Cloud Database Service on User

¶ … Cloud Database Service on User Intentions and Satisfaction for Business The pervasive nature of cloud computing is re-ordering the economics of enterprise software, leading to widespread integration and adoption of database services. Moving from capital expense-driven (CAPEX) based spending to operating expense (OPEX) funding models, cloud computing platforms are accelerating in adoption throughout all levels of businesses today. In large-scale enterprises, cloud computing is being increasingly relied on as the infrastructure layer of global computing platforms.

Examples of this include two-tier Enterprise Resource Planning (ERP) strategies that scale across Asia, Europe and North America. In addition, cloud-based database services are being used today for unifying the diverse Customer Relationship Management (CRM) platforms across diverse business models and cultural boundaries. The same holds true for the integration of Supply Chain Management (SCM) systems on a global scale as well. The reliance on cloud database services is one of seeking to integrate systems of record and the many diverse workflows into a single, unified strategy.

Databases used to create the single system of record across enterprise that also rely on ERP, CRM and SCM systems require a standard level or quality of service in order to complete basic and advanced business strategies. These three application areas, ERP, CRM and SCM, are the most prevalently used throughout enterprises. They are also the three most critical application areas that are driving the global adoption of cloud database services as well.

The question this research proposal looks to address is how effective cloud database services are in defining user intentions of adoption and overall satisfaction with these technologies within a business context. Introduction The level of consistency and reliability that cloud database services deliver and the resulting levels of customer satisfaction attained is the focus of this research proposal.

Throughout this research proposal the critical role of cloud database services will be shown, including their influence on user intentions and satisfaction with computing performance in general, in addition to specific application performance as well. The user intention and customer satisfaction of cloud database services can be measured using quality of service (QoS) metrics of the performance of the database platform technologies which measures performance purely at a technology level. Additional research is needed to measure the attitudinal aspects of how cloud database services influence user intentions and customer satisfaction.

The intention of this research proposal is to evaluate the attributes of QoS performance at the technical level followed by attitudinal measures of performance for cloud database services in place today and how their performance is contributing to greater levels of productivity within enterprises who have adopted this platform globally. As user intention and customer satisfaction are attitudinal in nature, the methodology for this study concentrates on using Likert scaling to capture the relative ranking of these technologies and their relative contributions to enterprise performance.

Supplanting the Likert-based scaling analysis will be financial analysis of the performance of firms included in the research methodology. The correlation of attitudinal assessments to financial performance of cloud-based databases services will be included in this analysis. The pervasive use of metrics specifically in the application areas of ERP, CRM and SCM applications that require a cloud database service to operate across the enterprise will also be used as the foundation of this analysis.

Determining if there is a strong correlation between the attitudinal perceptions and valuation of cloud database services and their financial performance is at the center of this research proposal. Literature Review The foundation of cloud database services is based on the cloud technology stack as defined by the foundational elements of Infrastructure-as-a-Service (IaaS) which is used for supporting the Platform-as-a-Service (PaaS) layer of the cloud platform.

The most visible area of the cloud computing stack is the Software-as-a-Service (SaaS) layer which is where applications that users interact with are located (Armbrust, Fox, Griffith., et.al., 2010). The attitudinal measures of user intentions and customer satisfaction are measured from this specific level of the cloud computing technology stack. The SaaS layer of the cloud computing stack is also where the majority of enterprise software companies deliver their applications including those that are ERP, CRM and SCM-based (Marston, Li, Bandyopadhyay, Zhang, Ghalsasi, 2011).

The cloud computing technology stack and its customization and permeability across the enterprise are the primary factors that are revolutionizing the economics of cloud computing. The agility of the cloud computing stack is also re-ordering the capital expense (CAPEX) spending levels of companies, drastically reducing them as these series of technologies make it possible to subscribe to only those applications and features that deliver the greatest value to a given business or process.

