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Exploration of Influential Determinants for the Adoption of Business Intelligence System in the Textile and Apparel Industry

Author

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  • Sumera Ahmad

    (School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru, Johor 81310, Malaysia)

  • Suraya Miskon

    (Department of Information Systems, Azman Hashim International Business School, Universiti Teknologi Malaysia, Johor Bahru, Johor 81310, Malaysia)

  • Rana Alabdan

    (Department of Information Systems, College of Computer and Information Sciences, Majmaah University, Al Majmaah 11952, Saudi Arabia)

  • Iskander Tlili

    (Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
    Faculty of Civil Engineering, Duy Tan University, Da Nang 550000, Vietnam)

Abstract

The textile and apparel industry is prone to digitization with business intelligence systems (BIS) and big data concepts to contribute the global sustainability. BIS, an impactful and leading technology, is being implemented in many industrial sectors but almost 80% of BIS fail to give expected results due to unknown reasons. Although many scholars put effort into finding the influential determinants for the BIS implementation, they neglect the BIS adoption context, especially in the textile and apparel industry. A purposive and proportionate choice of potential determinants in the context of adoption would contribute significantly to the success of BIS. Multi-stage research is employed to identify and prioritize the significant determinants. In the first stage, twenty-two semi-structured in-depth interviews are conducted with seventeen textile and apparel companies. Ten significant determinants emerged after thematic analysis of interview data. The determinants are sustainability, competitive pressure, market trends, compatibility, technology maturity, leadership commitment and support, satisfaction with existing systems, sustainable data quality and integrity, users’ traits, and interpersonal communications that influence the adoption of BIS. In the second stage, the Best Worst Method (BWM) is used to calculate the weights for prioritizing the determinants based on experts’ opinion. These weights are then used to evaluate and rank the determinants. The findings of this research show that the leadership commitment and support, sustainability, users’ traits, and technology maturity, are the top-ranked determinants that influence the practitioners’ choice to adopt the BIS in the textile and apparel industry. The results of this study enable the BIS stakeholders to holistically comprehend the significant determinants that would drive or impede the success of BIS projects in the sustainable textile and apparel industry.

Suggested Citation

  • Sumera Ahmad & Suraya Miskon & Rana Alabdan & Iskander Tlili, 2020. "Exploration of Influential Determinants for the Adoption of Business Intelligence System in the Textile and Apparel Industry," Sustainability, MDPI, vol. 12(18), pages 1-21, September.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:18:p:7674-:d:414814
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    References listed on IDEAS

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    1. Tanko Ishaya, 2012. "Business Intelligence in Telecoms Industry: A Service Oriented Approach," Chapters, in: Daniel Catalan-Matamoros (ed.), Advances in Customer Relationship Management, IntechOpen.
    2. Asma I. Magaireah* & HidayahSulaiman & Nor’ashikin Ali, 2019. "Identifying the Most Critical Factors to Business Intelligence Implementation Success in the Public Sector Organizations," The Journal of Social Sciences Research, Academic Research Publishing Group, vol. 5(2), pages 450-462, 02-2019.
    3. Zied Jemai & Imen Safra & Aida Jebali & Hanen Bouchriha & Asma Ghaffari, 2018. "Capacity planning in textile and apparel supply chains," Post-Print hal-01791992, HAL.
    4. William Yeoh & Aleš Popovič, 2016. "Extending the understanding of critical success factors for implementing business intelligence systems," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(1), pages 134-147, January.
    5. Farzad Firouzi Jahantigh & Arash Habibi & Azam Sarafrazi, 2019. "A conceptual framework for business intelligence critical success factors," International Journal of Business Information Systems, Inderscience Enterprises Ltd, vol. 30(1), pages 109-123.
    6. Navodya Denuwara & Juha Maijala & Marko Hakovirta, 2019. "Sustainability Benefits of RFID Technology in the Apparel Industry," Sustainability, MDPI, vol. 11(22), pages 1-14, November.
    7. Sumera Ahmad & Suraya Miskon & Rana Alabdan & Iskander Tlili, 2020. "Towards Sustainable Textile and Apparel Industry: Exploring the Role of Business Intelligence Systems in the Era of Industry 4.0," Sustainability, MDPI, vol. 12(7), pages 1-23, March.
    8. Lu, Hsi-Peng & Weng, Chien-I, 2018. "Smart manufacturing technology, market maturity analysis and technology roadmap in the computer and electronic product manufacturing industry," Technological Forecasting and Social Change, Elsevier, vol. 133(C), pages 85-94.
    9. Iqbal, Muhammad & Alam Kazmi, Syed Hasnain & Manzoor, Dr. Amir & Rehman Soomrani, Dr. Abdul & Butt, Shujaat Hussain & Shaikh, Khurram Adeel, 2018. "A Study of Big Data for Business Growth in SMEs: Opportunities & Challenges," MPRA Paper 96034, University Library of Munich, Germany.
    10. Acheampong Owusu & Abbas Ghanbari-Baghestan & Abdolhossein Kalantari, 2017. "Investigating the Factors Affecting Business Intelligence Systems Adoption: A Case Study of Private Universities in Malaysia," International Journal of Technology Diffusion (IJTD), IGI Global, vol. 8(2), pages 1-25, April.
    11. Choi, Tsan-Ming, 2018. "Launching the right new product among multiple product candidates in fashion: Optimal choice and coordination with risk consideration," International Journal of Production Economics, Elsevier, vol. 202(C), pages 162-171.
    12. Magnus Boström & Michele Micheletti, 2016. "Introducing the Sustainability Challenge of Textiles and Clothing," Journal of Consumer Policy, Springer, vol. 39(4), pages 367-375, December.
    13. Rezaei, Jafar, 2015. "Best-worst multi-criteria decision-making method," Omega, Elsevier, vol. 53(C), pages 49-57.
    14. Catherine Le Roux & Marius Pretorius, 2016. "Navigating Sustainability Embeddedness in Management Decision-Making," Sustainability, MDPI, vol. 8(5), pages 1-23, May.
    15. Pamela S. Norum, 2017. "Towards Sustainable Clothing Disposition: Exploring the Consumer Choice to Use Trash as a Disposal Option," Sustainability, MDPI, vol. 9(7), pages 1-14, July.
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    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. Sher Jahan Khan & Saeed Badghish & Puneet Kaur & Rajat Sharma & Amandeep Dhir, 2023. "What motivates the purchasing of green apparel products? A systematic review and future research agenda," Business Strategy and the Environment, Wiley Blackwell, vol. 32(7), pages 4183-4201, November.

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