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Top1. Introduction
Cloud Computing (CC) has revolutionized software applications development. Owing to the growing popularity of this computing paradigm, time taken for implementing an application from ideation to production has shrunk considerably. CC is defined by the National Institute of Standards and Technology (NIST), USA as a model for enabling ubiquitous, convenient, and on demand network access to a shared pool of configurable computing resources; moreover, such resources can be provided quickly and released with minimal management effort or service provider interaction (Mell & Grance, 2011). With its three service models (Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS)) and four deployment models (Public, Private, Hybrid & Community Model), CC provides a solution that offers agility and savings in terms of both time and cost. These solutions are applicable to all verticals of the Information Technology (IT)-intensive industry, including automotive, banking, education, energy, healthcare, insurance, manufacturing and transportation. Today, CC has redefined the application development landscape as it allows developers to quickly and cost-effectively spin out new e-environments to test and design new applications. The application development is rapid and continuous within this emerging computing environment.
The fusion of Internet-connected devices and data processing software frameworks with cloud-based resources has led to the creation of novel applications in various domains, including healthcare. CC provides a channel for self-provision development and testing environments in which applications can be quickly produced and scaled, as and when required. With CC, there is a need for the software development (SD) process to adapt to the requirements of cloud technologies, that is, via PaaS which enables software developers to quickly design, develop, and test solutions to be deployed on the cloud (KPMG, 2011). In this sense, time and money needed to source and maintain the infrastructure required for SD can be saved. As well, new SD tools and techniques can be applied to meet the enterprise computing requirements. For example, cloud platforms allow the streamlining of the SD process and have the ability to get the development assets online quickly. These applications can also take advantage of the cloud native features such as multi-tenancy and auto scaling.
Cloud applications for heath monitoring, electronic health records (EHRs), health insurance packages and other medical alert systems are highly demanding in terms of organizational resources, thereby providing the primary motivation for focusing on this vertical in our research. Literature reviews and experiences of the developer communities reveal that the SD process for developing cloud applications possess certain characteristics that enable the process to fully realize and leverage the potential of this computing paradigm. This article aims at identifying the attributes of SD process for cloud application development and determining the causal effects of the identified attributes. To interpret the relationship and patterns amongst the identified attributes, we used factor analysis (FA) and then applied the DEMATEL technique to determine the causal effects. On the one hand, FA technique identifies the latent factors with the goal of finding the smallest number of common factors that will account for the correlations amongst the identified attributes; on the other hand, the DEMATEL approach purposes to perform direct comparisons of the relationships among attributes within a complex system. Hence, the DEMATEL result should illustrate analytically the interrelations of the attributes and find the significant attributes of the domain to aid decision-making by the practitioners.
The rest of the article is organized as follows. In section 2 on background, the authors discuss recent advancements in the area of Health IT (HIT) and review the extant literature to identify and extract the attributes. Section 3 on methodology details the research methodology employed whereas implementation details are presented in section 4 on data collection and analysis. Results are discussed in Section 5 on observation. In section 6 on concluding remarks, the authors highlight the implications of the findings as well as outline future extension of the current work.