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Investigating SaaS Providers’ market success based on the Multivariate LGCM Approach
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The International Academy of Information Technology and Quantitative Management, The International Academy the Peter Kiewit of Information Institute,Technology University of and Nebraska Quantitative Management, the Peter Kiewit Institute, University of Nebraska
Investigating SaaS Providers' market success based on the Multivariate Investigating SaaS Providers' market success based on the Multivariate LGCM Approach LGCMaApproach b Oh, Seong Tak , Sungbum Park * Oh, Seong Taka, Sungbum Parkb*
a National Information Society Agency, 53, Cheomdan-ro, Dong-gu, Daegu, Republic of Korea 40168 HoseoaUniversity, 20, Hoeo-ro 79 beon-gil, Baebang-eup, Asan-si, Chungcheongnam-do, Republic of 40168 Korea 31499 National Information Society Agency, 53, Cheomdan-ro, Dong-gu, Daegu, Republic of Korea b Hoseo University, 20, Hoeo-ro 79 beon-gil, Baebang-eup, Asan-si, Chungcheongnam-do, Republic of Korea 31499 b
Abstract Abstract
In this study, we longitudinally investigate the market success of Software-as-a-Service (SaaS) providers based on this customer and financial performance. Using success a multivariate latent growth curve(SaaS) modelproviders (LGCM),based we In study,growth we longitudinally investigate the market of Software-as-a-Service analyzed longitudinal survey data performance. gathered fromUsing 199 strategic business unitsgrowth (SBUs) of SaaS providers in we on customer growth and financial a multivariate latent curve model (LGCM), Korea forlongitudinal three years. survey Our results SaaS factors did (SBUs) not significantly enhance the analyzed data indicate gatheredthat from 199idiosyncratic strategic business units of SaaS providers in software business values in the early stage,idiosyncratic but did significantly affect the growth rate of their Korea forproviders’ three years. Our results indicate that SaaS factors did not significantly enhance the customerproviders’ base and financial the stage, software became affect more mature overrate time. software businessperformance values in theas early butproviders did significantly the growth of This their study presents additional insights for academia and practitioners customer base and financial performance as the software providers became more mature over time. This study presents additional insights for academia and practitioners
© 2018 The Authors. Published by Elsevier B.V. © 2018 The Authors. Published by Elsevier B.V. This is an open accessPublished article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) © 2018 The Authors. by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer is review under responsibility of the the CC scientific committee of The International Academy of Information Technology and This an open access article under BY-NC-ND license Peer review under responsibility of the scientific committee of(http://creativecommons.org/licenses/by-nc-nd/4.0/) The International Academy of Information Technology Quantitative Management, the Peter Kiewit Institute, University of Nebraska. Peer review under responsibility of the scientific committee of The and Quantitative Management, the Peter Kiewit Institute, UniversityInternational of Nebraska.Academy of Information Technology and Quantitative Management, the Peter Kiewit Institute, University of Nebraska.
Keywords: Software-as-a-Service, Application dimension, Technology maturity, Latent growth curve model, Business performance, Longitudinal study Keywords: Software-as-a-Service, Application dimension, Technology maturity, Latent growth curve model, Business performance, Longitudinal study
* Corresponding author. Tel.: +82-41-540-9956; fax: +82-41-540-9989 E-mail address: parks [email protected] * Corresponding author. Tel.: +82-41-540-9956; fax: +82-41-540-9989 E-mail address: parks [email protected] 1877-0509 © 2018 The Authors. Published by Elsevier B.V. This is an open access under the CC by BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) 1877-0509 © 2018 The article Authors. Published Elsevier B.V. Peer is review under responsibility of the committee of The International Academy of Information Technology and This an open access article under the scientific CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Quantitative Management, the Peter Kiewit Institute, University of Nebraska . Academy of Information Technology and Peer review under responsibility of the scientific committee of The International Quantitative Management, the Peter Kiewit Institute, University of Nebraska .
1877-0509 © 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer review under responsibility of the scientific committee of The International Academy of Information Technology and Quantitative Management, the Peter Kiewit Institute, University of Nebraska. 10.1016/j.procs.2018.10.255
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1. Introduction Software-as-a-Service (SaaS) is software hosted and accessible over the Internet [1] and is based on the idea of on-demand, pay-per-use, and utility computing [2, 3]. The SaaS model evolved from the Application Service Provider (ASP) approach, which originated in the mid-1990’s, when a new category of software vendors adopted – a “same-for-all” position in which vendors provided homogenous application portfolios that were largely undifferentiated for clients [4]. The expected benefit of the ASP model was to provide small to mid-size firms with the capabilities of a large-scale infrastructure without the cost and staffing requirements of developing and supporting such large-scale solutions in-house. These application portfolios were often developed with generic IT infrastructure designs in mind, and were delivered to clients as standard pre-packaged solutions that did not take the clients’ existing organizational business processes into account (Currie 2004). The ASP software model for centralized computing held tremendous promise but homogenous software solutions were not as popular as expected. Thus, the ASP model ultimately floundered [5], and ASP companies experienced significant merger and acquisitions [4, 6] in order to adjust to the changing industry. SaaS has the potential to address the limitations of the ASP approach so long as SaaS offers distinctive features and creates tangible value [7]. Empirical research that evaluates the proposed value of SaaS is lacking but is essential for the widespread diffusion of the technology. In this paper, we seek to quantify the value that SaaS has on business performance by longitudinally examining the entire population of SaaS providers in Korea over a three-year period. We begin by defining the SaaS application model and drawing from prior literature on product characteristics and application dimension, technology maturity, business strategy and financial performance. We apply these concepts to the SaaS application model to formulate our research hypotheses. To analyze the data, we employ a multivariate latent growth curve model (LGCM) to empirically assess longitudinal changes in business performance. We also identify a set of business, strategic, and technical considerations to guide the client decision-making process for selecting an appropriate SaaS vendor. 2. Theoretical Background and Hypotheses 2.1. Software-as-a-Service (SaaS) SaaS, like ASP, is a form of information systems (IS) software outsourcing, however, SaaS is different from traditional inforsmation systems (IS) outsourcing software in various ways, including target clients, customization, and application features [8]. Target clients for SaaS are typically small to mid-size firms that can benefit from having a functional software solution with minimal or no need for in-house IS staff. The software solution provided may allow different levels of features and technical support. Further, the SaaS vendor, not the client, primarily determines what features to include in the software solution, when to upgrade and the support levels that they will provide to the client. But, unlike ASP, SaaS is a more flexible and efficient approach to implementing systems. SaaS solutions include individual applications serving small to mid-size enterprises (SMEs) having a low level of complexity and integration with existing business processes [9]. SaaS solutions also include applications that serve large corporations and connect buyers with suppliers on several core business processes, including procurement, logistics, and supply chain management [10]. SaaS solutions exist for every organizational business function [5, 11], but often fail to capture a firm’s core competency features well.
