The purpose of this paper is to understand what inhibit or facilitate cloud computing (CC) assimilation.
The authors investigate the effects of two enablers, top management support (TMS) and government support (GS), and two inhibitors, organization inertia (OI) and data security risk (DSR) on CC assimilation. The authors posit that enablers and inhibitors influence CC assimilation separately and interactively. The research model is empirically tested by using the field survey data from 376 Chinese firms.
Both TMS and GS positively and DSR negatively influence CC assimilation. OI negatively moderates the TMS–assimilation link, and DSR negatively moderates the GS–assimilation link.
The results indicate that enablers and inhibitors influence CC assimilation in both separate and joint manners, suggesting that CC assimilation is a much more complex process and demands new knowledge to be learned.
For these firms with a high level of OI, only TMS is not enough, and top managers should find other effective way to successfully implement structural and behavioral change in the process of CC assimilation. For policy makers, they should actively play their supportive roles in CC assimilation.
A new framework is developed to identify key drivers of CC assimilation along two bipolar dimensions including enabling vs inhibiting and internal vs external.
Wang, N., Liang, H., Ge, S., Xue, Y. and Ma, J. (2019), "Enablers and inhibitors of cloud computing assimilation: an empirical study", Internet Research, Vol. 29 No. 6, pp. 1344-1369. https://doi.org/10.1108/INTR-03-2018-0126Download as .RIS
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To successfully compete in the networked economy, firms increasingly integrate business processes with information systems (IS) and build internet-based IT to conduct business with trading partners (Low et al., 2011). Cloud computing (CC) creates a new information service sourcing model where firms rent third-party, internet-hosted software applications, data storage and computing services for their computing requirements (Benlian et al., 2011; Bhattacherjee and Park, 2014; Mell and Grance, 2009). Firms rent IT resources and services in an on-demand manner without paying for the upfront cost of the IT infrastructure. CC has been viewed as one of the most promising IT advancements that could fundamentally change how IS are delivered (Kappelman et al., 2013; Vithayathil, 2018). Gartner (2017) predicted that the public CC market will grow 18 percent in 2017 to total $246.8bn, and cloud adoption strategies will influence more than 50 percent of IT outsourcing deals through 2020. Realizing the potential of CC to achieve strategic flexibility, innovation, and economic gains, more and more firms begin to adopt cloud-based IS to realize strategic flexibility and operational efficiencies. Although CC has been widely adopted (RightScale, 2017), firms are struggling to realize the business value of CC. An IT innovation must be appropriately assimilated into the adopting firm’s business processes and work routines to realize its potential benefits after the initial adoption (Liang et al., 2007; Lin, 2013). Hence, this research tries to understand what factors influence firms’ CC assimilation.
Although researchers have made much progress in understanding CC adoption and IT assimilation during the past decade, there are still some gaps in the literature. First, many studies have investigated the determinants of CC adoption (Lian et al., 2014; Low et al., 2011; Messerschmidt and Hinz, 2013; Oliveira et al., 2014). Yet, research on CC assimilation is scarce. CC must be appropriately assimilated into the adopting firm’s business processes to realize its potential benefits after its adoption (Liang et al., 2007). Therefore, it is imperative to understand how the post-adoption assimilation of CC is influenced by key factors. Second, there is a lack of a comprehensive framework to identify enablers and inhibitors of IT assimilation, resulting in some key factors neglected. On the one hand, many studies employed institutional theory and investigated the effects of external pressures (e.g. normative, mimetic and coercive pressures) on IT assimilation (Liang et al., 2007; Sodero et al., 2013), but the impacts of external support are vaguely understood. IT assimilation is a resource-dependence process, and firms in developing countries often suffer resource disadvantage (Zhu et al., 2006). Support from external entities is especially important in developing countries or emerging economies where organizations’ internal resources are lacking (Cai et al., 2010; Chen and Tan, 2011). On the other hand, prior studies examined the impacts of internal enablers (e.g. technology readiness, technology integration, quality of senior leadership and sophistication of IT infrastructures) on IT assimilation, but internal inhibitors have been largely neglected when investigating antecedents of IT assimilation. Despite the consensus among researchers that IT assimilation requires firms’ structural adjustment and employees’ behavioral change and organizational obstacles could hinder these changes (Devaraj and Kohli, 2003; Setia et al., 2011), to our knowledge little research has investigated the effects of internal inhibitors on IT assimilation. Therefore, more studies should be conducted to understand the effects of external support and internal inhibitors on IT assimilation. Third, few studies have examined the joint effects of enablers and inhibitors on IT assimilation. While IT adoption might only need a decision from top managers, IT assimilation is a much more complex process that requires firms’ structural adjustment and employees’ behavioral change (Kim and Kankanhalli, 2009; Lapointe and Rivard, 2005; Rai et al., 2009). Past studies only examined the separate impacts of external and internal factors on IT assimilation (Armstrong and Sambamurthy, 1999; Liang et al., 2007; Sodero et al., 2013; Zhu et al., 2006), and many attempts are expected to extend our understanding about the complexity of IT assimilation by examining the joint impacts of these enablers and inhibitors.
The purpose of this study is to investigate both enablers and inhibitors that influence CC assimilation. Since absorbing CC requires changes in organizational structures and processes and demands new knowledge to be learned, CC assimilation can be viewed as a type of organizational learning (Attewell, 1992; Fichman and Kemerer, 1997; Yi et al., 2018). Among many factors that could affect this learning process, we adopted an integrative view to develop a conceptual framework to select the critical enablers and inhibitors of CC assimilation. We identify four salient factors along two bipolar dimensions: internal vs external and enabling vs inhibiting. Top management support (TMS) (internal enabler) and government support (GS) (external enabler) can facilitate organizational learning, while organizational inertia (internal inhibitor) and data security risk (DSR) (external inhibitor) can suppress organizational learning when firms try to establish new routines of CC use. To deeply understand the impacts of these critical factors, we also investigate the joint effects of the enablers and the inhibitors besides their separate effects. The research model is empirically tested by using the field survey data from 376 Chinese firms. Results suggest that both TMS and GS positively and DSR negatively influence CC assimilation. Results also indicate that organization inertia (OI) negatively moderates the TMS–assimilation link, and DSR negatively moderates the GS–assimilation link.
