Understanding cloud computing adoption: A model comparison approach

Abstract

The purpose of this paper is to understand cloud computing adoption. Specifically, this paper aims to compare two theoretical models and an integrated model to identify which theoretical models best predicts cloud computing adoption. An empirical study was conducted through an survey from 280 companies in IT, manufacturing and finance sectors in India. The data was analysed using exploratory and confirmatory factor analyses. Further, structural equation modeling using AMOS 20.0 was used to test the proposed model. Results show that the integrated mediating TAM-TOE and Direct TAM-TOE model have greater explanatory power of cloud computing adoption intention than the technology acceptance model (TAM) and technological organisational environmental framework (TOE). Mediating TAM-TOE provides more complete understanding of adoption intention and Direct TAM-TOE provides the generic cognition of understanding adoption intention and determinants of managerial decision making. Thus, the Mediating TAM-TOE is a preferable model. Security concern and Third party control are salient factors influencing cloud computing adoption intention through perceived usefulness (PU) and perceived ease of use (PU).This study is one of the first studies to examine of influence change of variables and compare the relative ability of two competing theories, TAM and TOE, and an integrated model in explaining cloud computing adoption intention.

Keywords

Cloud computing technology acceptance model technological organisational environmental framework

Hemlata Gangwar is a research scholar in National Institute of Industrial Engineering, Mumbai. She has done Masters in Computer Applications and has 2 years of work experience in IT industry.

Prof. Hema Date is an associate professor in National Institute of Industrial Engineering, Mumbai. She has 17 years of academic experience. She is a Fellow from National Institute of Industrial Engineering Mumbai.

1 Introduction

Cloud Computing can be viewed as a way to deliver IT enabled services in the form of software, platform and infrastructure using internet technologies. Cloud computing is defined by NIST, 2009 as “Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. This cloud model promotes availability and is composed of five essential characteristics, three service models, and four deployment models.”

Cloud computing is a kind of computing service that is like e-mail, office software, and enterprise resource planning (ERP), servers, and uses ubiquitous resources that can be shared by the business employee or trading partners [49, 79]. Thus, cloud computing provide cost advantage, scalability, flexibility and access of the most advanced and latest technologies to organisations. From business perspective, it is important for companies to integrate business processes into their existing IS applications and to build internet-based technologies for transacting business with trading partners [115]. These ubiquitous data transformation practices improve operation efficiency, especially in case of high-tech industries. Further, development of cloud computing capability rapidly changes the buying and selling behaviour with customers, improves business tactics, thus, enhances competitive advantage [89]. Also, the execution of data transactions along value chain activities becomes effective by adopting cloud computing in companies [39 , 89]. This way, it provides several strategic and operational advantages. This way, cloud computing is found to be the future of computing that reduces cost of IT services and increases processing throughput, reliability, availability, and flexibility and decreasing processing time [49]. Though custom applications in the cloud are built,studies are needed to understand the reasons of not having fast adoption of cloud computing in firms [12 , 42]. Also, it is meaningful how cloud computing affected the companies’ operations in security integration areas [104]. Hence, this study develops an understanding on the process of adoption of cloud computing and identifies factors affecting the cloud computing adoption decision with high-tech industry in India.

In this study we focus on cloud computing adoption intention. Adoption is explained by various theories and models such as Technology acceptance model (TAM) proposed by Davis (1989), Innovation diffusion theory (IDT) proposed by Rogers(1995), Theory of reasoned action (TRA) proposed by Fishbein and Ajzen (1975), Theory of planned behavior (TPB) proposed by Ajzen (1991), Technology-Organization-Environment Framework (TOE Framework) proposed by Tornatzky and Fleischer (1990), and Unified Theory of Acceptance and Use of Technology (UTAUT. Literature have argued that TRA, TPB and UTAUT were originally developed for predicting individual adoption and there are lesser studies in organizational context [77, 85]. On the contrary, TAM and TOE are widely used in studying technology adoption at organizational level. According to Oliveira and Martins [85], IDT constructs are the same to the technology and organization context of the TOE framework, and TOE framework is found superior to IDT to explain technology adoption as it includes new constructs as well (i.e. environmental). Many researchers have used TAM to examine adoption intention [1 , 118]. Several authors have tested TOE framework for the adoption of several technologies [70 , 126]. Some researches have raised the limitation of such as unclear major construct in TOE framework and external variables of TAM are not clearly defined yet and extended [74]. Therefore, researchers in the IS domain have suggested to integrate TAM model and TOE framework so that some of their limitations can be overcome. Moreover, there is no empirical effort to integrate TAM and TOE framework and compares the different perspectives in term of influence of variables and their relative utility in understanding cloud computing adoption.

Thus, the purpose of this research attempts to understand adoption intention by comparing two theoretical prospective. This paper addresses two research questions:

RQ1. What are those factors that influence adoption intention of cloud computing?

RQ2. Which theoretical model predicts adoption intention better than the other models in a cloudcontext?

