Skip to main content
Log in

A Deeper Look at Cloud Adoption Trajectory and Dilemma

  • Published:
Information Systems Frontiers Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. 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.

  2. 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.

  3. 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).

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

References

  • Alshamaila, Y., Papagiannidis, S., & Li, F. (2013). Cloud computing adoption by SMEs in the north east of England: A multi-perspective framework. Journal of Enterprise Information Management., 26(3), 250–275.

    Google Scholar 

  • Armbrust, M., Fox, A., Griffith, R., Joseph., A. D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., Zaharia, M. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50–58.

  • Arthur, W. B. (1989). Competing technologies, increasing returns and lock-in by historical events. The Economic Journal, 99(394), 116–131.

    Google Scholar 

  • Bharadwaj, A. S. (2000). A resource-based perspective on information technology capability and firm performance: An empirical investigation. MIS Quarterly, 24(1), 169–196.

    Google Scholar 

  • Buyya, R., Yeo, C., & Venugopal, S. (2008). Market-oriented cloud computing: Vision, hype, and reality for delivering IT services as computing utilities. Proceedings of the 10th IEEE International Conference on High Performance Computing and Communications (pp. 5–13).

  • Cenfetelli, R. T., & Bassellier, G. (2009). Interpretation of formative measurement in information systems research. MIS Quarterly, 33(4), 689–707.

    Google Scholar 

  • Chin, W. (1998). Issues and opinion on structural equation modeling. MIS Quarterly, 22(1), vii–xvi.

    Google Scholar 

  • Chin, W. W. (2010). How to write up and report PLS analyses. In V. Esposito Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares (pp. 655–690). New York: Springer.

    Google Scholar 

  • Cho, V., & Chan, A. (2015). An integrative framework of comparing SaaS adoption for core and non-core business operations: An empirical study on Hong Kong industries. Information Systems Frontiers, 17(3), 629–644.

    Google Scholar 

  • Choudhary, V., & Vithayathil, J. (2013). The impact of cloud computing: Should the IT department be organized as a cost center or a profit center. Journal of Management Information Systems, 30(2), 67–100.

    Google Scholar 

  • Cohen, J. (1988). Statistical power analysis for behavioral sciences (2nd ed.). Hillsdale: Erlbaum.

    Google Scholar 

  • Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152.

    Google Scholar 

  • DiMaggio, P. J., & Powell, W. W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48(2), 147–160.

    Google Scholar 

  • Dhar, S. (2012). From outsourcing to cloud computing: Evolution of IT services. Management Research Review, 35(8), 664–675.

    Google Scholar 

  • Dhar, S., & Balakrishnan, B. (2006). Risks, benefits and challenges in global IT outsourcing: Perspectives and practices. Journal of Global Information Management, 14(3), 59–89.

    Google Scholar 

  • Ettlie, J. E., & Rubenstein, A. H. (1987). Firm size and product innovation. Journal of Product Innovation Management, 4(2), 89–108.

    Google Scholar 

  • Fang, Y., Qureshi, I., Sun, H., McCole, P., Ramsey, E., & Lim, K. H. (2014). Trust, satisfaction, and online repurchase intention. MIS Quarterly, 38(2), 407–4A9.

    Google Scholar 

  • Farrell, J., & Saloner, G. (1986). Installed Base and compatibility: Innovation, product preannouncements, and predation. American Economics Review, 76(5), 940–955.

    Google Scholar 

  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

    Google Scholar 

  • Garrison, G., Kim, S., & Wakefield, R. (2012). Success factors for deploying cloud computing. Communications of the ACM, 55(9), 62–68.

    Google Scholar 

  • Gefen, D., Rigdon, E. E., & Straub, D. (2011). Editor's comments: An update and extension to SEM guidelines for administrative and social science research. MIS Quarterly, 35(2), iii–xiv.

    Google Scholar 

  • Giessmann, A., & Legner, C. (2016). Designing business models for cloud platforms. Information Systems Journal, 26(5), 551–579.

    Google Scholar 

  • Gupta, P., Seetharaman, A., & Raj, R. (2013). The usage and adoption of cloud computing by small and medium businesses. International Journal of Information Management, 33(5), 861–874.

    Google Scholar 

  • Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., Ketchen, D. J., Hair, J. F., Hult, G. T. M., & Calantone, R. J. (2014). Common beliefs and reality about PLS: Comments on Rönkkö and Evermann (2013). Organizational Research Methods, 17(2), 182–209.

    Google Scholar 

  • Iacovou, C. L., Benbasat, I., & Dexter, A. S. (1995). Electronic data interchange and small organizations: Adoption and impact of technology. MIS Quarterly, 19(4), 465–485.

    Google Scholar 

  • Kaltenecker, N., Hess, T., & Huesig, S. (2015). Managing potentially disruptive innovations in software companies: Transforming from on-premises to the on-demand. The Journal of Strategic Information Systems, 24(4), 234–250.

