Integrate between information systems engineering and software engineering theories for successful quality engineering measurement of software: Valid instrument pre-results

Amani Ali Ibrahim Almetwaly, Ibrahim Eskandar Ibrahim Fadhel

Article ID: 3382
Vol 6, Issue 1, 2023

VIEWS - 198 (Abstract) 40 (PDF)

Abstract


An extensive number of instruments and systems assessment tools are weak and not good enough in the appraisal of systems’ quality engineer success measurement. Thus, the comprehension of systems’ success is very serious. One of the purposes of this research topic is to develop a successful, novel, validated instrument for measuring system quality success based on the integration between theories of information systems (Seddon and DeLone & McLean) and software engineering theory (ISO 25010). To ensure the quality of the instrument before use, eight academic experts have validated. The reason for expanding the number of experts to eight is to accurately build and evaluate the instrument because this instrument is the first one erring. After expert validation done successfully, researchers started the process of instrument pre-test. Pre-test verification and validation results done by test the instrument of 74 users. The results of the statistical testing were perfect. The Composite reliability proposed value is 0.7, The average variance extracted value is 0.5. Cronbach’s alpha is higher than 0.7. The value of Spearman’s reliable rhea is >0.6. Results approved that this instrument is strong and perfect to be used as a valid tool for system success measurement.


Keywords


information systems; systems success measurement; software engineering; quality engineering; ISO 25010

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References


1. Fadhel IEI, Idrus SZS, Ibrahim AAEA, et al. An integration between information systems engineering and software engineering theories towards engineering a novel framework of web-based systems success for institutions based on students’ perceptions. Journal of Physics: Conference Series 2018; 1019: 012081. doi: 10.1088/1742-6596/1019/1/012081

2. Fadhel IEI, Idrus SZS, Ibrahim AAAE, et al. Measuring system success in new context by adapting DM 2003 framework with the external factor management support. Journal of Physics: Conference Series 2018; 1019: 012003. doi: 10.1088/1742-6596/1019/1/012003

3. Jami Pour M, Mesrabadi J, Asarian M. Meta-analysis of the DeLone and McLean models in e-learning success: The moderating role of user type. Online Information Review 2022; 46(3): 590–615. doi: 10.1108/OIR-01-2021-0011

4. Bao Z, Zhu Y. Understanding customers’ stickiness of live streaming commerce platforms: An empirical study based on modified e-commerce system success model. Asia Pacific Journal of Marketing and Logistics 2022; 35(3): 775–793. doi: 10.1108/apjml-09-2021-0707

5. Zhang J, Zhong S, Wang T, et al. Blockchain-based systems and applications: A survey. Journal of Internet Technology 2020; 21(1): 1–14.

6. König CM, Karrenbauer C, Breitner MH. Critical success factors and challenges for individual digital study assistants in higher education: A mixed methods analysis. Education and Information Technologies 2023; 28(4): 4475–4503. doi: 10.1007/s10639-022-11394-w

7. DeLone WH, McLean ER. The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems 2003; 19(4): 9–30. doi: 10.1080/07421222.2003.11045748

8. Davis RB, Anderson JR. Exponential survival trees. Statistics in Medicine 1989; 8(8): 947–961. doi: 10.1002/sim.4780080806

9. Hsu L. What makes good LMOOCs for EFL learners? Learners’ personal characteristics and Information System Success Model. Computer Assisted Language Learning 2023; 36(1–2): 1–25. doi: 10.1080/09588221.2021.1899243

10. DeLone WH, McLean ER. Information systems success measurement. Foundations and Trends® in Information Systems 2016; 2(1): 1–116. doi: 10.1561/2900000005

11. Gelderman CJ, Kusters RJ. Measuring information systems success: A comment on the use of perceptions. In: Measuring Organizational Information Systems Success: New Technologies and Practices. IGI Global; 2012. pp. 23–38. doi: 10.4018/978-1-4666-0170-3.ch002

12. Gürkut C, Nat M. Important factors affecting student information system quality and satisfaction. Eurasia Journal of Mathematics, Science and Technology Education 2017; 14(3): 923–932. doi: 10.12973/ejmste/81147

13. Mardiana S, Tjakraatmadja JH, Aprianingsih A. Validating the conceptual model for predicting intention to use as part of information system success model: The case of an Indonesian government agency. Procedia Computer Science 2015; 72: 353–360. doi: 10.1016/j.procs.2015.12.150

14. McNab AL, Ladd DA. Information quality: The importance of context and trade-offs. In: Proceedings of 2014 47th Hawaii International Conference on System Sciences; 6–9 January 2014; Waikoloa, HI, USA. pp. 3525–3532. doi: 10.1109/hicss.2014.439

15. Mwangi EW. Information Quality Assessment Framework: Case of the National Safety Net Program Single Registry System [PhD thesis]. Strathmore University; 2016.

16. Fadhel IEI. The Development of Web-Based Systems Quality Engineering Framework in the Universities Domain [PhD thesis]. University Malaysia Perlis; 2019.

17. Fadhel IEI. An Evaluation of Information System Success Based on Students’ Perspective [Master’s thesis]. Universiti Utara Malaysia; 2015.

18. Fadhel IEI, Idrus SZBS, Abdullah MSY, et al. Nias-mukalla web based systems success measurement and students satisfaction evaluation based on security factor of systems quality engineering theory (ISO 25010) and other factors. Independent Journal of Management & Production 2019; 10(6): 2102–2123. doi: 10.14807/ijmp.v10i6.967

19. Fadhel IEI, Idrus SZBS, Abdullah MSY, et al. A new perspective of web-based systems quality engineering measure by using software engineering theory (ISO 25010): An initial study. Journal of Physics: Conference Series 2020; 1529(2): 022004. doi: 10.1088/1742-6596/1529/2/022004

20. Fadhel IEI, Idrus SZBS, Abdullah MSY, et al. Towards development a novel framework of web-based systems quality engineering by the integration between information systems and software engineering theories: Context of higher education. Journal of Physics: Conference Series 2020; 1529(2): 022005. doi: 10.1088/1742-6596/1529/2/022005

21. Fadhel IEI, Idrus SZBS, Abdullah MSY, et al. Systems success measurement: Instrument & framework a new perspective. Independent Journal of Management & Production 2019; 10(5): 1572–1606. doi: 10.14807/ijmp.v10i5.872

22. Fadhel IEI, Idrus SZBS, Ibrahim AAEA, et al. Ibrahim’s triangle model (satisfaction–benefit–loyalty) for systems success measurement. Independent Journal of Management & Production 2019; 10(2): 499–511. doi: 10.14807/ijmp.v10i2.867

23. MacCallum RC, Widaman KF, Preacher KJ, et al. Sample size in factor analysis: The role of model error. Multivariate Behavioral Research 2001; 36(4): 611–637. doi: 10.1207/S15327906MBR3604_06

24. Hair JF Jr, Hult GTM, Ringle C, Sarstedt M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Sage Publications; 2017.

25. Fornell C, Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research 1981; 18(1): 39–50. doi: 10.1177/002224378101800104

26. Huang CC, Wang YM, Wu TW, et al. An empirical analysis of the antecedents and performance consequences of using the moodle platform. International Journal of Information and Education Technology 2013; 3(2): 217–221. doi: 10.7763/ijiet.2013.v3.267

27. Pallant J. SPSS Survival Manual. McGraw-Hill Education; 2013.

28. Garson GD. Reliability analysis. Available online: http://faculty.chass.ncsu.edu/garson/pa765/statnote.htm (accessed on 18 January 2024).




DOI: https://doi.org/10.24294/csma.v6i1.3382

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