In order to evaluate the contributions of each of these platforms, each is briefly reviewed within the context of this literature review. First, the most commonly used one in start-up cloud database service providers, Amazon Web Services (AWS), is analyzed. AWS is comprised of the following components: Amazon DynamoDB and SimpleDB Services, Amazon Elastic Cloud Service (EC2), Amazon Elastic Block Store (EBS), Amazon Simple Storage Service (S3), Amazon Relational Database Service (RDS) and Amazon SQS. Amazon has also developed a cloud-based content sharing service that capitalizes on the depth of database expertise and support in this platform. Called Amazon CloudFront, this specific Web Service is designed to allow for Amazon ElastiCache configuration and use of a secured Cloud DNS service called Route 53. All of these technologies are combined to create the AWS architecture as shown in Figure 2.
The study and evaluation of this specific architecture is highly relevant to the level of user satisfactions attained with a cloud database service. The performance of this platform will be critical for bridging the gap between expectations and experiences of enterprise users of a cloud database service as well.
Figure 2: Amazon Web Services Architecture
Source: http://softwarestrategiesblog.com/2011/03/10/building-powerful-web-applications-in-the-aws-cloud/
AWS is structured for ease of customization through the use Application programmer Interfaces (APIs) which can be used for tailoring specific areas of the cloud architecture to the needs of a given enterprise (Marston, Li, Bandyopadhyay, Zhang, Ghalsasi, 2011). This is particularly useful in the development and launch of a cloud database service, which just align to the specific needs of a company to be useful. Measuring the relative levels of user intention and satisfaction for cloud-based database services hosted on the AWS platform will need to take into account the configuration requirements of this platform as well.
Google AppEngine also requires an extensive amount of customization to support cloud databases services, and is delivered free to developers and corporate accounts on an evaluation basis (Wang, Rashid, Chuang, 2011). In the interest of completeness, respondents using Google AppEngine and Microsoft Azure must also be included to see how user intentions and satisfaction vary by cloud platform. Microsoft Azure is the most comprehensive cloud stack provided by a software vendor today, incouding APIs for customization to the IaaS, PaaS and SaaS levels (Vance, 2011). The user experiences, intentions and satisfaction levels will vary significantly across all of these platforms as each has a significantly different set of development tools, platforms and techniques (Bowers, 2011). These variations will in turn drive completely different expectations of respondents to this research initiative. Measuring the differences between expectations and experiences is critical for understanding user intention and overall satisfaction levels. It is also critically important for understanding how the specific attributes of cloud database services impact overall customer satisfaction as well. These technology-based factors over time have a direct effect on customer satisfaction and need to be included in any evaluation of the performance of these platforms from a customer's perspective.
Expectation Theory and it Implications to
Measuring User Intentions and Satisfactions
The behavioral aspects of measuring user intention and satisfaction are predicated on understanding how expectations are created and maintained over time. A core aspect of this research proposal is measuring the difference between expectation and experience of enterprises users of cloud database services, used in an CRP, CRM and SCM context.
The development of expectations is based on the interaction of social exchange on the one hand and volitional or voluntary, performance-related behaviors on the other. Just as the formation of trust occurs between two people the trust that is inherent in the performance of any service or enterprise system must also be predicated on the stability, reliability and predictability of outcomes based on shared expectations. For expectations to be created and sustained the factors of continual customer satisfaction, trust in outcomes, commitment to continual social exchange behaviors and expectation fulfillment must be present (Hawes, Strong, Winick, 1996). There also must be a specific level of performance expectation defined and continually attained with any product or service which over time sets the new levels of anticipated performance levels (Hawes, Mast, Swan, 1989). Each interaction with a product or service reinforces or detracts from the previous level of expectation regarding its performance (Young, Wilkinson, 1989).
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