The reminder of the paper organizes as follows. Section 2 reviews the theoretical framework including learning challenges of CC assimilation, IT assimilation and drivers of CC assimilation. Section 3 is about the research model and hypotheses. Section 4 describes methodology and report results of hypothesis testing. This paper concludes after discussing the findings, theoretical and managerial implications, and limitations and future research directions.
2. Theoretical framework
2.1 Learning challenges of CC assimilation
In this study, we follow Purvis et al. (2001) to define assimilation as “the extent to which the use of technology diffuses across the organizational projects or work processes and becomes routinized in the activities of those projects and processes.” In developing an organizational learning perspective of innovation assimilation, Attewell (1992) asserts that the knowledge required by organizations to use technological innovation is difficult to transfer, and creates a “knowledge barrier” that inhibits assimilation. In the CC assimilation phase, both end-users and IT staff face many learning challenges, and many firms fail in assimilating CC into business routines because of resistance from end-users and IT staff (Kuo, 2011). As a new IT service delivery model, CC transforms the IT artifact from IT resource into IT service, which exerts a profound impact on IS research and practice (Wang, Liang, Jia, Ge, Xue and Wang, 2016). To fully capture the potential value of CC service, end-users need to learn how to shift task responsibilities, procure on-demand self-service, govern the relationship with CC service providers and design usage-based contracts for standardized services (Chen and Wu, 2013; Schneider and Sunyaev, 2016; Wang, Liang, Jia, Ge, Xue and Wang, 2016), which are all new practices for them.
Besides learning challenges for end-users, structural and behavioral changes in the process of CC assimilation inevitably increase learning resistance of IT staff. CC can be classified as a type of competence-destroying technology, because it renders many traditional client-hosted technologies and related expertise obsolete (Baker, 2012; Iyer and Henderson, 2010). Firms with competency in client-hosted systems may find that such a legacy competency is no longer needed and no longer a source of competitive advantage, which may result in strong resistance from IT staff. Since CC operation and maintenance is provided by external CC service providers, the task for IT staff will switch to the governance of relationships and contracts with CC service providers, which may lead to the layoff of IT staff in an organization and require the remaining IT professionals to abruptly change mindsets and acquire unfamiliar new capabilities including governing the relationship with service providers and designing service level agreements to ensure quality, reliability and other performance indicators (Vithayathil, 2018). Due to the threat of layoff and new knowledge requirements, the IT staff will be reluctant to assimilate CC.
2.2 IT assimilation
To substantiate the business value of CC, firms need to not only adopt CC, but also, and more importantly, assimilate the technical features of CC into their business routines in the post-adoption stage (Devaraj and Kohli, 2003; Setia et al., 2011). CC assimilation is a complex phenomenon that deserves more attention from the IS community, so that it can be thoroughly understood. We first review the antecedents of IT assimilation to shed some lights on the enablers and inhibitors of CC assimilation.
We reviewed key studies on the antecedents of IT assimilation that have mainly published in leading IS journals such as Decision Support Systems, European Journal of Information Systems, Information and Management, Information Systems Journal, Information Systems Research, Internet Research, Journal of Information Technology, Journal of Management Information Systems, Journal of Strategic Information Systems, Journal of the Association for Information Systems and MIS Quarterly. Our review indicates that previous studies have examined the antecedents of IT assimilation in various contexts of technology and management innovations including collaborative information technologies (Bajwa et al., 2008), e-commerce (Chatterjee et al., 2002; Lin, 2013), ERP (Ke and Wei, 2008; Lai et al., 2016; Liang et al., 2007; Liu et al., 2011; Mu et al., 2015; Saraf et al., 2013; Shao et al., 2017), electronic health records (Baird et al., 2017; Mishra et al., 2012; Reardon and Davidson, 2007), electronic procurement (Rai et al., 2006, 2009), electronic supply chain management (Wu and Chuang, 2010), interorganizational business process standards (Bala and Venkatesh, 2007), object-oriented programming languages (Cho and Kim, 2002), IS security (Hsu et al., 2012) and RFID technology (Wei et al., 2015). The literature has focused on two broad dimensions of factors that enable or inhibit IT assimilation within an organization: external and internal antecedents.
2.2.1 External antecedents
Forces from external influential entities and technological and competitive environments would influence IT assimilation. According to institutional theory, IT assimilation is driven by the need for organizational legitimacy rather than by competition and the desire for efficiency, and organizational legitimacy fosters the process of institutionalization (DiMaggio and Powell, 1983; Hsu et al., 2012). Many studies employed institutional theory, and examined impacts of three institutional pressures (mimetic, coercive and normative) on IT assimilation. For example, Liang et al. (2007) investigate the mechanism through which IT assimilation is influenced by the three institutional forces and the mediation effect of TMS in the link between institutional forces and IT assimilation.
Furthermore, competitive environments also present various forces for IT innovation assimilation (Zhu et al., 2006). By assimilating new IT innovation, firms may influence the industry structure, change the competition rules and leverage new ways to outperform their rivals. Firms in the high turbulent environments have to deal with the demanding processes of IT assimilation to maintain competitive edges (Wolf et al., 2012). Many studies confirmed that many environmental pressures such as environmental turbulence (Wolf et al., 2012), environmental uncertainty (Lai et al., 2016) and competition intensity (Zhu et al., 2006) increase a firm’s tendencies to assimilate new IT innovations.
