Thirty Six (36) Months. Researcher 01 Researcher 02. South Eastern University of Sri Lanka

Transcription

1 Title of the Research: Duration of the Research in Months: Field Covered by the Proposal: FACTORS INFLUENCING CLOUD COMPUTING ADOPTION BEHAVIOUR OF ADMINISTRATIVE STAFF: REFERENCE TO STATE UNIVERSITIES IN SRI LANKA Thirty Six (36) Months University Governance and Information Technology Adoption Researcher 01 Researcher 02 Name S. Sabraz Nawaz M.I. Nawfer Designation Lecturer in MIT Senior Assistant Registrar Institution s Address South Eastern University of Sri Lanka University Park Oluvil South Eastern University of Sri Lanka University Park Oluvil Date of Birth February 20, 1979 Telephone No: Ext: Fax No: sabraz@seu.ac.lk sabraznawaz@gmail.com nawfermi@seu.ac.lk 1

2 Introduction According to severe market competition and a dramatically changing business environment, firms have still prompted to adopt various state-of-the-art information technologies (IT) to improve their business operations (Pan and Jang, 2008; Sultan, 2010). In recent years, the term cloud computing has been critical in the world of IT. Cloud computing, or the use of internet-based technologies to conduct business, is recognized as an important area for IT innovation and investment (Armbrust et al., 2010; Goscinski and Brock, 2010; Tuncay, 2010). Cloud computing has spread out through the main areas related to information systems (IS) and technologies, such as operating systems, application software, and technological solutions for firms (Armbrust et al., 2010). Cloud computing, an innovative technology with dynamic scalability and usage of virtualized resources as a service through the internet, is regarded as potential solutions to advancing modern organizations IT competitiveness and performance (Catteddu and Hogben, 2009; Ercan, 2010; Goscinski and Brock, 2010; Wu, 2011; Thomas, 2011). In other words, cloud computing is a kind of computing application service that is like , office software, and enterprise resource planning (ERP) and uses ubiquitous resources that can be shared by the business employee or trading partners. Thus, a user on the internet can communicate with many servers at the same time, and these servers exchange information among themselves (Hayes, 2008). Moreover, telecommunication and network technology have been progressing fast; they contain 3G, 4G, etc. so the high speed infrastructures are integrated strongly. Cloud computing services can provide the user seamlessly, the convenience, and the quality stable technological support that can develop the enormous potential demand (Buyya et al., 2009; Pyke, 2009). Thus, cloud computing provides the opportunity of flexibility and adaptability to attract the market on demand. From a business point of view, firms are increasingly attempting to integrate business processes into their existing IS applications and build internet based technologies for transacting business with trading partners (Tuncay, 2010). To enhance competitive advantage, developing cloud computing capability is an important undertaking because it is not only rapidly changing the way that enterprises do business activities, but it is also becoming a more integral part of enterprises business tactics (Pyke, 2009). While cloud computing has been discussed as a new technology develop that can provide several advantages, both strategic and operational, to its adopters, the cloud computing adoption rate is not growing as fast as expected (Banerjee, 2009; Buyya et al., 2009; Goscinski and Brock, 2010). The 2