To answer research questions, this study proposes models including TOE framework, TAM model, and an integrated model (TAM-TOE) and empirically validates those models using data from a survey on cloud computing user. Integrated model can be integrated in two ways-TOE framework can be considered as external variables of TAM model. Also, TAM and TOE can be treated as two independent models determining adoption intention. Further both the approaches of integration were compared with individual TAM model and TOE framework. The primary contributions of this paper are its examination of influence change of variables, to assess theoretical differences between Mediating TAM-TOE and Direct TAM-TOE in explaining cloud computing adoption intention, to validate value of an integrated model combining these two models that are TAM Model and TOE framework. The findings of this study therefore help bridge the extant gap between TAM model and TOE framework by integrating and testing multiple models. The following section describes the research methodology used in this study, presents the results of data analysis, and discusses the key findings and limitations of the study. The final section provides a conclusion and discusses the implications of this study.

2 Technology acceptance model (TAM)

Among the many theoretical models, TAM is widely accepted model for understanding IT adoption and usage processes (Fig. 1). It predicts a user’s acceptance of information technology and its usage on the job [9] and explains the determinants of user acceptance of a wide range of end-user computing technologies [28].

2.1 Determinants of TAM model

TAM seeks to explain the relationship between individual’s technological acceptance and adoption and subsequently, their behavioral intention to use it [10, 61]. It poises the perceived usefulness (PU) and perceived ease of use (PEOU) as primary determinants of system use [9, 23]. PU is defined as ‘the prospective user’s subjective probability that using a specific application system will increase his or her job performance within an organizational context,’ and PEOU refers to ‘the degree to which the prospective user expects the target system to be free of effort’ [29]. The model also suggests that perceived ease of use influences perceived usefulness, because technologies that are easy to use can be more useful [103]. To examine firm-wide acceptance and adoption of IT, the extension of the basic framework (i.e., TAM2 and TAM3) has included broad categories of antecedents to the perceived usefulness and perceived ease of use.

2.2 Limitations of TAM

Nevertheless, TAM has been found with certain limitations. Studies on TAM have generated conflicting findings and have led to the confusion over moderating and external variables [23]. Further, TAM measures perceived adoption and self-reports on future behavior rather than measurement of actual behavior [122]. TAM contains restricted constructs and thus cannot handle the issue of adopting new services or solutions [122]. Also, TAM is known for its limited possibility of explanation and prediction, triviality and lack of practical value [37]. Legris et al. [74] highlighted that TAM-based empirical studies do not produce totally consistent or clear results; hence, significant factors are needed to be identified and included in the models. Hence, there is a scope of investigating role of other variables specific to technological influences, innovativeness of the firm, firm’s level of technology readiness, security and trust [10].

3 Technology-organisational-environmental (TOE) framework

TOE framework (Fig. 2) was developed by Tornatzky and Fleischer (1990) to examine firm-level adoption of various IS/IT products and services. It has emerged as a widespread theoretical perspective on IT adoption [126]. Inclusion of technological, organisational and environmental variables has made TOE advantageous over other adoption models in studying technology adoption, technology use and value creation from technology innovation [52 , 124]. Also, it is free from industry and firm-size restrictions [121]. Hence, it provides a holistic picture for user adoption of technology, its implementation, foreseeing challenges, its impact on value chain activities, the post-adoption diffusion among firms, factors influencing business innovation-adoption decisions and to develop better organisational capabilities using the technology [76 , 126].

4 Developing TAM-TOE framework

This study takes into consideration two of the technology adoption models i.e. TAM model and TOE framework which have been widely adopted for studies in organisational context. Though, a wide range of empirical and conceptual studies have justified the significant, dominant and relevant role of TAM model and TOE framework in explaining technology adoption at their individual levels, the models have individual limitations. The two constructs of TAM (PU and PEOU) explain about 40% of the system’s use [74] and the external variables in the extended models of TAM are not clearly defined yet. On the other side, TOE framework has unclear major constructs [119] and is too generic [94]. So, TOE framework is needed to be strengthened by integrating it with the models having clear constructs. Therefore, researchers have advocated the need of integrating TAM and TOE so that predictive power of the resulting model can be improved and some of their individual limitations can be overcome. Integrating the two models (TAM and TOE) is not simple because external variables of TAM model and variables of TOE framework vary across contexts and their significance as well. Thus, there is a lack of common set of variables which can be generalized to explain technology adoption and is applicable to any context and technology. To develop an integrated model, this study follows an approach of including all the variables (significant as well as insignificant) of TAM and TOE identified from various studies based on these two models. Further, specific variables to cloud computing i.e. Security and Third-party control are identified from the literature that describe its technical dimension and they are considered as separate set of variables while integrating the model. These variables are included so that integrated model can be more robust and relevant for cloud computing adoption.

Further, several approaches that can be followed to integrate the two models are proposed here. For example, one approach can be considering TOE framework as external variables of TAM model. Another approach is, TAM and TOE can be treated as two independent models determining adoption of information technologies. While including cloud computing specific variables, they can either be treated as independent variables, or they can act as external variable of TAM. The study proposes an integrated model (Fig. 1) which is so designed that it ensures that testing several approaches of integration is possible and the basic model remains the same irrespective of following any of the approaches of integration.

