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SEM–ANN based research of factors’ impact on extended use of ERP systems

Author

Listed:
  • Simona Sternad Zabukovšek

    (University of Maribor)

  • Zoran Kalinic

    (University of Kragujevac)

  • Samo Bobek

    (University of Maribor)

  • Polona Tominc

    (University of Maribor)

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.

Suggested Citation

  • Simona Sternad Zabukovšek & Zoran Kalinic & Samo Bobek & Polona Tominc, 2019. "SEM–ANN based research of factors’ impact on extended use of ERP systems," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 27(3), pages 703-735, September.
    • Handle: RePEc:spr:cejnor:v:27:y:2019:i:3:d:10.1007_s10100-018-0592-1
    DOI: 10.1007/s10100-018-0592-1
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    References listed on IDEAS

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    1. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    2. Fiona Fui-Hoon Nah & Xin Tan & Soon Hing Teh, 2004. "An Empirical Investigation on End-Users' Acceptance of Enterprise Systems," Information Resources Management Journal (IRMJ), IGI Global, vol. 17(3), pages 32-53, July.
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    6. Faith-Michael E. Uzoka & Richard O. Abiola & Rebecca Nyangeresi, 2008. "Influence of Product and Organizational Constructs on ERP Acquisition Using an Extended Technology Acceptance Model," International Journal of Enterprise Information Systems (IJEIS), IGI Global, vol. 4(2), pages 67-83, April.
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    Cited by:

    1. Andrej Kastrin & Janez Povh & Lidija Zadnik Stirn & Janez Žerovnik, 2021. "Methodologies and applications for resilient global development from the aspect of SDI-SOR special issues of CJOR," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 29(3), pages 773-790, September.
    2. Simona Sternad Zabukovšek & Samo Bobek & Uroš Zabukovšek & Zoran Kalinić & Polona Tominc, 2022. "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, MDPI, vol. 10(9), pages 1-28, April.
    3. Mirjana Pejić Bach & Amir Topalović & Lejla Turulja, 2023. "Data mining usage in Italian SMEs: an integrated SEM-ANN approach," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 31(3), pages 941-973, September.
    4. Amy H. I. Lee & He-Yau Kang & Yu-Ai Liu, 2021. "A Pilot Study on the Satisfaction of Long-Term Care Services in Taiwan," IJERPH, MDPI, vol. 19(1), pages 1-21, December.
    5. Albahri, A.S. & Alnoor, Alhamzah & Zaidan, A.A. & Albahri, O.S. & Hameed, Hamsa & Zaidan, B.B. & Peh, S.S. & Zain, A.B. & Siraj, S.B. & Alamoodi, A.H. & Yass, A.A., 2021. "Based on the multi-assessment model: Towards a new context of combining the artificial neural network and structural equation modelling: A review," Chaos, Solitons & Fractals, Elsevier, vol. 153(P1).
    6. Kamble, Sachin S. & Gunasekaran, Angappa & Kumar, Vikas & Belhadi, Amine & Foropon, Cyril, 2021. "A machine learning based approach for predicting blockchain adoption in supply Chain," Technological Forecasting and Social Change, Elsevier, vol. 163(C).

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