2.2.2 Internal antecedents
There are many internal factors that influence IT assimilation. First, literature on IT assimilation has reached the consensus that top management is responsible for both the technical and organizational changes during the process of IT assimilation. Past studies have repeatedly confirmed the critical roles of TMS in IT assimilation (Dong et al., 2009; Liang et al., 2007; Wolf et al., 2012), and some scholars examined the effects of top management leadership styles on IT assimilation (Ke and Wei, 2008; Shao et al., 2017). Second, organizational readiness refers to the technological, managerial and financial resources that can be used to support IT assimilation, which is assumed to be critical in IT assimilation. Many constructs representing organizational readiness have been found to be positively related with IT assimilation, for example IT sophistication (Armstrong and Sambamurthy, 1999; Wei et al., 2015), IT resource (Hsu et al., 2012), organizational capital (Neirotti and Paolucci, 2011), absorptive capability (Saraf et al., 2013; Wei et al., 2015), knowledge management capability (Lin, 2013) and financial resource (Rai et al., 2009). Third, organizational culture determines the attitudes of low-level managers and operational-level employees toward changes during the process of IT assimilation, and there is empirical support for the importance of organizational culture (Ke and Wei, 2008).
As mentioned above, the antecedents of IT assimilation have consistently been investigated in the various contexts of technology and management innovations, and the results indicate that many external and internal factors influence the process of IT assimilation. Past studies paid attention to external pressures and internal facilitators, but the impacts of external support and internal constraints on IT assimilation are neglected. This is probably due to the lack of a comprehensive framework that can be used to identify the enablers and inhibitors of IT assimilation from both external and internal perspectives. Furthermore, to our knowledge past studies only examined the separate impacts of external and internal factors, little attention has been paid to their joint effects on IT assimilation. Research on the joint effects would increase our understanding about the complexity of IT assimilation as a process of technical and organizational changes.
2.3 Drivers of CC assimilation
Studies on IT assimilation suggest that the business value of IT cannot be fully captured unless it is actually assimilated in key business processes and routinely used (Devaraj and Kohli, 2003; Setia et al., 2011). It is important to investigate the important factors that influence IT assimilation, since it is a precondition to streamline the process of IT assimilation. In this paper, we develop a conceptual framework to fully identify the critical drivers of CC assimilation along two bipolar dimensions, as shown in Figure 1. The two bipolar dimensions including internal vs external and enabling vs inhibiting factors produce four quadrants – internal enabler, external enabler, internal inhibitor and external inhibitor. We will select the most important driver in each quadrant to understand their separate and joint effects on CC assimilation.
Given the consensus regarding the critical role of TMS in IT innovation assimilation and the implementation of large-scale IT projects (Dong et al., 2009), we select TMS in the internal-enabler quadrant. Top managers largely determine a firm’s autonomy in the process of IT assimilation, since they perform the crucial functions of allocating resources, initiating implementation strategy and motivating end users (Akkermans and van Helden, 2002). According to Dong et al. (2009), there are three types of TMS actions: resource provision, change management and vision sharing. Resource provision refers to the actions that supply key resources, e.g., IT budget, staff and training, in the process of IT adoption and assimilation. Change management refers to the actions related to assisting individuals, teams and firms to accept a new IS. Vision sharing refers to actions, ensuring that low-level managers and operational-level employees develop a common understanding on the core objective of a new IS.
In the external-enabler quadrant, we select GS. Compared to other entities, e.g., customers and suppliers, government plays a more important role in facilitating CC assimilation because the open-standard and distributed nature of CC bring forth unique challenges regarding business law, data security and information privacy, which require active involvement of the government. GS provides social capital that can help firms overcome uncertainties (Luk et al., 2008) and become an indispensable source of support for IT assimilation, especially in developing countries (Zhu et al., 2006).
OI is identified as an internal inhibitor. It refers to firms’ tendency to maintain stability of their organizational arrangements such as strategy and structure in spite of environmental change (Hannan and Freeman, 1984). Assimilating new technologies into existing work routines inevitably leads to structural change of firms and behavioral change of low-level managers and operational-level employees.
Since DSR presents a major external threat to CC assimilation, we put it in the external inhibitor quadrant. To leverage CC, firms have to upload and store their data into the servers of service providers which are beyond their reach and control (Subashini and Kavitha, 2011). It is impossible for client firms to know where their data are stored and how the data are leveraged. Security risks will make end-users more reluctant to use CC (Oliveira et al., 2014). A KPMG (2014) survey indicates that data security is the greatest challenge for firms to use CC. Employing this framework, we are able to consider both internal and external factors that facilitate or inhibit CC assimilation, thus helping to achieve a holistic understanding of CC assimilation.
3. Research model and hypotheses
Based on the perspective of organizational learning, we develop a research model (Figure 2) to investigate the enablers and inhibitors of CC assimilation. Specifically, we argue that the CC assimilation is influenced by the separate and joint effects of enablers and inhibitors. On the one hand, a firm is subject to impetus forces of CC assimilation, e.g., the desire for competitive advantages or the advocating from top managers or governments. On the other hand, DSR and OI create obstacles for assimilating CC (Louis and Sutton, 1991). Although supports of top managers and governments signal a need for assimilating CC, DSR and OI often prevent the firm from embracing changes in the process of CC assimilation (Ghewamat, 1991). We elaborate on the underlying logic in detail as follows.
3.1 Enablers of CC assimilation
3.1.1 Top management support
TMS, the explicit and active support of top managers toward the use of new IS, plays a critical role in CC assimilation. The objective during the post-adoption stage is to assimilate the technical features of CC service into business routines, so that the expected benefits of CC can be actually realized. TMS has been verified as a requisite for ensuring the success of IT implementations (Elbanna, 2013). Drawing from previous research on TMS and IT implementation (Dong et al., 2009; Hwang and Schmidt, 2011; Sharma and Yetton, 2011; Thong et al., 1996), we argue that TMS facilitates CC assimilation by supporting the acquirement of knowledge that is needed to understand and use CC in a routine manner. The needed knowledge often lies outside the boundaries of the focal firms, and lower-level managers and operation-level staff can acquire the external knowledge through CC provider training and professional consulting companies (Ko et al., 2005), academic and practical CC conferences (Hirt and Swanson, 2001) and centralized help desks (Saraf et al., 2013). TMS can provide key resources to support these ways of knowledge acquisition and help to overcome knowledge barriers that inhibit CC assimilation (Liang et al., 2007; Rai et al., 2009). Therefore:
TMS is positively associated with the extent of CC assimilation within the firm.