    Google Scholar 

  • Ketokivi, M., & McIntosh, C. N. (2017). Addressing the endogeneity dilemma in operations management research: Theoretical, empirical, and pragmatic considerations. Journal of Operations Management, 52, 1–14.

    Google Scholar 

  • Khajeh-Hosseini, A., Greenwood, D., and Sommerville, I. (2010). Cloud migration: A case study of migrating an Enterprise IT system to IaaS. Proceedings of IEEE 3rd International Conference on Cloud Computing (pp. 450–457).

  • Kranz, J. J., Hanelt, A., & Kolbe, L. M. (2016). Understanding the influence of absorptive capacity and ambidexterity on the process of business model change–the case of on-premise and cloud-computing software. Information Systems Journal, 26(5), 477–517.

    Google Scholar 

  • Kung, L., Cegielski, C. G., & Kung, H. J. (2015). An integrated environmental perspective on software as a service adoption in manufacturing and retail firms. Journal of Information Technology, 30(4), 352–363.

    Google Scholar 

  • Lim, J., Stratopoulos, T., & Wirjanto, T. (2012). Path dependence of dynamic information technology capability: An empirical investigation. Journal of Management Information Systems, 28(3), 45–84.

    Google Scholar 

  • Lin, A., & Chen, N.-C. (2012). Cloud computing as an innovation: Percepetion, attitude, and adoption. International Journal of Information Management, 32(6), 533–540.

    Google Scholar 

  • Lee, N., & Cadogan, J. W. (2013). Problems with formative and higher-order reflective variables. Journal of Business Research, 66(2), 242–247.

    Google Scholar 

  • Malhotra, N., Kim, S. S., & Patil, A. (2006). Common method variance in IS research: A comparison of alternative approaches and a reanalysis of past research. Management Science, 52(2), 1865–1883.

    Google Scholar 

  • Marston, S., Li, Z., Bandyopadhyay, S., Zhang, J., & Ghalsasi, A. (2011). Cloud computing – The business perspective. Decision Support Systems, 51(1), 176–189.

    Google Scholar 

  • Mell, P. and Grance, T. (2009). The NIST definition of cloud computing. National Institute of Standards and Technology. Information Technology Laboratory, version, 15(10.07).

  • Messerschmidt, C. M., & Hinz, O. (2013). Explaining the adoption of grid computing: An integrated institutional theory and organizational capability approach. The Journal of Strategic Information Systems, 22(2), 137–156.

    Google Scholar 

  • Miller, M. (2008). Cloud computing: Web-based applications that change the way you work and collaborate online. Que Publishing.

  • Miller, L. E., & Smith, K. L. (1983). Handling nonresponse issues. Journal of Extension, 21(5), 45–50.

    Google Scholar 

  • Nelson, R., & Winter, S. (1982). An evolutionary theory of economic change. Cambridge: Harvard University Press.

    Google Scholar 

  • Podsakoff, P., MacKenzie, S., Lee, J., & Podsakoff, N. (2003). Common method biases in behavioral research: A critical review of hte literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.

    Google Scholar 

  • Ringle, C. M., Sarstedt, M., & Straub, D. W. (2012). Editor’s comments: A critical look at the use of PLS-SEM in MIS quarterly. MIS Quarterly, 36(1), iii–xiv.

    Google Scholar 

  • Rogers, E. M. (1995). Diffusion of innovations. New York: Free Press.

    Google Scholar 

  • Romanow, D., Rai, A., & Keil, M. (2018). CPOE-enabled coordination: Appropriation for deep structure use and impacts on patient outcomes. MIS Quarterly, 42(1), 189–212.

    Google Scholar 

  • Rosenberg, N. (1982). Inside the black box: Technology and economics. Cambridge: Cambridge University Press.

    Google Scholar 

  • Sabi, H. M., Uzoka, F. M. E., Langmia, K., Njeh, F. N., & Tsuma, C. K. (2018). A cross-country model of contextual factors impacting cloud computing adoption at universities in sub-Saharan Africa. Information Systems Frontiers, 20(6), 1381–1404.

    Google Scholar 

  • Schneider, S., & Sunyaev, A. (2016). Determinant factors of cloud-sourcing decisions: Reflecting on the IT outsourcing literature in the era of cloud computing. Journal of Information Technology, 31(1), 1–31.

    Google Scholar 

  • Seethamraju, R. (2015). Adoption of software as a service (SaaS) enterprise resource planning (ERP) systems in small and medium sized enterprises (SMEs). Information Systems Frontiers, 17(3), 475–492.

    Google Scholar 

  • Subramani, M. (2004). How do suppliers benefit from IT use in supply chain relationships. MIS Quarterly, 28(1), 45–74.

    Google Scholar 

  • Teece, D. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533.