3 future of computing lies in cloud computing, whose major goal is reducing the cost of IT services while increasing processing throughput, reliability, availability, and flexibility and decreasing processing time (Hayes, 2008). Until today, use of cloud services is not as popular as anticipated in most universities in Sri Lanka; despite that cloud computing has been thought useful in advancing the academic research, pedagogical materials and administrative tasks (Sultan, 2010). The adoption of a new technology is regarded as a promising way to enhance the universities IT competitiveness and performance. Owing to the lack of exploratory studies that explain the adoption of cloud computing in higher education industry, our research is to unveil factors influencing administrative staffs in Sri Lankan universities to the adoption decision of cloud computing services. Literature Review and Theoretical Framework The concept of cloud computing Cloud computing is similar to an electricity grid, where resources like hardware, software, information are pooled and shared with the end-user via the internet, which is used as a medium of exchange (Li et al., 2011). Users do not know the exact location of their digital data (McAfee, 2011). The framework provided by cloud computing is in the form of high quality leased IT resources instead of building the IT infrastructure from scratch. Thus, the in-house versus cloud computing comparison is, analogous to the make or buy decision. Cloud computing is analogous to outsourcing data center operations (Mahesh et al.,2011). This approach also typically implies renting software via the Internet instead of employing an in-house software development team (Payton, 2010). As a result, organizations inhouse IT parts are minimal (Li et al., 2011). Mahesh et al. (2011), Sultan (2011). McAfee (2011) observe that cloud computing comprises three services: Software-as-a-Service (SaaS): Instead of installing software on the client s machine and updating it with regular patches, frequent version upgrades etc., applications like Word processing, CRM (Customer Relationship Management), ERP (Enterprise Resource Planning) are made available (hosted) over the internet for the consumption of the end-user. It can achieve economies of scale. This is the biggest and most mature cloud model. Commercial vendors are Yahoo Mail, Gmail, Hotmail, TurboTax Online, Facebook, Twitter, Microsoft Office Live, Google Apps, Salesforce.com, Cisco WebEx web conferencing, antivirus, Success Factors (HRM tool) etc., Platform-as-a-Service (PaaS): Instead of buying the software licenses for platforms like operating systems, databases and middleware, these platforms and the software development kits (SDKs) and tools (like Java,.NET, Python, Ruby on Rails) are made available over the Internet. Commercial vendors include Microsoft Azure Services, Amazon Web 3

4 Services (AWS), Sales-force s Force.com, Google App Engine platform, IBM Cloud burst, Amazon s relational database services, Rackspace cloud sites, Infrastructure-as-a-Service (IaaS): This refers to the tangible physical devices (raw computing) like virtual computers, servers, storage devices, network transfer, which are physically located in data centers but they can be accessed and used over the internet using the login authentication systems and passwords from any dumb terminal or device. Commercial vendors include Amazon EC2 (Elastic Compute Cloud), Elastic Block Storage (EBS) and Simple Storage Service (S3), Rackspace cloud servers, etc. One of the biggest advantages of moving to cloud computing is the opportunity cost of freeing up some of the IT administrative time, which can now be applied to the business aspects of growing the core business (Creeger, 2009) of higher education institutions. Now, startups and small firms can use cloud computing resulting in introduction of these types of online applications and social media services such as YouTube and Facebook (Marston et al., 2011). There are four different cloud deployment models within organizations namely (Marston et al., 2011; Rath, 2012): Public cloud: It is available from a third party service provider via Internet and is very cost effective for universities to deploy IT solutions. For example, Google Apps. Private cloud: It is managed within an organization and is suitable for large enterprises. For example, the US government cloud product is in a segregated environment, both physically and logically and is being handled by a third party provider, Google. Private clouds provide the advantages of public clouds but still incur capital expenditures. Community cloud: It is used and controlled by a group of enterprises, which have shared interests. For example, the US federal government using community cloud for USA.gov, cars.gov, Apps.gov. Hybrid cloud: It is a combination of public and private cloud. TOE Framework Based on the Technology-Organization-Environment (TOE) framework (Tornatzky & Fleischer, 1990), this study proposes a three dimensional model (technology, organization and environment). Previous studies found that TOE frameworks are useful in understanding critical factors of new IT adoption in a given organization Therefore, application of this framework seemed to be suitable for this study. Tornatzky and Fleischer (1990) summarized previous studies on innovative information technology adoption and proposed the TOE framework to understand the critical factors affecting the introduction of new information technology. Their framework contains three major elements that affect the process 4