3.1.2 Government support
GS refers to the support from governments to encourage the use of IT innovation by firms (Oliveira et al., 2014; Zhu et al., 2006). GS may be realized by enacting regulations and policies or providing financial supports, and it can facilitate firms to assimilate CC. The open-standard and distributed nature of CC bring forth unique challenges regarding business law, data security and information privacy, which require active involvement of the government (Oliveira et al., 2014). Uncertainties with using CC may hinder middle-level managers and operational-level employees to adapt business processes and work routines to CC (Liang et al., 2007). GS enhances organizational legitimacy that can help firms overcome these uncertainties (Luk et al., 2008) and become an indispensable source of support for CC assimilation, especially in developing countries (Zhu et al., 2006). Furthermore, financial support from governments provides firms with extra resources to overcome knowledge barriers and motivate end users to learn and use CC in the process of assimilation (Rai et al., 2009). For example, China’s 12th Five Year Plan for the development of software and information technology services (Atkinson, 2014) sets CC innovation development as one of the eight key projects from 2013 to 2017, and provides financial supports for the CC service industry as well as firms’ demonstration projects. Some firms that successfully assimilate CC will be selected as “model” firms, which would increase their reputation and access to economic resources. These financial supports provide middle-level managers and operational-level employees with autonomy in deciding how to assimilate CC (Xue et al., 2008). Hence:
GS is positively associated with the extent of CC assimilation.
3.2 Inhibitors of CC assimilation
3.2.1 Organizational inertia
CC assimilation refers to the extent to which the use of CC diffuses across the organizational projects or work processes and becomes routinized in the activities of those projects and processes (Liang et al., 2007; Purvis et al., 2001). OI could impede CC assimilation through two different ways. On the one hand, OI comes from persuading exchange partners to support CC assimilation. Firms with higher OI are locked in relationship with their supplies and customers by using specific interorganizational IS (Clemons and Row, 1992). Thus, opposition from exchange partners would hinder lower-level managers and operational-level employees to learn how to use CC (Figueiredo et al., 2015). On the other hand, OI comes from implementation cost. IT routines develop over time and are costly to adapt. Firms with higher OI utilize established IT routines more heavily, and learning costs related CC assimilation should be greater for these firms (Deng and Chi, 2012). Higher learning cost would weaken firms’ tendency to assimilate CC and result in a lower successful rate of CC assimilation. Therefore, firms with higher level of OI will suffer more difficulties in assimilating CC:
OI is negatively associated with the extent of CC assimilation.
3.2.2 Data security risk
Firms who want to use advanced CC services have to upload their data into provider’s servers. Loss of data control rights leads to many security-related risks. We used perceived DSR to capture the concern. DSR refers to end-users’ risk perceptions about the loss of control over data and information (Ackermann et al., 2012; Benlian and Hess, 2011; Wang, Wang, Su and Ge, 2016). There are three types of DSR when using CC service. First, confidential information is used without permission. Second, data are altered accidentally or maliciously during the process of transmission. Third, malwares such as viruses and botnets that can attach themselves to the downloaded confidential data and infect client systems. Although most of the CC services offer security protection, such as firewalls, anti-virus, back-up servers and encryption, none of these mechanisms can assure immunity from all DSR. High DSR would weaken the tendency of lower-level managers and operational-level employees to use CC. The CC literature also suggests that DSR has discouraged CC assimilation behaviors (Subashini and Kavitha, 2011). Thus:
DSR is negatively associated with the extent of CC assimilation.
3.3 The joint effects of enables and inhibitors on CC assimilation
Besides their main effects, the enabling and inhibiting factors often interact with each other to influence CC assimilation. First, OI could negatively moderate the effects of TMS on CC assimilation, so that TMS is less influential in firms with high OI. Generally speaking, firms with lower OI usually adopt an organic organizational structure (Hannan and Freeman, 1984), and communication flows more freely throughout firms with the organic structure (Covin and Slevin, 2010). The advocacy of CC assimilation from top managers can easily reach and be accepted by lower-level managers and operational-level employees, while firm with higher OI often have more levels of organization hierarchies (Hannan and Freeman, 1984). Even if top managers advocate CC assimilation, it takes time to go through many hierarchies for the signal to reach lower-level managers and operational-level employees, which will weaken the fidelity of the signal. Therefore, firms with high OI are less likely to follow top managers’ advocacy to learn to use CC than firms with low OI:
The relationship between TMS and CC assimilation is negatively moderated by OI, so that the relationship is weaker in firms with high OI than those with low OI.
OI also negatively moderates the effects of GS on CC assimilation, so that GS is less influential in firms with high OI. Firms with low OI tend to be young organizations (Shimizu and Hitt, 2005), which often have not established client-hosted IT infrastructure and thus lower sunk costs. The advocacy of CC assimilation from governments can be easily responded, since the firms do not need to divest existing systems to embrace CC. In contrast, firms with high OI are often old organizations (Shimizu and Hitt, 2005). These firms have made significant investments on client-hosted IS and developed routines around these systems. Switching to CC means abandoning these IS and changes the entrenched routines. Even if governments advocate CC assimilation, the structural and cognitive rigidities would lead to strong reluctance to learn to use CC. Therefore, it is difficult for firms with high OI to follow governments’ advocacy to assimilate CC:
The relationship between GS and CC assimilation is negatively moderated by OI, so that the relationship is weaker in firms with high OI than those with low OI.
In this paper, we also propose that DSR can possibly moderate the influences of TMS on CC assimilation, so that TMS is more influential when firms have low DSR for CC services. For CC services with low DSR, top managers can advocate them without data security-related concerns, and lower-level managers and operational-level employees can focus on learning how to use these services in their routine work without the distraction of security issues. In contrast, as DSR gets higher, top managers should consider complimentary investment to avoid security risks, and end users need to simultaneously learn how to avoid DSR as well as how to use these services in their routine work. The dual learning pressures may cause anxiety and discomfort in the focal firm (Benlian and Hess, 2011; Subashini and Kavitha, 2011), and it could partially offset the forces of TMS on CC assimilation. Hence, TMS generates more impacts on CC assimilation when DSR is low:
The relationship between TMS and CC assimilation is negatively moderated by DSR, so that the relationship is weaker for firms perceiving high DSR than those perceiving low DSR.
