Factors Influencing Intent to Adopt Big Data Analytics in Malaysian Government Agencies

  • Mad Khir Johari Abdullah Sani Faculty of Information Management, UiTM Puncak Perdana Campus
  • Muhamad Khairulnizam Zaini Faculty of Information Management, UiTM Puncak Perdana Campus
  • Noor Zaidi Sahid Faculty of Information Management, UiTM Puncak Perdana Campus
  • Norshila Shaifuddin Faculty of Information Management, UiTM Puncak Perdana Campus
  • Tamara Adriani Salim Faculty of Humanities, Department of Library and Information Science, Universitas Indonesia
  • Noorazah Md. Noor Malaysian Administrative Modernisation and Management Planning Unit (MAMPU), Prime Minister's Department, Putrajaya
Keywords: Big Data Analytics, Public agencies, Library and Information Management, Adoption Model


In Big Data Analytics (BDA), many government agencies directly raised their ICT expenditure in their effort to understand the attitude of the users towards new technologies. This research is intended to analyze factors affecting IT practitioners’ behavioral intentions in adopting (BDA) using a combination of multiple technology acceptance models. The synergistic three IS theory strengths: (1) Task Technology Fit (TTF), (2) Unified Technology Acceptance and Utilization Theory (UTAUT), and the (3) Initial Trust Model (ITM). The concept was validated in Malaysian government agencies, one of the highly dependent BDA promoters and initiators. 186 respondents in the Information Management departments of public agencies were recruited as part of the rigorous methodology to gather rich data. Partial least squares were analyzed by the structural models (PLS). The two key factors determine behavioral intention to adopt BDA in government agencies. Firstly, the assumption that the technology is going to produce great results raises the expectation of performance. Technological fit was the second determinant factor. Initial trust, on the other hand, was found to be adversely related to the BDA intention. Implicitly, the proposed model would be useful to IT officers in public agencies in making investment choices and designing non-adopter-friendly outreach strategies because they have more barriers to acceptance than adopters and lead adopters in the reward ladder. All public agencies will benefit from the findings of this study in gaining awareness of BDA application and fostering psychological empowerment of employees to adopt this revolutionary approach. The article outlines how dynamic TTF, UTAUT and ITM are for researchers to integrate in their emerging decision support framework for the study of new technology adoption.


Abbasi, A., Sarker, S., & Chiang, R. H. L. (2016). Big Data Research in Information Systems: Toward an Inclusive Research Agenda. Journal of the Association for Information Systems, 17(2), 3. https://doi.org/10.17705/1jais.00423

Abbas Naqvi, M. H., Jiang, Y., Miao, M., & Naqvi, M. H. (2020). The effect of social influence, trust, and entertainment value on social media use: Evidence from Pakistan. Cogent Business & Management, 7(1), 1723825. https://doi.org/10.1080/23311975.2020.1723825

Abouelmehdi, K., Beni-Hessane, A., & Khaloufi, H. (2018). Big healthcare data: preserving security and privacy. Journal of Big Data, 5(1), 1-18. https://doi.org/10.1186/s40537-017-0110-7

Adair, B. (2019). Features of Big Data Analytics and Requirements. SelectHub. https://www.selecthub.com/big-data-analytics/big-data-analytics-requirements/

Alalwan, A. A., Dwivedi, Y. K., & Rana, N. P. (2017). Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust. International Journal of Information Management, 37(3), 99-110. https://doi.org/10.1016/j.ijinfomgt.2017.01.002

Alarcon, G. M., Lyons, J. B., & Christensen, J. C. (2016). The effect of propensity to trust and familiarity on perceptions of trustworthiness over time. Personality and Individual Differences, 94, 309-315. https://doi.org/10.1016/j.paid.2016.01.031

Alcácera, V., & Cruz-Machado, V. (2019). Scanning the Industry 4.0: A Literature Review on Technologies for Manufacturing Systems. Engineering Science and Technology, an International Journal, 22(3), 899-919. https://doi.org/10.1016/j.jestch.2019.01.006

Al-Shiakhli, S. (2019). Big data analytics: a literature review perspective (Unpublished Master's Thesis). The Luleå University of Technology.

