Enhancing PLS-SEM-Enabled Research with ANN and IPMA: Research Study of Enterprise Resource Planning (ERP) Systems’ Acceptance Based on the Technology Acceptance Model (TAM)

Mathematics

卷:10

Polona Tominc¹  Samo Bobek¹  Simona Sternad Zabukovšek¹  Uroš Zabukovšek¹  Zoran Kalinić² 

[1] Faculty of Economics and Business, University of Maribor, 2000 Maribor, Slovenia;

[2] Faculty of Economics, University of Kragujevac, 34000 Kragujevac, Serbia;

关键词: traditional PLS-SEM;    artificial neural network (ANN) analysis;    Importance–Performance Matrix Analysis (IPMA);    ERP system acceptance;    TAM model;   

DOI  :  10.3390/math10091379

来源: DOAJ

【 摘 要 】

PLS-SEM has been used recently more and more often in studies researching critical factors influencing the acceptance and use of information systems, especially when the technology acceptance model (TAM) is implemented. TAM has proved to be the most promising model for researching different viewpoints regarding information technologies, tools/applications, and the acceptance and use of information systems by the employees who act as the end-users in companies. However, the use of advanced PLS-SEM techniques for testing the extended TAM research models for the acceptance of enterprise resource planning (ERP) systems is scarce. The present research aims to fill this gap and aims to show how PLS-SEM results can be enhanced by advanced techniques: artificial neural network analysis (ANN) and Importance–Performance Matrix Analysis (IPMA). ANN was used in this research study to overcome the limitations of PLS-SEM regarding the linear relationships in the model. IPMA was used in evaluating the importance and performance of factors/drivers in the SEM. From the methodological point of view, results show that the research approach with ANN artificial intelligence complements the results of PLS-SEM while allowing the capture of nonlinear relationships between the variables of the model and the determination of the relative importance of each factor studied. On other hand, IPMA enables the identification of factors with relatively low performance but relatively high importance in shaping dependent variables.

【 授权许可】

Unknown