In addition, DSR can negatively moderate the effect of GS on CC assimilation, so that GS is more influential for CC services with low DSR. The focal firm and governments have different goals to advocate CC. The focal firm expects to realize strategic flexibility and operational efficiencies (Armbrust et al., 2010; Garrison et al., 2012; Sultan, 2010) without impairing its confidential data, while governments aim to improve computing resources utilization and reduce carbon dioxide emissions without taking DSR of specific firms into consideration (Sultan, 2010). If a firm has low DSR for CC, the CC assimilation goal of the focal firm can align with that of governments, which helps the focal firm comply with the advocacy of governments. In contrast, as DSR gets higher, the conflict between the goals of the focal firm and governments heightens, which could partially offset the force of GS on CC assimilation. Hence, GS generates stronger impact on the tendency of lower-level managers and operational-level employees to learn to use CC when firms have low DSR of CC:
The relationship between GS and CC assimilation is negatively moderated by DSR, so that the relationship is weaker for firms perceiving high DSR than those perceiving low DSR.
4.1 Construct operationalization
To ensure validity and reliability, we adapted all measurement items from the past literature, and some modifications were made to fit the context of CC assimilation. We organized an expert panel which is composed with three IS professors, two CEOs and two CIOs, and the expert panel examined the face validity of the measurement items. The measurement items of each construct are provided in Appendix 1.
4.1.1 CC assimilation
CC assimilation refers to the volume, diversity and depth of CC usage in firms, and its measures were adapted from Liang et al. (2007).
TMS refers to explicit and active support of the top management toward the introduction and deployment of CC, and its scale was adapted from Ramamurthy and Premkumar (1995), Bruque-Camara et al. (2004) and Oliveira et al. (2014).
OI refers to firms’ inability to enact internal change in face of a significant external change. Past studies propose that both firm size and firm age influence OI (Hannan and Freeman, 1984; Kelly and Amburgey, 1991; Ruef, 1997). Large-sized firms tend to have more layers in the hierarchical structure and more rules and policies, thus lead to greater level of OI and slower decision making. Meanwhile, firms with higher age tend to solidify the cognitive mind-set of managers and employees and also institutionalize the routines. Therefore, we used firm size and firm age to measure OI. According the classification standard of National-Bureau-of-Statistics-of-China (2012), a number of employees and annual income are used to determine firm size into three categories: small, medium and large. To reduce kurtosis and skewness, we used the logarithm of the firm age measure.
DSR is defined as the potential loss of control over data and information, such as when confidential information is used without your knowledge or permission. We adapted its measures from Kim et al. (2008) and Saya et al. (2010).
We also include some control variables to capture the differences among firms. To account for the variances attributable to institutional pressures, we control for the differences among firms in terms of their three institutional pressures (e.g. mimetic, coercive and normative) for CC assimilation. Besides, we include industry type, number of IT staff, IT budget, CC compatibility, IT sophistication and time since implementation as another six control variables for the reason that these variables have been verified as the determinants of IT innovation assimilation.
4.1.6 Three institutional pressures
4.1.7 IT resources
IT resources refer to the IT-related resource that can be leveraged in the process of CC adoption and assimilation. In this work, we used the number of IT staff and IT budget to measure IT resources. Since IT department with large number of IT staff and IT budget represents the abundance of IT resources, one firm possesses to effectively assimilate an IT innovation (Damanpour, 1991; Zmud, 1982). To reduce kurtosis and skewness, we used the logarithm of IT budget and the number of IT staff measures.
4.1.8 CC compatibility
CC compatibility refers to the degree to which CC aligns with adopters’ previous practices and current needs. We adapted its measures from Oliveira et al. (2014).
4.1.9 IT sophistication
4.1.10 Time since implementation
Time since implementation (in months) was considered to control for the temporal nature of CC assimilation (Saraf et al., 2013).
4.2 Data collection
We hired a reputable survey company in China to collect data from senior business or IT managers as key informants. The survey was conducted in August, 2014 for three weeks. A total of 604 responses were received. After checking the contents and time used to finish the questionnaire, 57 responses were deleted because of obvious errors, all same answers throughout the questionnaire, or too short time used to finish the questionnaire. Among the 547 valid responses, 171 were deleted because these firms have not adopted any cloud service, leaving 376 firms that have already adopted cloud services for data analysis.
Table I shows the responding firms’ demographics. Among the 376 firms are 185 manufacturers, 177 service vendors and 14 other firms. On average, a firm adopted 5.53 CC services, and these companies had been using CC for 10.26 months (SD=7.53 months).
Non-response bias was checked by following Armstrong and Overton (1977). t-Test was used to compare the earliest 25 percent with the last 25 percent respondents on four demographical variables: firm size, firm age, number of IT employee and annual IT budget. No significant differences were found between the two groups, suggesting that non-response bias is unlikely to be serious.
5.1 Measurement model
Before testing our hypotheses, validity and reliability of the measurement models were assessed. The validity of the measurement models was tested by following Gefen et al. (2011). First, as shown in Table II, the square root of each construct’s average variance extracted (AVE) is much greater than the construct’s correlations with any other construct, suggesting sufficient discriminant validity (Fornell and Larcker, 1981). Second, factor loadings and cross-loadings were calculated for all of the constructs. As Table AI shows, loadings of all items on their substantive constructs are over 0.60, suggesting sufficient convergent validity. In addition, each item’s factor loading is much higher than its cross-loadings on other constructs, confirming the sufficiency of discriminant validity (Hair et al., 1998). The reliability of the measurements was examined by computing composite reliability and Cronbach’s α coefficients. As Table II shows, all reliability scores exceed Nunnally’s (1978) recommended cut-off of 0.70.
