Measurement and structural model of agile software development critical success factors

Purpose: Agile methodologies have emerged as an innovative and successful business changing way for software development companies since the success rate for completing the software projects on time and budget is better than conventional methodologies. This study proposes a theoretical framework of success factors for agile software development and validates the proposed framework using structural equation modeling. Design Methodology: A survey based random sampling was performed for data collection from 201 respondents identified from the pool of agile practitioners in software companies. Structural Equation Modeling performed on the collected data to validate measurement model as well as the structural model. Findings: The theoretical model was confirmed with modifications and the results showed that required level of fitness indexes have been achieved for the measurement model and structural model. The validation of the factors has also been done. Originality/Value: This study will guide the agile practitioners, academicians and project managers to focus more on the particular success factors which have high weight towards project success.

[1]  Hsin Hsin Chang,et al.  Social capital and individual motivations on knowledge sharing: Participant involvement as a moderator , 2011, Inf. Manag..

[2]  Yehuda Baruch,et al.  Response Rate in Academic Studies — A Comparative Analysis , 1999 .

[3]  Weidong Xia,et al.  Confirmatory Factor Analysis of the End-User Computing Satisfaction Instrument: A Replication , 1997 .

[4]  M. Matsunaga How to factor-analyze your data right: do’s, don’ts, and how-to’s. , 2010 .

[5]  Jen-Her Wu,et al.  An organizational memory information systems success model: an extension of DeLone and McLean's I/S success model , 1998, Proceedings of the Thirty-First Hawaii International Conference on System Sciences.

[6]  Judy E. Scott The measurement of information systems effectiveness: evaluating a measuring instrument , 1995, DATB.

[7]  David F. Larcker,et al.  Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics: , 1981 .

[8]  S. IiI. “Redesigning the future” , 2007, Journal of General Internal Medicine.

[9]  James B. Schreiber,et al.  Reporting Structural Equation Modeling and Confirmatory Factor Analysis Results: A Review , 2006 .

[10]  P. Bentler,et al.  Cutoff criteria for fit indexes in covariance structure analysis : Conventional criteria versus new alternatives , 1999 .

[11]  Chin-Lung Hsu,et al.  Acceptance of blog usage: The roles of technology acceptance, social influence and knowledge sharing motivation , 2008, Inf. Manag..

[12]  R. P. McDonald,et al.  Principles and practice in reporting structural equation analyses. , 2002, Psychological methods.

[13]  Ali F. Farhoomand,et al.  A structural model of end user computing satisfaction and user performance , 1996, Inf. Manag..

[14]  Amitoj Singh,et al.  Agile Enablers and Adoption Scenario in Industry Context , 2015 .

[15]  Christian Homburg,et al.  Applications of structural equation modeling in marketing and consumer research: A review , 1996 .

[16]  Afzaal H. Seyal,et al.  Determinants of academic use of the Internet: A structural equation model , 2002, Behav. Inf. Technol..

[17]  Hsiu-Fen Lin,et al.  A stage model of knowledge management: an empirical investigation of process and effectiveness , 2007, J. Inf. Sci..

[18]  Magid Igbaria,et al.  The effects of self-efficacy on computer usage , 1995 .

[19]  Fatin Izzati Khairushalimi,et al.  Assessing the Fitness of a Measurement Model Using Confirmatory Factor Analysis (CFA) , 2016 .

[20]  Jodie B. Ullman,et al.  Structural Equation Modeling: Reviewing the Basics and Moving Forward , 2006, Journal of personality assessment.

[21]  P. Bentler,et al.  Significance Tests and Goodness of Fit in the Analysis of Covariance Structures , 1980 .

[22]  Amitoj Singh Design of distributed agile development System for indian software industry , 2013 .