This research proposal looks to determine how the selection of a given cloud platform impacts user intention, satisfaction and long-term adoption.
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.
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).
The greater the depth and breadth of complexity in a given network or system, the greater the level of consistency and reliability there must be in its operation for expectations to be solidified over time. For a highly complex system to be effective in meeting and exceeding expectations, its systemic structure must allow for enough agility to respond to widely varying requirements while also staying consistent with the overall architecture of the platform (Saint Germain, Valckenaers, Van Belle, Verstraete, Van Brussel, 2012). This is precisely what a cloud database service must do as well; it must be agile enough to respond to specific user requirements yet capable of providing a foundational basis for continued operation as well.
When the aspects of cloud-based architectures are taken into account in conjunction with the aspects of expectation theory a proposed Cloud Database Service Customer Satisfaction Model emerges from the research completed. This model seeks to balance the expectations and experiences of customers while anchoring each in application design and usability on the one hand, and application performance and reliability on the other. This model must also take into account the specific attributes of how trust is created and maintained in the context of a cloud database service's performance over time. Finally there needs to be integration to the process level and ability to create enough cooperative advantage between system uses so that the variation between expectations and experiences can be measured. The proposed model can be used for completing the research in addition to defining the interrelationships of expectations to experiences over the long-term within populations of cloud database service users. Figure 3, Proposed Cloud Database Service Customer Satisfaction Model illustrates these concepts graphically, within the form of an ecosystem that represents how the user dynamics in a cloud database service interact.
Figure 3: Proposed Cloud Database Service Customer Satisfaction Model
The methodology for this research study centers on defining hypotheses and a research plan to validate the model shown in Figure 3, while also seeking to define the specific aspects of user intention and customer satisfaction. The balancing of expectations and experiences, anchored in application design and usability on one side and application performance (including reliability and scalability) give the model balance. This model is also ideally designed for measuring the effects of system fidelity and performance over time, specifically how effective a given cloud database service is in meeting or exceeding expectations. Future research with regard to the proposed model would center on creating an aggregate custom satisfactions core based on the average or mean differences between expectations and experiences. The following section on methodology explains hwo this can be achieved using a series of Likert scales and multidimensional scaling techniques.
Methodology sampling frame is defined as those enterprises who have successfully implemented a cloud computing platform. The platform is defined as Amazon Web Services Google AppEngine or Microsoft Windows Azure cloud platform. These three platforms each support cloud database services and serve a the prerequisite for user intention and satisfaction research.
Sampling Methodology and Sample Frame
The sampling frame is defined as all enterprises who have currently adopted cloud computing platforms from Amazon, Google and Microsoft. The sampling methodology is based on those enterprises that are currently using one of the three following software systems in conjunction with a cloud database service: Enterprise Resource Planning (ERP); Customer Relationship Management (CRM) or Supply Chain Management (SCM). These applications are all heavily reliant on databases for their performance, which will provide immediate insights into the levels of satisfaction with cloud database service performance.
The Chief Information Officers (CIOs) and directors of it will be the primary respondents, and will be contacted through the use groups of the three cloud platforms that form the foundation of the sampling frame.
An online survey created in Zoomerang or comparable online survey tool will be used for completing the research. Contacts obtained from the trade organizations will be used for completing an initial round of surveys initiated through telephone calls and followed up with an e-mail survey. The initial contact will concentrate on explaining the confidentiality of responses and the anonymity of each respondent. A description of data analysis routines will be provided to also show how the data will be aggregated and not tracked back to any individual respondent.
The survey instrument itself will be a questionnaire that will include several questions with regard to demographics, both at an industry and individual company level. The secondary segment of the survey will be a series of Likert scales that measure on a five point attitudinal scale the…