Ballantyne, A., & Stewart, C. (2019). Big data and public-private partnerships in healthcare and research. Asian Bioethics Review, 11(3), 315-326. https://doi.org/10.1007/s41649-019-00100-7

Bahari, B. (2016, September 19). Malaysia on Track to Become Southeast Asian Hub on Big Data & Analytics. New Straits Times. http://www.nst.com.my/news/2016/09/174270/malaysia-track-become-southeast-asian-hub-bigdata-analytics

Behl, A., Dutta, P., Lessmann, S., Dwivedi, Y. K., & Kar, S. (2019). A conceptual framework for the adoption of big data analytics by e-commerce startups: a case-based approach. Information Systems and e-Business Management, 17(2), 285-318. https://doi.org/10.1007/s10257-019-00452-5

Bere, A. (2018). Applying an extended task-technology fit for establishing determinants of mobile learning: an instant messaging initiative. Journal of Information Systems Education, 29(4), 239-252.

Bibri, S. E., & Krogstie, J. (2017). The core enabling technologies of big data analytics and context-aware computing for smart sustainable cities: a review and synthesis. Journal of Big Data, 4(1), 1-50. https://doi.org/10.1186/s40537-017-0091-6

Bolonne, H., & Wijewardene, P. (2020). Critical Factors Affecting the Intention to Adopt Big Data Analytics in Apparel Sector, Sri Lanka. https://doi.org/10.14569/IJACSA.2020.0110620

Bozan, K., Parker, K., & Davey, B. (2016, January 5-8). A closer look at the social influence construct in the UTAUT Model: An institutional theory based approach to investigate health IT adoption patterns of the elderly. In the Proceedings of 2016 49th Hawaii International Conference on System Sciences (HICSS) (pp. 3105-3114). USA, Koala. https://doi.org/10.1109/HICSS.2016.391

Brock, V., & Khan, H. U. (2017). Big data analytics: does organizational factor matters impact technology acceptance?. Journal of Big Data, 4(1), 1-28. https://doi.org/10.1186/s40537-017-0081-8

Brünink, L. (2016). Cross-functional Big Data integration: Applying the UTAUT model. University of Twente (The Netherlands).

Cabrera-Sánchez, J. P., & Villarejo-Ramos, Á. F. (2019). Fatores que afetam a adoção de análises de Big Data em empresas. Revista de Administração de Empresas, 59, 415-429. https://doi.org/10.1590/s0034-759020190607

Cao, Q., & Niu, X. (2019). Integrating context-awareness and UTAUT to explain Alipay user adoption. International Journal of Industrial Ergonomics, 69, 9-13. https://doi.org/10.1016/j.ergon.2018.09.004

Chao, C. M. (2019). Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT model. Frontiers in psychology, 10, 1652. https://doi.org/10.3389/fpsyg.2019.01652

D'Ambra, J., Wilson, C. S., & Akter, S. (2013). Application of the task‐technology fit model to structure and evaluate the adoption of E‐books by Academics. Journal of the American society for information science and technology, 64(1), 48-64. https://doi.org/10.1002/asi.22757

Debussche, J., César, J. & Moortel, I. D. (2019, April). Big Data & Issues & Opportunities: Discrimination. Bird & Bird. https://www.twobirds.com/en/news/articles/2019/global/big-data-and-issues-and-opportunities-discrimination

Desjardins, J. (2017). The 100 websites that rule the internet. Visual Capitalist. www.visualcapitalist.com

Elragal, A., & Klischewski, R. (2017). Theory-driven or process-driven prediction? Epistemological challenges of big data analytics. Journal of Big Data, 4(1), 1-20. https://doi.org/10.1186/s40537-017-0079-2

Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big Data consumer analytics and the transformation of marketing. Journal of business research, 69(2), 897-904. https://doi.org/10.1016/j.jbusres.2015.07.001

Falahat, M., Lee, Y. Y., Foo, Y. C., & Chia, C. E. (2019). A model for consumer trust in e-commerce. Asian Academy of Management Journal, 24(2), 93-109. https://doi.org/10.21315/aamj2019.24.s2.7

Feng, M., Zheng, J., Ren, J., Hussain, A., Li, X., Xi, Y., & Liu, Q. (2019). Big data analytics and mining for effective visualization and trends forecasting of crime data. IEEE Access, 7, 106111-106123. https://doi.org/10.1109/ACCESS.2019.2930410

