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Artificial Intelligence in Accounting and Firm Effectiveness Among Manufacturing Companies in Nigeria

Ijeoma Scholastica Omemgbeoji, Dr. Nkechi Ofor Abstrac

Abstract

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.

Keywords

Artificial IntelligenceMachine Learning AutomationRobotic Process

References

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