Impact of a firm’s physical and knowledge capital intensities on its selection of a cloud computing deployment model

https://doi.org/10.1016/j.im.2019.103259Get rights and content

Abstract

Cloud computing has become an increasingly common computing infrastructure for contemporary firms. An important decision for firms to make in adopting a cloud computing model is whether to build it in-house (a private cloud) or outsource it (a public cloud). Prior literature has focused on the impact of firms’ characteristics but generated inconsistent results regarding the selection of cloud computing models. To add to this line of inquiry, we consider the relative resource structure, which reflects the importance of physical and knowledge resources for individual firms, and examine their respective effects on the selection of cloud computing deployment models (CCDMs). Using data from 520 companies deploying cloud computing in mainland China, we find that firms with higher physical capital intensity (PCI) tend to outsource cloud computing, whereas those with higher knowledge capital intensity (KCI) tend to use private clouds. Firms with higher codified knowledge capital intensity (CKCI) are found to be more susceptible to the negative relationship between KCI and public cloud selection than those with higher tacit knowledge capital intensity (TKCI). The direct positive influence of regional legal protection on a firm’s preferences for a public cloud is also confirmed, as well as its indirect moderating effect on alleviating the negative relationships between CKCI and deploying a public cloud.

Introduction

Cloud computing is an innovative information service model [1] that reshapes the access to and application of a firm’s information resources.1 It can lower costs and improve efficiency [4], and allow firms to focus on core competitiveness and improved performance [5].

Some researchers have discussed the main deployment models for cloud computing. Armbrust et al. [6] distinguished between private cloud and public cloud, and Mell and Grance [7] identified four types of cloud: private, public, hybrid, and community. The first three deployment models are more common.2 The public cloud deployment model refers to cloud infrastructures that are owned, managed, and operated by external providers, and openly used by the general public. The private cloud deployment model usually refers to cases where the cloud infrastructure is mainly owned, managed, operated, and used exclusively by a single organization. The hybrid cloud deployment model refers to a cloud infrastructure composed of an internal private cloud computing infrastructure and an external public cloud computing infrastructure, where the data and applications are portable between private and public cloud computing [[6], [7], [8]].

The literature on organizations’ cloud adoption processes, involving evaluation, adoption decisions, implementation, and governance, is quite abundant [9,10]. Prior studies on the cloud deployment model selection have been primarily conceptual [6,9,11], with few studies further discussing how firms choose among different deployment models. Cloud Computing deployment model (CCDM) refers to the arrangement of the organization’s cloud computing resources, that is, whether the organization builds or outsources the cloud computing center. Hence, the choice of a cloud deployment model is one of the important issues firms have to consider when adopting cloud computing. In this study, we examine the extent to which a firm’s physical and knowledge capital intensity (KCI) influence its selection behavior, with an aim toward identifying the kind of cloud deployment model most likely to be selected by a firm.

In theory and in practice, the study of a firm’s choice of a cloud deployment model is highly significant. Two important practical reasons, organizational and social, make it imperative to study cloud deployment model choices. First, from an organizational perspective, the focal firm’s choice of a cloud deployment model is a key decision when adopting cloud computing, because different models require drastically different considerations of information technology (IT) infrastructure, human resources, and service providers [10,12]. This decision in turn influences the focal firm’s cost structure [13] and information risks [14], and further affects the return on the firm’s investment in cloud computing. Hence, the organizational decision maker needs guidance to ensure that the choice of a CCDM can be adapted to the internal and external environmental characteristics of the firm, and will ensure the best return on the firm’s investment in cloud computing. In addition, a firm’s model choice can help cloud vendors build profiles of potential clients and recommend cloud solutions accordingly, to more effectively target clients with different cloud service needs. Second, from a social perspective, cloud computing can achieve economies of scale and reduce carbon emissions by centralizing computing resources in shared infrastructures through virtualization technology, especially in the public cloud [15]. Understanding a firm’s selection behavior regarding the cloud deployment model and its influencing factors, can help reduce risk concerns in choosing a public cloud. This can in turn promote the firm’s use of and the role of cloud computing in the firm’s operations, thus impacting economies of scale and the reduction of carbon.

