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Investigating the Market Success of Software-as-a-Service Providers: the Multivariate Latent Growth Curve Model Approach

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Abstract

Software-as-a-Service (SaaS) Cloud computing as the next stage of internet evolution provides all the computing resources as services over the Internet. In the SaaS cloud computing research area, there are many studies from the user’s point of view, but there is relatively little research on the supplier’s success strategy. The purpose of this study is to empirically analyze the factors that determine the market competitiveness necessary for SaaS cloud computing providers to survive in the mid to long term. We presented application dimension and technology maturity as SaaS idiosyncratic factors and show how these factors influence the business performance of SaaS providers through a differentiation strategy and low-cost strategy. Using a multivariate latent growth curve model, this study analyzed 199 strategic business units of SaaS cloud computing providers in Korea for three years. Our results find that SaaS cloud computing idiosyncratic factors did not significantly enhance the software providers’ business performance in the early stage. However, they significantly affected the growth rate of their customer base and financial performance as the SaaS technology became mature over time. In addition, this study identifies a set of business, strategic, and technical considerations to guide the practitioners’ decision-making process for selecting an appropriate SaaS cloud computing model.

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Acknowledgements

This research was supported by the Academic Research fund of Hoseo University in 2017 (20170362)

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  1. Department of Management of Technology, Hoseo Graduate School of Management of Technology, 20, Hoeo-ro 79 beon-gil, Baebang-eup, Asan-si, Chungcheongnam-do, 3149, Republic of Korea

    Sun-Pyo Lee & Sungbum Park

  2. Department of Electrical and Computer Engineering (ECE) and Inter-university Semiconductor Research Center (ISRC), Seoul National University, Gwanak-Gu Gwanakro 1, Seoul, 08826, South Korea

    Kyungji Kim

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This paper is based on the revision of Sun-Pyo Lee's doctoral dissertation (Hoseo University Graduate School of Management of Technology)

Appendices

Appendix A

Table 9 Measurement items for key constructs
Full size table

Appendix B: Point estimation analysis

We obtained mediation coefficients based on the point estimation approach. To verify the statistical significance, we conducted interval estimation based on the Sobel test. There are three principal versions of the Sobel test. The Arojan test adds the third denominator term (Aroian, 1947), and the Goodman test subtracts it (Goodman, 1960). The Sobel test equation omits the third term of the variance estimate in the denominator. We drew formulae for the tests provided here from (MacKinnon & Dwyer, 1993) and from (MacKinnon et al., 1995):

$$ \mathrm{Sobel}\ \mathrm{test}\ \mathrm{equation}:z=\frac{a\times b}{\sqrt{b^2\times {s_a}^2+{a}^2\times {s_b}^2}} $$
(4)
$$ \mathrm{Aroian}\ \mathrm{test}\ \mathrm{equation}:z=\frac{a\times b}{\sqrt{b^2\times {s_a}^2+{a}^2\times {s_b}^2+{s_a}^2\times {s_b}^2}} $$
(5)
$$ \mathrm{Goodman}\ \mathrm{test}\ \mathrm{equation}:z=\frac{a\times b}{\sqrt{b^2\times {s_a}^2+{a}^2\times {s_b}^2-{s_a}^2\times {s_b}^2}} $$
(6)

Where, a = raw (unstandardized) regression coefficient for the association between IV (independent variable) and mediator,

b = raw coefficient for the association between the mediator and the DV (dependent variable) when the IV is also a predictor of the DV,

sa = standard error of a,

sb = standard error of b

Baron and Kenny (1986) recommended the Aroian version of the Sobel test because it does not make the unnecessary assumption that the product of sa and sb is vanishingly small. Meanwhile, the Goodman version of the test subtracts the third term for an unbiased estimate of the variance of the mediated effect, but this can sometimes have the unfortunate effect of yielding a negative variance estimate.

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Lee, SP., Kim, K. & Park, S. Investigating the Market Success of Software-as-a-Service Providers: the Multivariate Latent Growth Curve Model Approach. Inf Syst Front 25, 639–658 (2023). https://doi.org/10.1007/s10796-021-10188-8

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