Influences of the Input Factors towards Success of An Information System Project

A'ang Subiyakto, Abd. Rahman Ahlan, Mira Kartiwi, Husni Teja Sukmana


This study examines the input factors that were reputed theoretically affecting the information system (IS) project success in term of the processional and causal perspectives. Adopting three of the four dimensions from the McLeod and MacDonell’s (M&M’s) classification project framework dimensions, the study is initiated by inviting the internal project stakeholders in a sampled institution. A stratified sampling then identified 130 people who experienced in the projects as the sample, contacted 90 of the samples via e-mail and  distributed the paper-based questionnaire into 40 certain people especially who are on the managerial level. A number of 62 (48%) valid responses, then were analyzed using the partial least squares-structural equation modeling  (PLS-SEM) software, i.e. SmartPLS. The significances of the whole path coefficients, the acceptances of the overall hypotheses, the relevances of the three predictors relevances, and the moderate coefficient determination of the IS project success variable may present acceptability of the proposed model for the subsequent studies.


IS, project success, survey, project stakeholders, PLS-SEM

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Lim CS, Mohamed MZ. Criteria of project success: an exploratory re-examination. International Journal of Project Management (IJPM). 1999; 17(4): 243-248. DOI:10.1016/S0263-7863(98)00040-4

Kerzner HR. Project management: a systems approach to planning, scheduling, and controlling. John Wiley & Sons. 2013.

Howsawi EM, Eager D, Bagia R. Understanding project success: The four-level project success framework. IEEE International Conference on IEEM. Singapore. 2011; 620-624. DOI: 10.1109/IEEM.2011.6117991

McLeod L, MacDonell SG. Factors that affect software systems development project outcomes: A survey of research. ACM Computing Surveys CSUR. 2011; 43(4): 24. DOI: 10.1145/1978802.1978803

Subiyakto A, Ahlan AR. Implementation of input-process-output model for measuring information system project success. TELKOMNIKA IJEE. 2014; 12(7): 5603-5612. DOI: 10.11591/telkomnika.v12i7.5699.

DeLone WH, McLean E.The DeLone and McLean model of information systems success: a ten-year update. Journal of Management Information Systems. 2003; 19(4): 9-30. Retrieved at:

Davis WS. HIPO Hierarchy Plus Input-Process-Output. The information system consultant’s handbook: Systems Analysis and Design. CRC, Florida. 1998; 503-510.

Kellogg WK. Logic model development guide. Michigan: WK Kellogg Foundation. 2004.

De Wit A. Measurement of project success. IJPM. 1988; 6: 164-170. DOI: 10.1016/0263-7863(88)90043-9.

Dvir D, Lipovetsky S, Shenhar A, Tishler A. In search of project classification: a non-universal approach to project success factors. Research Policy. 1988; 27(9): 915-35. DOI: 10.1016/S0048-7333(98)00085-7.

McLeod L, Doolin B, & MacDonell SG. A perspective-based understanding of project success. Project Management Journal (PMJ). 2012; 43(5): 68-86. DOI: 10.1002/pmj.21290

Hussein BA & Klakegg OJ. Measuring the impact of risk factors associated with project success criteria in early phase. Procedia-Social and Behavioral Sciences. 2014; 119: 711-718. DOI: 10.1016/j.sbspro.2014.03.079.

Azimi A, Manesh FS. A new model to identify and evaluate critical success factors in the IT projects; Case study: using RFID technology in Iranian fuel distribution system. IJISM. January-June 2010; 99-112.

Atkinson R. Project management: cost, time and quality, two best guesses and a phenomenon, its time to accept other success criteria. IJPM. 1999; 17: 337-342. DOI: 10.1016/S0263-7863(98)00069-6.

Sudhakar GP. A model of critical success factors for software projects. JEIM. 2012; 25(6): 537-558. DOI: 10.1108/17410391211272829.

Xu X, Zhang W, Barkhi R. IT infrastructure capabilities and IT project success: a development team perspective. Information Technology and Management. 2010; 11(3): 123-142. DOI: 10.1007/s10799-010-0072-3.

Belout A, Gauvreau C. Factors influencing project success: the impact of human resource management. IJPM. 2004; 22(1): 1-11. DOI: 10.1016/S0263-7863(03)00003-6.

Jugdev K, MÜller R. A retrospective look at our evolving understanding of project success. Project Management Journal. 2005; 36: 19-31. DOI: 10.1109/EMR.2006.261387.

Subiyakto A, Ahlan AR. A Coherent Framework for Understanding Critical Success Factors of ICT Project Environment. Proceeding of ICRIIS. Kuala Lumpur. 2013: 342 – 347. DOI: 10.1109/ICRIIS.2013.6716733.

