A metrics suite of cloud computing adoption readiness

Abstract

Recent research on cloud computing adoption suggests the lack of a deep understanding of its benefits by managers and organizations. We present a firm-level cloud computing readiness metrics suite and assess its applicability for various cloud computing service types. We propose four relevant categories for firm-level adoption readiness, including technology and performance, organization and strategy, economic and valuation, and regulatory and environmental dimensions. We further define sub-categories and measures for each. Our evidence of the appropriateness of the metrics suite is derived based on a series of empirical cases developed from our project work, which encompasses input from field interviews, business press sources, industry white papers, non-governmental organizations, and government agency sources. We also assess how the application of the metrics suite supports organizational users of cloud computing.

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Notes

  1. 1.

    Discussants of a prior version of this research (Kauffman et al. 2014) presented at the 2014 International Conference on the Economics of Grids, Clouds, Systems and Services (GECON 2014) emphasized the importance of adapting measurement approaches that tie in more closely with the various cloud computing services for data storage, software, infrastructure, data analytics, and so on. These comments encouraged us to develop new results related to the different business models of cloud computing in our own metrics suite work.

  2. 2.

    To distinguish our research from what is often seen in academic publications, we sought to create a balanced view through the use of non-academic sources, in addition to the interactions and information from our field study interviews. The benefit of working to triangulate a meaningful set of findings that cuts across academic, industry and government sources is that it brings retrospective assessments, current views and prognostications of future outcome all to the present – to create valuable insights into technology-induced firm and industry transformation.

  3. 3.

    When a client’s use of cloud computing is non-essential because the new technology only supplements existing capabilities, versus when a client relies on the technology to be essential for supporting the primary outcomes of its business, the evaluation of the adoption decision is likely to shift dramatically. For these reasons, we will draw on a variety of aspects of different cloud computing services, including contrasts in the architectural models, the current level of maturity of cloud services in the marketplace, and the heterogeneous perceptions of uncertainty and risk with respect to different cloud services of the potential adopting organizations.

  4. 4.

    This term, metrics suite, is used in engineering, software systems, and business process management contexts. Metrics suites can capture and quantify complex aspects of operational processes, help managers to evaluate business performance, and enable them to make effective adjustments and achieve desirable outcomes. In addition, metrics suites have been used to create measurement approaches to capture quantitative and financial performance, and qualitative and intangible organizational capacities (Kaplan and Norton 1996), measure interdependent aspects of systems design in software development (Chidamber and Kemerer 1994), and simplify financial risks based on a set of numerical measures (Jorion 2000).

  5. 5.

    The idea of using exploratory research methods in this work is heavily founded on the use of industry cases, which anchors our research in the central domain of organization IS research scholarship. The exploratory study of cases to learn about relevant issues that bear on theory related to the management of IT in various kinds of settings was pioneered by authors in organizational and management studies (e.g., Eisenhardt and Graebner 2007), information systems (e.g., Benbasat et al. 1987).

  6. 6.

    With this intermediate theory perspective, researchers typically work on areas with no mature theories, and only separate bodies of literature, as we have noted. Their purpose is to explore the theoretical basis, test provisional explanations, and propose testable propositions. Prior literature, archival data, industry reports and interviews are the major data sources of data, and the method tend to be qualitative. With this approach, different authors deploy slightly different methods. This description matches our general approach, though we have proposed an artefact for cloud adoption readiness.

  7. 7.

    We focused on reports from major consulting firms such as Gartner, McKinsey, and Ernest & Young, and the major cloud services vendors – IBM, HP, Salesforce, Amazon Web Services, Oracle, Fujitsu, Citrix, Insightly, RedHat OpenShift and Heroku – based on a variety of industry sources (e.g., Curtis 2014, Panettieri 2013).

  8. 8.

    Although it initially made sense to us to use quotations as a way to represent their assessments, we later decided that it was more appropriate to extract the essential elements of their comments only. In too many cases, quoting their words gave an impression that the people who were quoted might be “cherry-picked” to support the marketing of the cloud services vendors they used, which was not appropriate for our narratives. Since the authors had no direct access to the senior managers in the materials that we acquired, and our main emphasis was on the relevance of the measures we identified – to assess whether they were meaningful for the intended purpose. We were somewhat less concerned with their specific values: “less important,” of average importance” or “more important.” We thank the review team for their guidance on this issue.