4.1 Relative advantage

Rogers (2003) describes relative advantage as adopters’ perception of a technology to be superior to existing technologies. It focuses upon the degree of users’ perceptions on a technology to be better than its precursor while perceived usefulness reflects the belief that a technology performs a function in a better manner [71]. Relative advantage is expected to affect adoption intentions directly as well as indirectly. It affects adoption intentions mediated through perceived usefulness and ease of use with the fact that users perceive a technology useful if they believe to have considerable advantages over other technologies. Though a technology offers a wide range of advantages, all of them may not be useful for the users and but they may influence adoption intentions. In other words, since some apparent advantages of a technology may not be considered very useful from functional perspective but can influence adoption intention. So,the following hypotheses are proposed:

H4.1a: Relative advantage has positive effect on perceived usefulness.

H4.1b: Relative advantage has positive effect on perceived ease of use.

H4.1c: Relative advantage has positive effect on adoption.

4.2 Compatibility

Beatty et al. [13] described compatibility as “the extent (or ease) with which IT is integrated with the existing technological infrastructure, culture, values, and preferred work practices of an organization”. In this study, compatibility highlights compatibility of cloud computing with existing organisational architecture and technical aspects of the work environment. A number of studies support that increase in compatibility of a technology with organisational architecture decreases the efforts put by the users in using the technology [6 , 120]. Perceptions of usefulness of the technology are a function of the fit between the innovation, one’s existing practices, and work style. The users perceive a technology easy to use it does not demand major change in existing practices. Further, diffusion of a technology is rapid with increase in compatibility which leads to facilitating its adoption. This results in generating competitive benefits of the technology, and greater technological, functional and economic benefits. However, compatibility is found to be firm-specific and dependent upon managerial intentions to adopt a technology [31].

Thus, following hypotheses are proposed:

H4.2a: Compatibility has positive effect on perceived usefulness.

H4.2b: Compatibility has positive effect on perceived ease of use.

H4.2c: Compatibility has positive effect on adoption intention.

4.3 Complexity

Complexity is described as the degree of difficulty in understanding and using an innovation [82]. Complexity focuses on perceptions of using a system rather than perceptions of the system itself. While introducing a new technology in an organisation, it requires employees to change their existing business practices or acquire new skills. Also, they need to integrate new application development software into the existing IT infrastructure [13]. This has further impact on achieving the outcomes associated with the task [22]. Thus, complexity deals with an interaction between a task (e.g., using a system) and the task-doer (e.g., user) [19]. In context of cloud computing,complexity of integrating the existing system to cloud solution and using the cloud solution may requires a level of expertise that may add complexity. Many of the studies on similar technologies have witnessed that organisations are less likely to adopt a technology if it is complex to use [16 , 58]. Accordingly, complexity is posited to have negative effects on perceptions of usefulness and ease of use [46, 47].

So, following hypotheses are proposed:

H4.3a: Complexity has negative effect on perceived usefulness.

H4.3b: Complexity has negative effect on perceived ease of use.

H4.3c: Complexity has negative effect on adoption intention.

4.4 Organisational readiness

Technology infrastructure and IT human resources can be defined as technological readiness for organizations [84, 129]. Technology infrastructure is related with establishing platforms on which internet technologies can be built and IT human resources are related to providing the knowledge and skills to develop web applications [124]. It is also related to accessibility to resources necessary to facilitate a service [117]. Also, financial resources have an important role in effective adoption of a technology in an organisation [56, 91]. Organisations willing to adopt cloud computing must have suitable infrastructures and technical skills, as well as financial resources [8 , 126–127].

So, following hypothesis is proposed:

H4.4a: Organisational readiness has positive effect on perceived usefulness.

H4.4b: Organisational readiness has positive effect on adoption intention.

4.5 Top management support

Top management support refers to the degree to which top management understands the importance of the IS innovation and the extent to which it is involved in IS activities [81, 90]. It provides a vision, support, and a commitment to create a positive environment for innovation [119]. It is meaningful for ensuring sufficient allocation of resources and for driving change that creates a more conducive environment for adoption of technology. Top management support is more critical for cloud computing since the cloud computing implementation requires adequate resources, process reengineering, and user coordination.Thus, management support is one of most important factors for adoption of a technology or an innovation in organisations that further affects perceived usefulness and perceived ease of use [30 , 119]. So, following hypothesis is proposed:

H4.5a: Top management support has positive effect on perceived usefulness.

H4.5b: Top management support has positive effect on perceived ease of use.

H4.5c: Top management support has positive effect on adoption intention.