Fornell, 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/10.2307/3151312

Galetto, M. (2018, March 8). What is Business Analytics? NGDATA. https://www.ngdata.com/ what-is-business-analytics/

Gao, L., & Waechter, K. A. (2017). Examining the role of initial trust in user adoption of mobile payment services: an empirical investigation. Information Systems Frontiers, 19(3), 525-548. https://doi.org/10.1007/s10796-015-9611-0

Giest, S. (2017). Big Data for Policymaking: Fad or Fasttrack? Policy Sciences, 50(3), 367-382. https://doi.org/10.1007/s11077-017-9293-1

Gong, Z., Han, Z., Li, X., Yu, C., & Reinhardt, J. D. (2019). Factors influencing the adoption of online health consultation services: the role of subjective norm, trust, perceived benefit, and offline habit. Frontiers in public health, 7, 286. https://doi.org/10.3389/fpubh.2019.00286

Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS quarterly, 213-236. https://doi.org/10.2307/249689

Günther, W. A., Mehrizi, M. H. R., Huysman, M., & Feldberg, F. (2017). Debating big data: A literature review on realizing value from big data. The Journal of Strategic Information Systems, 26(3), 191-209. https://doi.org/10.1016/j.jsis.2017.07.003

Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (6th ed). Upper Saddle River, NJ: Pearson Prentice Hall.

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis (7th ed). Prentice Hall, Upper Saddle River, New Jersey.

Hair, J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial Least Squares Structural Equation Modeling (PLS-SEM): An Emerging Tool for Business Research. European Business Review, 26, 106-121. https://doi.org/10.1108/EBR-10-2013-0128

Hasan, M. M., Popp, J., & Oláh, J. (2020). Current landscape and influence of big data on finance. Journal of Big Data, 7(1), 1-17. https://doi.org/10.1186/s40537-020-00291-z

Heidari, H., Moosakhani, M., Alborzi, M., Divandari, A., & Radfar, R. (2018). Investigating the Effective Factors on the Customers' Behavioral propensity to Use Blockchain Capabilities as financial instrument. Journal of Money and Economy, 13(2), 195-219. http://jme.mbri.ac.ir/article-1-423-en.html

Ingrams, A. (2019). Public values in the age of big data: A public information perspective. Policy & Internet, 11(2), 128-148. https://doi.org/10.1002/poi3.193

International Telecommunication Union. (ITU) (2018). Big data, machine learning, consumer protection and privacy. Report of Trust Workstream. Geneva, Switzerland. https://www.itu.int/en/ITU-T/extcoop/figisymposium/Documents/FIGI_SIT_Techinical%20report_Big%20data%2C%20Machine%20learning%2C%20Consumer%20protection%20and%20Privacy_f.pdf

Jibril, A. B., Kwarteng, M. A., Chovancova, M., & Pilik, M. (2019). The impact of social media on consumer-brand loyalty: A mediating role of online based-brand community. Cogent Business & Management, 6(1), 1-19. https://doi.org/10.1080/23311975.2019.1673640

Kaabachi, S., Mrad, S. B., & O'Leary, B. (2019). Consumer's initial trust formation in IOB's acceptance: The role of social influence and perceived compatibility. International Journal of Bank Marketing, 37(2), 507-530. https://doi.org/10.1108/IJBM-12-2017-0270

Kennedy, H., Moss, G., Birchall, C., & Moshonas, S. (2015). Balancing the potential and problems of digital methods through action research: Methodological reflections. Information, Communication & Society, 18(2), 172-186. https://doi.org/10.1080/1369118X.2014.946434

Khan, N., Yaqoob, I., Hashem, I. A., Inayat, Z., Ali, W. K., Alam, M., Shiraz, M., & Gani, A. (2014). Big data: survey, technologies, opportunities, and challenges. The Scientific World Journal, 712826. https://doi.org/10.1155/2014/712826

Kim, K. K., & Prabhakar, B. (2004). Initial trust and the adoption of B2C e-commerce: The case of internet banking. ACM SIGMIS Database: the DATABASE for Advances in Information Systems, 35(2), 50-64. https://doi.org/10.1145/1007965.1007970

Kim, E. S., Choi, Y., & Byun, J. (2020). Big Data Analytics in Government: Improving Decision Making for R&D Investment in Korean SMEs. Sustainability, 12(1), 202. https://doi.org/10.3390/su12010202

Klopping, I. M., & McKinney, E. (2004). Extending the technology acceptance model and the task-technology fit model to consumer e-commerce. Information Technology, Learning, and Performance Journal, 22(1), 35-48.