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2.2. SaaS Application Dimension The product characteristics of a firm’s internal environment influence its differentiation strategy, such as product uniqueness and supply channel diversification [12, 13]. Client firms that utilize IS broadly in their business showed more advantages in cost reduction, increased task efficiency, and better strategic effect than those that did not [14, 15]. The application product characteristic has been used as the major measurement criterion to evaluate business performance that increased from IS adoption [16]. From the IS perspective, this application dimension has a relationship with both application complexity and system integrity [17]. An application that can integrate a complex or wider dimension of a business process will lead to more efficient coverage of a niche market and to more specialized service toward customers through the firm’s differentiation strategy. In this study, we apply the term “application dimension” as the system integrating capacity of a SaaS solution and we categorize application dimension as a product characteristic. We suggest that the application dimension influences strategies and business performances of SaaS providers. Based on previous studies on product characteristics and IS adoption, we posit that the wider support of applications in actual business transactions and tasks of clients can bring about a positive effect on the customer acquisition of SaaS providers. Table 1 summarizes multiple patterns of the SaaS application dimension. Table 1 Perspectives of application dimension Application Venkatraman [18] Tan [19] Leem and Kim [20] Patnayakuni and Dimension Seth [9] Localized Functional Functional Individual Low exploitation integration integration application Internal integration Cross-functional Process integration Functional integration application Business process Process integration Business Enterprise-wide redesign integration application Business network Business process Industry integration Vertical industry redesign redesign and e-commerce application Business scope Business scope Role-model High redefinition redefinition generation (Note) Grayed cells are not included as a research topic because the SaaS applications surveyed in this study had yet to reach this stage. SaaS is now being applied to support the core competencies of clients in areas ranging from the individual productivity area to the vertical industry and e-commerce area. This trend will be intensified further as time goes by [10]. However, adopting SaaS to support core functions requires a higher contract maintenance cost because of the high complexity, asset specificity and importance of IS applications. Thus, SaaS with a low level of application dimension, such as individual applications, are likely to have a low level of asset specificity and are more likely to be sourced. Similarly, compared to the lower ones, coordination costs are likely to be higher for complicated applications that require customization [5, 21]. 2.3. SaaS Maturity Technology maturity defined is an assessment of a firm’s facilitating conditions that influence system success, ranging from unfavorable to optimal [22]. Technology maturity resembles perceived ease of use [23, 24] and is related to IS utilization and performance [25]. Characteristics of a mature SaaS solution include simple setup procedures, easy content presentation, navigation structures, user guidance, and a comfortable user interface [9, 26].
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SaaS, as a form of hosted application market, is segmented into three phases of technology maturity [27]. The first generation of SaaS aligns with the ASP model. Software vendors in this stage host and manage packaged applications either through the Internet or a dedicated line and offer a price strategy consisting of a one-time license and setup fee or a monthly, periodic subscription [28, 29]. The ASP model had great potential for SMEs due to the low cost of system setup and maintenance [30], but provided a solution that delivered the same solution to all clients without consideration of specific business functions or processes [4, 31]. The second generation of SaaS enables easy software access through web-browser distribution. Second generation SaaS offer web-based deployment with relatively small setup fees and lower maintenance costs compared to traditional on-premise enterprise software solutions. The primary difference between the first to second generation SaaS is that the latter partially incorporates a ‘one-to-many’ application sharing environment meaning that one instance of the software is made available to multiple clients [11]. The second generation of SaaS are narrowly designed to support specific business functions or processes [28, 29]. The third generation of SaaS is known as the web service stage and facilitated the commercialization of SaaS solutions [32, 33]. The web service stage employs a multi-tenant business model where software components are distributed individually or in groups to support specific work functions. The third generation of SaaS is based on open Internet standards and facilitates high performance, fast system updates [34], secure access, reliability, and availability [2]. Technological evolution is an important factor for corporate strategies [35]. In a volatile technological environment, the maturity of SaaS technology – which progressed from ASP to the web-native application and web-service – brings about a positive effect on the differentiation strategy of SaaS providers [36]. For example, web-service technologies better alleviate software versioning issues that hinder differentiation strategy practice compared to the prior maturity stage. There is no competition between the present and future versions of software because the vendors automatically upgrade to up-to-date technologies. Thus individual brand-new technologically advanced features can be released as soon as they are completed, whereas the existing IS software requires them to be withheld until a new version of the software is completed [2]. The web service stage offers on-demand application selection and uses a true pay-as-you-go price model. These web-service characteristics at a higher level of SaaS technological maturity led software vendors to develop more customized applications and enhance the application’s degree of the differentiation. Clients may have a greater willingness to pay because they expect to receive further enhancements to software features. This will increase the positive effect on customer acquisition that SaaS provides. Thus, publishers who distribute the web-service application typically invest more in software quality compared to publishers who develop traditional IS software. The increased investment yields higher software quality and higher client-perceived benefit. These incentive effects are unique to the web-service stage application and do not arise in the ASP stage because the software publisher provides upgrades to the user or ASP in almost the same way as in the perpetual licensing model [2]. We propose that SaaS solutions reach the higher maturity phase over an extended time period, and this ultimately makes SaaS more attractive in the software market. Yet, a considerable portion of SaaS applications remain in the early-growth stage. Whereas many SaaS companies were marketing themselves as full SaaS vendors, they turned out to be SaaS “pretenders” meaning that they were still in the early stages [37]. This misrepresentation of technology maturity creates failures in implementing a successful SaaS business model. Attaining a higher SaaS maturity stage also helps enable clients to become less dependent upon IS support resources. For both the client and vendor, this can lead to lower transaction costs and result in a decrease in product price [9]. Conversely, lower SaaS maturity, such as the ASP stage, is on-premise based software, and leads to a higher product price as compared to the customer’s software utilization level [38]. This relationships between SaaS maturity, transaction costs and product price is more significant as the number of application functions provided by the vendor increase. Finally, the pay-as-you-go SaaS price model, which was fully incorporated in the web-service stage, can lead to a rental fee curtailment for clients, further reducing costs [39].
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2.4 SaaS Vendor Strategy The SaaS market is highly accessible and open to new entrants who want to penetrate it. As a consequence, the vendors in the SaaS market need to differentiate their business drivers. In the traditional software market, both vendors’ trust and market power are major factors for customers to choose their software based on viewing the software as a conventional packaged product. However, in the SaaS market, since software is usually packaged as services and delivered through the Internet, clients are less likely to depend on the SaaS vendor. The only lock-in effect for the SaaS customer may be the nature of differentiated technical and application support provided by the vendors [5]. This would enhance clients’ dependence on the SaaS and create switching costs that inhibit a change in their outsourcing decision. Clients are likely to maintain an ongoing relationship with the SaaS providers only when they can provide and support differentiated application and service extending beyond the existing corporate information system [9]. It can also be argued – as the SaaS industry matures – that vendors continuously need to make greater efforts to allow customers to re-adapt their software rather than switching to that of another vendor. Traditionally, corporations adopt mixed strategies, which can pursue differentiation and low-cost strategies at the same time, given the mass customization with the economy of scale features of the modern product [40]. It is believed that companies are more likely to outsource when the perceived cost benefit is high [41]. Software vendors may enjoy mass-production owing to the innate characteristics of the web-based software distribution which enable the SaaS vendor to offer the multi-user support represented by the term “one-to-many model” (single instance, multi-tenant architecture) [11]. In this way, the SaaS business model decreases the production cost as well as the business operating costs of clients. Thus, clients could obtain a cost benefit regarded as a cost advantage by adopting the SaaS business model (Jayatilaka, Schwartz [42]. SaaS providers’ low-cost strategy has a long-term influence not only on customer acquisition and but also on the expected life span of a solution [5, 43]. 2.5 SaaS and Financial Performance The performance indicators, such as customer acquisition and revenue growth, are considered generally as corporate performance [44]. According to the proponents of the Balanced Scorecard (BSC) approach, if companies are to continuously improve their financial capabilities, they should first achieve better performance from the perspective of their customers. The customer’s performance has been proven to be the decisive antecedent for financial performance, as customers can recognize whether the company provides the best customer value and thus their level of satisfaction can be increased accordingly. Customer satisfaction and perceived profitability facilitates a market share growth in which the financial performance can be achieved. Furthermore, according to the product life cycle theory in the introduction stage, firms cannot make enough profit from a customer increase due to the cost occurrences for forming and promoting a distribution network as well as solving the technical problems of brand-new products. However, with entering the growth stage, the promotion cost is diversified into the sales volume. The service cost decreases further as the producer's learning effect increases. Accordingly, revenue gradually grows [44, 45]. Then, the causal relationship between customer performance and financial performance will be reinforced as SaaS diffuses in the market. 3. Research Model This study combines two LGCM models – showing the number of customers and financial performances as SaaS providers’ business values – into the multivariate LGCM and verifies their relationship changes by the effect of the time elapsed and various predictors. The multivariate LGCM is useful not only for validating the
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time trend of research variables, but also for verifying causal relationship changes among variables [46]. The research model in Figure 2 couples two LGCM variables composed of a pair of constructs, which are the intercepts (or initial status: CP-I and FP-I), and the slopes (or change status: CP-S and FP-S) constructs. In addition, a pair of three repeated measurements at each time point (CP1-CP3, FP1-FP3) is connected to the intercept and slope constructs. According to Meredith and Tisak [47], the intercept variable’s factor loadings were set to 1 while those in the slope variable were zero initially at times 1 and 2 at the last time point. Meanwhile, the path coefficients between two intercept variables (from CP-I to FP-I) and two slopes variables (from CP-S to FP-S) show the relationship changes over time between two corporate performances. Figure 2 also postulates that SaaS providers’ business performances are determined by (or aligned with) the application dimension and the technology maturity, as well as the firm strategies. The particular theoretical perspective adopted here is the principle of strategy-environment co-alignment [48-50], which states that the ‘fit’ between strategy and its context (technological and product characteristics) has significant implications for firm performance [12, 51, 52]. In contrast to previous studies that postulate direct links from context characteristics to firm performance [53, 54], the proposed conceptualization posits that these links are mediated by firm strategies. This research highlights the central role of firm strategies to co-align the various perspectives of strategy-environment and subsequently to achieve positive performances as Cavusgil and Zou [12] claimed and verified. 4. Conclusion The recent emergence of ubiquitous Internet technologies have facilitated the delivery of software applications over the network and raised SaaS vendors as the new type of outsourcing providers. Given this trend of SaaS market activation, this research provides an empirical analysis of the factors to determine the market competitiveness that would enable SaaS vendors to survive in a fiercely competitive environment. We longitudinally measured the performances of the SaaS application from the vendor’s perspective, which enabled us to infer that the SaaS providers’ corporate performances change with time. This study conducted unconditional LGCM and found that customer and financial performance of SaaS providers were depicted as a linear growth curve with the passage of time. Through the conditional multivariate LGCM analysis, we found that SaaS idiosyncratic factors did not significantly enhance the software providers’ business values in the early stage, but did significantly affect the growth rate of the customer and financial performance over time. In addition, the existing firm strategies were the strong antecedents for the initial stage of SaaS distribution, while their influence on the change rate of business values decreased with the passage of time. More importantly, firm strategies were the key mediators of the SaaS idiosyncratic features on the corporate performances and their growth rate simultaneously. Results of the analysis outlined above suggests that SaaS providers, in order to survive in the fierce market competition, need to supplement and reinforce not only the traditional strategy factors that determine the market competitiveness of SaaS providers in the initial stages but also SaaS idiosyncratic features differentiated from the existing software in the long term without ignoring any of those factors. The contribution of this research is demonstrating the relevance of traditional strategic theory to the SaaS business model and, in particular, to show that from the vendor perspective, different priorities and preferences will be placed according to the technological maturity and customers’ business environment. Furthermore, by proposing a systematic structure in the SaaS providers corporate performances, these findings will help SaaS vendors make decisions as to which aspects of SaaS need to be focused on to better satisfy their customers – different priorities and preferences will be placed on SaaS-specific indicators and constructs over time. The results also suggest that a ‘one size fits all’ approach is unlikely to be sustainable for SaaS providers. The findings from this study will also help the customer in selection of a SaaS vendor. We identified a set of business, strategic,
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and economic considerations that can help guide interested companies in their evaluation and decision making process about whether or not to opt for SaaS. However, it is important to recognize the limits of the generalizability of this study. We conducted the study within the domain of a specific organization and industry. We collected data exclusively in Korea to examine and identify influencing factors of SaaS providers’ business success. The results of this study may not be able to be generalized to the experiences in other countries, though the findings should still have value for such countries. Instead of partial samples, this research surveyed the entire population of business professionals. This extends the generalizabilty of the current IS success model. Second, the longitudinal data of three years depict only the part of the introductory and growth stages of the entire product life cycle. Within this period of time, the SaaS vendors’ business performance curve could afford to grow. However, from the Product Life Cycle (PLC) perspective, the performance curve will eventually slow down, flatten, and ultimately decline. Therefore, a longer time frame is needed to assess antecedents’ impact on maturity and even declining stages. Additionally, the impact of firm strategy factors on the relationships between SaaS idiosyncratic factors and firm performance are worth further study. In addition, sufficient control variables for corporate performances were not mentioned. Recent studies related to web-based learning specifically mentioned that social influence and intrinsic value (or playfulness) are the variables that may affect our dependent variables. If those variables can be included as control variables in a future, the explanatory power of the study would be enhanced and the effect of SaaS vendors’ corporate performance as the core construct of this study would be further emphasized. Acknowledgements This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2018-08-01417) supervised by the IITP(Institute for Information & communications Technology Promotion) References 1. Turner, M., D. Budgen, and P. Brereton, Turning software into a service. Computer, 2003. 36(10): p. 38-44. 2. Choudhary, V., Comparison of software quality under perpetual licensing and software as a service. Journal of Management Information Systems, 2007. 24(2): p. 141-165. 3. Marsan, C.D., Gauging the network effects of grids. Network World, 2003. 04: p. 21-21. 4. Currie, W., Value creation from the application service provider e-business model: the experience of four firms. Journal of Enterprise Information Management, 2004. 17(2): p. 117-130. 5. Chan, A.Y.M. and V. Cho, Application Service Providers (ASP) Adoption in Core and Non-Core Functions. International Journal of Engineering, 2009. 1(2): p. 35-40. 6. Yao, Y., E. Watson, and B.K. Kahn, Application service providers: market and adoption decisions. Communications of the ACM, 2010. 53(7): p. 113-117. 7. Desai, B., et al. Market entry strategies of application service providers: identifying strategic differentiation. in Proceedings of the 36th Annual Hawaii International Conference on System Sciences. 2003. Hawaii: Citeseer. 8. Yao, Y. and L. Murphy, A state-transition approach to application service provider client-vendor relationship development. ACM SIGMIS Database, 2005. 36(3): p. 8-25. 9. Patnayakuni, R. and N. Seth. Why license when you can rent? Risks and rewards of the application service provider model. Proceedings of the 2001 ACM SIGCPR conference on Computer personnel research. 2001. ACM New York, NY, USA. 10. Dubey, A. and D. Wagle, Delivering software as a service, in The McKinsey Quarterly2007, McKinsey & Company. p. 1-12. 11. Jaiswal, M., R. Prasad, and D. Nath. A Multi Influence framework of Enterprise systems adoption on Service mode (SaaS) and opportunities for future research. in 7th Annual Conference on Information Science & Technology Management. 2009. New Delhi, India: cistm.org. 12. Cavusgil, S. and S. Zou, Marketing strategy-performance relationship: an investigation of the empirical link in export market ventures. The Journal of Marketing, 1994. 58(1): p. 1-21.
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44. Kotler, P., Marketing management2007, New Jersey: Pearson Prentice Hall. 45. Weber, J.A., Planning corporate growth with inverted product life cycles. Long range planning, 1976. 9(5): p. 12-29. 46. MacCallum, R.С., et al., Studying Multivariate Change Using Multilevel Models and Latent Curve Models. Multivariate Behavioral Research, 1997. 32(3): p. 215-253. 47. Meredith, W. and J. Tisak, Latent curve analysis. Psychometrika, 1990. 55(1): p. 107-122. 48. Aldrich, H., Organizations and environments1979, Stanford, California: Stanford Business Books. 49. Porter, M., Competitive strategy: techniques for analyzing industries and competitors: with a new introduction1998: Free Pr. 50. Venkatraman, N. and J. Prescott, Environment-strategy coalignment: an empirical test of its performance implications. Strategic Management Journal, 1990. 11(1): p. 1-23. 51. Dos Santos, B., K. Peffers, and D. Mauer, The impact of information technology investment announcements on the market value of the firm. Information Systems Research, 1993. 4(1): p. 1-23. 52. Pfeffer, J., Competitive advantage through people: Unleashing the power of the work force1994: Boston, Massachusetts:Harvard Business School Pr. 53. Cooper, R. and E. Kleinschmidt, The impact of export strategy on export sales performance. Journal of International Business Studies, 1985. 16(1): p. 37-55. 54. Madsen, T., Empirical export performance studies: a review of conceptualizations and findings, in Advances in international marketing, S.T. Cavusgil, Editor 1987, JAI Press. Inc: New York. p. 177-198.
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The International Academy of Information Technology and Quantitative Management, The International Academy the Peter Kiewit of Information Institute,Technology University of and Nebraska Quantitative Management, the Peter Kiewit Institute, University of Nebraska
Investigating SaaS Providers' market success based on the Multivariate Investigating SaaS Providers' market success based on the Multivariate LGCM Approach LGCMaApproach b Oh, Seong Tak , Sungbum Park * Oh, Seong Taka, Sungbum Parkb*
a National Information Society Agency, 53, Cheomdan-ro, Dong-gu, Daegu, Republic of Korea 40168 HoseoaUniversity, 20, Hoeo-ro 79 beon-gil, Baebang-eup, Asan-si, Chungcheongnam-do, Republic of 40168 Korea 31499 National Information Society Agency, 53, Cheomdan-ro, Dong-gu, Daegu, Republic of Korea b Hoseo University, 20, Hoeo-ro 79 beon-gil, Baebang-eup, Asan-si, Chungcheongnam-do, Republic of Korea 31499 b
Abstract Abstract
In this study, we longitudinally investigate the market success of Software-as-a-Service (SaaS) providers based on this customer and financial performance. Using success a multivariate latent growth curve(SaaS) modelproviders (LGCM),based we In study,growth we longitudinally investigate the market of Software-as-a-Service analyzed longitudinal survey data performance. gathered fromUsing 199 strategic business unitsgrowth (SBUs) of SaaS providers in we on customer growth and financial a multivariate latent curve model (LGCM), Korea forlongitudinal three years. survey Our results SaaS factors did (SBUs) not significantly enhance the analyzed data indicate gatheredthat from 199idiosyncratic strategic business units of SaaS providers in software business values in the early stage,idiosyncratic but did significantly affect the growth rate of their Korea forproviders’ three years. Our results indicate that SaaS factors did not significantly enhance the customerproviders’ base and financial the stage, software became affect more mature overrate time. software businessperformance values in theas early butproviders did significantly the growth of This their study presents additional insights for academia and practitioners customer base and financial performance as the software providers became more mature over time. This study presents additional insights for academia and practitioners
© 2018 The Authors. Published by Elsevier B.V. © 2018 The Authors. Published by Elsevier B.V. This is an open accessPublished article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) © 2018 The Authors. by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer is review under responsibility of the the CC scientific committee of The International Academy of Information Technology and This an open access article under BY-NC-ND license Peer review under responsibility of the scientific committee of(http://creativecommons.org/licenses/by-nc-nd/4.0/) The International Academy of Information Technology Quantitative Management, the Peter Kiewit Institute, University of Nebraska. Peer review under responsibility of the scientific committee of The and Quantitative Management, the Peter Kiewit Institute, UniversityInternational of Nebraska.Academy of Information Technology and Quantitative Management, the Peter Kiewit Institute, University of Nebraska.
Keywords: Software-as-a-Service, Application dimension, Technology maturity, Latent growth curve model, Business performance, Longitudinal study Keywords: Software-as-a-Service, Application dimension, Technology maturity, Latent growth curve model, Business performance, Longitudinal study
* Corresponding author. Tel.: +82-41-540-9956; fax: +82-41-540-9989 E-mail address: parks [email protected] * Corresponding author. Tel.: +82-41-540-9956; fax: +82-41-540-9989 E-mail address: parks [email protected] 1877-0509 © 2018 The Authors. Published by Elsevier B.V. This is an open access under the CC by BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) 1877-0509 © 2018 The article Authors. Published Elsevier B.V. Peer is review under responsibility of the committee of The International Academy of Information Technology and This an open access article under the scientific CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Quantitative Management, the Peter Kiewit Institute, University of Nebraska . Academy of Information Technology and Peer review under responsibility of the scientific committee of The International Quantitative Management, the Peter Kiewit Institute, University of Nebraska .
1877-0509 © 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer review under responsibility of the scientific committee of The International Academy of Information Technology and Quantitative Management, the Peter Kiewit Institute, University of Nebraska. 10.1016/j.procs.2018.10.255
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1. Introduction Software-as-a-Service (SaaS) is software hosted and accessible over the Internet [1] and is based on the idea of on-demand, pay-per-use, and utility computing [2, 3]. The SaaS model evolved from the Application Service Provider (ASP) approach, which originated in the mid-1990’s, when a new category of software vendors adopted – a “same-for-all” position in which vendors provided homogenous application portfolios that were largely undifferentiated for clients [4]. The expected benefit of the ASP model was to provide small to mid-size firms with the capabilities of a large-scale infrastructure without the cost and staffing requirements of developing and supporting such large-scale solutions in-house. These application portfolios were often developed with generic IT infrastructure designs in mind, and were delivered to clients as standard pre-packaged solutions that did not take the clients’ existing organizational business processes into account (Currie 2004). The ASP software model for centralized computing held tremendous promise but homogenous software solutions were not as popular as expected. Thus, the ASP model ultimately floundered [5], and ASP companies experienced significant merger and acquisitions [4, 6] in order to adjust to the changing industry. SaaS has the potential to address the limitations of the ASP approach so long as SaaS offers distinctive features and creates tangible value [7]. Empirical research that evaluates the proposed value of SaaS is lacking but is essential for the widespread diffusion of the technology. In this paper, we seek to quantify the value that SaaS has on business performance by longitudinally examining the entire population of SaaS providers in Korea over a three-year period. We begin by defining the SaaS application model and drawing from prior literature on product characteristics and application dimension, technology maturity, business strategy and financial performance. We apply these concepts to the SaaS application model to formulate our research hypotheses. To analyze the data, we employ a multivariate latent growth curve model (LGCM) to empirically assess longitudinal changes in business performance. We also identify a set of business, strategic, and technical considerations to guide the client decision-making process for selecting an appropriate SaaS vendor. 2. Theoretical Background and Hypotheses 2.1. Software-as-a-Service (SaaS) SaaS, like ASP, is a form of information systems (IS) software outsourcing, however, SaaS is different from traditional inforsmation systems (IS) outsourcing software in various ways, including target clients, customization, and application features [8]. Target clients for SaaS are typically small to mid-size firms that can benefit from having a functional software solution with minimal or no need for in-house IS staff. The software solution provided may allow different levels of features and technical support. Further, the SaaS vendor, not the client, primarily determines what features to include in the software solution, when to upgrade and the support levels that they will provide to the client. But, unlike ASP, SaaS is a more flexible and efficient approach to implementing systems. SaaS solutions include individual applications serving small to mid-size enterprises (SMEs) having a low level of complexity and integration with existing business processes [9]. SaaS solutions also include applications that serve large corporations and connect buyers with suppliers on several core business processes, including procurement, logistics, and supply chain management [10]. SaaS solutions exist for every organizational business function [5, 11], but often fail to capture a firm’s core competency features well.
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2.2. SaaS Application Dimension The product characteristics of a firm’s internal environment influence its differentiation strategy, such as product uniqueness and supply channel diversification [12, 13]. Client firms that utilize IS broadly in their business showed more advantages in cost reduction, increased task efficiency, and better strategic effect than those that did not [14, 15]. The application product characteristic has been used as the major measurement criterion to evaluate business performance that increased from IS adoption [16]. From the IS perspective, this application dimension has a relationship with both application complexity and system integrity [17]. An application that can integrate a complex or wider dimension of a business process will lead to more efficient coverage of a niche market and to more specialized service toward customers through the firm’s differentiation strategy. In this study, we apply the term “application dimension” as the system integrating capacity of a SaaS solution and we categorize application dimension as a product characteristic. We suggest that the application dimension influences strategies and business performances of SaaS providers. Based on previous studies on product characteristics and IS adoption, we posit that the wider support of applications in actual business transactions and tasks of clients can bring about a positive effect on the customer acquisition of SaaS providers. Table 1 summarizes multiple patterns of the SaaS application dimension. Table 1 Perspectives of application dimension Application Venkatraman [18] Tan [19] Leem and Kim [20] Patnayakuni and Dimension Seth [9] Localized Functional Functional Individual Low exploitation integration integration application Internal integration Cross-functional Process integration Functional integration application Business process Process integration Business Enterprise-wide redesign integration application Business network Business process Industry integration Vertical industry redesign redesign and e-commerce application Business scope Business scope Role-model High redefinition redefinition generation (Note) Grayed cells are not included as a research topic because the SaaS applications surveyed in this study had yet to reach this stage. SaaS is now being applied to support the core competencies of clients in areas ranging from the individual productivity area to the vertical industry and e-commerce area. This trend will be intensified further as time goes by [10]. However, adopting SaaS to support core functions requires a higher contract maintenance cost because of the high complexity, asset specificity and importance of IS applications. Thus, SaaS with a low level of application dimension, such as individual applications, are likely to have a low level of asset specificity and are more likely to be sourced. Similarly, compared to the lower ones, coordination costs are likely to be higher for complicated applications that require customization [5, 21]. 2.3. SaaS Maturity Technology maturity defined is an assessment of a firm’s facilitating conditions that influence system success, ranging from unfavorable to optimal [22]. Technology maturity resembles perceived ease of use [23, 24] and is related to IS utilization and performance [25]. Characteristics of a mature SaaS solution include simple setup procedures, easy content presentation, navigation structures, user guidance, and a comfortable user interface [9, 26].
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SaaS, as a form of hosted application market, is segmented into three phases of technology maturity [27]. The first generation of SaaS aligns with the ASP model. Software vendors in this stage host and manage packaged applications either through the Internet or a dedicated line and offer a price strategy consisting of a one-time license and setup fee or a monthly, periodic subscription [28, 29]. The ASP model had great potential for SMEs due to the low cost of system setup and maintenance [30], but provided a solution that delivered the same solution to all clients without consideration of specific business functions or processes [4, 31]. The second generation of SaaS enables easy software access through web-browser distribution. Second generation SaaS offer web-based deployment with relatively small setup fees and lower maintenance costs compared to traditional on-premise enterprise software solutions. The primary difference between the first to second generation SaaS is that the latter partially incorporates a ‘one-to-many’ application sharing environment meaning that one instance of the software is made available to multiple clients [11]. The second generation of SaaS are narrowly designed to support specific business functions or processes [28, 29]. The third generation of SaaS is known as the web service stage and facilitated the commercialization of SaaS solutions [32, 33]. The web service stage employs a multi-tenant business model where software components are distributed individually or in groups to support specific work functions. The third generation of SaaS is based on open Internet standards and facilitates high performance, fast system updates [34], secure access, reliability, and availability [2]. Technological evolution is an important factor for corporate strategies [35]. In a volatile technological environment, the maturity of SaaS technology – which progressed from ASP to the web-native application and web-service – brings about a positive effect on the differentiation strategy of SaaS providers [36]. For example, web-service technologies better alleviate software versioning issues that hinder differentiation strategy practice compared to the prior maturity stage. There is no competition between the present and future versions of software because the vendors automatically upgrade to up-to-date technologies. Thus individual brand-new technologically advanced features can be released as soon as they are completed, whereas the existing IS software requires them to be withheld until a new version of the software is completed [2]. The web service stage offers on-demand application selection and uses a true pay-as-you-go price model. These web-service characteristics at a higher level of SaaS technological maturity led software vendors to develop more customized applications and enhance the application’s degree of the differentiation. Clients may have a greater willingness to pay because they expect to receive further enhancements to software features. This will increase the positive effect on customer acquisition that SaaS provides. Thus, publishers who distribute the web-service application typically invest more in software quality compared to publishers who develop traditional IS software. The increased investment yields higher software quality and higher client-perceived benefit. These incentive effects are unique to the web-service stage application and do not arise in the ASP stage because the software publisher provides upgrades to the user or ASP in almost the same way as in the perpetual licensing model [2]. We propose that SaaS solutions reach the higher maturity phase over an extended time period, and this ultimately makes SaaS more attractive in the software market. Yet, a considerable portion of SaaS applications remain in the early-growth stage. Whereas many SaaS companies were marketing themselves as full SaaS vendors, they turned out to be SaaS “pretenders” meaning that they were still in the early stages [37]. This misrepresentation of technology maturity creates failures in implementing a successful SaaS business model. Attaining a higher SaaS maturity stage also helps enable clients to become less dependent upon IS support resources. For both the client and vendor, this can lead to lower transaction costs and result in a decrease in product price [9]. Conversely, lower SaaS maturity, such as the ASP stage, is on-premise based software, and leads to a higher product price as compared to the customer’s software utilization level [38]. This relationships between SaaS maturity, transaction costs and product price is more significant as the number of application functions provided by the vendor increase. Finally, the pay-as-you-go SaaS price model, which was fully incorporated in the web-service stage, can lead to a rental fee curtailment for clients, further reducing costs [39].
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2.4 SaaS Vendor Strategy The SaaS market is highly accessible and open to new entrants who want to penetrate it. As a consequence, the vendors in the SaaS market need to differentiate their business drivers. In the traditional software market, both vendors’ trust and market power are major factors for customers to choose their software based on viewing the software as a conventional packaged product. However, in the SaaS market, since software is usually packaged as services and delivered through the Internet, clients are less likely to depend on the SaaS vendor. The only lock-in effect for the SaaS customer may be the nature of differentiated technical and application support provided by the vendors [5]. This would enhance clients’ dependence on the SaaS and create switching costs that inhibit a change in their outsourcing decision. Clients are likely to maintain an ongoing relationship with the SaaS providers only when they can provide and support differentiated application and service extending beyond the existing corporate information system [9]. It can also be argued – as the SaaS industry matures – that vendors continuously need to make greater efforts to allow customers to re-adapt their software rather than switching to that of another vendor. Traditionally, corporations adopt mixed strategies, which can pursue differentiation and low-cost strategies at the same time, given the mass customization with the economy of scale features of the modern product [40]. It is believed that companies are more likely to outsource when the perceived cost benefit is high [41]. Software vendors may enjoy mass-production owing to the innate characteristics of the web-based software distribution which enable the SaaS vendor to offer the multi-user support represented by the term “one-to-many model” (single instance, multi-tenant architecture) [11]. In this way, the SaaS business model decreases the production cost as well as the business operating costs of clients. Thus, clients could obtain a cost benefit regarded as a cost advantage by adopting the SaaS business model (Jayatilaka, Schwartz [42]. SaaS providers’ low-cost strategy has a long-term influence not only on customer acquisition and but also on the expected life span of a solution [5, 43]. 2.5 SaaS and Financial Performance The performance indicators, such as customer acquisition and revenue growth, are considered generally as corporate performance [44]. According to the proponents of the Balanced Scorecard (BSC) approach, if companies are to continuously improve their financial capabilities, they should first achieve better performance from the perspective of their customers. The customer’s performance has been proven to be the decisive antecedent for financial performance, as customers can recognize whether the company provides the best customer value and thus their level of satisfaction can be increased accordingly. Customer satisfaction and perceived profitability facilitates a market share growth in which the financial performance can be achieved. Furthermore, according to the product life cycle theory in the introduction stage, firms cannot make enough profit from a customer increase due to the cost occurrences for forming and promoting a distribution network as well as solving the technical problems of brand-new products. However, with entering the growth stage, the promotion cost is diversified into the sales volume. The service cost decreases further as the producer's learning effect increases. Accordingly, revenue gradually grows [44, 45]. Then, the causal relationship between customer performance and financial performance will be reinforced as SaaS diffuses in the market. 3. Research Model This study combines two LGCM models – showing the number of customers and financial performances as SaaS providers’ business values – into the multivariate LGCM and verifies their relationship changes by the effect of the time elapsed and various predictors. The multivariate LGCM is useful not only for validating the