5 of adopting innovation technology and it includes the organization dimension, technology dimension and environment dimension (Tornatzky & Fleischer, 1990). Previous Studies on Higher Education. Based on the theoretical background information and the review of findings in the literature, this study proposes a three dimensional model (Figure 1), which incorporates the variables of technology, organization and environment in understanding the decision to adopt cloud computing in the higher education industry. This study infers that these three dimensions will affect university administrative staffs decisions toward the adoption of cloud computing technology in a positive manner. Research model and hypotheses In this study, the research model incorporates technological, organizational, and environmental contexts as important determinants of cloud computing adoption. Technology dimension Figure 1: Research Model The technology dimension entails the internal and external influences of adopting specific information technology in the organization. Due to the nature of cloud computing technology, data security and privacy are the major concerns for adoption (Kuo, 2011); it applies to the higher education industry and also as a general concern. As a result, establishing a secure environment for cloud based enterprise resource planning system data integration and sharing is indeed critical (Chen, Lu, & Jan, 2012). For this reason, data security is included as one of the critical key variables in the technology dimension. 5

6 Rogers (1983) defined relative advantage as the degree to which a technological factor is perceived as providing greater benefit for firms. It is reasonable that firms take into consideration the advantages that stem from adopting innovations (To and Ngai, 2006). Cloud computing services, which allow operations to be generalized and mobilized through internet transactions, can substitute for or complement ERP software. The study of Premkumar and Roberts (1999) indicated that relative advantages will affect businesses and push them to adopt new information technologies. Firms may not have confidence in a cloud computing system because it is relatively new to them (Buyya et al., 2009). It may take users a long time to understand and implement the new system. Thus, complexity of an innovation can act as a barrier to implementation of new technology; complexity factor is usually negatively affected (Premkumar et al., 1994). Compatibility refers to the degree to which innovation fits with the potential adopter s existing values, previous practices and current needs (Rogers, 1983). Compatibility has been considered an essential factor for innovation adoption (Cooper and Zmud, 1990; Wang et al., 2010). When technology is recognized as compatible with work application systems, firms are usually likely to consider the adoption of new technology. When technology is viewed as significantly incompatible, major adjustments in processes that involve considerable learning are required. Previous studies have also indicated that IS complexity (Changet al., 2007) and compatibility will affect IT adoption decision positively (Lin et al., 2012; Liu, 2011) for those in the higher education industry. Therefore, perceived system complexity will be a key criterion when making an adoption decision. Furthermore, higher education ERP modules that handle students enrollment, examination and researches by staff, etc. are rather unique by nature. How to migrate these data with the cloud computing platform will also be a critical factor these organizations need to consider. Consequently, the level of system compatibility is another key factor in the technical dimension. If cloud computing technology can be compatible with the existing systems or applications of the university, then it will be more helpful and also more feasible for the adoption of cloud computing technology. Because of the varied and extensive nature of the costs, organizations can find the expenses associated with this type of project to be very sizable. For this reason, costs will also be a critical factor for the adoption decision. Based on the above discussions, this dimension is composed of four variables. These variables are Data Security, Relative Advantage, Complexity and Compatibility. From the aforementioned theory, the following hypotheses have been developed: H1: There will be a positively significant relationship between Data Security and the adoption of cloud computing. 6

7 H2: There will be a positively significant relationship between Relative Advantage and the adoption of cloud computing. H3: There will be a negatively significant relationship between Complexity and the adoption of cloud computing. H4: There will be a positively significant relationship between Compatibility and the adoption of cloud computing. Organization dimension Organizational factors will affect the universities intention to adopt new information systems technology (Chang, Hwang, Yen, & Lian, 2006; Hsiao et al., 2009). In this study, the organizational dimension represents different organizational conditions including variables such as Top Management s Support, Adequate Resources, and Benefits for adoption. Top management s support refers to whether or not the executives understand the nature and functions of cloud computing technology and therefore fully support the development of it. The study of Chang et al. (2006) found that top management s support will affect new IS adoption. Additionally, the adoption of cloud computing technology is usually a large project and a huge undertaking for universities. Top management support is critical for creating a supportive climate and for providing adequate resources for the adoption of new technologies (Lin and Lee, 2005; Wang et al., 2010). As the complexity and sophistication of technologies increase, top management can provide a vision and commitment to create a positive environment for innovation (Lee and Kim, 2007; Pyke, 2009). Top management plays an important role because cloud computing implementation may involve integration of resources and reengineering of processes. If a given university has a sufficient budget, adequate human resource support, ample time, and good top management s involvement, then the adopting of cloud computing technology will be met in a positive manner. To this end, adequate resources are also critical to the success of adoption (Chang et al., 2007). Finally, potential benefits such as improving a university s image, gaining strategic advantage over other universities, improving their service quality, and enhancing the efficiency of internal operations will also be critical. The study of Chang et al. (2006) found that the benefits of IS will lead to a positive adoption. The measurements used for this dimension were adapted from the studies of Premkumar and Roberts (1999), Chang et al. (2007), and Kuan and Chau (2001) respectively. 7