Koufaris, M., & Hampton-Sosa, W. (2004). The development of initial trust in an online company by new customers. Information & management, 41(3), 377-397. https://doi.org/10.1016/j.im.2003.08.004

KPMG (2016). Building trust in analytics. https://home.kpmg.com/xx/en/home/insights/2016/10/building-trust-in-analytics.html

Kubina, M., Varmus, M., & Kubinova, I. (2015). Use of big data for competitive advantage of company. Procedia Economics and Finance, 26, 561-565. https://doi.org/10.1016/S2212-5671(15)00955-7

Kurt, Ö. E., & Tingöy, Ö. (2017). The acceptance and use of a virtual learning environment in higher education: an empirical study in Turkey, and the UK. International Journal of Educational Technology in Higher Education, 14(1), 1-15. https://doi.org/10.1186/s41239-017-0064-z

Lai, Y., Sun, H., & Ren, J. (2018). Understanding the determinants of big data analytics (BDA) adoption in logistics and supply chain management: An empirical investigation. International Journal of Logistics Management, 29(2), 676-703. https://doi.org/10.1108/IJLM-06-2017-0153

Latif, Z., Tunio, M. Z., Pathan, Z. H., Jianqiu, Z., Ximei, L., & Sadozai, S. K. (2018, March). A review of policies concerning development of big data industry in Pakistan: Subtitle: Development of big data industry in Pakistan. In Proceedings of 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) (pp. 1-5). IEEE. https://doi.org/10.1109/ICOMET.2018.8346315

Laugesen, J., & Hassanein, K. (2017). Adoption of personal health records by chronic disease patients: A research model and an empirical study. Computers in Human Behavior, 66, 256-272. https://doi.org/10.1016/j.chb.2016.09.054

Lee, C. -C., Cheng, H. K., & Cheng, H.-H. (2007). An empirical study of mobile commerce in insurance industry: Task-technology fit and individual differences. Decision Support Systems, 43(1), 95-110. https://doi.org/10.1016/j.dss.2005.05.008

Lee, J.-H., & Song, C.-H. (2013). Effects of trust and perceived risk on user acceptance of a new technology service. Social Behaviour and. Personality an International Journal, 41(4), 587-597. https://doi.org/10.2224/sbp.2013.41.4.587

Lehrer, C., Wieneke, A., Vom Brocke, J. A. N., Jung, R., & Seidel, S. (2018). How big data analytics enables service innovation: materiality, affordance, and the individualization of service. Journal of Management Information Systems, 35(2), 424-460. https://doi.org/10.1080/07421222.2018.1451953

Lewicki, R. J., & Bunker, B. B. (1996). Developing and maintaining trust in working relationships. In R. M. Kramer & T. R. Tyler (Eds.), Trust in organizations. Frontiers of theory and research. Thousand Oaks: Sage Publications.

Liebenberg, J., Benade, T., & Ellis, S. (2018). Acceptance of ICT: Applicability of the unified theory of acceptance and use of technology (UTAUT) to South African Students. The African Journal of Information Systems, 10(3), 1.

Lin, W. R., Wang, Y. H., & Hung, Y. M. (2020). Analyzing the factors influencing adoption intention of internet banking: Applying DEMATEL-ANP-SEM approach. Plos one, 15(2), e0227852. https://doi.org/10.1371/journal.pone.0227852

Löfgren, K., & Webster, C. W. R. (2020). The value of Big Data in government: The case of 'smart cities.' Big Data & Society, 7(1), 1-14. https://doi.org/10.1177/2053951720912775

Longo, J., & McNutt, K. (2018). From Policy Analysis to Policy Analytics. Policy Analysis in Canada, 367-389. https://doi.org/10.1332/policypress/9781447334910.003.0018

Mahfuz, M. A., Khanam, L., & Hu, W. (2016, September). The influence of culture on m-banking technology adoption: An integrative approach of UTAUT2 and ITM. In 2016 Portland International Conference on Management of Engineering and Technology (PICMET) (pp. 824-835). IEEE. https://doi.org/10.1109/PICMET.2016.7806814

MAMPU (2014). National Big Data Analytics Initiative. http://tinyurl.com/z3ffgdt.

