Technology, Organization and Environment as Strategic Factors of Big Data Analytics Readiness and Acquisition Intention to Adopt Big Data Analytics in Malaysian Libraries

Authors

  • Mohamad Salbihan Salman Jabatan Perpustakaan, Universiti Sains Islam Malaysia, Bandar Baru Nilai, 71800, Negeri Sembilan, Malaysia
  • Mad Khir Johari Ahdullah Sani College of Computing, Informatics and Mathematics, Al-Khawarizmi Building, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
  • Noor Zaidi Sahid College of Computing, Informatics and Mathematics, Al-Khawarizmi Building, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia

DOI:

https://doi.org/10.21834/e-bpj.v9iSI18.5489

Keywords:

Big Data Analytics Readiness (BDAR), TOE Factors Readiness, Library Science , Data Analytics

Abstract

Libraries can learn BD analytics. The report examines Malaysian library big data analytics for new research. Complete subject debate recordings enhance comprehension. Adopt TOE-BDA. Multiple research gaps led this study. Malaysian libraries use new BDA. Not predictive, most data analysis is descriptive. Libraries and information science rarely use big data analytics (BDA). Initial measurement analysis revealed framework concerns. TOE, BDAR, and big data analytics acquisition propensity were linked in structural model analysis. This study made three empirical, theoretical, and practical contributions. The study empirically tests Malaysian libraries' relationship. Future researchers may explore TOE, BDA, and AITABDA using the paradigm. Measure TOE/BDAR.

References

Adrian, C., Rusli Abdullah, Atan, R., & Yusmadi Yah Jusoh. (2017). Factors influencing to the implementation success of big data analytics: A systematic literature review. 2017 International Conference on Research and Innovation in Information Systems (ICRIIS), 1–6. DOI: https://doi.org/10.1109/ICRIIS.2017.8002536

Ahmad, N. (2018). Annual Report of National Library of Malaysia (Vol. 1986, Issue Act 331). www.pnm.gov.my

Al-Barashdi, H., & Al-Karousi, R. (2019). Big Data in academic libraries: literature review and future research directions. Journal of Information Studies & Technology (JIS&T), 2018(2). https://doi.org/10.5339/jist.2018.13 DOI: https://doi.org/10.5339/jist.2018.13

Ali, A., Qadir, J., ur Rasool, R., Sathiaseelan, A., Zwitter, A., Crowcroft, J., Rasool, R. ur, Sathiaseelan, A., & Zwitter, A. (2016). Big Data For Development: Applications and Techniques. Big Data Analytics, 1(1), 2. https://doi.org/10.1186/s41044-016-0002-4 DOI: https://doi.org/10.1186/s41044-016-0002-4

Blummer, B., & Kenton, J. M. (2018). Big Data and Libraries: Identifying Themes in the Literature. Internet Reference Services Quarterly, 23(1–2), 15–40. https://doi.org/10.1080/10875301.2018.1524337 DOI: https://doi.org/10.1080/10875301.2018.1524337

Byrne, B. M. (2010). Structural equation modelling with AMOS: Basic concepts, applications and programming (2nd Editio). Taylor and Francis Group.

Campbell, D. G., & Cowan, S. R. (2016). The Paradox of Privacy: Revisiting a Core Library Value in an Age of Big Data and Linked Data. Library Trends, 64(3), 492–511. https://doi.org/10.1353/lib.2016.0006 DOI: https://doi.org/10.1353/lib.2016.0006

Chang, C. C. (2018). Hakka genealogical migration analysis enhancement using big data on library services. Library Hi Tech, 36(3), 426–442. https://doi.org/10.1108/LHT-08-2017-0172 DOI: https://doi.org/10.1108/LHT-08-2017-0172

Chen, H. L., Doty, P., Mollman, C., Niu, X., Yu, J. C., & Zhang, T. (2015). Library assessment and data analytics in the big data era: Practice and policies. Proceedings of the Association for Information Science and Technology, 52(1), 1–4. https://doi.org/10.1002/pra2.2015.14505201002 DOI: https://doi.org/10.1002/pra2.2015.14505201002

Evolution, T., & Osman, R. R. (2018). The Evolution of Data , from data to big data: are we ready for the big data technology in the library community ? March, 1–8. https://bigdatainarabic.wordpress.com/

Fornell, C. C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3150980 DOI: https://doi.org/10.1177/002224378101800104

Frederick, D. E. (2017). Library data: what is it and what changes do libraries need to make? (the Data Deluge Column). Library Hi Tech News, 34(8), 1–7. https://doi.org/10.1108/LHTN-06-2017-0044 DOI: https://doi.org/10.1108/LHTN-06-2017-0044

Gamage, P. (2016). New development: Leveraging ‘big data’ analytics in the public sector. Public Money and Management, 36(5), 385–390. https://doi.org/10.1080/09540962.2016.1194087 DOI: https://doi.org/10.1080/09540962.2016.1194087

Gefen, D., & Straub, D. (2005). A practical guide to factorial validity using PLS-Graph: Tutorial and annotated example. Communications of the Association for Information Systems, 16(1), 5. DOI: https://doi.org/10.17705/1CAIS.01605

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modelling (PLS-SEM) (2nd Editio). SAGE Publication.