4.6 Training and education

It plays an important role in enabling employees to gain expertise for adjusting to new technologies and for building positive attitude towards the system [4]. It facilitates conditions to enhance their knowledge and skills that has direct impact on their perceptions on usefulness of the technologies [65]. In other words, employees appreciate the benefits offered the new technologies and usability [92]. Schepers and Ruyter [102] argue that hands-on sessions on the new technologies and feedback illustrates functions and features of the technology, and perceived usefulness further. In addition, the user’s experience with technology and the fit between task and technology could have further contribution to PEU [92]. The importance of training and education in technology adoption is well recognized in earlier studies in several contexts [54, 116]. So,following hypotheses are proposed:

H4.6a: Training and education has positive effect on perceived usefulness.

H4.6b: Training and education has positive effect on perceived ease of use.

H4.6c: Training and education has positive effect on perceived ease of use.

4.7 Competitive pressure

It is related to the level of competition within the environment the organizations operate [36, 45]. It is necessary for the organisations to build a competitive advantage in the market place by adopting competent technologies and innovations [69] and to maintain market position in highly competitive markets [27, 40]. Strategically, it results in altering the rules of competition and leveraging new ways to outperform rivals [87]. It is perceived as one of the important reasons for organisations to adopt new technologies and is positively related [27 , 127]. So, following hypotheses are proposed:

H4.7: Competitive pressure has positive effect on cloud computing adoption.

4.8 Trading partner pressure

Technology adoption is also motivated by the pressure exerted by the trading partners of organizations [56]. This is driven by the fact that adoption of same technology amongst the business partners facilitatesfully utilizing the innovation at an inter-organizational level [45]. Addressing trading partner pressure enables organisations to effectively respond to customer needs and market demands [55]. Earlier studies have found positive relationship of trading partner pressure with technology adoption [20 , 111]. So, following hypotheses are proposed:

H4.8: Trading partner pressure has positive effect on cloud computing adoption.

4.9 Security concern

Security related issues include confidentiality, auditability, integrity and availability aspects that serve as building blocks in designing secure systems [67, 123]. Security issues related to cloud computing are related to the cloud providers’ resources, application security, data transmission security (e.g. network infrastructure security) and data storage security (server security) [107]. It also involves issues related to third parties’ access to the data and assets, and the issues regarding the data transmission and data storage [107]. In general, three broad categories of assets i.e. data, software and hardware resources need to be secured [128]. Cloud computing adoption poses unique security challenges such as availability and reliability issues, data integrity, recovery, and privacy and auditing. It is important to alleviate these concerns to ensure that users have the same security and privacy controls over their applications and services, provide evidence to customers that their organization are secure and they can meet their service-level agreements, and that they can prove compliance to auditors [48]. Also, the strategic policies of the cloud providers addressing security challenges in cloud computing should ensure building trust through location transparency, technical security and other related issues [62 , 110]. This ensures the credibility and authenticity of the cloud service providers, becoming the key to establish a successful cloud computing environment [2, 110].

4.9.1 Confidentiality

It refers to providing access to protected data to only authorized parties or systems. Confidentiality issues are more meaningful in case of cloud computing as it involves an increased number of parties, devices and applications leading to an increase in the number of points of access [128]. It further results in the needs of addressing issues of multitenancy, data remanence, application security and privacy. In case of cloud computing, the issues related to confidentiality is addressed by defining user authentication that controls access to memory, devices and software. Thus, it establishes confidence in user identities, electronically presented to an information system and protects from breaching in privacy.

4.9.2 Privacy

Since data are stored at multiple locations in the cloud across the world, it increases the risk of confidentiality and privacy breaches. So, organisations dealing with personal data are required to implement legal framework of several countries that controls the disclosure of personal information and address legal challenges towards privacy issues [128].

4.9.3 Integrity

Integrity in information security is related to providing the authorization to modify assets (data, software and hardware) to authorized parties in authorized ways. It protects the data from unauthorized deletion, modification or fabrication. It results in greater visibility in tracing back the content and authorizer of altered data or system information to ensure accountability [128]. In other words, it enables a system to determine several levels of assessing by authenticated usersto secured resources controlled by the system. In a cloud computing environment, the authorization mechanism addresses a number of threats including sophisticated insider attacks on these data attributes.

H4.9a: Security concernhas negative effect on perceived usefulness.

H4.9b: Security concern has negative effect on perceived ease of use.

H4.9c: Security concern has negative effect on perceived ease of use.

4.10 Third party control

Addressing third party related issues ensure reliability, availability of cloud services and compliance laws and legal regulations. It is need to counter denial of service attacks, performance slowdowns, equipment outages and natural disasters [100]. Availability means accessibility and usability of cloud services upon demand by an authorized entity [128]. In other words, it ensures the possibility of obtaining the resources whenever they are needed with the consideration to the time it takes for these resources to be provisioned [100]. The organisations demand from cloud owners the ability to ensure availability of information and information processing to them upon demand, and continuity of operations in the possibility of a security breach so that they can rely on the resource infrastructures and network availability at all times.

Organisations adopting cloud computing also need to address issues related to complying with existing IT laws and legal regulations, and with the division of compliance responsibilities [14]. The legal regulations include Statement on Auditing Standards(SAS) Number 70 Type II and the ISO certificate 27001. Complying with these regulations ensures the presence and documentation of monitoring activities for IT technologies and processes. It also evaluates security measures in Plan-Do-Check-Act cycles on a regular basis. Thus, it is hypothesized as:

H4.10a: Third party control has positive effect on perceived usefulness.

