Abstract
The main objective of this research is to test the hypothesis that the two-step structural equation modelling (SEM) and artificial neural network (ANN) approach enables better in-depth research results as compared to the single-step SEM approach. This approach was used to determine which factors have statistically significant influence on extended use of enterprise resource planning (ERP) systems. The research model and the hypothesized relationships are based on the technology acceptance model (TAM). Majority of research on ERP acceptance has been conducted with SEM based research approaches. The purpose of this paper is to extend basic TAM research which is traditionally based on SEM technique with ANN approach. In the first step of the present research the SEM technique was used to determine which factors have statistically significant influence on extended use of the ERP systems; in the second step, ANN models were used to rank the relative influence of significant predictors obtained from SEM. The main finding of this research is that the use of multi-analytical two step SEM–ANN approach provides two important benefits. First, it enables additional verification of the results obtained by the SEM analysis. Second, this approach enables capturing not only linear but also complex nonlinear relationships between antecedents and dependent variables and more precise measure of relative influence of each predictor.
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Acknowledgements
This research was partially supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia, as a part of Research Project III-44010, titled: Intelligent Systems for Software Product Development and Business Support based on Models and by the Slovenian Research Agency (research core funding No. P5-0023).
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Sternad Zabukovšek, S., Kalinic, Z., Bobek, S. et al. SEM–ANN based research of factors’ impact on extended use of ERP systems. Cent Eur J Oper Res 27, 703–735 (2019). https://doi.org/10.1007/s10100-018-0592-1
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DOI: https://doi.org/10.1007/s10100-018-0592-1