There are also important theoretical reasons to examine cloud deployment model choices. Cloud computing has been examined in the literature as a new form of IT outsourcing (ITO) [[16], [17], [18]]. The choice of a cloud deployment model is understood as a firm’s ITO decision in the cloud context, that is, the trade-off between cloud outsourcing (choosing the public cloud deployment model) and cloud insourcing (choosing the private cloud deployment model). Transaction cost theory (TCT) is often used to explain a firm’s ITO behavior [16,17,19,20]. On the basis of principal attributes of transactions, those of asset specificity, frequency, and uncertainty [20,21], TCT focuses on comparing the costs of information resources operation3 in different cloud deployment models. It argues that different information resources operations have different transactions, production, and management costs in different firms, which can lead to diverse property rights arrangements—that is, operating cloud computing inside or outside the organization. TCT has good explanatory power in the ITO field [20], yet some empirical results regarding the determinants of cloud deployment models are inconsistent, in not conforming to the logic of TCT. Schneider and Sunyaev [18] summarized these inconclusive influence factors as asset specificity, client firm characteristics, and external environment. TCT mainly addresses the transaction cost of outsourcing information resources operation from the aspects of asset specificity, frequency, and uncertainty, but does not thoroughly analyze the impact of relative resource structure on the transaction cost of information resources for firms, which is related to firms’ characteristics.

The resource-based view (RBV) posits that a firm should outsource a resource based on its importance and strategic value to the firm [24]. Barney [24] and Wernerfelt [25] believed that enterprise resources are the sum of various elements owned by enterprises, that can bring competitive advantages or disadvantages, and that include intangible resources and tangible resources, such as firm brand, technology, knowledge, employee skills, information, social capital, land, plant, equipment, process, and financial resources, and others. However, in the set of enterprise resources, not all resources play a consistent role. As firms prefer retaining important and valuable resources internally [24,26], the importance and value of the resource could affect the firm’s sourcing decisions. If a firm’s competitive advantage mainly comes from intangible knowledge resources, it is more likely to keep the operation of information resources internal. That’s because information resources [2], such as literature and data, are one of the carriers and embodiments of knowledge resources [27], and closely related to the firm’s value creation. In other words, the information resources operation might not be outsourced if the internal knowledge resources contained in information resources are important to a firm [28].

K. Foss and N.J. Foss et al. [29] and others [30,31] proposed that RBV and TCT should be combined in the study of organizational outsourcing behavior. Pitelis and Pseiridis [31] suggested that a firm’s resource value could influence transaction costs, because opportunism—that is, market transactions of high-value resources—brings greater uncertainty to firms and the consequent cost of supervision, which leads to increased transaction costs. Therefore, in a firm’s resource structure, the proportion and importance of knowledge resources directly affect the value of information resources to the firm, and thus, encourage it to choose different CCDMs. Prior literature has examined some important factors affecting cloud deployment selection based on a firm’s characteristics, such as size and IT budget [18,[32], [33], [34]], but has not addressed the influence of the firm’s relative resource structure characteristics. RBV considers how firms tend to internalize and continuously strengthen their strategic resources to sustain a competitive advantage [3,24,26], which can push up the proportion of important resources they access. A firm’s relative resource structure will thus reflect the different degrees of importance it assigns to internal resources. It is, therefore, meaningful from a theoretical perspective to further study how the firm’s relative resource structure influences its cloud computing transaction costs, and further influences its cloud computing deployment selection. By opening the enterprise’s black box, such analysis can explain some inconsistent study results in ITO, greatly improve the research framework of TCT’s transaction costs, and promote research on the influencing factors of transaction costs.

A firm’s selection of a cloud deployment model is also impacted by the external environment, especially the institutional environment. The firm’s outsourcing behavior regarding information resources involves many participants, such as cloud service vendors, clients, subcontractors, and internet service providers, whose behaviors are mainly governed by contracts protected by the legal system. The risk of opportunistic behavior by these external partners is one of the main sources of information resources transaction costs. The size of the risk is restricted by the external institutional environment, which in turn can affect the willingness of a firm to outsource its cloud service. Hence, the degree of legal protection (DLP), especially in information security systems, is an important external environment factor for promoting ITO [35]. The DLP can reduce the risk of cloud outsourcing and further moderate cloud deployment model selection behaviors affected by the relative resource structure. DiMaggio et al. [36] argued that the institutional environment affects an organization’s structure through external coercive and normative pressures. Moreover, some scholars have confirmed the relationship between cloud computing outsourcing and governmental regulation [33,37,38]. Drawing on previous studies, we further discuss the direct influence and indirect moderating effects of the DLP on a firm’s selection of a cloud deployment model. This information could help the government understand the influencing factors and mechanisms of a firm’s CCDM selection behavior. The government could then benefit from being able to make adjustments at the institutional level and reduce the risks of information outsourcing.