Following the formative measure assessment guideline recommended by Petter et al. (2007), we evaluated validity and reliability of CC assimilation. First, the PLS analysis results show that five indicators of CC assimilation have significant weights, providing evidence for construct validity (Diamantopoulos and Winklhofer, 2001). Second, we tested its variance inflation factor (VIF) of each assimilation indicator to assess multicollinearity. The VIFs for five indicators range from 1.687 to 1.841, well below the recommended VIF statistic of 3.3 (Diamantopoulos and Siguaw, 2006). Hence, the measurement model of CC assimilation has adequate reliability.
5.2 Common method bias
Since a single informant was employed to collect organizational data, common method variance (CMV) is a potential issue. Following Podsakoff et al. (2003), we applied procedural remedies to reduce CMV, e.g., protecting respondent anonymity, counterbalancing some items’ order and retesting some questions. Additionally, Harman’s single-factor test was performed, and the results show that a single dominant factor was not present and the largest factor accounted for only 21.44 percent of total variance, respectively. Therefore, CMV does not seem to be a serious issue for this study.
5.3 Hypothesis testing
Hierarchical regression analysis is used to test our hypotheses. The independent variables and moderator variables are mean-centered to minimize the possibility for multicollinearity (Aiken et al., 1991). Following Perrone et al. (2003), three models were estimated separately with incrementally more predictors included. Model 1 only included the control variables, and Model 2 included the control variables and independent variables. Model 3 is the full model with all variables and hypothesized interaction effects as independent variables.
Table III shows the estimation results. For the separate effects of enablers (TMS and GS), we found support for both H1a and H1b, which proposed that TMS and GS are positively associated with CC assimilation. For the separate effects of inhibitors (OI and DSR), we found support for H2b but not for H2a, which proposed that neither firm size nor firm age is significantly associated with CC assimilation, but DSR is negatively associated with CC assimilation.
The results also reveal the joint effects of enablers and inhibitors on the CC assimilation. Firm size and firm age negatively moderate the relationship between TMS and CC assimilation, suggesting support for H3a. Firm size does not significantly moderate the relationship between GS and CC assimilation, and firm age does not significantly moderate the relationship between GS and CC assimilation, suggesting not support for H3b. DSR does not significantly moderate the relationship between TMS and CC assimilation, suggesting not support for H4a, while DSR negatively moderates the relationship between GS and CC assimilation, suggesting support for H4b. In summary, these results partially support for H3 and H4, suggesting that enablers and inhibitors do have joint effects on CC.
To further analyze the moderation effects, we followed Aiken et al. (1991) graphical procedure to draw Figures 3–5. We assigned OI (firm size and firm age) and DSR to the values of one standard deviation above and below their means to plot their moderation effects. Figure 3 shows that the sloped regression line for the relationship between TMS and CC assimilation is positive and significant for small firms, and it is insignificant for large firms. Figure 4 shows that the sloped regression line for the TMS–assimilation link is positive and significant for low firm age, and it is insignificant for high firm age.
Figure 5 shows that the sloped regression line for the GS–assimilation link is positive and significant for low DSR, and it is insignificant for high DSR.
The objective of this work is to understand the enablers and inhibitors of CC assimilation. Taking an organizational learning perspective, we study the effects of two enablers (TMS and GS) and two inhibitors (OI and DSR) on CC assimilation. Overall, our results show that enablers and inhibitors influence CC assimilation in both separate and joint manners. Below, we discuss these results in the order of our hypotheses.
First, the two enablers, TMS and GS, positively affect CC assimilation, suggesting that both internal and external support can facilitate focal firms to overcome knowledge barriers in the process of CC assimilation. The results are consistent with previous research on the roles of top managers (Liang et al., 2007; Purvis et al., 2001; Rai et al., 2009) and governments (Cai et al., 2010; Sodero et al., 2013; Zhu et al., 2006) in IT innovation assimilation.
Second, the two inhibitors, OI and DSR, have different effects on CC assimilation. OI has no significant influence on CC assimilation. Instead, it affects CC assimilation in an indirect way. As our results indicate, OI weakens the effects of TMS on CC assimilation. That is probably because when top management considers the age and size of the organization, they may take a less aggressive approach to promote CC assimilation. DSR is negatively associated with CC assimilation, which is consistent with our hypothesis and previous studies on CC literature (Subashini and Kavitha, 2011).
Third, OI negatively moderates the relationship between TMS and CC assimilation, and further analysis of the moderating role of OI shows that TMS has no effects on CC assimilation (see Figures 3 and 4) for the firms with a high level of OI. Prior studies on IT innovation assimilation which typically find a significant positive relationship between TMS and IT assimilation (Armstrong and Sambamurthy, 1999; Chatterjee et al., 2002; Hsu et al., 2012; Liang et al., 2007; Purvis et al., 2001; Rai et al., 2009; Wolf et al., 2012). The inconsistency between our study and the literature is mainly because OI was neglected in the prior studies. We added OI as an inhibitor and the results indicate that OI will weaken the impacts of TMS on CC assimilation. The negative moderation effects of organizational inertia in the GS–assimilation links were not supported. This is possibly because GS would help to overcome uncertainties of data security and information privacy, which can directly facilitate middle-level managers and operational-level employees to assimilate CC no matter what the level of OI is.
Forth, DSR negatively moderates the relationship between GS and CC assimilation, which extend our understanding about the roles of DSR in CC assimilation. Our results indicate that DSR has both direct and indirect impacts on CC assimilation by moderating the effects of GS. But DSR does not moderate the relationship between TMS and CC assimilation. This may be because top managers have the authority to enforce the use of CC and their influence on CC assimilation is too strong to be contingent on DSR (Dong et al., 2009). Middle-level managers and operational-level employees will use CC without taking DSR into consideration if top managers advocate CC.
