low et al ., 2011 ). W r i t i n g

Referencing and reference listing

Complexity has been extensively studied in the IS literature (Compeau, Meister, & Higgins, 2007). Rogers defines complexity as “the degree to which an innovation is perceived as relatively difficult to understand and use” (Rogers, 2003). The longer it takes to understand and to implement an innovation, the more likely it is that complexity turns into a barrier for adoption of a new technology. This is why complexity usually negatively affects adoption of technologies (Borgman, Bahli, Heier, & Schewski, 203; Low, Chen, & Wu, 2011; Premkumar, Ramamurthy, & Nilakanta, 1994). However, a study among small and medium enterprises (SMEs) revealed that experts do not consider cloud computing as a very complex technology to implement due to simple administration tools, high usability, as well as a high degree of automation, Stieninger and Nedbal (2014). In technology acceptance model (TAM), Davis describes complexity from a positive point of view and uses the term ease-of-use. He defines it as “the degree to which an individual believes that using a particular system would be free of physical and mental effort” (Davis, 1987). Even though there are general differences between Rogers’ discusion of innovation (DoI) theory and Davis TAM (i.e., Rogers focuses on the organizational and Davis on the individual perspective, concerning complexity and ease-of-use), they are both discussing the perception of individuals. Several studies suggest that individuals will see greater relative advantage in innovations that are perceived as easy to use (e.g., Compeau et al. (2007), (Karahanna, Agarwal, & Angst, 2006)). Hence, increased complexity probably inhibits the adoption of technological innovations. For that purpose, the factors are negatively correlated in the proposed hypotheses (Borgman et al., 203; Low et al., 2011).

References

Borgman, H. P., Bahli, B., Heier, H., & Schewski, F. (203). Cloudrise: Exploring cloud computing adoption and governance with the TOE framework. In Proceedings, 46th Hawaii International Conference on System Sciences (HICSS) (p. 4425-4435). Wailea, HI, USA.

Compeau, D. R., Meister, D. B., & Higgins, C. A. (2007). From prediction to explanation: Reconceptualizing and extending the perceived characteristics of innovating. Journal of the Association for Information Systems(8), 409 -439.

Davis, F. (1987). User acceptance of information systems: The technology acceptance model (TAM) ( Tech. Rep.). Ann Arbor, MI, USA: School of Business Administration, University of Michigan.

Karahanna, E., Agarwal, R., & Angst, C. M. (2006). Reconceptualizing compatibility beliefs in technology acceptance research. MIS Quarterly, 30(-), 781 -804.

Low, C., Chen, Y., & Wu, M. (2011). Understanding the determinants of cloud computing adoption. Industrial Management & Data Systems, 111(7), 1006 -1023.

Premkumar, G., Ramamurthy, K., & Nilakanta, S. (1994). Implementation of electronic data interchange: An innovation diffusion perspective. Journal of Management Information Systems(11), 157 -186.

Rogers, E. M. (2003). Diffusion of innovations (5th ed.). New York: Free Press.

Stieninger, M., & Nedbal, D. (2014). Diffusion and acceptance of cloud computing in smes: Towards a valence model of relevant factors. In Proceedings, 47th Hawaii International Conference on System Sciences (HICSS) (p. 3307-3316). Waikoloa, HI, USA.