Firm resources are diverse, and are thus classified into multiple categories. When studying outsourcing behavior [[39], [40], [41], [42]], some researchers have divided firm resources into two types: knowledge capital and physical capital. The current study uses this classification to examine CCDM selections of firms with different relative knowledge and physical resource structures. Based on RBV, a firm’s relative knowledge and physical resource structure could reflect the varying degrees of importance of knowledge and physical resources. We introduce the firm’s physical capital intensity (PCI) and KCI into the transaction cost analysis of TCT, and focus on two questions: First, how do the firm’s knowledge capital intensity and physical capital intensity influence its cloud computing deployment model selection? Second, what are the direct influences and indirect moderating effects of the DLP as a factor of the external environment on the firm’s cloud computing deployment model selection?

Answering these questions is important for developing a holistic view of the influence of inner resource structure characteristics and outer legal protections on the choice of a cloud deployment model; it also further promotes the study of ITO by combining TCT and RBV. From a practical perspective, the potential findings will promote cloud computing implementation and offer insights for cloud vendors and government policy makers.

The rest of the paper is organized as follows. In Section 2, we review the main related theories on ITO and cloud outsourcing, which provides the theoretical foundation for our study; we also develop the research model and propose the research hypotheses. Then, in Section 3, we discuss the research methodology used to test the hypotheses. In Section 4, we present the results of our empirical testing. Section 5 discusses the findings, implications for theory and practice, limitations, and future directions. The last section introduces the main conclusions of our study.

Section snippets

Theoretical background and research hypotheses

A firm’s decision to deploy cloud computing is related to the boundaries of the organization, where firm-level decisions are reflected in the chosen IT organizational model. Both TCT and RBV have been widely used to explain organizational decisions regarding IT [16,19,20,26].

Research data and sample

This study uses data from Chinese-listed companies to examine our hypotheses. By 2013, there were nearly 2600 listed companies in mainland China. First, we browsed the websites and annual reports of those listed companies and searched for keywords such as cloud computing and cloud service. This produced a list of companies that had been involved in cloud computing as of December 2013. Second, we collected telephone numbers or e-mail addresses of these companies’ IT departments (from websites or

Results

This study employs maximum likelihood estimation (MLE) to estimate the parameters of the logistic model and evaluate the hypotheses. MLR is sensitive to sample size because MLE requires larger samples. Hosmer and Lemeshow [82] recommended that the sample size should be greater than 400. Because the empirical data of this study are drawn from 520 listed companies, it meets the MLR sample size requirement. MLR is also sensitive to data multicollinearity, which means a high correlation among

Discussion

This research considers the reasons why firms choose different CCDMs, and hypothesizes that the intensities of physical capital and knowledge capital could be important factors influencing this decision. The hypotheses are tested on a sample of 520 firms’ observations from mainland China. The results of the initial hypotheses tests are shown in Table 7. Generally, a firm’s relative resource structure, which reflects the importance and proportion of physical versus knowledge resources, is found

Conclusions

This study proposes that a firm’s physical and knowledge capital intensities impact the selection of a CCDM. Empirical tests support most of our hypotheses. The selection of a CCDM reflects the property rights arrangements for the organization’s ITA—self-building or outsourcing. Choosing a deployment model is the key issue for firms that adopt cloud computing. Studying this issue could promote ITO research in the context of cloud computing and, to some extent, explain inconsistent results

CRediT authorship contribution statement

Qian Zheng: Data curation, Writing - original draft, Conceptualization, Supervision. Dongxiao Gu: Conceptualization, Supervision, Writing - original draft, Data curation. Changyong Liang: Conceptualization. Yulin Fang: Methodology, Writing - review & editing.

Declaration of Competing Interest

The authors declare that they have no competing interests. All persons who have made substantial contributions to the work reported in the manuscript.

Acknowledgment

This research is partially supported in data collection, analysis, and interpretation by the National Natural Science Foundation of China (NSFC) under grants Nos. 71331002, 71771075, and 71771077.