    Google Scholar 

  • Temme, D., Diamantopoulos, A., & Pfegfeidel, V. (2014). Specifying formatively-measured constructs in endogenous positions in structural equation models: Caveats and guidelines for researchers. International Journal of Research in Marketing, 31(3), 309–316.

    Google Scholar 

  • Thomas, M., Costa, D., & Oliveira, T. (2016). Assessing the role of IT-enabled process virtualization on green IT adoption. Information Systems Frontiers, 18(4), 693–710.

    Google Scholar 

  • Vithayathil, J. (2018). Will cloud computing make the information technology (IT) department obsolete? Information Systems Journal, 28(4), 634–649.

    Google Scholar 

  • Wang, N., Liang, H., Jia, Y., Ge, S., Xue, Y., & Wang, Z. (2016). Cloud computing research in the IS discipline: A citation/co-citation analysis. Decision Support Systems, 86, 35–47.

    Google Scholar 

  • Whetten, D. A. (1987). Organizational growth and decline process. Annual Review of Sociology, 13, 335–358.

    Google Scholar 

  • Wu, Y., Cegielski, C., Hazen, B., & Hall, D. (2013). Cloud computing in support of supply chain information system infrastructure: Understanding when to go to the cloud. Journal of Supply Chain Management, 49(3), 25–41.

    Google Scholar 

  • Yang, H, and Tate, M. (2012). A descriptive literature review and classification of cloud computing research, Communications of the Association for Information Systems, 31, article 2.

  • Zhu, K., Kraemer, K., Gurbaxani, V., & Xu, S. (2006). Migration to open-standard interorganizational systems: Network effects, switching costs, and path dependency. MIS Quarterly, 30(1), 515–539.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Pei-Fang Hsu.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendices

Appendix 1: Common Method Variance

Table 8 Marker variable analysis (correlations among variables)

Appendix 2: Measurement Items

Table 9 Measurement items

Using a 5-point Scale where 1 is “not at all” and 5 is “a great deal,” please rate to what extent, does your company perceive the following benefits or concerns of cloud computing?

 

Perceived Benefits

Resources

Reduce IT deployment time

Armburst et al. (2010), Sabi et al. (2018)

Reduce IT infrastructure costs

Reduce IT personnel cost

Adjust information system scale according to company’s needs quickly

Get ubiquitous access to company data and information

Receive professional technical support from cloud vendors

Business concerns

Unexpected service failure

Buyya et al. (2008), Marston et al. (2011), Sabi et al. (2018)

Vendor lock-in

Leakage of confidential information

Concerns of service quality

Internet bandwidth bottleneck (low transfer speed)

Incompatibility of legacy IT systems

Underperformance of the software and hardware

Current IT Position

Using a 5-point Scale where 1 is “not at all” and 5 is “a great deal,” please rate to what extent,

Bharadwaj (2000), Zhu et al. (2006)

1. Do your company’s IT personnel have sufficient IT skills and abilities to develop and customize IT to support company operations?

2. Does your company have sufficient IT infrastructure to support company operations?

3. How many IT employees are there in your company? (1 = 0~2 employees, 2= 3~5 employees, 3= 6~10 employees, 4= 11~50 employees, 5= over 50 employees)

4. How much is the annual budget for the IT division of your company? (1 = Below 1 million, 2= Between 1 to 5 million, 3= Between 5 to 10 million, 4= Between 10 to 20 million, 5= Over 20 million)

IT Outsourcing Experience

[ITO1] Sum of the following questions:(Note: “Y” = 1; “N” = 0)

Bharadwaj (2000), Zhu et al. (2006)

Has your company ever outsourced data center? (Y/N)

Has your company ever outsourced IT servers? (Y/N)

N/N)

Has your company ever outsourced any of your IT solutions? (Y/N) please specify _______

[ITO2] Using a 5-point Scale where 1 is “not at all” and 5 is “a great deal,” please rate to what extent is your company satisfied with each of the above-mentioned IT outsourcing experiences? (Average of the satisfaction levels of each outsourcing project when applicable)

Environmental forces

Using a 5-point Scale where 1 is “not at all” and 5 is “a great deal,” please rate to what extent, does your company perceive the following environmental forces?

DiMaggio and Powell (1983)

Cognitive force

My competitors are adopting or have adopted cloud

Normative force

My business partners are adopting or have adopted cloud

Firms in my industry are adopting or have adopted cloud

Regulative force

Government policy support cloud

Government regulation support cloud

IT infrastructure in the country is completed to support cloud

Appendix 3: Research Results of Full Model

Table 10 Research results of full model (proposed model)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hsu, PF. A Deeper Look at Cloud Adoption Trajectory and Dilemma. Inf Syst Front 24, 177–194 (2022). https://doi.org/10.1007/s10796-020-10049-w

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10796-020-10049-w

Keywords

Navigation