  9. 9.

    An anonymous reviewer suggested to us that it may not be easy to evaluate relative importance especially when the sources, interview partners and documents differ from case to case. We agreed, but wish to note that this is similar to other evaluative methods in different contexts, both qualitative and quantitative. One example is the quantitative method of valuation via net present value (NPV) analysis, where many different assessments, different outcome numbers, and different assumptions are likely to be used by different analysts in different settings. Only in the simplest cases will there be easily agreed upon quantitative outcomes from the analysis.

  10. 10.

    We also want to indicate that the weighting scheme that we used (*, **, ***) is worthwhile to examine carefully for biases. They include: (1) possible data source biases since we conducted an “arm’s length” analysis; (2) potential author-introduced biases based on the coding process; and (3) information availability biases due to variation from case to case. These biases will all be mimicked when senior managers use our approach though: so there is no complete escape from this kind of problem. But senior managers will not be at “arm’s length” from their organizations: they will be them. The consistency of coding by senior managers may be subject to differences in their organizational experience, technical knowledge, and risk management experience. Nevertheless, we proffer that these issues will be less of a problem when managers in organizations are driving and participating in the metrics suite-based evaluative process. For more information on coding, see Recker (2013).

  11. 11.

    Models that explain or characterize processes will work best when they have orthogonal or maximally distinct components. An example is econometric regression models; they have greater explanatory power for a given number of variables when the independent variables are correlated with the dependent variable, but are uncorrelated with one another. Also, frameworks that aim to provide explanatory capability are considered to be robust when they employ relatively independent elements and components that sustain their relevance over time, in different settings, and for different purposes of use. Our choices of categories in the metric suite follow the same logic, and the categories have the least overlap among one another.

  12. 12.

    The information sources for our analysis work in this section are as follows. (1) Industry reports and relevant cloud computing websites and forums provide information on vendor and technology performance. They help firms to get an idea about the technological maturity of a specific type of cloud computing service at the aggregate level. In addition, some vendors provide free trials of their services on their websites, which offers firms an opportunity to get a sense about vendor-specific technology information. (2) When considering the firm’s own organizational factors, senior managers such as the CIO and the CEO will mainly rely on internal support from their staff to acquire relevant information. The firm also needs to have an appropriate understanding and self-evaluation of its overall strategy, organizational culture and capability, as well as a longer-term orientation and targets. (3) Cloud vendors’ websites typically provide pricing information, which assists users to estimate the economic value of using cloud services. In addition, for a cloud computing services vendor, company reports are typically publicly available, so that firms can gauge their financial status and stability. And (4), to get a good understanding about regulatory constraints, firms need to pay attention to announcements on cloud-related government regulations and laws. For example, a government’s requirements about security in terms of the boundaries of data storage are a key concern.

  13. 13.

    It is worthwhile to note that, rather than interpret these entries as being definitive, the reader should view them as broadly representative of relevant considerations for a specific type of cloud computing service, and more narrowly representative of our application of the metrics to the cases.

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Acknowledgments

The authors would like to thank Singapore Management University’s School of Information Systems Research Centre (SISRC) for research funding. We also benefitted from the helpful comments of the special issue guest editors, the anonymous reviewers, and the co-chairs, discussants and participants of the 2014 International Conference on the Economics of Grids, Clouds, Systems and Services (GECON 2014). We also had useful input from Jun Liu, Kustini Lim-Wavde and Dan Geng. All of the errors are our own.

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Correspondence to Dan Ma.

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Responsible Editor: Jörn Altmann

Appendices

Appendix A. Cloud computing services types, by market share of services

Table 3 Cloud computing services types with relative market shares in 2012

Appendix B. Estimated economic impacts of cloud computing services

(Table 4).

Table 4 University, industry and government assessments: works published from 2009–2014

Appendix C. Details of the cloud adoption readiness metrics suite

Table 5 Categories, sub-categories, measures, supporting literature and related disciplines

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Kauffman, R.J., Ma, D. & Yu, M. A metrics suite of cloud computing adoption readiness. Electron Markets 28, 11–37 (2018). https://doi.org/10.1007/s12525-015-0213-y

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Keywords

  • Adoption readiness
  • Cloud computing
  • Empirical assessment
  • Managerial decision-making
  • Metrics suite
  • Technology adoption

JEL Classification

  • L86