Mining significant factors affecting the adoption of SaaS using the rough set approach

Despite that Software as a Service (SaaS) seems to be the most tempting solution among different types of cloud services, yet it has not been adopted to-date with as much alacrity as was originally expected. A variety of factors may influence the adoption of SaaS solutions. The objective of this study is thus to explore the significant factors affecting the adoption of SaaS for vendors and enterprise users. An analytical framework is proposed containing two approaches-Technology Acceptance Model (TAM) and Rough Set Theory (RST). An empirical study on the IT/MIS enterprises in Taiwan is carried out. The results have revealed a considerable amount of meaningful information, which not only facilitates the SaaS vendors to grasp users' needs and concerns about SaaS adoption, but also helps the managers to introduce effective marketing strategies and actions to promote the growth of SaaS market. Based on the findings, some managerial implications are discussed.

[1]  E. Rogers,et al.  Diffusion of Innovations , 1964 .

[2]  Paul T. Jaeger,et al.  Identifying the security risks associated with governmental use of cloud computing , 2010, Gov. Inf. Q..

[3]  Fred D. Davis.  A technology acceptance model for empirically testing new end-user information systems : theory and results , 1985 .

[4]  Marjan Hericko,et al.  An empirical examination of application frameworks success based on technology acceptance model , 2010, J. Syst. Softw..

[5]  Z. Pawlak.  Rough set approach to knowledge-based decision support , 1997 .

[6]  Susan Wiedenbeck,et al.  On-line trust: concepts, evolving themes, a model , 2003, Int. J. Hum. Comput. Stud..

[7]  Subhas C. Misra,et al.  Identification of a company's suitability for the adoption of cloud computing and modelling its corresponding Return on Investment , 2011, Math. Comput. Model..

[8]  William R. King,et al.  A meta-analysis of the technology acceptance model , 2006, Inf. Manag..

[9]  V. Kavitha,et al.  A survey on security issues in service delivery models of cloud computing , 2011, J. Netw. Comput. Appl..

[10]  Chi-Jen Lin,et al.  A causal analytical method for group decision-making under fuzzy environment , 2008, Expert Syst. Appl..

[11]  Daniele Catteddu and Giles Hogben.  Cloud Computing. Benefits, risks and recommendations for information security , 2009 .

[12]  Fred D. Davis,et al.  User Acceptance of Computer Technology: A Comparison of Two Theoretical Models , 1989 .

[13]  Annette Mills,et al.  A model of Internet adoption by SMEs , 2001, Inf. Manag..

[14]  Viswanath Venkatesh,et al.  Technology Acceptance Model 3 and a Research Agenda on Interventions , 2008, Decis. Sci..

[15]  Siani Pearson,et al.  A client-based privacy manager for cloud computing , 2009, COMSWARE '09.

[16]  Sandro Morasca,et al.  A hybrid approach to analyze empirical software engineering data and its application to predict module fault-proneness in maintenance , 2000, J. Syst. Softw..

[17]  Harry Bouwman,et al.  An assessment of advanced mobile services acceptance: Contributions from TAM and diffusion theory models , 2008, Inf. Manag..

[18]  Markus Klems,et al.  Do Clouds Compute? A Framework for Estimating the Value of Cloud Computing , 2008, WEB.

[19]  Andrzej M. Goscinski,et al.  Toward dynamic and attribute based publication, discovery and selection for cloud computing , 2010, Future Gener. Comput. Syst..

[20]  Yu-Cheng Lee,et al.  Analysis of adopting an integrated decision making trial and evaluation laboratory on a technology acceptance model , 2010, Expert Syst. Appl..

[21]  A. Evans,et al.  Elements of trust: Risk and perspective-taking , 2011 .

[22]  Wei-Wen Wu,et al.  Developing global managers' competencies using the fuzzy DEMATEL method , 2007, Expert Syst. Appl..

[23]  Elena Mugellini,et al.  Advances in Intelligent Web Mastering - 2 , 2007 .

[24]  Dong-Hee Shin,et al.  Towards an understanding of the consumer acceptance of mobile wallet , 2009, Comput. Hum. Behav..

[25]  Yu-Ting Lee,et al.  Exploring high-performers' required competencies , 2010, Expert Syst. Appl..

[26]  Su-Chao Chang,et al.  An extension of trust and TAM model with IDT in the adoption of the electronic logistics information system in HIS in the medical industry , 2008, Int. J. Medical Informatics.

[27]  Zdzislaw Pawlak,et al.  Rough sets, decision algorithms and Bayes' theorem , 2002, Eur. J. Oper. Res..

[28]  George Feuerlicht,et al.  Next Generation SOA: Can SOA Survive Cloud Computing? , 2010 .

[29]  Fred D. Davis,et al.  A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies , 2000, Management Science.

[30]  I. Y.-F. Huang,et al.  Simplifying essential competencies for Taiwan civil servants using the rough set approach , 2008, J. Oper. Res. Soc..

[31]  Fred D. Davis,et al.  A Model of the Antecedents of Perceived Ease of Use: Development and Test† , 1996 .

[32]  Wei-Wen Wu.  Exploring core competencies for R&D technical professionals , 2009, Expert Syst. Appl..

[33]  Wei-Zhi Wu,et al.  Approaches to knowledge reduction based on variable precision rough set model , 2004, Inf. Sci..

[34]  I. Ajzen,et al.  Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research , 1977 .

[35]  Gordon B. Davis,et al.  User Acceptance of Information Technology: Toward a Unified View , 2003, MIS Q..