바로가기메뉴

본문 바로가기 주메뉴 바로가기

ACOMS+ 및 학술지 리포지터리 설명회

  • 한국과학기술정보연구원(KISTI) 서울분원 대회의실(별관 3층)
  • 2024년 07월 03일(수) 13:30
 

logo

  • P-ISSN1738-3110
  • E-ISSN2093-7717
  • SCOPUS, ESCI

Investigating the Determinants of Major IT Incident Tickets: A Case Study of an IT Service Provider Firm for Logistics and Distribution Industry

Investigating the Determinants of Major IT Incident Tickets: A Case Study of an IT Service Provider Firm for Logistics and Distribution Industry

The Journal of Distribution Science(JDS) / The Journal of Distribution Science, (P)1738-3110; (E)2093-7717
2016, v.14 no.12, pp.61-69
https://doi.org/https://doi.org/10.15722/jds.14.12.201612.61
Ro, Mohamad Izham Che (School of Business and Administration, Wawasan Open University)
Lau, Wee-Yeap (Department of Applied Statistics, Faculty of Economics and Administration, University of Malaya)

Abstract

Purpose - This study investigates the determinants that affect the number of IT Incident tickets of an IT Service Provider ("ITSP") to logistics industry in order to improve its management process by reducing the incident tickets. Research design, data, and Methodology - This study uses weekly data of IT incident tickets from September 2012 to June 2015. Correlation and regression analyses are conducted. Six identified determinants i.e., IT Change, User Errors, Shipment Volume, Network, Hardware and Software Issues are used as the explanatory variables. Results - Our findings show as following. First, our analysis indicates that IT Change is not a significant determinant as opposed to what commonly believed by many as the most important factor. Second, Software issue is the highest contributor to the Major IT incident tickets, followed by User Error, Network and Hardware issues. Third, it seems there is lead-lag relationship between IT Change and Major IT Incidents tickets as indicated by earlier studies. Fourth, the relationship between IT Change and Major IT tickets is also affected by shipment volume. Conclusions - As policy recommendation, all identified determinants should be treated according to priority. In addition, improving the way IT Changes are implemented will definitely reduce the IT incident tickets.

keywords
IT Service Provider, IT Changes, IT Incident Tickets, Logistics and Distribution Industry, Network, Enterprise Cloud Computing

참고문헌

1.

Armstrong & Associates (2014). Top 25 Global Freight Forwarders - Largest Providers by 2013 Gross Revenues and Freight Forwarding Volumes. Retrieved August 6, 2016, from http://www.3plogistics.com/Top_25_Global_FF.htm

2.

Ash, J. S. (2004). Some unintended consequences of information technology in health care: The nature of patient care information system-related errors. Journal of the American Medical Informatics Association, 11(2), 104-112.

3.

Beekman, G., & Quinn, M. J. (2008). Tomorrow's Technology and You. New Jersey, USA: Pearson Prentice Hall.

4.

Bhimani, A. (1996). Securing the commercial Internet. Communications of the ACM , 39(6), 29-35.

5.

Clark, C. Y. (2013). A study on Corporate Security Awareness and Compliance Behavior Intent. Pace University. Ann Arbor, MI: ProQuest Dissertation Publishing.

6.

Computer History Museum. (2004). Internet History. Retrieved September 1, 2015, from Computer History Museum: http://www.computerhistory.org/internet_history/index.html, as accessed on August 6, 2016.

7.

Crosby, P. (1984). Quality Without Tears. New York:McGraw-Hill Book Company.

8.

Davis, F. (1989). Perceived Usefulness, Perceived Ease of User, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-340.

9.

Druebert, J. (2010, February 22). Changes, Incidents and Unintended Consequences Index. Retrieved September 1, 2015, from ITSM Watch:http://www.itsmwatch.com/itil/article.php/3866396/Chan ges-Incidents--Unintended-Consequences.htm, as accessed on August 15, 2016.

10.

Evolven (2016). Unintended Consequences Index by Jason Druebert. Retrieved January 5, 2016, from http://www.evolven.com/blog/changes-incidents-uninte nded-consequences-index.html.

11.

Henriquez, J. (1996). Misunderstandings About Computers as a Factor in Computer-Related Incidents. Atlanta, USA: Thesis for Doctorate in Emory University.

12.

Humphrey, W. (1995). A Discipline for Software Engineering. New York: Addison-Wesley.

13.

Janaki, K. (2010). Quality Market: Design and Field Study of Prediction Market for Software Quality Control. Florida, USA: Thesis for Doctorate in Nova Southeastern University.

14.

Kanfer, R. (1990). Motivation Theory and Industrial and Organizational Psychology. In M. Dunnette, & L., Hough (eds.). Handbook of Industrial and Organizational Psychology, 2, 75-150. Palo Alto, CA: Consulting Psychology Press.

15.

Latham, G., & Pinder, C. (2005). Work Motivation Teory and Research at the Daen of the Twenty First Century. The Annual Review of Psychology, 56, 485-516.

16.

Leveson, N. G. (1995). Safeware: System safety and computers. New York, NY, USA: ACM New York.

17.

Mitchell, M. W. (1997). The Effects of Embedded Question Type and Locus of Control on Processing Dept, Knowledge Gain and Attitude Change in a Computer-based Interactive Video Environment. Virginia Polytechnic Institute and State University. Blacksburg, Virginia: Virginia Polytechnic Institute and State University.

18.

Potharaju, R. (2014). Data-driven approaches to improve dependability of cloud services. Purdue University. Indiana: ProQuest Dissertations Publishing.

19.

Roberts, J., Hann, I., & Slaughter, S. (2006). Understanding the Motivations, Participations, and Performance of Open Source Software Developers:A longitudinal study of Apache Project. Management Science , 52(7), 984-999.

20.

Rola, M. (2002). Monitoring mayhem or the right to see?. Computer Dealer News, 18, 6-7.

21.

Tira, D. E. (1970). An Introduction to the Theory and Application of the Product-Moment Family of Correlations via a Computer Assisted Instructional System. Ohio: Ohio State University.

22.

Turban, E., King, D., Lee, J., Liang, T.-P., & Turban, D. (2010). Electronic Commerce 2010. New Jersey, USA: Pearson.

23.

Tian, W. D., & Zhao, Y. D. (2014). Optimized Cloud Resource Management and Scheduling: Theories and Practices. Waltham, MA, USA: Morgan Kaufmann.

24.

Wickens, C. D. (2000). Engineering psychology and human performance (3rd ed.). New York: Harper Collins Publishers Inc.

25.

Venkatesh, Davis, F., & Morris, M. (2007). Dead or alive? The development, trajectory and future of technology acceptance adoption research. Journal of the association for Information Systems, 8(4), 268–286.

The Journal of Distribution Science(JDS)