Exploring decisive factors affecting an organization’s SaaS adoption: A case study

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Wu, Wei-Wen, Lan, Lawrence W. & Lee, Yu-Ting (2011): Exploring decisive factors affecting an organization’s SaaS adoption: A case study, International Journal of Information Management, Vol. 31, Issue 6, p.556-563. 6p. DOI: 10.1016/j.ijinfomgt.2011.02.007

Keywords: Software as a Service (SaaS), Adoption, Trust, Decision Making Trial and Evaluation, Laboratory (DEMATEL)

Wu, Wei-Wen, Lan, Lawrence W. & Lee, Yu-Ting start by defining cloud services as a group of service solutions involving computing, data storage, and software available through the Internet where customers do not own or operate the service provided. The cloud process all the information given by the user to then send back its results. This model allows organizations to focus on its core business and lessens the burden of developing and maintaining complicated IT systems.

Cloud computing can be divided into three categories: Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). Nevertheless, SaaS is regarded as potentially the most important model for scaling IT performance. Despite this information, organizations are still hesitant to use it due to trust concerns. It is stated that SaaS has an attractive growth potential as SMEs have yet to start using SaaS extensively. Among the reasons why it is not yet widely-spread in this segment, concern about data security represents the stronger factor. Therefore, focusing on trust to highlight perceived benefits and diminish perceived risks is the approach recommended for all marketing efforts. Nonetheless, adopting new technologies or services solutions is still commonly seen as a way to improve competitiveness in an organization.

Trust plays a decisive role in the acceptance of perceived benefits and the lessening of perceived risks for this business model. This is why, the authors propose a solution framework where they treat perceived benefits and perceived risks as two different arguments in order to develop a visible cause-effect graph to aid organizations in their decision making towards SaaS. They define trust as the willingness to behave risky in uncertain situations as it is believed that expected benefits might overcome the negative aspects.

Although trust is a main driver for the accomplishment of any e-commerce, perceived risks, technical and subjective, act as barriers for the adoption of this service, while perceived benefits represent a motivation for adoption. The study aims to select the most important set of perceived benefits and perceived risks for SaaS adoption using the DEMATEL model to conduct a cause-effect analysis. To do so, they use a case study centering on a Taiwanese technological company. Out of all the factors studied, it is concluded that an easy and fast deployment of the service, and its potential in the future are the most relevant benefits to business. On the other hand, data locality and security, and authentication and authorization are mentioned as the most important perceived risks preventing adoption. These perceived risks are, however, subjective rather than technical, as organizations commonly dislike the lack of ownership and control on cloud computing deployment.

These results tell us that SaaS businesses should emphasize the subjective but strategical aspects of delegating security control to SaaS. The writers divide two types of SaaS customers: the organizations focusing on perceived benefits, where SaaS vendors can focus on their strategical competitive advantage, and the organizations focusing on perceived risks, where vendors should reduce security concerns by communicating best practices of successful businesses using SaaS, and expert recommendations.

 

References:

Catteddu, D., & Hogben, G. (2009). Cloud computing: Benefits, risks and recom- mendations for information security. European Network and Information Security Agency (ENISA), 1–125.

Chang, B., Chang, C. W., & Wu, C. H. (2011). Fuzzy DEMATEL method for developing supplier selection criteria. Expert Systems with Applications, 38(3), 1850–1858.

Chen, J. K., & Chen, I. S. (2010). Using a novel conjunctive MCDM approach based on DEMATEL, fuzzy ANP, and TOPSIS as an innovation support system for Taiwanese higher education. Expert Systems with Applications, 37(3), 1981–1990.

Cho, S. E. (2010). Perceived risks and customer needs of geographical accessibil- ity in electronic commerce. Electronic Commerce Research and Applications, 9(6), 495–506.

Crespo, A. H., & del Bosque, I. R. (2010). The influence of the commercial features of the Internet on the adoption of e-commerce by consumers. Electronic Commerce Research and Applications, 9(6), 562–575.

Davis, F. D. (1986). A technology acceptance model for empirically testing new end- user information systems: Theory and results. Doctoral dissertation. Cambridge, MA: Sloan School of Management, Massachusetts Institute of Technology.

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.

Deutsch, M. (1962). Cooperation and trust: Some theoretical notes. In M. R. Jones (Ed.), Nebrasca symposium on motivation (pp. 275–318). Nebrasca: University Press.

Elahi, S., & Hassanzadeh, A. (2009). A framework for evaluating electronic commerce adoption in Iranian companies. International Journal of Information Management, 29(1), 27–36.

Evans, A. M., & Krueger, J. I. (2010). Elements of trust: Risk and perspective-taking. Journal of Experimental Social Psychology, doi:10.1016/j.jesp.2010.08.007

Feuerlicht, G. (2010). Next generation SOA: Can SOA survive cloud computing? In V. Snasel, V. Snasel, et al. (Eds.), Advances in intelligent web mastering – 2, AISC 67 (pp. 19–29).