8 Based on the above discussions, this dimension is composed of three variables. These variables are Top Management s Support, Adequate Resource and Benefits. From the aforementioned theory, the following hypotheses have been developed: H5: There will be a positively significant relationship between Top Management s Support and the adoption of cloud computing. H6: There will be a positively significant relationship between Adequate Resources and the adoption of cloud computing. H7: There will be a positively significant relationship between Benefits and the adoption of cloud computing. Environment dimension The environmental dimension represents the current operating environment of the higher education industry. This will no doubt impact universities as they adopt new information systems (Hsiaoet al., 2009; Chang et al., 2006). In Sri Lanka, universities try to become better positioned in Webometric as well as other rankings for universities. These competitive pressures will force universities to adopt new information systems quickly to provide better services and gain strategic advantages. Previous studies also confirm the fact that business competition significantly affects organizations to adopt new technologies (Hsiaoet al., 2009). One of the main objectives that the Ministry of Higher Education is trying to meet is determining the best way to avoid wasting resources. The advance in clouding computing technology is highly correlated with the application of cloud based ERP systems in government universities. From the above discussion, the environment dimension includes the following two variables: Government Policy and Industry Pressure. Each of these two variables was measured by using the studies of Kuan and Chau (2001) and Premkumar and Roberts (1999) accordingly. Based on the above discussions, this dimension is composed of two variables. These variables are Top Government Policy and Industry Pressure. H8: There will be a positively significant relationship between Government Policy and the adoption of cloud computing. 8

9 H9: There will be a positively significant relationship between Industry Pressure and the adoption of cloud computing. The variables considered for this study and their sources are shown in Table 1. Table 1: Variables and Their Sources Dimension Variables Sources Technology Data Security Kuo, 2011 Relative advantage Complexity Compatibility Premkumar and Roberts (1999), Tan et al. (2008), To and Ngai (2006), Wang et al. (2010) Premkumar and Roberts (1999) and Chang et al. (2007), Tan et al. (2008), Wang et al. (2010) Premkumar and Roberts (1999) and Premkumar and Ramamurthy (1995), Hong and Zhu (2006), Tan et al. (2008), To and Ngai (2006), Oliveira and Martins (2010), Wang et al. (2010) Organization Top Management s Support Chang et al. (2006), Lee and Kim (2007), Wang et al. (2010) Adequate Resource Chang et al. (2006) Benefits Chang et al.(2006) and Kuan and Chau (2001) Environment Government Policy Kuan and Chau (2001) Industry Pressure Premkumar and Roberts (1999) Research Method The research study will employ quantitative study based on questionnaire survey. According to Bernard and Bernard (2012), a quantitative analysis allows for the classifying of features, counting them, and constructing more complex statistical models in an attempt to explain what is observed, findings can be generalised to a larger population, since quantitative data is in numerical form it allows researchers to analyse more easily, provides high level of accuracy and also allows to present analysis graphically. Quantitative method enables the researcher to test the relationships between the variables identified in the model and thereby let him provide evidence to support or disprove the hypotheses (Carter and Belanger, 2005). Survey research is an important area of measurement in applied social research (Pinsonneault and Kraemer, 1993). Scholars mentioned that the main purpose of a survey is to produce quantitative statistics about some aspects of a study and a survey is a systematic method for assembling information from a sample of the population for the rationale of 9