H4.10b: Third party control has positive effect on perceived ease of use.

4.11 Perceived usefulness (PU)

PU is defined as the prospective user’s subjective probability that using a specific application system will increase his or her job performance within an organizational context. So, following hypothesis is proposed:

H4.11: Perceived usefulness has positive effect on cloud computing adoption.

4.12 Perceived ease of use (PEOU)

PEOU refers to “the degree to which the prospective user expects the target system to be free of effort” [29]. The TAM model suggests that perceived ease of use influences perceived usefulness, because technologies that are easy to use can be more useful [103]. So, following hypotheses are proposed:

H12a: Perceived ease of use has positive effect on cloud computing adoption.

H12b: Perceived ease of use has positive effect on perceived usefulness.

5 Methodology

This study measures thirteen constructs: Security, Third-party control, Relative advantage, Compatibility, Complexity, Organizational readiness Training and education, Top management support, Competitive pressure, Trading partner pressure, PU and PEOU. The items in these constructs were adapted from relevant prior research. The questionnaire was developed by by reviewing previous research on different adoption theories and models in the related area. Since enough empirical work has not been reported in cloud computing adoption related to the variables, the researchers therefore created items to measure the constructs, and used five-point multi-item Likert-type scales for each item. A self-administered questionnaire was prepared taking measurement scales for eight constructs (Table 1).

5.1 Data collection procedure

A Questionnaire survey was used to collect the empirical data for this study. The list of 1000 random organisation was obtained from the Bombay Chamber of Commerce and of Industry of India. Mails or telephone calls were then made to screened the organisations on the basis of questions like they are aware of cloud computing, whether they are willing to adopt cloud computing or they are in the process of adoption. Out of total 1000 companies, 433 were found to be eligible for this survey on the basis of screening question. Most of the responses were collected through personal visits to the respondents and a round of conversation was held before seeking their responses on the questionnaire. Other responses were collected through email. Out of 433 organisation 300 responses were gathered and 280 responses were found valid. For the data analysis, several data analysis techniques were applied.

6 Result and disscussion

Reliability analysis revealed Cronbach’s Alpha value as 0.7 and is comparable with the reliabilities reported in earlier studies. Further, the scale was factor analyzed using Principal component analysis and Varimax rotation. The result for Bartlett’s test of sphericity was 0.000 and the KMO value 0.6705 (Table 2). This value is more than 0.5 which shows high measure of sampling adequacy and ensures factorability of the data.

From a total of 64 items, 3 of the items were dropped in the exploratory factor analysis. The reliabilities of sub-scales varied between 0.620 and 0.947; which exceeded the recommended level of 0.6. Exceeding this threshold proves that the factors emerged are reliable and valid for their factor structure. The variables were grouped in thirteen factors and all together accounted for 76.23 per cent of the total variance. This value of total variance explained means that the set of factors extracted from the data explain adoption intention to a very high extent and a very less part of the adoption remains unexplained. Four confirmatory factor analyses using AMOS V20.0 were performed on the measurement models of TAM, TOE, Direct Integrated TAM-TOE, Mediating Integrated TAM-TOE. Table 3 shows accepted threshold for fit indices.

The fit of the measurement models was estimated with various indices (Table 4). For TAM, χ2 statistic, χ2 /df, the goodness-of-fit (GFI), adjusted goodness-of-fit(AGFI), comparative fit index (CFI) and root mean square error of approximation (RMSEA) were within the recommended levels, indicating good model fit. For TOE model, we observed values of 0.94, 0.85, 0.80, and 0.07 for CFI, GFI, AGFI, and RMSEA respectively, also representing good model fit. Finally, the fit indices suggested good model fit for integrated TAM-TOE. The observed values for CFI, GFI, AGFI, and RMSEA were 0.93, 0.83, 0.79 and 0.041 all within the recommended levels. The GFI value were 0.836, 0.855 and 0.896 for integrated TAM-TOE,TOE and TAM respectively which is nearer to the suggested value of >0.9 and considered as reasonable fit [32, 63].

The psychometric properties of the constructs are summarized in Tables 5 and 6. The composite reliability for each construct ranged from 0.713 to 0.925, suggesting acceptable levels of reliability [44].

The averagevariances extracted (AVEs), ranging from 0.50 to 0.81,were above the recommended 0.50 level [44]. All the indicater had significant loading onto the respective construct with values varying between 0.50 to 0.98. Construct validity represent accurate measurement represented by convergent validity and discriminant validity. According to Hair et al. [44], convergent validity means indicator of a specific construct should share a high proportion of variance by using three criteria- All standardized factor loading should be more than 0.5 or higher; Construct reliability should be more than 0.7 to indicate adequate internal consistency; average variance extracted should be 0.5 or greater to indicate adequate convergent validity; Discriminant validity was obtained by comparing the shared variance between factors with the average variance extracted from the individual factors [35]. This analysis showed that the shared variances between factors were less than the average variance extracted for the individual factors. From Tables 5 and 6 it can be inferred that the square root of the AVE values of every construct is greater than the inter–construct correlations which supports the discriminant validity of the construct.