6.1 Theoretical implications
Our results have three theoretical implications. First, we developed a new framework to identify key drivers of CC assimilation along two dimensions including enabling vs inhibiting and internal vs external. Most of the studies on IT innovation assimilation relied on institution theory (Hsu et al., 2012; Liang et al., 2007) and/or TOE framework (Zhu et al., 2006) to investigate the determinants of IT innovation assimilation. Our new framework provides an alternative way to identify both enablers and inhibitors of IT innovation assimilation in both internal and external contexts, which can give a holistic understanding how IT innovation assimilation as a complex organizational learning is influenced. The results indicate that enablers and inhibitors influence CC assimilation in both separate and joint manners, suggesting that CC assimilation is a much more complex process and demands new knowledge to be learned (Attewell, 1992; Fichman and Kemerer, 1997).
Second, this study integrates OI, one important inhibitor which was neglected by previous studies on IT innovation diffusion, into the process of CC assimilation. As an organizational learning process, CC assimilation will lead to structural change of firms and behavioral change of low-level managers and operational-level employees, and is inevitably affected by OI. The finding suggests that TMS has no effects on CC assimilation for these firms with a high level of OI, which is opposite to the general consensus regarding the critical role of top management in the IT innovation diffusion (Dong et al., 2009). This finding delineates the boundaries of TMS’ effects in the CC assimilation context.
Third, this paper also extends our understanding about the role of governments in IT innovation diffusion. Past studies have found that the pressures or supports from governments play an important role in IT innovation diffusion (Cai et al., 2010; Liang et al., 2007; Oliveira et al., 2014; Saraf et al., 2013; Zhu et al., 2006) and the effects vary at different levels of national economic development, as studies find that GS and regulatory forces play more significant roles in developing countries than in developed countries (Zhu et al., 2006). This study further indicates that the effects of GS have no effect on assimilation for CC services with high DSR, suggesting that the effects of GS also depend on firms’ technological concerns.
6.2 Practical implications
This study also provides guidance for IT practitioners and related policy makers. Support and participation of top managers should be encouraged, since TMS still works for these firms with a low level of OI in the process of CC assimilation. But for these firms with a high level of OI, only TMS is not enough, and top managers should find other effective way to successfully implement structural and behavioral change in the process of CC assimilation, or CC assimilation is likely to fail. For example, top managers should provide some specific supports (e.g. help desk, training, communication and retraining workshops) for end-users to access necessary knowledge and overcome learning challenges of CC assimilation. For IT staff, top managers should design some policies to smooth their resistances from threat of layoff and new knowledge requirements in the process of CC assimilation.
For policy makers, they should actively play their supportive roles in CC assimilation. Since GS can directly facilitate CC assimilation, especially for these CC services with low DSR, governments should actively initiate some funds or privileged policies to encourage CC assimilation. In addition, governments should enact specific law and regulation on data security and information privacy to overcome firm’s DSR concerns in using CC services.
6.3 Limitations and future research directions
Although we strive to conduct a rigor study, limitations are inevitable. First, we collected our data from one specific developing country, which will limit the generability of our results. Our model should be tested in other developing countries and even in developed countries, and culture orientations can be included in our research model. Second, the use of self-reports of single respondents may lead to common method bias. Although the results of the Harman one-factor and maker variable test indicate that common method bias is not a significant issue, this issue should be kept in mind when interpreting the findings. We suggest that future research should collect data from multiple sources. Third, we measured TMS as a first reflective construct in terms of top managers’ participation in CC assimilation. Actually, TMS is a multi-dimensional construct, including resource provision, change management and vision sharing (Dong et al., 2009). In order to fully capture the role of TMS in CC assimilation, we encourage future studies to examine the effects of specific TMS dimensions on CC assimilation. Forth, we indeed found that TMS has no effects on CC assimilation for the firms with a high level OI. One possible explanation is that the profound impacts of CC lead to the resistances of IT and business function. We did not collect data about the resistances of IT and business function during the process of CC assimilation. Future studies could investigate reasons for the irrelevant relationship between TMS and CC assimilation, and explore the new mediators of the TMS–assimilation link and the GS–assimilation link.
Because of great potential to strategic flexibility and operational efficiencies, CC has been attracted many attentions from IS academics and practitioners. Drawing from the perspective of organizational learning, this study examines both enablers and inhibitors of CC assimilation. Furthermore, our empirical findings offer evidences for what constrain or facilitate CC adoption and assimilation. From a theoretical perspective, this study provides a good starting point for theoretical refinement on the different facilitators and inhibitors of CC assimilation. It also provides an analytical tool that can be used for top managers’ and governments’ intervention in the process of CC assimilation.
Responding firm demographics
|Number of employee|
|Annual incoming of 2013 (million RMB)|
Notes: C’s α means Cronbach’s α; CR means composite reliability. Diagonal italic numbers are square roots of AVE
Results of hierarchical regression analysis
|Model 1||Model 2||Model 3|
Notes: *p<0.05; **p<0.01; ***p<0.001; ****p<0.10
Item descriptive statistic and cross-loadings of key variables
|Cloud computing assimilation (ASS)||ASS1||3.83||0.766||0.332||0.795||0.349||0.383||0.126||0.030||−0.005||0.405||0.233||0.434||−0.034||0.126||0.074||−0.049|
|Top management support (TMS)||TMS1||4.150||0.666||0.335||0.298||0.736||0.250||0.103||0.059||−0.014||0.414||0.204||0.296||−0.140||0.088||−0.045||0.085|
|Government support (GS)||GS1||3.880||0.803||0.163||0.417||0.236||0.819||0.074||0.030||0.055||0.390||0.341||0.432||−0.032||0.083||0.121||−0.206|
|Firm size (SIZE)||SIZE||3.560||1.719||−0.028||0.007||0.062||0.027||0.409||1.000||0.375||−0.066||−0.013||−0.028||−0.261||0.347||0.011||−0.007|
|Firm age (AGE)||AGE||1.074||0.382||−0.045||0.031||−0.008||0.006||0.080||0.375||1.000||−0.011||−0.005||0.014||−0.201||0.151||0.048||0.025|
|Perceived data security risk (DSR)||SEC1||3.860||0.802||0.256||0.449||0.429||0.399||0.109||−0.053||−0.008||0.803||0.349||0.462||−0.076||0.068||0.047||−0.005|
Appendix 1. Scale and items
Cloud computing assimilation (ASS) (1= very low; 5= very high)
ASS1: percentage of our firm’s business processes that are using cloud computing services.