Dr. Qian Zheng is a doctoral student of MIS in Hefei University of Technology (HFUT) as well as an associate professor and deputy director of the Department of Management at Anhui Science and Technology University (AHSTU). She obtained her M.S. and B.S. in economics from HFUT. Her research interests focus on cloud service, information economics, and the value of IT in business. She has over 20 publications in journals or conference proceedings.

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    Dr. Qian Zheng is a doctoral student of MIS in Hefei University of Technology (HFUT) as well as an associate professor and deputy director of the Department of Management at Anhui Science and Technology University (AHSTU). She obtained her M.S. and B.S. in economics from HFUT. Her research interests focus on cloud service, information economics, and the value of IT in business. She has over 20 publications in journals or conference proceedings.

    Dr. Dongxiao Gu is an associate professor of Management Information Systems in the School of Management at HFUT where he obtained his Ph.D. and M.S. He received his B.S. in MIS from Jilin University. Dr. Gu held visiting scholar positions in Indiana University Bloomington in 2018 and the University of Wisconsin-Milwaukee in 2010. His research interests focus on cloud computing, medical big data analytics, health intelligence, and smart health. Dr. Gu received Emerald LIS Research Highly Commented Award of China and the best paper nomination of Wuhan International Electronic Commerce Conference in 2016. He is an author/editor of 6 books and over 80 publications in journals/proceedings such as Bulletin of Chinese Academy of Sciences, Information & Management, International Journal of Production Research, Journal of Medical Internet Research, Computers & Industrial Engineering, International Journal of Medical Informatics, International Journal of Project Management, Knowledge-Based Systems, Applied Soft Computing, Expert Systems with Applications, and etc. He serves on the editorial board of Data Intelligence hosted by CAS and MIT. He is a council member of China Association for Information (CNAIS).

    Dr. Changyong Liang is a professor of MIS and Director of the Institute for the IT Management at HFUT. He is the vice Dean of Academy of Science & Research and the director of the Office for Social Science Administration at HFUT. He was the former Dean of the School of Management at HFUT. He received his Ph.D. in management science and engineering from the Harbin Institute of Technology. He received National Scientific and Technological Progress Award of China and the Ministry of Education’ Science Prize of China in 2008. He is one of vice presidents of Chinese Society of Optimization, Overall Planning and Economic Mathematics, a member of the Ministry of Education’ Teaching Steering Committee for Management Science and Engineering Related Majors, one of executive committee members of China Association for Information Systems, as well as one of committee members of China Operations Research Society. He is also vice editor-in-chief of the Chinese Journal of Operations Research and Management. He is the co-chair for the 9th China Summer Workshop on Information Management (CSWIM2015). Dr. Liang has over 30 years of experience in examining, analyzing, designing, evaluating, developing, and implementing various information systems for enterprises, hospitals, and governments. His research focuses on information management, the role of emerging technologies such as cloud computing in smart business and society. He is an author/editor of 7 books and over 130 publications in high-level journals.

    Dr. Yulin Fang is a full professor and a program co-director for Master of Business Information Systems at Department of Information Systems, City University of Hong Kong. He earned his PhD at Richard Ivey School of Business, The University of Western Ontario in Canada. He conducts research in the areas of digital innovation and transformation, platform economy, e-commerce, and social media. Yulin is currently serving as a Senior Editor for Information Systems Research and Information Systems Journal, and a co-editor-in-chief for Information Technology & People. He is also on the Editorial Board of Journal of Strategic Information Systems and Information & Management. He was an Associate Editor for several leading journals, including MIS Quarterly (2013-2016)(2013–2016), Information Systems Research (2013–2016), and Information Systems Journal (2012–2015). He was awarded the Associate Editor of the Year at Information Systems Research in 2015. Yulin has published over 50 research articles in renowned management and information systems journals, including MIS Quarterly (MISQ), Information Systems Research (ISR), Journal of Management Information Systems (JMIS), Journal of the Association for Information Systems (JAIS), Journal of Operations Management (JOM), Strategic Management Journal (SMJ), Journal of Management Studies (JMS), Organizational Research Methods (ORM), among others. His research on open source software communities won the 2009 Senior Scholars Best IS Publication Award by the Association for Information Systems (AIS), one out of the five in that year. His work on e-commerce published at MIS Quarterly was one of the Citation of Excellent Winners of Emerald Citations of Excellence in 2017. Yulin is a professional case writer, having developed business cases for major technology and financial corporations operating in Asia markets. His cases were recognized among best-sellers at Ivey Business School Publishing and European Case Clearing House (ECCH).

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