A THEORETICAL FRAMEWORK FOR CLOUD AND DATA ANALYTICS CONVERGENCE IN SCALABLE MANAGEMENT INFORMATION SYSTEMS
DOI:
https://doi.org/10.53555/xt7g0888Keywords:
cloud computing, data analytics, management information systems, scalability, higher education, theoretical framework, conceptual modelAbstract
With the growing use of data-intensive environments in the operations of an organizations and the higher education institutions, Management Information Systems (MIS) must shift away from the known-data-processing-environment to the higher analytical support and timely decision-making environment. Despite the fact that data analytics and cloud computing are recognized as crucial agents of organizational intelligence and scalability, a large portion of the literature currently in publication views the two technologies as distinct disciplines. These has been the focus on elaborating on how cloud and analytics capabilities interact in systematic relations to serve scalable MIS and this is specifically in complex and multidisciplinary academic settings.
The paper proposes a theoretically grounded conceptual framework that clarifies how data analytics and cloud computing can be integrated to provides a platform for scalable MIS. The study incorporates cloud computing, data analytics, distributed system, and information system literature using a theory synthesis approach. It is designed based on three construct domains, including cloud infrastructure elements, data analytics capabilities and MIS scalability requirements and operationalized by five convergence mechanisms: dynamic resource orchestration, analytics-support scalability, bidirectional feedback loops, data-compute co-location, and adaptive optimization. Seven theoretical propositions are developed to inform future empirical research.
The research work also adds to MIS theory by explaining structural functional relationship between cloud infrastructure and analytics capabilities in scalable system design. Furthermore, it provides viable guidelines to the institutions that seek to achieve the development of analytics-enabled MIS designs that can withstand the increasing levels of data, changing decision needs, and available resource limitations.
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