MAMPU (2016). Analitis Data Raya Sektor Awam (DRSA). http://www.mampu.gov.my/ms/data raya-sektor-awam-drsa

Maroufkhani, P., Wan Ismail, W.K. & Ghobakhloo, M. (2020). Big data analytics adoption model for small and medium enterprises. Journal of Science and Technology Policy Management, 11(4), 483-513. https://doi.org/10.1108/JSTPM-02-2020-0018

Mayer, R. C., & Davis, J. H. (1999). The effect of the performance appraisal system on trust for management: A field quasi-experiment. Journal of Applied Psychology, 84(1), 123-136. https://doi.org/10.1037/0021-9010.84.1.123

McCole, P., Ramsey, E., Kincaid, A., Fang, Y., & Li, H. (2019). The role of structural assurance on previous satisfaction, trust and continuance intention: The case of online betting. Information Technology and People, 32(4), 781-801. https://doi.org/10.1108/ITP-08-2017-0274

McFarland, D. A., & McFarland, H. R. (2015). Big Data and the danger of being precisely inaccurate. Big Data and Society, 2(2), 1-4. https://doi.org/10.1177/2053951715602495

McKnight, D. H., & Chervany, N. L. (2006). Reflections on an initial trust-building model. In Handbook of Trust Research (pp. 29-51). Edward Elgar Publishing. https://doi.org/10.4337/9781847202819.00008

Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). Big data analytics and firm performance: Findings from a mixed-method approach. Journal of Business Research, 98, 261-276. https://doi.org/10.1016/j.jbusres.2019.01.044

Mikalef, P., Pappas, I. O., Krogstie, J., & Giannakos, M. (2018). Big data analytics capabilities: A systematic literature review and research agenda. Information Systems and e-Business Management, 16(3), 547-578. https://doi.org/10.1007/s10257-017-0362-y

Mithas, S., Tafti, A., Bardhan, I., & Goh, J. M. (2012). Information technology and firm profitability: mechanisms and empirical evidence. Mis Quarterly, 36(1), 205-224. https://doi.org/10.2307/41410414

Moore, G.C., & Benbasat, I. (1991). Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation. Information Systems Research, 2(3), 192-222. https://doi.org/10.1287/isre.2.3.192

Munné, R. (2016). Big data in the public sector. In New Horizons for a Data-Driven Economy (pp. 195-208). Springer, Cham. https://doi.org/10.1007/978-3-319-21569-3_11

Nantais, J. (2019). Data Science for Government Performance. Towards Data Science. https://towardsdatascience.com/@joelnantais

Ngampornchai, A., & Adams, J. (2016). Students' acceptance and readiness for E-learning in Northeastern Thailand. International Journal of Educational Technology in Higher Education, 13(1), 1-13. https://doi.org/10.1186/s41239-016-0034-x

Khidzir, N. Z., Wan Abdul Ghani, W. S. D., Tan, T. G. (2017). Cloud-Based Mobile-Retail Application for Textile Cyberpreneurs: Task-Technology Fit Perspective Analysis. In Proceedings of the International Conference on High Performance Compilation, Computing and Communications (HP3C-2017). Association for Computing Machinery, New York, NY, USA, 65-70. https://doi.org/10.1145/3069593.3069609

O'Connor, Y., Andreev, P., & O'Reilly, P. (2020). MHealth and perceived quality of care delivery: a conceptual model and validation. BMC medical informatics and decision making, 20(1),1-13. https://doi.org/10.1186/s12911-020-1049-8

OECD (2018). Digital Government Review of Colombia: Towards a Citizen-Driven Public Sector, OECD Digital Government Studies, OECD Publishing, Paris. https://doi.org/10.1787/9789264291867-en

OECD (2017). OECD Digital Economy Outlook 2017, OECD Publishing, Paris, https://doi.org/10.1787/9789264276284-en

Okello, D. R., & Gilson, L. (2015). Exploring the influence of trust relationships on motivation in the health sector: a systematic review. Human resources for health, 13(1), 1-18. https://doi.org/10.1186/s12960-015-0007-5

