바로가기메뉴

본문 바로가기 주메뉴 바로가기

logo

  • E-ISSN2233-5382
  • KCI

Facilitating Conditions in Adopting Big Data Analytics at Medical Aid Organizations in South Africa

The Journal of Industrial Distribution & Business / The Journal of Industrial Distribution & Business, (E)2233-5382
2022, v.13 no.11, pp.1-10
https://doi.org/https://doi.org/10.13106/jidb.2022.vol13.no11.1
VELA, Junior Vela
SUBRAMANIAM, Prabhakar Rontala
OFUSORI, Lizzy Oluwatoyin

Abstract

Purpose: This study measures the influence of facilitating conditions on employees' attitudes towards the adoption of big data analytics by selected medical aid organizations in Durban. In the health care sector, there are various sources of big data such as patients' medical records, medical examination results, and pharmacy prescriptions. Several organizations take the benefits of big data to improve their performance and productivity. Research design, data, and methodology: A survey research strategy was conducted on some selected medical aid organizations. A non-probability sampling and the purposive sampling technique were adopted in this study. The collected data was analysed using version 23 of Statistical Package for Social Science (SPSS) Results: the results show that the "facilitating conditions" have a positive influence on employees' attitudes in the adoption of big data analytics Conclusions: The findings of this study provide empirical and scientific contributions of the facilitating conditions issues regarding employee attitudes toward big data analytics adoption. The findings of this study will add to the body of knowledge in this field and raise awareness, which will spur further research, particularly in developing countries.

keywords
Big Data Analytics, Facilitating Conditions, Medical Aid, Employees Perception

Reference

1.

Agarwal, R., & Prasad, J. (1997). The Role of Innovation Characteristics and Perceived Voluntariness in the Acceptance of Information Technoin the Acceptance of Information Technologies. Decision Sciences, 28(3), 557-582.

2.

Akbar, F. (2013). What affects students’ acceptance and use of technology? Honours thesis, Dietrich College, Carnegie Mellon University, Pennsylvania, USA.

3.

Anshari, M., Almunawar, M. N., Lim, S. A., & Al-Mudimigh, A.(2019). Customer relationship management and big data enabled: Personalization & customization of services. Applied Computing and Informatics, 15(2), 94-101.

4.

Bag, S., Pretorius, J. H. C., Gupta, S., & Dwivedi, Y. K. (2021). Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities. Technological Forecasting and Social Change, 163(2021), 120420. https://doi.org/10.1016/j.techfore.2020.120420

5.

Brock, V., & Khan, H. U. (2017). Big data analytics: does organizational factor matters impact technology acceptance? Journal of Big Data, 4(21), 1-28.

6.

Cabrera-Sanchez, J.-P., & Villarejo-Ramos, A. F. (2019). Factors affecting the adoption of big data analytics in companies. Revista de Administração de Empresas, 59(6), 415-429.

7.

Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165-1188

8.

Cox, M., & Ellsworth, D. (1997). Managing big data for scientific visualization. Paper presented at the ACM Siggraph. 97(1), 21-38.

9.

Cunningham, E. (2021). Artificial intelligence-based decision making algorithms, sustainable organizational performance, and automated production systems in big data-driven smart urban economy. Journal of Self-Governance and Management Economics, 9(1), 31-41.

10.

Dai, H.-N., Wang, H., Xu, G., Wan, J., & Imran, M. (2019). Big data analytics for manufacturing internet of things:opportunities, challenges and enabling technologies. Enterprise Information Systems, 14(9-10), 1279-1303.

11.

Dai, H.-N., Wong, R. C.-W., Wang, H., Zheng, Z., & Vasilakos, A. V. (2019). Big data analytics for large-scale wireless networks:Challenges and opportunities. ACM Computing Surveys (CSUR), 52(5), 1-36.

12.

Eyisi, D. (2016). The usefulness of qualitative and quantitative approaches and methods in researching problem-solving ability in science education curriculum. Journal of education and practice, 7(15), 91-100.

13.

Javanmard, H., Iranmanesh, A., & Bastaki, S. B. (2014). New Clothing Adoption in an Islamic Market. The Journal of Industrial Distribution & Business, 5(4), 13-22.

14.

Jubeir, M. B., Ismail, M. A., Kasim, S., & Amnur, H. (2020). Big healthcare data: Survey of challenges and privacy. JOIV:International Journal on Informatics Visualization, 4(4), 184-190.

15.

Kumar, R. (2018). Research methodology: A step-by-step guide for beginners (2nd ed.). London, United Kingdom: Sage publication.