Fontela, E., & Gabus, A. (1976). The DEMATEL observer, DEMATEL 1976 report. Geneva, Switzerland: BATTELLE Institute, Geneva Research Center.

Gabus, A., & Fontela, E. (1973). Perceptions of the World Problematique: Communi- cation Procedure. In Communicating with those bearing collective responsibility (DEMATEL Report No. 1). Geneva, Switzerland: BATTELLE Institute, Geneva Research Centre.

Gabus, A., & Fontela, E. (1972). World problems, an invitation to further thought within the framework of DEMATEL. Geneva, Switzerland: BATTELLE Institute, Geneva Research Centre.

Goscinski, A., & Brock, M. (2010). Toward dynamic and attribute based publica- tion, discovery and selection for cloud computing. Future Generation Computer Systems, doi:10.1016/j.future.2010.03.009

Ho, W. R. J., Tsai, C. L., Tzeng, G. H., & Fang, S. K. (2011). Combined DEMATEL technique with a novel MCDM model for exploring portfolio selection based on CAPM. Expert Systems with Applications, 38(1), 16–25.

Hu, H. Y., Lee, Y. C., Yen, T. M., & Tsai, C. H. (2009). Using BPNN and DEMATEL to modify importance–performance analysis model—A study of the computer industry. Expert Systems with Applications, 36(6), 9969–9979.

King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740–755.

Lin, Ch. J., & Wu, W. W. (2008). A causal analytical method for group decision-making under fuzzy environment. Expert Systems with Applications, 34(1), 205–213. Lopez-Nicolas, C., Molina-Castillo, F. J., & Bouwman, H. (2008). An assessment of

advanced mobile services acceptance: Contributions from TAM and diffusion

theory models. Information & Management, 45(6), 359–364.

Mehrtens, J., Cragg, P. B., & Mills, A. M. (2001). A model of Internet adoption by SMEs.

Information & Management, 39(3), 165–176.

Misra, S. C., & Mondal, A. (2010). Identification of a company’s suitability for the

adoption of cloud computing and modelling its corresponding return on invest-

ment. Mathematical and Computer Modelling, doi:10.1016/j.mcm.2010.03.037 Shen, Y. C., Lin, G. T. R., & Tzeng, G. H. (2011). Combined DEMATEL techniques with novel MCDM for the organic light emitting diode technology selection. Expert

Systems with Applications, 38(3), 1468–1481.

Shieh, J. I., Wu, H. H., & Huang, K. K. (2010). A DEMATEL method in identifying

key success factors of hospital service quality. Knowledge-Based Systems, 23(3), 277–282.

Slovic, P. (1992). Perception of risk: Reflections on the psychometric paradigm. In S. Krimsky, & D. Golding (Eds.), Social theories of risk (pp. 117–152). New York: Praeger.

Subashini, S., & Kavitha, V. (2010). A survey on security issues in service deliv- ery models of cloud computing. Journal of Network and Computer Applications, doi:10.1016/j.jnca.2010.07.006

Sultan, N. (2010). Cloud computing for education: A new dawn? International Journal of Information Management, 30(2), 109–116.

Tarafdar, M., & Vaidya, S. D. (2006). Challenges in the adoption of E-Commerce technologies in India: The role of organizational factors. International Journal of Information Management, 26(6), 428–441.

Tsai, W. H., & Chou, W. C. (2009). Selecting management systems for sustainable development in SMEs: A novel hybrid model based on DEMATEL, ANP, and ZOGP. Expert Systems with Applications, 36(2), 1444–1458.

Tseng, M. L. (2009). A causal and effect decision making model of service quality expectation using grey-fuzzy DEMATEL approach. Expert Systems with Applica- tions, 36(4), 7738–7748.

Tzeng, G. H., Chiang, C. H., & Li, C. W. (2007). Evaluating intertwined effects in e- learning programs: A novel hybrid MCDM model based on factor analysis and DEMATEL. Expert Systems with Applications, 32(4), 1028–1044.

Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315.

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.

Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451–481.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. Wu, H. H., Chen, H. K., & Shieh, J. I. (2010). Evaluating performance criteria of Employ- ment Service Outreach Program personnel by DEMATEL method. Expert Systems

with Applications, 37(7), 5219–5223.

Wu, F., Li, H. H., & Kuo, Y. H. (2010). Reputation evaluation for choosing a trustworthy

counterparty in C2C e-commerce. Electronic Commerce Research and Applications,

doi:10.1016/j.elerap.2010.09.004

Wu, W. W., & Lee, Y. T. (2007). Developing global managers’ competencies

using the fuzzy DEMATEL method. Expert Systems with Applications, 32(2), 499–507.

Wu, W. W. (2008). Choosing knowledge management strategies by using a combined ANP and DEMATEL approach. Expert Systems with Applications, 35(3), 828–835. Zhou, Q., Huang, W., & Zhang, Y. (2011). Identifying critical success factors in

emergency management using a fuzzy DEMATEL method. Safety Science, 49(2), 243–252.