This study uses structural model analysis to compare the four models (TAM, TOE, integrated TAM-TOE model Judged by its direct and indirect on adoption intention) employing the same measurement model as the earlier CFA. Figure 4 show the results of these analyses. TAM explains 62.9 percent of the variance in intention to adopt cloud computing (Fig. 4). The results showed that PU significantly influenced Adoption Intention (β= 0.958, P < 0.001), supporting hypothesis H4.11. PEU was found to be significant in influencing PU (β= 0.721, P < 0.001), supporting hypotheses H12a. Also, cloud computing adoption intention was found to be significantly influenced by PEOU(β= 0.256, P < 0.01).

TOE explains 26 percent variance in intention to adopt cloud computing (Fig. 5). The paths from Top management support, competitive pressure and trading partner pressure were significant. Cloud computing adoption intention was found to be significantly influenced by three exogenous factors: TMS (β= 0.24, P < 0.001), CPP (β= 0.21, P < 0.001), and TPP (β= 0.28, P < 0.1).

The Third model presents direct paths from Adoption Intention to the ten exogenous variables (see Fig. 6). No indirect effects were hypothesized or tested. Only the paths of top management support, competitive pressure, trading partner pressure perceived usefulness and perceived ease of use with cloud computing adoption intention were supported. The effect of the newly added security and third party control were salient in the model. The model explained 74.7% of the variance in users’ intentions to adopt cloud computing.

The fourth model (Fig. 7) shows perceived usefulness and percieved ease os use playing a mediation role between ten exogenous variables and cloud computing adoption intention. Relative advantage does not significantly affect adoption intention. The direct effect of Relative advantage on adoption intention, 0.032, is less than the indirect effect 0.13(0.104*0.468 +0.142*0.578) through (PEOU and PU). Therefore, RA has a greater influence on PU and PEOU than adoption intention. The direct effect of compatibility on adoption intention 0.023 is less than the indirect effect of compatibility on adoption intention 0.12 (0.134*0.468 +0.108*0.578). The direct effect of complexity on adoption intention, 0.061, is less than the indirect effect –0.23(–0.299*0.468+–0.156*0.578) through (PEOU and PU). The total direct effect of top management support adoption intention is 0.13 and the total indirect effect is 0.21(0.161*468 +0.241*0.578), resulting in a total effect of 0.33. The indirect effect of organizational readineness, training and education are 0.072(0.125*0.578) and 0.17(0.224*0.468 +0.117*0.578) respectively. The total direct effect of competitive pressure and trading partner pressure adoption intention are 0.103 and 0.134 respectively. The total direct effect of security on adoption intention is 0.06 and the total indirect effect is 0.26 (0.217*468 +0.273*0.578), resulting in a total effect of 0.49. The total direct effect of third party control on adoption intention is 0.04 and the total indirect effect is 0.23 (0.217*468 +0.273*0.578), resulting in a total effectof 0.39.

Since the purpose of this study is to study cloud computing adoption intention, it is important to examine the direct and indirect influences of factors on adoption intention. TAM model explains 62 percent of the variance in continuance intention, while TOE explains 26 percent, and integrated model explains, 77 percent when TOE framework is considered as external variable to tam model, 74 percent when both the model is considered as two independent model and direct effect on adoption intention was examined. This indicates that the addition of TOE variable to TAM model provide additional explanatory power. This integrated model has examined the indirect effect of TOE variable through perceived usefulness and perceived ease of use. The result shows that TOE variable has indirect significant effect on adoption intention. In the direct approach of integration (TAM-TOE) only the path from TMS, CPP, TPP, SEC and third party were found to be significant. Some of the TOE variable does not effect adoption intention directly but has indirect effect through perceived usefulness and perceived ease of use. The possible reason for this can be that two construct of TAM model include more specific cognition of cloud computing adoption.

Another notable finding in this study was the strong influence of perceived usefulness on user intention in all three models having effect 0.958, 0.634, 0.578, (model 4,6,7) respectively. This is because people are believed to form adoption intentions toward using an IT on a cognitive appraisal of how it will help them achieve valued objectives. In addition, PEOU has a significant total effect 0.95, 0.55 and 0.61 on adoption intention (model 4, 6 and 7). The strong direct impact of perceived ease of use on continuance intention is due to the nature of the technology that is cloud computing. With cloud computing, you need not manage hardware and software— that’s the responsibility of an provider. Shared infrastructure allows you to only pay for what you need, upgrades are automatic, and scaling up or down is easy.

