Sarah Aouhassi
ABSTRACT
Organizations nowadays focus more and more on business quality for various reasons, such as increasing their competitiveness, their productivity or their turnover, and since we are living on the digital era, information system quality requires great attention from all decision-makers.
Information System (IS) is by definition composed of five components namely human resources, hardware, software, procedures and data, therefore its qualification rely on the qualification of each component apart. A set of indicators has been defined and interpreted as questions to allow data gathering from a larger population including all IS players which are managers, technical staff, functional staff and users. Four types of surveys has been created to fit every IS player.
In this contribution, we emphasize on the survey designed for technical staff by explaining the reasons for not including the whole set of indicators and by finding mathematical formulas relating questions of the survey to indicators in the model, in order to quantify them numerically and aggregate them by component.
- Aouhassi, S., & Hanoun, M. (2015). Information System Quality: State of the Art and New Model. International Journal of Engineering Research and Technology, 4(3), 589--594.Google Scholar
- Bakota, T., Beszédes, Á., Ferenc, R., & Gyimóthy, T. (2008). Continuous Software Quality Supervision Using SourceInventory and Columbus, 1--2.Google ScholarDigital Library
- Besson, P., & Rowe, F. (2012). Strategizing information systems-enabled organizational transformation: A transdisciplinary review and new directions. The Journal of Strategic Information Systems, 21(2), 103--124. Google ScholarDigital Library
- Chawla, S. (2013). Review of MOOD and QMOOD metric sets, 3(3), 448--451.Google Scholar
- Gencel, C., Petersen, K., Ahmad, A., & Imran, M. (2013). The Journal of Systems and Software A decision support framework for metrics selection in goal-based measurement programs: GQM-DSFMS. The Journal of Systems & Software, 86(12), 3091--3108. Google ScholarDigital Library
- Gorla, N., Somers, T. M., & Wong, B. (2010). Organizational impact of system quality, information quality, and service quality. The Journal of Strategic Information Systems, 19(3), 207--228. Google ScholarDigital Library
- Heidari, F., & Loucopoulos, P. (2014). Quality evaluation framework (QEF): Modeling and evaluating quality of business processes. International Journal of Accounting Information Systems, 15(3), 193--223.Google ScholarCross Ref
- Letouzey, J.-L., & Coq, T. (n.d.). The «SQALE» Analysis Model An analysis model compliant with the representation condition for assessing the Quality of Software Source Code.Google Scholar
- Letouzey, J. L., & Ilkiewicz, M. (2012). Managing technical debt with the SQALE method. IEEE Software, 29(6), 44--51. Google ScholarDigital Library
- Maiga, A. S., Nilsson, A., & Ax, C. (2015). Relationships between internal and external information systems integration, cost and quality performance, and firm profitability. International Journal of Production Economics.Google ScholarCross Ref
- Marinescu, R. (n.d.). Detection Strategies: Metrics-Based Rules for Detecting Design Flaws.Google Scholar
- Mordal-Manet, K., Laval, J., Ducasse, S., Anquetil, N., Balmas, F., Bellingard Laurent Bouhier, F., ... McCabe, T. J. (n.d.). An empirical model for continuous and weighted metric aggregation.Google Scholar
- Nagendra, A., & Deshpande, M. (2014). Human Resource Information Systems (HRIS) in HR Planning and Development in Mid to Large Sized Organizations. Procedia - Social and Behavioral Sciences, 133, 61--67.Google ScholarCross Ref
- Nieves Pérez-Aróstegui, M., Bustinza-Sánchez, F., & Barrales-Molina, V. (2015). Exploring the relationship between information technology competence and quality management. Cuadernos de Economía Y Dirección de La Empresa, 18, 4--17.Google Scholar
- Peppard, J., & Ward, J. (2004). Beyond strategic information systems: towards an IS capability. The Journal of Strategic Information Systems, 13(2), 167--194.Google ScholarCross Ref
- Rothenberger, M. A., Kao, Y.-C., & Wassenhove, L. N. Van. (2010). Total quality in software development: An empirical study of quality drivers and benefits in Indian software projects. Information & Management, 47, 372--379. Google ScholarDigital Library
- Sarrab, M., & Hussain Rehman, O. M. (2014). Empirical study of open source software selection for adoption, based on software quality characteristics. ADVANCES IN ENGINEERING SOFTWARE, 69, 1--11. Google ScholarDigital Library
- Zheng, Y., Zhao, K., & Stylianou, A. (n.d.). The impacts of information quality and system quality on users' continuance intention in information-exchange virtual communities: An empirical investigation.Google Scholar
Index Terms
- Information System Qualification by component
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