The ability to align specific applications, processes and workflows to a given series of business problems, giving the business owner the flexibility of customizing just the specific parts of the application they need, is changing the economics of cloud computing. This approach to using only those aspects of the cloud computing technology stack and respective applications underscores the ability to charge only for the elements used. The economics of these decisions lead to operating expense (OPEX)-based payment for these services (Wang, Rashid, Chuang, 2011).

The cloud technology stack has made this customization and tailoring of enterprise software applications possible. The degree of customization is particularly appealing to large-scale enterprises that have in the past been forced to use monolithic, difficult to customize ERP, CRM and SCM applications. The inherent advantages of cloud computing and the agility of configuration for the applications lead to greater levels of customer satisfaction and greater user intention to adopt advanced technologies.

It is anticipated that this research will show that there is statistically significant higher levels of satisfaction with cloud-based applications relative to their on-premise counterparts precisely due to the greater levels of customization and tailoring to specific internal needs, a key finding from previous research (Arinze, Anandarajan, 2010). In the nascent and rapidly growing area of cloud computing there is evidence however of significant gaps between expectations and the experiences of customers.

this is especially the case throughout the enterprise, where the mix of on-premise, old-line systems are converging with cloud-based database services. This convergence of existing-generation enterprise systems and cloud computing systems including cloud database services creates an exceptional level of hype as well. Gartner Group, one of the leading it consultancies, has created a Hype Cycle for Cloud Computing, which is shown in Figure 1. This graphic shows how expectations of cloud computing are accelerating, and the proliferation of technologies in this area of computing.

Figure 1: Gartner's Hype Cycle for Cloud Computing Source: http://softwarestrategiesblog.com/2011/07/27/gartner-releases-their-hype-cycle-for-cloud-computing-2011/ The myriad of technologies shown in the Hype Cycle for Cloud Computing can be attributed to the massive level of investment occurring in this market due to its exponential growth. Venture capitalists see significant value in the business models of companies in these segments as the economics of cloud computing combined with early adopters show significant potential for growth. The Hype Cycle also indicates where early adopters are most and least satisfied with these emerging technologies as well.

From a customer an expectation, user intentions and satisfaction perspective, the further a given technology is on the hype cycle curve the more mature it is, therefore the greater value it is generating. The most mature cloud technologies are on the Slope of Enlightenment and Plateau of Productivity. These areas of the hype cycle indicate where enterprise can expect to gain the greatest value of adopting new technologies over the long-term.

Conversely the Technology Trigger and Peak of Inflated Expectations are those areas that have the greatest distance between expectations and experiences. The greater the distance between these two areas of early adopters' experiences the more costly mistakes are from an enterprise software implementation standpoint. The greater the level of successful adoption of a given technology, the faster it progresses down the adoption curve of the hype cycle.

Cloud databases services are evident in several areas of the hype cycle including database platform as a service (dbPaaS), multitenancy, Infrastructure-as-a-Service (IaaS), Public Cloud Storage and virtualization. These areas are all directly related to the cloud services architecture. What is particularly challenging about orchestrating these technologies are creating a unified strategic plan that takes into account the relative merits each emerging and established technology.

As cloud database services are comprised of a series of technologies with several being nascent as defined by their location on the Hype Cycle for Cloud Computing, the relative maturity of each technology must be taken into account in any research methodology to measure user intention and satisfaction. Structural aspects of cloud computing in general and cloud database services specifically have the potential to influence user intentions, user satisfaction and long-term adoption and use of these technologies.

The agility of these technologies to meet complex strategies and workflows throughout an organization has a direct effect on user intention and satisfaction. The intent of this research proposal is to evaluate the impact of varying cloud computing infrastructure platforms as well. The three most dominant in use throughout industries today are the Amazon Web Services platform, Google AppEngine and Microsoft Azure. 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. 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). 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. Method 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 relative level of satisfaction with each enterprise application that relies on a cloud database service to function. These Likert scales will measure each aspect of performance of the applications, from responsiveness and speed to reliability and validity of computing results for applications. The respondents will also be asked to evaluate and assess the perceived security level of each specific application that is based on the cloud database service.

The final section fo the questionnaire will ask the respondents to forecast how and where cloud database services will be.