Relative Advantages does not influence adoption intention directly; it impacts adoption intention indirectly through perceived usefulness as well as perceived ease of use. Though a technology demonstrates various advantages, adoption intentions for the technology depend upon how these advantages are useful. Usefulness is pivotal for an organisation because it achieves competitive advantages through various strategic and operational advantages. For example scalability, mobility pay per use model of cloud computing could provide user more control over their operations and IT expenditure which improves efficiency and productivity of business operation Also, ease of use offered by computing leads to adoption because cloud computing services can be easily accessed by its users. Relative advantage also has effect on PEOU because cloud computing allows the user to use computing resources and IT solution without going into detail which in turn increase ease of use in performing organisational task.

Compatibility has a mediating effect on cloud computing adoption intentions rather than the direct effect because if the managers believes in the benefits of cloud computing, they will try to meet its compatibility by making changes in their existing practices for their business environment so that cloud computing becomes compatible before actually using it. Organisations that perceive cloud computing as a technical innovation aligned with their existing infrastructure and business environment will influence its usefulness such as improving efficiency or effectiveness. The results show that increase in compatibility has effect on ease of use as less effort are required to use and learn cloudcomputing.

Similar to earlier studies on technology adoption intentions, this study also witnessed a substantial indirect effect of complexity on adoption intentions [47 , 73]. This indicates that Indian organisations accept the challenge of complexity of cloud computing and take necessary steps while adopting, implementing and using it. This way, they reduce the perceptions of complexity of cloud computing and enhance beliefs of their employees about the technology. It further results in improving its ultimate acceptance and use. Since the perceptions of system complexity are closely related to the activities using cloud computing and the lack of knowledge about cloud computing, it is suggested demonstration of simple activities to the employees helps organisations to minimize perceptions of complexity amongst them.

Organisational readiness is found to be a significant predictor of perceived usefulness and perceived ease of use rather than a direct influence upon adoption intentions. This is supported by the fact that the data was collected from those who are in the process of cloud computing adoption and they possess necessary technical, human and financial resources. This indicates that in cloud computing adoption, organisational competency enables organisations to leverage existing IT applications and data resources across key processes along the value chain. In other words, organisations may obtain necessary technical and financial resources, and a team of experts with suitable skill sets to address cloud computing adoption challenges. This will further enable employees to demonstrate discretionary innovative work behaviours. Thus organisation with sophisticated technological resources may influence the extent of implementation but not the initial cloud computing adoption.

Management support is found to have direct effect on the cloud computing adoption intentions as well as indirect effect through perceived ease of use and perceived usefulness in the organisations. The strong direct of top management support may be due to the fact that as the management recognizes strategic importance of cloud computing, they have willingness to take risks associated with cloud computing adoption, to make necessary infrastructural changes and to develop industry specific standards for its implementation. Supportive top management improves cloud computing effectiveness by sharing information among the employees. Thus, it also enhances the easier use of cloud computing by allocating resources at right place and channelizing information in right manner.

The study found that training and education has direct impact on perceived usefulness and perceived ease of use and does not effect on cloud computing adoption intention. It enhances business and technical skills of the employees together with specific knowledge to effectively utilize cloud computing for the set objectives. The training for cloud computing needs to include a system-thinking approach to integrating cloud computing into an existing IT infrastructure, and developing project coordination and leadership skills to work with the technical skills. It is important to illustrate that skill sets necessary for cloud computing are different from traditional security skills, and needs specialized skills to develop a detailed understanding of cloud computing.

This study determines that competitive pressure is significant for cloud computing adoption in organisations. In other words, the greater the competition among similar organizations, the more likely the organizations adopt cloud computing to gain a competitive edge. This is in line with earlier studies such as [56] and [127].

The significant relationship between trading partner pressure and cloud computing adoption intentions recommends that pressure from strategic trading partners motivates organisations to adopt cloud computing. It can also be argued that cloud computing adoption is driven by the convincing and bargaining power of the trading partners.

The finding shows that concern of security is found to have mediating effect on adoption intention through perceived usefulness and perceived ease of use but does not affect adoption intention directly. The possible reason might be companies were willing to adopt cloud computing services despite security concerns, as they rely on the element of trust. The data result shows that security risk can be mitigated by Standardization of cloud specific protocols (e.g., access control, configuration management, incident response, security audit, risk assessment and communications, effectively integration of the security controls). The finding shows that reducing the concern of security encourage the usefulness of cloud computing services. A good comprehensive SLA includes both threats and opportunities of the service,can minimize the risks that are involved with cloud computing services. As per the findings, the relationship between third party control and cloud computing adoption intentions indicate that ensuring high availability to cloud services is important to increase the efficiency of organisations and demonstrating compliance that improves the effectiveness of cloud services. This also ensures the quality, availability, reliability, and performance of these resources which are owned by the cloud service providers. In this respect, service level agreements determine the appropriate levels of granularityso that most of the consumer expectations is covered and is relatively simple to be weighted, verified, evaluated, and enforced by the resource allocation mechanism on the cloud. Since different cloud offerings (IaaS, PaaS and SaaS) need to define different SLA meta specifications, cloud providers as well as users need to address the number of implementation issues. Also, advanced SLA mechanisms can be used to constantly incorporate user feedback and customization features into the SLA evaluation framework. In other words, consolidation and alignment of different compliance programs with organization’s overall information security strategy play an important role in effective adoption of cloud computing for the set organisational objectives such as improving operational efficiency, optimizing costs and increasing sustainability.