16.

Mahardika, H., Thomas, D., Ewing, M. T., & Japutra, A. (2019). Experience and facilitating conditions as impediments to consumers’ new technology adoption. The International Review of Retail, Distribution and Consumer Research, 29(1), 79-98.

17.

Muni Kumar, N., & Manjula, R. (2014). Role of Big data analytics in rural health care-A step towards svasth bharath. International Journal of Computer Science and Information Technologies, 5(6), 7172-7178.

18.

Okuyucu, A., & Yavuz, N. (2020). Big data maturity models for the public sector: a review of state and organizational level models. Transforming Government: People, Process and Policy, 14(4), 681-699.

19.

Pantano, E., Giglio, S., & Dennis, C. (2020). Integrating Big Data Analytics Into Retail Services Marketing Management: The Case of a Large Shopping Center in London, UK. In S. Dadwal (Ed.), Handbook of Research on Innovations in Technology and Marketing for the Connected Consumer (pp. 205-222), London, United Kingdom: IGI Global. https://doi.org /10.4018/978-1-7998-0131-3.ch010

20.

Peñarroja, V., Sánchez, J., Gamero, N., Orengo, V., & Zornoza, A. M. (2019). The influence of organisational facilitating conditions and technology acceptance factors on the effectiveness of virtual communities of practice. Behaviour &Information Technology, 38(8), 845-857.

21.

Ragab, M. A., & Arisha, A. (2018). Research methodology in business: A starter’s guide. Management and Organizational Studies, 5(1), 1-14.

22.

Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health information science and systems, 2(1), 1-10.

23.

Simsek, Z., Vaara, E., Paruchuri, S., Nadkarni, S., & Shaw, J. D. (2019). New ways of seeing big data. Academy of Management Journal, 62(4), 971-978.

24.

Singh, C. S., & Gupta, D. (2019). Handling Enormous Informationin Healthcare: A Study on Big Data Analysis in Healthcare Applications. Journal of Innovation in Computer Science and Engineering, 9(1), 24-28.

25.

Taiwo, A. A., & Downe, A. G. (2013). the theory of user acceptance and use of technology (utaut): a meta-analytic review of empirical findings. Journal of Theoretical & Applied Information Technology, 49(1), 48-58.

26.

Rehman, M. H., Yaqoob, I., Salah, K., Imran, M., Jayaraman, P. P., & Perera, C. (2019). The role of big data analytics in industrial Internet of Things. Future Generation Computer Systems, 99(2019), 247-259. doi.org/10.1016/j.future.2019.04.020

27.

Vela, V., Subramaniam, P. R., Ofusori, L., & Venugopal, C. (2020). The Influence of Performance Expectancy on Employees’Attitude towards the Adoption of Big Data Analytics by Selected Medical Aid Organisations in South Africa. International Journal of Advanced Science and Technology, 29(3), 7466-7480.

28.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 27(3), 425-478.

29.

Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly, 36(1), 157-178.

30.

Verkijika, S. F. (2018). Factors influencing the adoption of mobile commerce applications in Cameroon. Telematics and Informatics, 35(6), 665-1674.

31.

Verma, S., & Bhattacharyya, S. S. (2017). Perceived strategic value-based adoption of big data analytics in emerging economy. Journal of Enterprise Information Management. 30(3), 354-382.

32.

Von Hippel, P. T. (2005). Mean, median, and skew: Correcting a textbook rule. Journal of statistics Education, 13(2), 1-13.

33.

Walker, R. S., & 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.

34.

Yadegaridehkordi, E., Hourmand, M., Nilashi, M., Shuib, L., Ahani, A., & Ibrahim, O. (2018). Influence of big data adoption on manufacturing companies' performance: An integrated DEMATEL-ANFIS approach. Technological forecasting and social change, 137(2018), 199-210. https://doi:10.1016/j.techfore.2018.07.043

35.

Zikopoulos, P., Parasuraman, K., Deutsch, T., Giles, J., & Corrigan, D. (2012). Harness the power of big data The IBM big data platform (pp. 3-247), New York, USA: McGraw-Hill Professional.

36.

Zhou, L. L., Owusu-Marfo, J., Antwi, H. A., Antwi, M. O., Kachie, A. D. T., & Ampon-Wireko, S. (2019). Assessment of the social influence and facilitating conditions that support nurses’adoption of hospital electronic information management systems (HEIMS) in Ghana using the unified theory of acceptance and use of technology (UTAUT) model. BMC medical informatics and decision making, 19(1), 1-9.

The Journal of Industrial Distribution & Business