Oliveira, T., Faria, M., Thomas, M.A. and Popovič, A. (2014). Extending the understanding of mobile banking adoption: When UTAUT meets TTF and ITM. International Journal of Information Management, 34(5), 689-703. https://doi.org/10.1016/j.ijinfomgt.2014.06.004

Omotayo, F. O., & Haliru, A. (2020). Perception of task-technology fit of digital library among undergraduates in selected universities in Nigeria. The Journal of Academic Librarianship, 46(1), 102097. https://doi.org/10.1016/j.acalib.2019.102097

Paramasivam, S. (2016, May 11). 85% of Malaysian Businesses Feel the Need For a Modern Data Culture - Yet 44% Have a Limited Digital Strategy in Place. Microsoft Malaysia News Center. https://news.microsoft.com/en-my/2016/05/11/85-malaysian-businesses-feel-need-modern-data culture-yet-44-limited-digital-strategy-place/#_ftn1.

Pencheva, I., Esteve, M., & Mikhaylov, S. J. (2020). Big Data and AI - A transformational shift for government: So, what next for research? Public Policy and Administration, 35(1), 24-44. https://doi.org/10.1177/0952076718780537

Queiroz, M. M., & Farias, S. C. (2019). Intention to adopt big data in supply chain management: A Brazilian perspective. RAE-Revista de Administração de Empresas, 59(6), 389-401. https://doi.org/10.1590/s0034-759020190605

Queiroz, M. M., & Wamba, S. F. (2019). Blockchain adoption challenges in supply chain: An empirical investigation of the main drivers in India and the USA. International Journal of Information Management, 46, 70-82. https://doi.org/10.1016/j.ijinfomgt.2018.11.021

Queiroz, M. M., & Telles, R. (2018). Big data analytics in supply chain and logistics: an empirical approach, The International Journal of Logistics Management, 29(2), 767-783. https://doi.org/10.1108/IJLM-05-2017-0116

Rai, R. S., & Selnes, F. (2019). Conceptualizing task-technology fit and the effect on adoption-a case study of a digital textbook service. Information & Management, 56(8), 103161. https://doi.org/10.1016/j.im.2019.04.004

Raja Mohd Ali, R. H., Mohamad, R., & Sudin, S. (2016). A proposed framework of big data readiness in public sectors. AIP Conference Proceedings, 1761, 020089. https://doi.org/10.1063/1.4960929

Rialti, R. & Zollo, L., Ferraris, A. & Alon, I. (2019). Big data analytics capabilities and performance: Evidence from a moderated multi-mediation model. Technological Forecasting and Social Change, 149, 119781. https://doi.org/10.1016/j.techfore.2019.119781

Said, G. R. E. (2015). Understanding knowledge management system antecedents of performance impact: Extending the task-technology fit model with intention to share knowledge construct. Future Business Journal, 1(1-2), 75-87. https://doi.org/10.1016/j.fbj.2015.11.003

Sam, K. M., & Chatwin, C. R. (2018). Understanding Adoption of Big Data Analytics in China: From Organizational Users Perspective. 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 507-510. https://doi.org/10.1109/IEEM.2018.8607652

Sbaffi, L., & Rowley, J. (2017). Trust and Credibility in Web-Based Health Information: A Review and Agenda for Future Research. Journal of Medical Internet Research, 19(6), e218. https://doi.org/10.2196/jmir.7579

Schindler, L. A., Burkholder, G. J., Morad, O. A., & Marsh, C. (2017). Computer-based technology and student engagement: a critical review of the literature. International journal of educational technology in higher education, 14(1), 1-28. https://doi.org/10.1186/s41239-017-0063-0

Schintler, L. A., & Kulkarni, R. (2014). Big Data for Policy Analysis: The Good, the Bad, and the Ugly. Review of Policy Research, 31(4), 343-348. https://doi.org/10.1111/ropr.12079

Schneider, J., Handali, J. P., & vom Brocke, J. (2018, June). Increasing trust in (big) data analytics. In R. Matulevicius & R. Dijkman (Eds.), International Conference on Advanced Information Systems Engineering (pp. 70-84). Springer, Cham. https://doi.org/10.1007/978-3-319-92898-2_6

Schüll, A., & Maslan, N. (2018). On the Adoption of Big Data Analytics: Interdependencies of Contextual Factors. In the Proceedings of the 20th International Conference on Enterprise Information Systems (ICEIS 2018) (pp. 425-431). SCITEPRESS - Science and Technology Publications, Lda. https://doi.org/10.5220/0006759904250431