7 Comparison of four models

All four models exhibit a reasonable fit to the data. Thus, other criteria are considered to examine and determine which model is the best. The best model is one which provides good prediction and its explanatory power and parsimony are acceptable [83, 105]. TAM model explains 62 of variance, TOE model explains 26 percent, while integrated model explains 74 when both the model is considered as two independent model and 77 percent when TOE act as external variable to TAM model.The integrated model exhibits statistically significant explanatory power improvement compared to individual TAM and TOE framework. The integrated model with 13 determinants of adoption intention is more complex than the individual TAM model and TOE framework. According to Tylor and Todd [109], a degree of parsimony can be sacrificed to understand complete phenomenon. In our case, while the individual TAM model with 2 constructs and TOE frame work with 10 constrcts are more parsimonious than integrated model with 13 constructs. But in term of increased explanatory power and better understanding of determinants, the integrated model is preferable. In comparing the integrated model by using both the approaches of integration (model 6,7), a number of factors need to be considered. Both the model include specific construct which provide a detail understanding of adoption intention. Mediating TAM-TOE (Model 7) has 3% improved explanatory power than direct TAM-TOE (Model 6). Relative advantage, compatibility, complexity, organisational readiness,training and education were found signification in mediating TAM-TOE but not in direct TAM-TOE. These determinants can provide leverage point to managers for the successful adoption of cloud computing. However Direct TAM-TOE can be used when the focus is on ascertaining the determinants of managerial decision making because the variable such as top management support,competitive pressure,trading partner pressure, usefulness were found significant in this approach. Based on these reasons, Mediating TAM-TOE is a preferable model for explaining cloud computing adoption intention because it has better explanatory power and provide a comprehensive understanding of adoptionintention.

8 Conclusion

This study compares four theoretical models i.e. TAM, TOE, Mediating integrated model and Direct integrated model in specic context of cloud computing. A preferable model should have a better explanatory power and be most parsimonious. The explanatory power of the integration model is better than that of individual models, but individual TAM model and TOE framework are more parsimonious models. Thus considering the better understanding and explanatory power integrated model is preferable. However the mediating TAM-TOE provide more complete understanding of adoption intention and Direct TAM-TOE provides the generic cognition of understanding adoption intention and determinants of managerial decision making. Both Mediating TAM-TOE and Direct TAM-TOE provide additional information to increase our understanding of adoption intention and can be used according to the objective of research. Overall the result indicate that Mediating TAM-TOE provides a fuller understanding of cloud computing adoption intention. The results of this study help advance the understanding of cloud computing adoption intention and provide suggestions to managers and researchers interested in cloud computing adoption. Further, an integrated model approach resulted in information technology adoption literature by proposing novel way of integrating two popular adoption frameworks.

9 Implications of the study

The theoretical contribution of this study is filling theoretical gap of TAM of external variables and major constructs of TOE framework. For this, an integrated model was developed and tested in the context of cloud computing. The potential utility of integrated model was tested and compared with the individual TAM model and TOE framework to advance the understanding of cloud computing adoption intention.

The results highlight influence changes of variables through model comparison. Few variables significantly influences adoption intention in Direct TAM-TOE. Compared with more variables, cloud computing adoption intention was influenced through perceived usefulness and perceived ease of use. Researchers should take this change into consideration. Most researches investigate influences of variables on certain factors. Few researches study influencing change of variables. This represents a potential way for future research. Second, our findings draw attention to the explanatory power under a specific context of cloud computing adoption intention. Mediating TAM-TOE is preferable model than other models in this study. It is suggested that researchers may be conducted to further test and compare competing models from other theories, such as IDT (Rogers, 1983), TRA (Fishbein and Ajzen,1975), and TPB (Ajzen, 1991), to explain and understand cloud computing adoption intention. Third, in the study,we incorporated two new constructs into the integrated model i.e. security and third party. This integrated model provides a more comprehensive understanding of the cloud computing adoption intention and expands the theoretical continuous by demonstrating security and third party control related issue. We did not consider all possible variables, such as lock-in, trust and risk, into the integrated model. This leaves a good way to advance more understanding of the cloud computing adoption. From the practical point of view the model can e used as a guideline to ensure a positive outcome of the cloud computing adoption in organisations.

The findings offer cloud computing users with a better understanding of how usefulness, perceived ease of use, relative advantage, compatibility, complexity, organizational readiness, training and education, top management commitment, competitive pressure and trading partner pressure, security, cloud service provider related factors affect cloud computing adoption intentions. It also provides relevant recommendations to achieve conducive implementation environment for cloud computing adoption.

Footnotes

Acknowledgments

The authors are grateful to the anonymous referees of the journal for their extremely useful suggestions to improve the quality of the paper. I also wish to thank all my teachers who were instrumental in shaping up my research acumen with their meticulous approach.

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