Ghapanchi H, Aurum A. The impact of project capabilities on project performance: Case of open source software projects. IJPM. 2012; 30(4): 407–417. DOI: 10.1016/j.ijproman.2011.10.002.

Gable GG, Sedera D, Chan T. Re-conceptualizing information system success: The IS-impact measurement model. JAIS. 2008; 97: 377–408. Retrieved at:

Belassi W, Tukel OI. A new framework for determining critical success/failure factors in projects. IJPM. 1996; 14(3): 141-151. DOI: 10.1016/0263-7863(95)00064-X

Hussein BA, Silva PP, & Pigagaite G. Perception of complexities in development projects. IEEE 7th IDAACS. 2013; 2: 537-542. DOI: 10.1109/IDAACS.2013.6662982.

Randeree K & Faramawy ATE. Islamic perspectives on conflict management within project managed environments. IJPM. 2011; 29(1), 26-32. DOI: 10.1016/j.ijproman.2010.01.013.

Liu JYC, Chen HG, Chen CC, & Sheu TS. Relationships among interpersonal conflict, requirements uncertainty, and software project performance. IJPM. 2011;29(5), 547-556. DOI: 10.1016/j.ijproman.2010.04.007.

Marnewick C. & Les Labuschagne. Factors that influence the outcome of information technology projects in South Africa: An empirical investigation. Acta Commercii. 2009; 9(1): 78-89. DOI: 10.4102/ac.v9i1.98.

Chandrasekaran A, Linderman K, & Schroeder R. The role of project and organizational context in managing high‐tech R&D projects. Production and Operations Management. 2014. DOI: 10.1111/poms.12253.

Qureshi SM & Kang C. Analysing the organizational factors of project complexity using structural equation modelling. IJPM. 2015; 33(1):165–176. DOI: 10.1016/j.ijproman.2014.04.006.

Nasir MH, Sahibuddin S. Critical success factors for software projects: A comparative study. Scientific research and essays. 2011; 6(10): 2174-2186. DOI: 10.5897/SRE10.1171.

Amiri M, Sarfi A, Kahreh MS, & Maleki MH. Investigation the Critical Success Factors of CRM Implementation in the Urban Management; Case Study: Tehran Municipality. International Bulletin of Business Administration. 2011; Issue 9: 120-132.

M. C. Kaptein, et al., Powerful and consistent analysis of likert-type ratingscales. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM. 2010: 2391-2394. DOI: 10.1145/1753326.1753686.

Creswell JW, Research design: Qualitative, quantitative, and mixed methods approaches. Sage Publications, 2013.

Marshall C. & Rossman GB. Designing qualitative research. Sage Publications, 2010.

Fornell C. & Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research. 1981; 39-50. Retrieved at:

Afthanorhan WMABW. A Comparison Of Partial Least Square Structural Equation Modeling (PLS-SEM) and Covariance Based Structural Equation Modeling (CB-SEM) for Confirmatory Factor Analysis. IJESIT, 2013; 2(5): 198- 205. Retrieved at:

Beringer C, Jonas D, & Kock A. Behavior of internal stakeholders in project portfolio management and its impact on success. IJPM. 2013; 31(6): 830-846. DOI: 10.1016/j.ijproman.2012.11.006.

Henseler J, Ringle CM, & Sinkovics RR. The use of partial least squares path modeling in international marketing. Advances in International Marketing. 2009; 20: 277-319. DOI: 10.1108/S1474-7979(2009)0000020014.

Urbach N, & Ahlemann F. Structural equation modeling in information systems research using partial least squares. JITTA. 2010; 11(2): 5-40. Retrieved at:

Hair JF, Ringle CM, & Sarstedt M. PLS-SEM: Indeed a silver bullet. JMTP. 2011; 19(2): 139-152. DOI: 10.2753/MTP1069-6679190202

Hair JF, Sarstedt M, Ringle CM, & Mena JA. An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science. 2012; 40(3): 414-433. DOI: 10.1007/s11747-011-0261-6.

Wong KKK. Partial Least Squares Structural Equation Modeling (PLS-SEM) Techniques Using SmartPLS. Marketing Bulletin. 2013; 24: 1-32. Retrieved at:

Nunnally JC & Bernstein IH. Psychometric Theory. New York: McGraw-Hill, 1994.

Chin WW. The partial least squares approach to structural equation modelling. In: Marcoulides GA. Editor. Modern Methods for Business Research. NY: Psychology Press; 2013: 295-336.


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