ASS2: number of functional areas that are using cloud computing services.
ASS3: for each functional area, identify the level cloud computing services are used in operation.
ASS4: for each functional area, identify the level cloud computing services are used in management.
ASS5: for each functional area, identify the level cloud computing services are used in decision making.
Top management support (TMS) (1= strongly disagree; 5= strongly agree)
TMS1: our top management is likely to support the investment on cloud computing.
TMS2: our top management is willing to take risks of adopting cloud computing.
TMS3: our top management might be interested in adopting cloud computing to gain competitive advantage.
TMS4: our top management may consider adopt cloud computing as a strategic weapon.
Government support (GS) (1= strongly disagree; 5= strongly agree)
GS1: government has enacted regulations to protect the use of cloud computing.
GS2: government has set up some relevant funds to support the companies to adopt cloud computing.
GS3: government has made some privileged policies for firms who have adopt cloud computing.
GS4: government will give financial support for firms who plan to adopt cloud computing.
Firm size1: the number of our firm’s employee is about….
Firm size2: our firm’s annual business income of 2013 is about… million.
Firm age: our firm was established in the year of….
Data security risk (DSR) (1= strongly disagree; 5= strongly agree)
DSR1: the use of cloud computing makes us to lose the right of data control.
DSR2: the use of cloud computing will result in disclosure of information privacy.
DSR3: using cloud computing gives possibility to other firms or individuals to check our confidential data.
DSR4: using cloud computing gives possibility to illegally use our firm’s confidential data.
Mimetic pressure (MP) (1= strongly disagree; 5= strongly agree)
MP1: our main competitors who have adopted cloud computing have greatly benefitted.
MP2: our main competitors who have adopted cloud computing are favorably perceived by their suppliers.
MP3: our main competitors who have adopted cloud computing are favorably perceived by their customers.
MP4: our main competitors who have adopted cloud computing are favorably perceived by other firms.
Coercive pressure (CP) (1= strongly disagree; 5= strongly agree)
CP1: the local government requires our company to use cloud computing.
CP2: the industry associations require our company to use cloud computing.
CP3: our main suppliers require our company to use cloud computing.
CP4: our main customers require our company to use cloud computing.
Normative pressure (NP) (1= strongly disagree; 5= strongly agree)
NP1: our suppliers have been widely used cloud computing.
NP2: our customers have been widely used cloud computing.
NP3: our competitors have been widely used cloud computing.
NP4: government promotion of information technology influences our firm to use cloud computing.
Cloud computing compatibility (COM) (1= strongly disagree; 5= strongly agree)
COM1: the use of cloud computing fits the work style of our firm.
COM2: the use of cloud computing is fully compatible with current business operations.
COM3: using cloud computing is compatible with our firm’s corporate culture and value system.
COM4: the use of cloud computing will be compatible with existing hardware and software in our firm.
IT sophistication (SOP) (1= strongly disagree; 5= strongly agree)
SOP1: in our firm, information technology is important for operational costs reduction.
SOP2: in our firm, information technology is important for productivity improvements.
SOP3: in our firm, information technology is important for improved quality of decision making.
SOP4: in our firm, information technology is important for improved service to customers.
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This research is partly supported by the Key Program of National Science Foundation of China (Grant No. 71331003) and the General Program of National Science Foundation of China (Grant Nos 71471079, 71471080, 71302140 and 71371013).
About the authors
Nianxin Wang is Associate Professor of Management Information Systems in the College of Economics and Management at Jiangsu University of Science and Technology. He received his PhD from Southeast University of China. His research has been appeared in such journals as Information Systems Research, Journal of Management Information Systems, Decision Support Systems and Communications of the Association for Information Systems. His current research interests include cloud computing management, IT business value, business-IT alignment, IT use and crowdfunding.
Huigang Liang is Professor of Information Systems and Teer Chair in Research in the College of Business at East Carolina University. He gained his PhD in Healthcare Information Systems from Auburn University. His research interests focus on IT issues at both individual and organizational levels, and include IT strategy, assimilation, decision process, IT avoidance, adoption, compliance and healthcare informatics. His research has appeared in scholarly journals, including MIS Quarterly, Information Systems Research, Decision Support Systems, Journal of the Association for Information Systems and Communications of the ACM. He is serving an associate editor for Information & Management and on the editorial board of JAIS.
Shilun Ge is Chair Professor of MIS in the School of Economics and Management at Jiangsu University of Science and Technology. His research has focused on the issues of enterprise modeling, enterprise data model and IT project management. He received his PhD from the School of Economics and Management at Nanjing University of Science and Technology. He has authored and co-authored 3 books and more than 100 papers.
Yajiong Xue is Professor of Information Systems in the College of Business at East Carolina University. She received PhD in Management Information Technology and Innovation from Auburn University. Her research appears in MIS Quarterly, Information Systems Research, Journal of the Association for Information Systems, Communications of the ACM, Communications of the Association for Information Systems, Decision Support Systems, IEEE Transactions on Information Technology in Biomedicine, Journal of Strategic Information Systems, International Journal of Production Economics, Drug Discovery Today and International Journal of Medical Informatics. Her current research interests include strategic management of IT, IT governance, IT security and healthcare information systems. She is serving an associate editor for Communications of the Association for Information Systems.
Jing Ma is Assistant Professor in the College of Business and Public Management at Wenzhou Kean University. She is interested in information management and e-commerce, focusing on analyzing and evaluating different information technologies and their impacts on group collaboration and innovation. She also works actively in the industry world as a consultant and mentor. As the project manager of 10,000 women entrepreneurship program, she designed the curriculum providing training to 300 women entrepreneurs. Some of her latest research works have been published in International Journal of Information Management, ACM Transactions on Computer-Human Interaction and many peer-reviewed conferences.