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Exploring business intelligence and its depth of maturity in Thai SMEs

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  • Waranpong Boonsiritomachai
  • G. Michael McGrath
  • Stephen Burgess

Abstract

Increased complexities in making effective and timely business decisions in highly competitive markets have driven organisations worldwide to adopt business intelligence (BI) technologies. Large enterprises have reached a mature stage of BI adoption while small- and medium-sized enterprises (SMEs) still lag behind—despite organisations of all sizes can benefit from the use of this technology to aggregate, manage and analyse data for assisting decision-making that enhances profitability. This study proposes a BI maturity model for SMEs that distinguishes different levels of BI maturity and identifies the factors that currently impact their levels of BI adoption. The proposed model is empirically tested using survey data from 427 SMEs and analysed using multinomial logistic regression. Results indicate that BI adoption in Thai SMEs is still at an initial stage, with the majority being classified in the lowest level of BI maturity. Significant factors that impact the levels of BI adoption are relative advantage, complexity, organisational resource availability, competitive pressure, vendor selection and owner-managers’ innovativeness. Results from the study can be used by government agencies to develop strategies to increase the rate of BI adoption among SMEs. IT vendors also can use the results to determine which SMEs they should target.

Suggested Citation

  • Waranpong Boonsiritomachai & G. Michael McGrath & Stephen Burgess, 2016. "Exploring business intelligence and its depth of maturity in Thai SMEs," Cogent Business & Management, Taylor & Francis Journals, vol. 3(1), pages 1220663-122, December.
    • Handle: RePEc:taf:oabmxx:v:3:y:2016:i:1:p:1220663
    DOI: 10.1080/23311975.2016.1220663
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    Cited by:

    1. Ana-Marija Stjepić & Mirjana Pejić Bach & Vesna Bosilj Vukšić, 2021. "Exploring Risks in the Adoption of Business Intelligence in SMEs Using the TOE Framework," JRFM, MDPI, vol. 14(2), pages 1-18, February.
    2. 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.
    3. Peetu Virkkala & Martti Saarela & Kai Hänninen & Jaakko Kujala & Anna-Mari Simunaniemi, 2020. "Business Maturity Models for Small and Medium-Sized Enterprises: A Systematic Literature Review," Management, University of Primorska, Faculty of Management Koper, vol. 15(2), pages 137-155.

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