Presenthing Metasynthesis and Interpretive Structural Modelling Approaches in Modeling the Cloud Business Intelligence in SMEs
Subject Areas : Development of business models in small and medium enterprises
Fatemeh Hamidinava
1
,
َAbdolhamid Ebrahimy
2
*
,
ROOHALLAH SAMIEE
3
,
HOSSEIN Dide Khani
4
1 -
2 - Allameh Tabataba’I University
3 -
4 -
Keywords: Meta-synthesis Business IntelligenceCloud ComputingCloud Business IntelligenceTSMEsISM,
Abstract :
Tourism small and medium enterprises (TSMEs( are more responsive to market demand when compared with larger companies. And if they use business intelligence system, they can enjoy high competition. The cloud platform for implementing business intelligence reduces maintenance and implementation costs for these businesses.The globalization of the markets requires the adaptation of the firm for business sustainability . Cloud deployments of BI and analytics platforms have the potential to reduce cost of ownership and speed time to deployment. In order to subsist, TSMEs have to take advantage of the new technology and new concepts for survival .The statistical population in the qualitative phase included 19 academics and experienced experts in the area of business intelligence .chosen by the purposive sampling approach. Further, in the quantitative phase, 393 people among the mangers of small and medium-sized enterprises in the Mazandaran Province, Iran, were participated. In the current study, the Meta-synthesis method has been utilized to identify the fundamental categories of business intelligence (BI). Fuzzy Delphi method (FDM) has been applied for parameter validation purposes, and eventually, the Cloud business intelligence model has been presented through exploiting the interpretive structural modeling. Final indices, 6 main factors, 27 sub-factors and 34 identifiers were obtained. In this regard, the data analysis process has been performed by MATLAB and MicMac software. Our research has shown that the two main themes of business stimuli and characteristics have the highest influence on other variables..
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