He, B., & Zhang, H. (2016). Research on Personalized Information Recommendation of Library. 2016 IEEE International Conference of Online Analysis and Computing Science (ICOACS), 289–292. https://doi.org/10.2991/icmmct-16.2016.245 DOI: https://doi.org/10.1109/ICOACS.2016.7563099

Heidron, P. B. (2011). The emerging role of libraries in data curation and e-science. Journal of Library Administration, 51(7–8), 662–672.

https://doi.org/10.1080/01930826.2011.601269 DOI: https://doi.org/10.1080/01930826.2011.601269

Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: updated guidelines. Industrial Management & Data Systems. DOI: https://doi.org/10.1108/IMDS-09-2015-0382

Jantti, M., & Heath, J. (2016). What role for libraries in learning analytics? Performance Measurement and Metrics, 17(2), 203–210. https://doi.org/10.1108/PMM-04-2016-0020 DOI: https://doi.org/10.1108/PMM-04-2016-0020

Kalema, B. M., & Mokgadi, M. (2017). Developing countries organizations’ readiness for Big Data analytics. Problems and Perspectives in Management, 15(1), 260–270. https://doi.org/10.21511/ppm.15(1-1).2017.13 DOI: https://doi.org/10.21511/ppm.15(1-1).2017.13

Karno, M. R. bin. (2022). Big Data Analytic in Libraries. https://doi.org/https://www.facebook.com/watch/?ref=search&v=344352227277084&external_log_id=14d6999e-e7da-414d-b2f5-1fee71dcefa9&q=Big%20Data%20in%20Malaysia%20Library

Katal, A., Wazid, M., & Goudar, R. H. (2013). Big data: Issues, challenges, tools and Good practices. 2013 6th International Conference on Contemporary Computing, IC3 2013, September 2013, 404–409. https://doi.org/10.1109/IC3.2013.6612229 DOI: https://doi.org/10.1109/IC3.2013.6612229

Kiconco, C. (2018). Implications of Big Data on the Role of Libraries in the Realization of Sustainable Development Goals (SDGs). In SCECSAL Conference. http://www.un.org/sustainabledevelopment/sustainable-development-

Kim, Y., & Cooke, L. (2017). Big data analysis of public library operations and services by using the Chernoff face method. Journal of Documentation, 73(3), 466–480. https://doi.org/10.1108/JD-08-2016-0098 DOI: https://doi.org/10.1108/JD-08-2016-0098

Klievink, B., Romijn, B. J., Cunningham, S., & de Bruijn, H. (2017). Big data in the public sector: Uncertainties and readiness. Information Systems Frontiers, 19(2), 267–283. https://doi.org/10.1007/s10796-016-9686-2 DOI: https://doi.org/10.1007/s10796-016-9686-2

Kline, R. B. (2016). Principles and practice of structural equation modeling, 4th ed. In Principles and practice of structural equation modeling, 4th ed. Guilford Press.

Lalic, B., & Marjanovic, U. (2017). Organizational Readiness/Preparedness. In E-Business Issues, Challenges and Opportunities for SMEs (Issue May, pp. 101–116). IGI Global. https://doi.org/10.4018/978-1-61692-880-3.ch007 DOI: https://doi.org/10.4018/978-1-61692-880-3.ch007

Lavoie, J. R., & Daim, T. U. (2018). Technology readiness levels enhancing R&D management and technology transfer capabilities: insights from a public utility in Northwest USA. International Journal of Transitions and Innovation Systems, 6(1), 48. https://doi.org/10.1504/ijtis.2018.10011690 DOI: https://doi.org/10.1504/IJTIS.2018.10011690

Li, J., Lu, M., Dou, G., & Wang, S. (2017). Big data application framework and its feasibility analysis in library. Information Discovery and Delivery, 45(4), 161–168. https://doi.org/10.1108/IDD-03-2017-0024 DOI: https://doi.org/10.1108/IDD-03-2017-0024

Motau, M. (2016). Assessment of Big Data Analytics Readiness in South African Governmental Parastatals. May. https://pdfs.semanticscholar.org/dc6d/4ac0db1733402797d0500daaf0b0eb936f4c.pdf

