalaquias RMalaquias FHa Y(2021)A Cross-Country Study on Intention to Use Mobile BankingJournal of Global Information Management10.4018/JGIM.202103010629:2(102-117)Online publication date: 1-Mar-2021
https://dl.acm.org/doi/10.4018/JGIM.2021030106
Marikyan DPapagiannidis SAlamanos E(2021)“Smart Home Sweet Smart Home”International Journal of E-Business Research10.4018/IJEBR.202104010117:2(1-24)Online publication date: 1-Apr-2021
https://dl.acm.org/doi/10.4018/IJEBR.2021040101
Tuu HKhoi NOlsen S(2021)How consumer innovativeness, technological expertise, and consideration set size can explain mobile commerce use: An extended understanding using a moderation–mediation modelInformation Systems and e-Business Management10.1007/s10257-021-00528-119:3(1015-1038)Online publication date: 1-Sep-2021
https://dl.acm.org/doi/10.1007/s10257-021-00528-1
Darban MPolites G(2020)Why Is It Hard to Fight Herding?ACM SIGMIS Database: the DATABASE for Advances in Information Systems10.1145/3433148.343315451:4(93-122)Online publication date: 2-Nov-2020
https://dl.acm.org/doi/10.1145/3433148.3433154
Bond ALéger PSénécal S(2020)The Effect of Device-Affordance Alignment with the User Goal on User ExperienceHCI International 2020 - Late Breaking Papers: User Experience Design and Case Studies10.1007/978-3-030-60114-0_3(42-65)Online publication date: 19-Jul-2020
https://dl.acm.org/doi/10.1007/978-3-030-60114-0_3
Spies RGrobbelaar SBotha A(2020)A Scoping Review of the Application of the Task-Technology Fit TheoryResponsible Design, Implementation and Use of Information and Communication Technology10.1007/978-3-030-44999-5_33(397-408)Online publication date: 6-Apr-2020
https://dl.acm.org/doi/10.1007/978-3-030-44999-5_33
Erskine MGregg DKarimi JScott J(2019)Individual Decision-Performance Using Spatial Decision Support Systems: A Geospatial Reasoning Ability and Perceived Task-Technology Fit PerspectiveInformation Systems Frontiers10.1007/s10796-018-9840-021:6(1369-1384)Online publication date: 1-Dec-2019
https://dl.acm.org/doi/10.1007/s10796-018-9840-0
Marikyan DPapagiannidis SAlamanos E(2019)Smart Home Technology Acceptance: An Empirical InvestigationDigital Transformation for a Sustainable Society in the 21st Century10.1007/978-3-030-29374-1_25(305-315)Online publication date: 18-Sep-2019
https://dl.acm.org/doi/10.1007/978-3-030-29374-1_25
Show More Cited By

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents