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Abstract
Cloud-based information management is one of the leading competitive differentiation strategies for firms. With the increasing criticality of information management in value creation and process support, establishing an integrated capability with cloud computing is vital for organizational success in the changing landscape of business competition. These issues have received scant attention, however. We draw on the resource-based view, dynamic capability hierarchy concepts, and the perspective of operand and operant resources to suggest a cloud value appropriation model for firms. We argue that, to appropriate business value from cloud computing, the firm needs to effectively deploy cloud computing and leverage cloud operant resources as firm capabilities in a hierarchical fashion toward the development of cloud computing-based service models in order to reliably achieve the desired business outcomes. We propose a model encompassing the principles of infrastructure and cloud platform deployment, integration and service orientation, and alignment with business processes that explain the linkage from cloud computing to firm performance. We test this approach to value creation with a cloud computing implementation assessment model using a sample of 147 firms that have implemented cloud computing in India. Our analysis uncovers a strategic value appropriation path from cloud technological capability to firm performance via cloud integration capability, cloud service portfolio capability, and business flexibility. This research offers new insights regarding the underlying mechanisms for how cloud computing affects firm performance via cloud-enabled capabilities and the business functions that are supported by cloud capabilities.
Acknowledgments
We thank the Guest Editors, Associate Editor, and the three reviewers for their helpful suggestions and insights during the review process. We thank Actuate Business Consulting for the collaborative efforts of their team regarding data collection. We also thank Ai-Phuong Hoang at the School of Information Systems, Singapore Management University for helpful comments and research assistance. We are grateful to participants at the 2016 Americas Conference on Information Systems (AMCIS) for valuable comments on a preliminary version of this paper.
Supplemental Material
Supplemental material for this article (Appendix Tables A1–A3 and Figure A1) can be accessed on the publisher’s website.
Notes
1. Our resource-based view (RBV) of cloud computing is consistent with prior cloud computing research [28]. See Barney [3] for a review of RBV theory, and for RBV theory in IS research, see Wade and Hulland [89].
2. Madhavaram and Hunt [53, p. 70] state that “what a researcher might label as a “composite, operant resource” in one schema might be considered as a basic operant resource, a building block, in another schema.” Similar schematic representation differences may exist for different cloud integration or service conceptualizations. Our objective is to validate a model for cloud computing as a resource hierarchy-based, service-oriented value-creation strategy that is novel yet granular. There is still quite a bit of scope to conceptualize operant resource hierarchies differently though.
3. Service offerings can result from or be supported by the provision of computing capabilities, such as dynamic on-demand allocation of server time and network storage, automatically without human interaction by the provider. Cloud resources also are pooled to serve multiple consumers using a multitenant model [93, 94], with dynamic resource assignment.
4. Business execution through cloud mobile platforms can support rich mobile applications and an engaging user experience. When a retailer opens a mobile store front, it needs to enable cloud services for secure mobile payments through a cloud-enabled mobile payment gateway system that allows it to connect with other market participants. This is more of a market orientation than a service orientation though. Other characteristics that enable cloud market offerings are interoperability, compatibility, and collaborative or complementing services across a variety of systems.
5. Our hierarchical taxonomy of cloud computing capabilities is illustrated for hybrid cloud adoption by EasyJet, a leading European low-fare airline [70]. EasyJet utilized cloud-based services offered by Microsoft Azure to enhance its existing on-premises registration system by adding a cloud-based seat allocation solution. For successful deployment, it was fully integrated and synchronized with its legacy system to be able to appropriate the benefits of the cloud implementation. EasyJet got a flexible, scalable infrastructure it can use to introduce new features quickly, enhancing its service portfolio and creating value.
6. Jindal Steel and Power, a steel and energy provider in India, is an example of a firm that effectively integrated cloud-based auction systems with its existing legacy systems to support better business decisions and improve execution efficiency in inventory management across its various warehouses [26, 44]. This example suggests that to achieve transformation and value-creation from cloud computing, a firm must go beyond cloud technology and architecture to create fully-integrated cloud capabilities that enable alignment of cloud service functionality with other systems. Cloud technology may be an enabler but will not suffice to achieve outcomes for a supply chain business function, without integration of the cloud platform.
7. Elasticity and on-demand provisioning of cloud resources are beneficial because services experience seasonal and periodic demand variation. Their value shows up in reduced time to introduce, deploy, and develop new services; maintain existing services; procure hardware and software platforms; and avoid the cost of higher use and reuse of existing resources. For example, cloud technology capabilities enabled Art-World [45] (a company that connects art dealers to collectors) to rapidly deploy and scale its service offerings through reliable, scalable, and dynamically-allocated IT resources. Another example is the photo website, SmugMug. Cloud technology capabilities have allowed it to meet demand spikes during the two months of the year when demand goes to five times the usual load [56].
8. Complexity in cloud utilization is present because of multiple deployment models (public, private, hybrid cloud), service options (SaaS, IaaS, etc.), and utilization choices (fully cloud, legacy-based, or a hybrid mix). In most firms, IT departments lead the implementation, test the delivery model and develop the capabilities to manage distributed implementation. Functional areas often adopt cloud functionality independently for quicker implementation and may adopt without prior approval.
9. A reason for the incompatibility of cloud service providers is that the cloud market is developing. No single vendor has a dominant position, especially for small and medium enterprises. Most enterprises use services from disparate vendors.
10. Synchronization of data across cloud and legacy systems is achieved via mechanisms such as transaction and application handoffs using transaction coordination, central authentication, control and change management, and standardization apps.
11. CIC enabled the governing council for technical education in India to develop CMOC by using streamlined data sharing across cloud offerings to support collaborative research across various institutes under its umbrella and with partner institutions [21].
12. Apeejay Stya and Svrán Group, an Indian conglomerate, serves as an exemplar for CIC leading to BusFlex. Integration of cloud-based systems with other systems helped the group to gather information from a variety of sources, and improve flexibility of its business processes and efficiency of its operations [2].
13. Dr. Lal Pathlabs, an Indian healthcare company, is example of how effective integration increases access of key ITs and scale economies for technical resources. The firm effectively integrated a cloud-based patient registration system with its resource planning system to increase efficiency by 15% [9].
14. India is the world’s fastest growing major economy. Business flexibility, responsiveness, and scalability are competitive priorities for firm performance and survival in India [39]. The growth of cloud services has resulted in the big providers setting up data centers there. Microsoft set up three Azure data centers to cater to new demand [79]. Meanwhile, public cloud computing services reached US$731 million in 2015, with cloud management, SaaS, and IaaS slated to reach US$1.9 billion in 2019 [34].
15. We are interested in measuring cloud capabilities and not simply cloud implementation; therefore, a survey is suitable. It allows us to measure nuances of internal firm capabilities more effectively than objective measures of implementation [33]. Similar to prior firm-level IS research [67], subjective measures were used for firm performance as senior managers have reasonable information and perspective of firm performance [47], and differences in accounting conventions and practices can confound comparisons of financial metrics, particularly in emerging markets such as India where accounting procedures are less developed.
16. We took multiple steps to mitigate common method bias. We used different scales to measure different constructs. Although use of similar scale formats and anchors requires less cognitive processing, this may increase method bias due to consistency in scale properties. We measured all key constructs using multi-item, 7-point or 5-point Likert scales. Using scales with different anchors reduces common method biases caused by commonalities in scale endpoints and anchoring effects [12, 65]. The 7-point scales were used for constructs when there was a precedent in prior work, while 5-point scales were used for new constructs. This approach benefits from reduced survey weariness for the respondent. Research has precedents for utilizing different scales during data collection and analysis, without reconciliation. To ensure our results are independent of scales, we re-scaled all items to a 5-point scale and re-ran our analysis. The results were similar and are omitted for brevity. We used a matched pair design, ensuring that independent and dependent variables were collected from different respondents in the same firm.
17. We refined our initial questionnaires based on results of the pretest. Pretest respondents filled out prototype questionnaires and were then interviewed and asked questions on their interpretation of the items. They offered comments on content validity, appearance, terminology, clarity of instructions, organization, and response format. We then made adjustments to the questionnaires based on the comments. We conducted a further pilot test with a small sample from the targeted population for reliability, convergent and discriminant validity, and predictability. We then made final revisions for items based on the pilot test results.
18. Firms from which we did not receive responses from both the CEO and CIO were dropped from the sample. The survey was done in-person and, as a result, we could identify who responded; if the CEO and CIO did not respond, their organizations were also dropped.
19. Individuals who answered our survey were acting as agents of their firms and provided responses to firm-level questions. To adhere to ethical principles regarding research-related data collection and to enhance response rates, we did not collect personal information from respondents, such as their demographics or job tenure. This ensured confidentiality and privacy for respondents, who could answer questions free from legal risks and report the actual, rather than the desired state of their firms.
20. We were careful in this research to ensure that the paths in the analysis were established based on appropriate evidence. For example, the importance of the CTC → CIC → CSPC → BusFlex path is further supported by a simple mediation analysis that we conducted, which was significant at the p < 0.10 level. The Sobel test for the product of coefficients approach [77] was used to assess the significance of simple mediation relationships. However, recent methods advances in PLS-SEM research suggest that this is not a valid method for assessing mediation in these contexts. First, the product of coefficients approach identifies two types of mediation, whereas recent advances propose three types of mediation and two types of non-mediation, requiring a series of different analyses. Second, the product of coefficients approach was developed for evaluating simple mediation, consisting of a single mediator. Structural models that contain more than one mediator require running a series of separate simple mediation analyses, which leads to biased and inaccurate results [31]. Third, the Sobel test assumes that the data for each of the variables follow a normal distribution, which is inconsistent with PLS. Fourth, the parametric assumptions of the Sobel test do not hold for indirect effects. Fifth, the Sobel test requires unstandardized coefficients as inputs. Finally, the test has low statistical power for small sample sizes. Instead, an alternate method to assess mediation in PLS has been proposed—which we use—in which the sampling distributions for the indirect effects are bootstrapped and multiple mediation analysis is conducted.
21. A minor contribution of this study is the dual online-offline mode; to our knowledge, we are the first to employ this method, which is suitable for the unique Indian context. In the future, other researchers can follow this approach to improve authenticity and response rates for primary data collection efforts in India.
22. We conducted a parametric test to compare mature and immature firms with respect to cloud computing technologies. The intuition for doing this analysis is to assess whether the relationships were weaker for immature firms than for mature firms, given that mature firms may have had more time to develop their cloud capabilities. The t-tests of standard errors derived from bootstrapping indicate that our hypothesized relationships are weaker (albeit significant) for immature firms. Although this suggests the temporal evolution of cloud capabilities, we note several caveats. The small size of subsamples restricts our ability to draw meaningful implications from such an analysis. Furthermore, this traditional approach is inappropriate for PLS as it suffers from Type I errors and inconsistent distributional assumptions. We are also restricted from employing the nonparametric permutation test or the PLS-multigroup analysis (PLS-MGA) technique: our subsamples are not of equal size and limit statistical power to detecting only large-sized effects (R2 > 25 percent). Due to these limitations, we did not add this analysis to our main narrative. Even so, they present an interesting avenue for future inquiry.
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Notes on contributors
Abhishek Kathuria
Abhishek Kathuria ([email protected]) is an Assistant Professor of Innovation and Information Management at the Faculty of Business and Economics, The University of Hong Kong. He holds a Ph.D. from the Goizueta Business School at Emory University. His research interests include the business value of IT and the role of IT in innovation, with a focus on emerging economies. His work has been published in Journal of Management Information Systems, the Communications of the Association for Information Systems and Health Systems and received multiple best paper nominations and awards at academic conferences. Dr. Kathuria is an advisor to and co-founder of multiple startups and consults on business transformation, organizational turnarounds, and IT strategy with public and private corporations in Hong Kong, India, and the Middle East.
Arti Mann
Arti Mann ([email protected]) is an Assistant Professor of Management at the College of Business, University of Northern Iowa. She holds a Ph.D. from Arizona State University. Her research interests span the business value of IT, theory, and empirical research in information systems (IS) and IT outsourcing, and the use of spatial analysis and econometrics techniques from regional economics and geography. Dr. Mann’s work has appeared in Decision Support Systems, Applied Geography, and various academic conference proceedings. Prior to her graduate studies, she worked in advertising and the IT industry.
Jiban Khuntia
Jiban Khuntia ([email protected]) is the Ph.D. Program Director and Assistant Professor of Information Systems in the Business School at the University of Colorado, Denver. He received his Ph.D. from the Robert H. Smith School of Business, University of Maryland. Dr. Khuntia’s research is in the areas of digital service innovation and health IT. His work has appeared in journals including Production and Operations Management, Decision Support Systems, Communications of the AIS, and others. Earlier, he had a decade of professional and consulting experience in supercomputing, the IT industry, and government organizations.
Terence J.V. Saldanha
Terence J.V. Saldanha ([email protected]) is an Assistant Professor of Information Systems in the Carson College of Business at Washington State University. He received his Ph.D. in Information Systems from the Stephen Ross School of Business at the University of Michigan. His research interests include the business value of IS, and the role of IS in innovation. His research has been published in journals such as MIS Quarterly, Journal of Operations Management, and Production and Operations Management, among others, and in various academic conference proceedings. Prior to his graduate studies, he worked in the software development industry.
Robert J. Kauffman
Robert J. Kauffman ([email protected]; corresponding author) is Professor of Information Systems at the School of Information Systems, Singapore Management University. He also recently served as Associate Dean (Faculty). He holds a Ph.D. from Carnegie Mellon University. Dr. Kauffman was a Distinguished Visiting Fellow at the Center for Digital Strategies at the Tuck School of Business at Dartmouth and served on the faculty at business schools of New York University, the University of Rochester, the University of Minnesota, and Arizona State University, where he held the W.P. Carey Chair in Information Systems. His interdisciplinary research spans strategy, information, IT, economics, marketing and consumer behavior, fintech, environmental sustainability, and computational social science and data analytics. His papers have appeared in Management Science, Information Systems Research, Journal of Management Information Systems, MIS Quarterly, Organization Science, and the Review of Economics and Statistics, among others.