INTERNATIONAL JOURNAL OF SOCIAL SCIENCES AND MANAGEMENT RESEARCH (IJSSMR )
E-ISSN 2545-5303
P-ISSN 2695-2203
VOL. 10 NO. 8 2024
DOI: 10.56201/ijssmr.v10.no8.2024.pg30.46
Ijeoma Scholastica Omemgbeoji, Dr. Nkechi Ofor Abstrac
The study examined the influence of Artificial Intelligence in Accounting on firm effectiveness among manufacturing companies in Nigeria. The specific objective was to assess the influence of machine learning automation and robotic process automation on firm effectiveness of manufacturing companies in Nigeria. This study employed a descriptive survey design. The study targeted staff that work in manufacturing companies in Nigeria. Cochran’s formula for determining sample size was employed to calculate the required sample size of 271 respondents from manufacturing companies across Nigeria. Primary data for the study were collected using structured questionnaire administered on the respondents. . Descriptive analysis technique, including frequency distribution, was used to summarize the research questions and present an overview of the respondents' perspectives. Spearman Ranked Order correlation was employed to test the hypotheses. The findings showed that: Machine Learning Automation has a positive influence on the firm effectiveness of manufacturing companies in Nigeria (Correlation Coefficient = 0.586, p-value = 0.000); Robotic Process Automation has a positive influence on the firm effectiveness of manufacturing companies in Nigeria (Correlation Coefficient = 0.504, p-value = 0.000). In conclusion, firms boost their performance and competitiveness by integrating these advanced automation technologies which offer a promising avenue for achieving operational excellence and sustained growth. Therefore, the study recommends that Operations Managers and Accounting Department Heads should deploy Robotic Process Automation tools to automate repetitive tasks such as data entry and transaction processing in order to reduce manual errors, increase efficiency, and allow employees to focus on more strategic activities, thereby enhancing firm effectiveness.
Artificial Intelligence, Machine Learning Automation, Robotic Process
Akanbi, P. A. (2014). Nexus between organizational culture and perceived firm effectiveness
in a manufacturing firm in Nigeria. Nexus, 4.
Badghish, S., & Soomro, Y. A. (2024). Artificial intelligence adoption by SMEs to achieve
sustainable business performance: application of
technology–organization–
environment framework. Sustainability, 16(5), 1864.
Chukwuemeka-Onuzulike, N. (2023). Artificial Intelligence and SMEs Growth in Anambra
State, Nigeria. AMITY BUSINESS REVIEW, 8.
Durão, D., & dos Reis, A. P. (2024). How does robotic process automation create value for
firms?. Information Systems and e-Business Management, 1-20.
Elegunde, A. F., & Oladimeji, I. (2020). Effects of artificial intelligence on business
performance in the banking industry (a study of access bank plc and united bank for
africa-uba). IOSR Journal of Business and Management (IOSR-JBM) Ser. IV, 22(5),
41-49.
Hasan, A. R. (2021). Artificial Intelligence (AI) in accounting & auditing: A Literature
review. Open Journal of Business and Management, 10(1), 440-465.
Hashem, F., & Alqatamin, R. (2021). Role of artificial intelligence in enhancing efficiency of
accounting information system and non-financial performance of the manufacturing
companies. International Business Research, 14(12), 1-65.
Hofmann, P., Samp, C., & Urbach, N. (2020). Robotic process automation. Electronic
markets, 30(1), 99-106.
Idu, M. A., Onodi, B. E., & Nwosu, U. (2022). Artificial Intelligence And Business
Sustainability In Sub-Sahara Africa. JORMASS| Journal of Research in Management
and Social Sciences, 8(2), 64-78.
Ifekanandu, C. C., Ezirim, A. C., & Kingsley, U. A. (2023). Artificial Intelligence Adoption
and MarketingPerformance of Quoted Manufacturing Firms in Nigeria. Int. J. Innov.
Sci. Res. Technol, 8, 1194-1207.
Khaled AlKoheji, A., & Al-Sartawi, A. (2022, May). Artificial intelligence and its impact on
accounting systems. In European, Asian, Middle Eastern, North African Conference on
Management & Information Systems (pp. 647-655). Cham: Springer International
Publishing.
Lasisi, J. O., Shodiya, O. A., & Raji, O. A. (2014). Business Relationships, Capability, and
Firm Effectiveness: A Study of Nigeria Firms. Nigerian Chapter of Arabian Journal of
Business and Management Review, 2(3), 38-48.
Mahesh, B. (2020). Machine learning algorithms-a review. International Journal of Science
and Research (IJSR).[Internet], 9(1), 381-386.
Menon, N. M., & Sujatha, I. (2021, March). Influence of Rogers’ theory of innovation of
diffusion on customer’s purchase intention–a case study of solar photovoltaic panels.
In IOP Conference Series: Materials Science and Engineering (Vol. 1114, No. 1, p.
012059). IOP Publishing.
Michael, O. O., Oluwafunmilayo, B. G., & Oyedepo, O. T. (2023). The adoption and impact
of Internet-based technological innovations on the performance of the industrial cluster
firms. Journal of Economy and Technology.
Mmadubuobi, L. C., Nworie, G. O., & Aziekwe, O. P. (2024). Industry 4.0 and Corporate
Technological Responsibility of Manufacturing Firms in Nigeria.”. Central Asian
Journal of Innovations on Tourism Management and Finance, 5(4), 67-80.
Pap, J., Mako, C., Illessy, M., Kis, N., & Mosavi, A. (2022). Modeling organizational
performance with machine learning. Journal of Open Innovation: Technology, Market,
and Complexity, 8(4), 177.
Seidu, S., Edwards, D. J., Owusu-Manu, D. G., & Buertey, J. I. (2024). An innovative
diffusion-theory based assessment of inherent barriers in urban green drainage
infrastructure systems. Hydrological Sciences Journal, 69(4), 426-437.
Wang, S., Huang, X., Xia, M., & Shi, X. (2024). Does Artificial Intelligence Promote Firms’
Innovation Efficiency: Evidence from the Robot Application. Journal of the
Knowledge Economy, 1-22.
Yadegari, M., Mohammadi, S., & Masoumi, A. H. (2024). Technology adoption: an analysis
of the major models and theories. Technology Analysis & Strategic Management, 36(6), 1096-1110.
Zhou, Z. H. (2021). Machine learning. Springer nature.