10 constructing quantitative attributes. In social sciences research the questionnaire based survey has become one of the most widely used techniques of data collection for reasons such as economy, ability to reach a larger sample of the population, saving time, feasibility to collect data within a predetermined framework, and feasibility to process the data using a coding system that could be easily used on computers (Ranasinghe and Fonseka, 1998). Questionnaires are an efficient data collection mechanism when the researcher knows exactly what is required and how to measure the variables of interest (Sekaran and Bougie, 2010). The most appropriate applications for surveying are those where participants are uniquely qualified to provide the desired information. In the case of a questionnaire based survey the unit of analysis is the individual. This study focuses the unit of analysis as the individual; the administrative officers of Sri Lankan universities and they are expected to provide information by responding to the survey questionnaire. Population and Sample The population of this study includes all administrative staff of government universities in Sri Lanka. According to a trusted source there are 375 administrative staff including Registrars and Bursars. Sampling frame is also available and the sampling method will be probability sample with random selection. According to Hair et al. (1998) as cited by Rehman et al. (2012), each independent variable is expected to have ten data records ; since this study had nine independent variables, 90 respondents would have sufficed however the sample size is calculated to be 194 and also sample sizes larger than 30 and less than 500 are appropriate for most research (Sekaran and Bougie, 2010). According to Arnold (1990), achieving a statistical power of at least 95% is common in social science researches. Using the formula provided by Yamane (1967) and bearing a confidence level of 95% and 5% for margin of error, the appropriate sample size for this study was 400, which was calculated as shown next: n=n/[1+n(e) 2 ] where: n = Sample size N = Population size (375) e = Error of sampling (.05) Substitution in the formula: n = 375 / [1+375(0.05) 2 ]

11 Data Collection Though there are many data collection methods available, because of constraints in terms of time, costs and human resources, the questionnaires will be administered personally using drop-off and pick-up approach for printed questionnaires and published on web-forms. In order to minimize the bias in the data collection, data triangulation will be followed. The use of different research approaches, methods and techniques in the same study is known as triangulation and can overcome the potential bias and sterility of a single method approach and in data triangulation, data is collected at different times or from different sources in the study of a phenomenon (Hussey and Hussey, 1997). The respondents of the study are expected to voluntarily to fill up printed questionnaire which will be personally administered by the researchers and also ed using an online survey, Google Docs, for those in far away. Data Analysis The data to be collected will be examined for outliers, coded and scored appropriately. The initial analysis will include an examination of descriptive statistics of demographic variables with frequencies and percentages. For this analysis work MS Excel 2010, SPSS 20 and AMOS 20 will be used. Reliability tests will be conducted to see the consistency of data, factor analyses will also be performed. The resultant variables of the factor analysis will be used to see the strength of relationship as well as strength of explaining ability of the variables. These regression tests will involve calculating and comparing to gain insight into the nature of the relationship between independent variables and dependent variables. Tentative Financial Budget for the Research This research project is estimated to cost SLR.117, and its detail is given in Table 2 below. Table 2: Estimated Budget Description Amount Literature Survey Rs Stationary & Postage Rs. 14, Consumables Rs. 8,

12 Data Processing Rs. 10, Traveling & Subsistence Rs. 25, Miscellaneous (Printing, etc.) Rs. 15, Total Amount: Rs. 117, Scheduled Work-plan for the Research Project This research project will be started in January 2014 and is scheduled to be completed by the end of December Hence the total duration of the project is almost 36 months (three years). The Figure 2 below depicts the scheduled plan of the project. Activities Research Proposal Development Review of Literature and Development of Theoretical Framework Questionnaire Development and Pilot-Testing Data Collection Data Entry and Analysis Write up, Scrutiny and Publication of the findings Figure 2: Gaunt Chart Duration