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Information Systems Frontiers

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06-10-2020

A Deeper Look at Cloud Adoption Trajectory and Dilemma

Author: Pei-Fang Hsu

Published in: Information Systems Frontiers | Issue 1/2022

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Abstract

Different from previous cloud adoption studies that focus on the benefits and concerns of cloud computing from a technology point of view, this study takes a deeper look at two additional firm-specific forces that could better explain firms’ cloud adoption trajectory and dilemma: Path dependency and Institutional forces. Path dependency theory argues that if a firm has invested intensively in traditional IT, it may be more capable of adopting and utilizing new IT since it has accumulated knowledge. However, the firm could also be trapped in its previous path and reluctant to migrate to cloud to avoid sunk costs and switching costs. On the other hand, institutional theory provides an external view and posit that a firm facing more institutional forces from its trading community will have more incentives, as well as pressure, to adopt cloud. We developed a cloud adoption model that features benefits, concerns, path dependency, and institutional forces as prominent antecedents to understand their competing and complementary effects, and empirically tested the proposed model using 177 firms. The results show that, path dependence is indeed an important factor affecting firms’ cloud adoption behaviors; a firm with a better IT position and more satisfying IT outsourcing experiences will have greater cloud adoption intention. Institutional forces do not directly affect cloud adoption intention. Instead, institutional forces increase perceived benefits, through which, indirectly influence cloud adoption intention. The findings delineate the trajectory and dilemma that firms face when migrating to cloud and provide insights to cloud vendors in choosing their target market.

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We thank one reviewer for pointing out that, in real IT environments, many firms use mixed sourcing options including some in-house systems, some traditional IT-outsourcing solutions, and some cloud applications.

National Institute of Standards and Technology (NIST) defines Software as a Service (SaaS) as: consumers can access software or applications from various client devices through the Internet, and do not manage or control underlying cloud infrastructure such as servers, operating systems, etc. (Mell and Grance 2009). The NIST definition of Platform as a Service (PaaS) is: consumers can deploy onto the cloud infrastructure their own-created or acquired applications using programming languages and tools supported by cloud vendors (Mell and Grance 2009). The NIST definition of Infrastructure as a Service (IaaS) is: consumers are provided with processing, storage, networks, and other fundamental computing resources from cloud vendors.

We used PLS-SEM (SmartPLS) for our analysis for four reasons: (1) PLS enables us to estimate the measurement model and the structural model simultaneously, thereby reducing estimation bias (Ringle et al. 2012; Romanow et al. 2018), (2) PLS is deemed appropriated for formatively measured latent variables (Chin 2010), while covariance-based SEM can only form latent variables reflectively. PLS can formatively measure latent variables with fewer problems related to identification (Temme et al. 2014), constraining structural parameters, and underrepresenting the variance of the underlying constructs (Lee and Cadogan 2013). (3) PLS is regarded as an appropriate tool for exploratory models involving relatively newly created measures or constructs, such as the path dependency construct in our study (Fang et al. 2014), and (4) PLS does not impose stringent restrictions and has fewer distributional assumptions (Gefen et al. 2011; Henseler et al. 2014).

Effect size (f²) and Q² are reported in Appendix C.

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Title: A Deeper Look at Cloud Adoption Trajectory and Dilemma
Author: Pei-Fang Hsu
Publication date: 06-10-2020
Publisher: Springer US
Published in: Information Systems Frontiers / Issue 1/2022
Print ISSN: 1387-3326
Electronic ISSN: 1572-9419
DOI: https://doi.org/10.1007/s10796-020-10049-w

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