Abstract
The concept of critical success factors (CSFs) has been widely used as a measure to tackle the hurdles associated with numerous implementations of enterprise resource planning (ERP) systems. This study evaluates the morphological variability of CSFs using the analytical principal component analysis technique to identify principal components (PCs) that can be adopted for a successful ERP system implementation. The dataset of 205 CSFs from 127 different studies was evaluated for the morphological variability in those studies. According to the results, 66 PCs were identified and ranked accordingly. The first 49 PCs with eigenvalues greater than 1 accounted for 89.67 % of the variability recorded. The first 6 PCs respectively accounted for 13.67%, 19.37%, 24.67%, 29.41%, 33.52% and 36.94% cumulative variations. In general, the graphical illustration of the study results show the palpable division between the taxonomic groups for 3 PCs.
Highlights
Critical success factors (CSFs) have been identified to be an essential precept for a successful enterprise resource planning (ERP) system implementation [1]
The significance of CSFs classification has been emphasised in various related studies with a lot of attentions paid to the importance of CSFs and the success of ERP system implementations [9]
Employing different markers of the CSFs, diversification was estimated by exploring the morphological attributes that provide essential preliminary method for gauging different CSFs while concurrently elucidating their performance under successful implementation
Summary
Critical success factors (CSFs) have been identified to be an essential precept for a successful ERP system implementation [1]. Saxena and McDonagh [6], have contended that CSFs remain the most-researched areas over the past years within the domain of enterprise systems Despite this contention, there exists a consensus among researchers that CSF is a highly significant concept that can help address the inherent challenges associated with ERP system implementation [7]. We provide the background discussion with respect to the related literature This is followed by the description of the material and methods of the study. Epizitone and Olugbara [11] highlighted this need by emphasising on the holistic nature of CSFs in different application settings This view is further supported by the adoption of a mixed method research approach to tackle the complex phenomena of CSFs [12]. The application of PCA to extract relevant information regarding CSFs from a large dimensional dataset is considered to enhance a deeper understanding of the intrinsic characteristics of CSFs [13]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