Motau, M., & Kalema, B. M. (2016). Big Data Analytics readiness: A South African public sector perspective. 2016 IEEE International Conference on Emerging Technologies and Innovative Business Practices for the Transformation of Societies, EmergiTech 2016, September, 265–271. https://doi.org/10.1109/EmergiTech.2016.7737350 DOI: https://doi.org/10.1109/EmergiTech.2016.7737350

Olendorf, R., & Wang, Y. (2018). Big data in libraries. In Big Data and Visual Analytics (pp. 191–202). Springer International Publishing. https://doi.org/10.1007/978-3-319-63917-8_11 DOI: https://doi.org/10.1007/978-3-319-63917-8_11

Parasuraman, A. (2000). Technology Readiness Index (Tri). Journal of Service Research, 2(4), 307–320. https://doi.org/10.1177/109467050024001 DOI: https://doi.org/10.1177/109467050024001

Park, N., Rhoads, M., Hou, J., & Lee, K. M. (2014). Understanding the acceptance of teleconferencing systems among employees: An extension of the technology acceptance model. Computers in Human Behavior, 39, 118–127. https://doi.org/10.1016/j.chb.2014.05.048 DOI: https://doi.org/10.1016/j.chb.2014.05.048

Putrawan, N. A. (2015). Relevansi Big Data dan Ilmu Perpustakaan: Sebuah Pendekatan Baru. Diakses Dari https://www.linkedin.com/pulse/relevansi-big-data-dan-ilmu-perpustakaan-sebuah-baru-a-putrawan

Rajasekar, A. (2014). The Librarian & the Big Data: Bridging the Gap. 2014 E-Science Symposium. https://drive.google.com/file/d/0BwVuBK4FRW2zcFI3eVNpYngyMmM/edit?usp=sharing

Ramayah, T., Cheah, J., Chuah, F., Ting, H., & Memon, M. A. (2018). Partial least squares structural equation modeling (PLS-SEM) using smartPLS 3.0. Kuala Lumpur: Pearson.

Rani, B. R. (2016). Big Data and Academic Libraries. International Conference on Big Data and Knowledge Discovery. Indian Statistical Institute. http://km.ptar.uitm.edu.my/documents/10180/1099975/Big+data+and+Academic+Libraries.pdf/74bbc20c-cfc0-4cae-ba4b-ead791b8f406

Romijn, B. (2014). Using Big Data in the Public Sector. Public Policy and Administration, 32(4), 1–16. https://doi.org/10.1177/0952076716687355 DOI: https://doi.org/10.1177/0952076716687355

Simović, A. (2018). A Big Data smart library recommender system for an educational institution. Library Hi Tech, 36(3), 498–523. https://doi.org/10.1108/LHT-06-2017-0131 DOI: https://doi.org/10.1108/LHT-06-2017-0131

Taber, K. S. (2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48(6), 1273–1296. DOI: https://doi.org/10.1007/s11165-016-9602-2

Tornatzky, L. G., Fleischer, M., & Chakrabarti, A. K. (1990). The processes of technological innovation. Issues in organization and management series. Lexington Books, 10, 2013.

Wang, C., Xu, S., Chen, L., & Chen, X. (2016). Exposing library data with big data technology: A review. 2016 IEEE/ACIS 15th International Conference on Computer and Information Science, ICIS 2016 - Proceedings, 1–6. https://doi.org/10.1109/ICIS.2016.7550937 DOI: https://doi.org/10.1109/ICIS.2016.7550937

Weiner, B. J. (2009). A theory of organizational readiness for change. Implementation Science, 4(1), 1–9. https://doi.org/10.1186/1748-5908-4-67 DOI: https://doi.org/10.1186/1748-5908-4-67

Zetterlund, B. (2016). Big Data and libraries: getting the most from your library data. Axiell Group. https://www.axiell.com/uk/blog-post/big-data-and-libraries-getting-the-most-from-your-library-data-2/

Zhan, M., & Widén, G. (2019). Understanding big data in librarianship. Journal of Librarianship and Information Science, 51(2), 561–576. https://doi.org/10.1177/0961000617742451 DOI: https://doi.org/10.1177/0961000617742451

Downloads

Published

2024-01-18

How to Cite

Salman, M. S., Ahdullah Sani, M. K. J., & Sahid, N. Z. (2024). Technology, Organization and Environment as Strategic Factors of Big Data Analytics Readiness and Acquisition Intention to Adopt Big Data Analytics in Malaysian Libraries . Environment-Behaviour Proceedings Journal, 9(SI18), 233–246. https://doi.org/10.21834/e-bpj.v9iSI18.5489