Shahbaz, M., Gao, C., Zhai, L., Shahzad, F., & Hu, Y. (2019). Investigating the adoption of big data analytics in healthcare: the moderating role of resistance to change. Journal of Big Data, 6(6), 1-20. https://doi.org/10.1186/s40537-019-0170-y

Shahbaz, M., Gao, C., Zhai, L., Shahzad, F., Abbas, A., & Zahid, R. (2020). Investigating the Impact of Big Data Analytics on Perceived Sales Performance: The Mediating Role of Customer Relationship Management Capabilities. Complexity, 2020, 1-17. https://doi.org/10.1155/2020/5186870

Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of big data challenges and analytical methods. Journal of Business Research, 70, 263-286. https://doi.org/10.1016/j.jbusres.2016.08.001

Smithson, M. (2018). Trusted Autonomy Under Uncertainty. In H. A. Abbass, J. Scholz, D. J. Reid (Eds.), Foundations of Trusted Autonomy. Studies in Systems, Decision and Control (pp. 185-201). Springer, Cham. https://doi.org/10.1007/978-3-319-64816-3_10

Spies, R., Grobbelaar, S., & Botha, A. (2020). A Scoping Review of the Application of the Task-Technology Fit Theory. Responsible Design, Implementation and Use of Information and Communication Technology, 12066, 397-408. Springer, Cham. https://doi.org/10.1007/978-3-030-44999-5_33

Stedman, C. (2017). Eyeing the future with predictive analytics can pay dividends now. http://searchbusinessanalytics.techtarget.com/ehandbook/Predictive-data-analytics-advances-businesses-ahead-of-the-game

Tam, C. & Oliveira, T. (2016). Understanding the impact of m-banking on individual performance: DeLone & McLean and TTF perspective. Computers in Human Behavior, 61, 233-244. https://doi.org/10.1016/j.chb.2016.03.016

Talwar, S., Dhir, A., Kaur, P., & Mantymaki, M. (2020). Why do people purchase from online travel agencies (OTAs)? A consumption values perspective. International Journal of Hospitality Management, 88, 102534. https://doi.org/10.1016/j.ijhm.2020.102534

The Multimedia Development Corporation. (MDeC) (2016). MDeC To Make Malaysia Regional Hub for Big Data Analytics. http://smeam.gomalaysia.com.my/en/news/27010

Uddin, M. A., Alam, M. S., Mamun, A. A., Khan, T. U. Z., & Akter, A. (2020). A study of the adoption and implementation of enterprise resource planning (ERP): Identification of moderators and mediator. Journal of Open Innovation: Technology, Market, and Complexity, 6(1), 1-18. https://doi.org/10.3390/joitmc6010002

UNESCO (2017). The Data Revolution in Education. Montreal: UNESCO Institute for Statistics. https://doi.org/10.15220/978-92-9189-213-6-en https://doi.org/10.15220/978-92-9189-213-6-en

Venkatesh, V., & Davis, F. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46, 186-204. https://doi.org/10.1287/mnsc.

Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540

Verma, S., & Chaurasia, S. (2019). Understanding the determinants of big data analytics adoption. Information Resources Management Journal, 32(3), 1-26. https://doi.org/10.4018/IRMJ.2019070101

Vitari, C., & Raguseo, E. (2020). Big data analytics business value and firm performance: linking with environmental context. International Journal of Production Research, 58(18), 5456-5476. https://doi.org/10.1080/00207543.2019.1660822

Vongjaturapat, S. (2018). Application of the Task-Technology Fit Model to Structure and Evaluation of the Adoption of Smartphones for Online Library Systems. Science & Technology Asia, 23(1), 39-56. https://ph02.tci-thaijo.org/index.php/SciTechAsia/article/view/124831

Wahab, S. N., Olugu, E. U., Lee, W. C., & Tan, S. Y. (2018, November 15-16). Big data analytics adoption in Malaysia warehousing industry. The 32nd International Business Information Management Association Conference (IBIMA) (pp. 2349-2365). Seville, Spain.