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time trend of research variables, but also for verifying causal relationship changes among variables [46]. The research model in Figure 2 couples two LGCM variables composed of a pair of constructs, which are the intercepts (or initial status: CP-I and FP-I), and the slopes (or change status: CP-S and FP-S) constructs. In addition, a pair of three repeated measurements at each time point (CP1-CP3, FP1-FP3) is connected to the intercept and slope constructs. According to Meredith and Tisak [47], the intercept variable’s factor loadings were set to 1 while those in the slope variable were zero initially at times 1 and 2 at the last time point. Meanwhile, the path coefficients between two intercept variables (from CP-I to FP-I) and two slopes variables (from CP-S to FP-S) show the relationship changes over time between two corporate performances. Figure 2 also postulates that SaaS providers’ business performances are determined by (or aligned with) the application dimension and the technology maturity, as well as the firm strategies. The particular theoretical perspective adopted here is the principle of strategy-environment co-alignment [48-50], which states that the ‘fit’ between strategy and its context (technological and product characteristics) has significant implications for firm performance [12, 51, 52]. In contrast to previous studies that postulate direct links from context characteristics to firm performance [53, 54], the proposed conceptualization posits that these links are mediated by firm strategies. This research highlights the central role of firm strategies to co-align the various perspectives of strategy-environment and subsequently to achieve positive performances as Cavusgil and Zou [12] claimed and verified. 4. Conclusion The recent emergence of ubiquitous Internet technologies have facilitated the delivery of software applications over the network and raised SaaS vendors as the new type of outsourcing providers. Given this trend of SaaS market activation, this research provides an empirical analysis of the factors to determine the market competitiveness that would enable SaaS vendors to survive in a fiercely competitive environment. We longitudinally measured the performances of the SaaS application from the vendor’s perspective, which enabled us to infer that the SaaS providers’ corporate performances change with time. This study conducted unconditional LGCM and found that customer and financial performance of SaaS providers were depicted as a linear growth curve with the passage of time. Through the conditional multivariate LGCM analysis, we found that SaaS idiosyncratic factors did not significantly enhance the software providers’ business values in the early stage, but did significantly affect the growth rate of the customer and financial performance over time. In addition, the existing firm strategies were the strong antecedents for the initial stage of SaaS distribution, while their influence on the change rate of business values decreased with the passage of time. More importantly, firm strategies were the key mediators of the SaaS idiosyncratic features on the corporate performances and their growth rate simultaneously. Results of the analysis outlined above suggests that SaaS providers, in order to survive in the fierce market competition, need to supplement and reinforce not only the traditional strategy factors that determine the market competitiveness of SaaS providers in the initial stages but also SaaS idiosyncratic features differentiated from the existing software in the long term without ignoring any of those factors. The contribution of this research is demonstrating the relevance of traditional strategic theory to the SaaS business model and, in particular, to show that from the vendor perspective, different priorities and preferences will be placed according to the technological maturity and customers’ business environment. Furthermore, by proposing a systematic structure in the SaaS providers corporate performances, these findings will help SaaS vendors make decisions as to which aspects of SaaS need to be focused on to better satisfy their customers – different priorities and preferences will be placed on SaaS-specific indicators and constructs over time. The results also suggest that a ‘one size fits all’ approach is unlikely to be sustainable for SaaS providers. The findings from this study will also help the customer in selection of a SaaS vendor. We identified a set of business, strategic,
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and economic considerations that can help guide interested companies in their evaluation and decision making process about whether or not to opt for SaaS. However, it is important to recognize the limits of the generalizability of this study. We conducted the study within the domain of a specific organization and industry. We collected data exclusively in Korea to examine and identify influencing factors of SaaS providers’ business success. The results of this study may not be able to be generalized to the experiences in other countries, though the findings should still have value for such countries. Instead of partial samples, this research surveyed the entire population of business professionals. This extends the generalizabilty of the current IS success model. Second, the longitudinal data of three years depict only the part of the introductory and growth stages of the entire product life cycle. Within this period of time, the SaaS vendors’ business performance curve could afford to grow. However, from the Product Life Cycle (PLC) perspective, the performance curve will eventually slow down, flatten, and ultimately decline. Therefore, a longer time frame is needed to assess antecedents’ impact on maturity and even declining stages. Additionally, the impact of firm strategy factors on the relationships between SaaS idiosyncratic factors and firm performance are worth further study. In addition, sufficient control variables for corporate performances were not mentioned. Recent studies related to web-based learning specifically mentioned that social influence and intrinsic value (or playfulness) are the variables that may affect our dependent variables. If those variables can be included as control variables in a future, the explanatory power of the study would be enhanced and the effect of SaaS vendors’ corporate performance as the core construct of this study would be further emphasized. Acknowledgements This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2018-08-01417) supervised by the IITP(Institute for Information & communications Technology Promotion) References 1. Turner, M., D. Budgen, and P. Brereton, Turning software into a service. Computer, 2003. 36(10): p. 38-44. 2. Choudhary, V., Comparison of software quality under perpetual licensing and software as a service. Journal of Management Information Systems, 2007. 24(2): p. 141-165. 3. Marsan, C.D., Gauging the network effects of grids. Network World, 2003. 04: p. 21-21. 4. Currie, W., Value creation from the application service provider e-business model: the experience of four firms. Journal of Enterprise Information Management, 2004. 17(2): p. 117-130. 5. Chan, A.Y.M. and V. Cho, Application Service Providers (ASP) Adoption in Core and Non-Core Functions. International Journal of Engineering, 2009. 1(2): p. 35-40. 6. Yao, Y., E. Watson, and B.K. Kahn, Application service providers: market and adoption decisions. Communications of the ACM, 2010. 53(7): p. 113-117. 7. Desai, B., et al. Market entry strategies of application service providers: identifying strategic differentiation. in Proceedings of the 36th Annual Hawaii International Conference on System Sciences. 2003. Hawaii: Citeseer. 8. Yao, Y. and L. Murphy, A state-transition approach to application service provider client-vendor relationship development. ACM SIGMIS Database, 2005. 36(3): p. 8-25. 9. Patnayakuni, R. and N. Seth. Why license when you can rent? Risks and rewards of the application service provider model. Proceedings of the 2001 ACM SIGCPR conference on Computer personnel research. 2001. ACM New York, NY, USA. 10. Dubey, A. and D. Wagle, Delivering software as a service, in The McKinsey Quarterly2007, McKinsey & Company. p. 1-12. 11. Jaiswal, M., R. Prasad, and D. Nath. A Multi Influence framework of Enterprise systems adoption on Service mode (SaaS) and opportunities for future research. in 7th Annual Conference on Information Science & Technology Management. 2009. New Delhi, India: cistm.org. 12. Cavusgil, S. and S. Zou, Marketing strategy-performance relationship: an investigation of the empirical link in export market ventures. The Journal of Marketing, 1994. 58(1): p. 1-21.
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