Walker, R., & Brown, I. (2019). Big data analytics adoption: A case study in a large South African telecommunications organisation. South African Journal of Information Management, 21(1), 1-10. https://doi.org/10.4102/sajim.v21i1.1079

Wang, S. J., & Moriarty, P. (2018). Barriers to the Implementation of Big Data. In Big Data for Urban Sustainability (pp. 65-80). Springer, Cham. https://doi.org/10.1007/978-3-319-73610-5_4

Wang, S. L., & Lin, H. I. (2019). Integrating TTF and IDT to evaluate user intention of big data analytics in mobile cloud healthcare system. Behaviour & Information Technology, 38(9), 974-985. https://doi.org/10.1080/0144929X.2019.1626486

Wayne, M. (2018). Assessment of Factors Influencing Intent-to-Use Big Data Analytics in an Organization: A Survey Study. [Doctoral dissertation, Nova Southeastern University]. NSUWorks. https://nsuworks.nova.edu/gscis_etd/1054/

Alabdallat, W. I. M. (2020). Toward a mandatory public e-services in Jordan. Cogent Business & Management, 7(1), 1727620. https://doi.org/10.1080/23311975.2020.1727620

Woolley, J. P. (2019). Trust and Justice in Big Data Analytics: Bringing the Philosophical Literature on Trust to Bear on the Ethics of Consent. Philosophy & Technology, 32(1), 111-134. https://doi.org/10.1007/s13347-017-0288-9

World Bank Group. (2017). The Malaysia Development Experience Series: Open Data Readiness Assesment. https://documents1.worldbank.org/curated/en/529011495523087262/pdf/115192-WP-PUBLIC-MALAYSIA-DEVELOPMENT-EXPERIENCE-SERIES.pdf

Wu, B., & Chen, X. (2017) Continuance Intention to Use MOOCs: Integrating the Technology Acceptance Model (TAM) and Task Technology Fit (TTF) Model. Computers in Human Behavior, 67, 221-232. https://doi.org/10.1016/j.chb.2016.10.028

Yang, H. H., Feng, L., & MacLeod, J. (2019). Understanding College Students' Acceptance of Cloud Classrooms in Flipped Instruction: Integrating UTAUT and Connected Classroom Climate. Journal of Educational Computing Research, 56(8), 1258-1276. https://doi.org/10.1177/0735633117746084

Yoo, S. K., & Kim, B. Y. (2019). The effective factors of cloud computing adoption success in organization, Journal of Asia Finance, Economics and Business, 6(1), 215-227. https://doi.org/10.13106/jafeb.2019.vol6.no1.217

Yu, S. & Lee, J. (2019). The effects of consumers' perceived values on intention to purchase upcycled products. Sustainability, 11, 1034. https://doi.org/10.3390/su11041034

Yu, C.-S., (2012). Factors affecting individuals to adopt mobile banking: empirical evidence from the UTAUT model. Journal of Electronic Commerce Research, 13(2), 104-121.

Yunus, Y. (2018, November). Harnessing Data Science for Data Driven Public Service Delivery. 6th Malaysia Statistics Conference, Kuala Lumpur, Malaysia.

Zaini, M. K., Masrek, M. N., Abdullah Sani, M. K. J. (2020). The impact of information security management practices on organisational agility. Information and Computer Security, 28(5), 681-700. https://doi.org/10.1108/ICS-02-2020-0020

Zhou, T. (2013). An empirical examination of continuance intention of mobile payment services. Decision Support Systems, 54(2), 1085-1091. https://doi.org/10.1016/j.dss.2012.10.034

Zhou, T., Lu, Y., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in human behaviour, 26(4), 760-767. https://doi.org/10.1016/j.chb.2010.01.013

Zhuang, Y., Wu, F., Chen, C., & Pan, Y. (2017). Challenges and opportunities: from big data to knowledge in AI 2.0. Frontiers of Information Technology & Electronic Engineering, 18(1) 3 - 14. https://doi.org/10.1631/FITEE.1601883

How to Cite
Mad Khir Johari Abdullah Sani, Muhamad Khairulnizam Zaini, Noor Zaidi Sahid, Norshila Shaifuddin, Tamara Adriani Salim, & Noorazah Md. Noor. (2021). Factors Influencing Intent to Adopt Big Data Analytics in Malaysian Government Agencies. International Journal of Business and Society, 22(3), 1315-1345. https://doi.org/10.33736/ijbs.4304.2021