The Role of AI and Machine Learning in Enhancing Decision-Making and Operational Efficiency in SMEs in Emerging Markets

Purpose: Nevertheless, most SMEs in Nigeria still operate with analogue operations as they have not embraced technological innovations. The purpose of this study will be to examine the impact that AI has brought, specifically machine learning, on the sustainability and operations of SMEs operating in South-East Nigeria. In particular, the study aims to determine the impact that machine learning has on the performance of these enterprises.Research Methodology: The research methodology used in this study was the descriptive method. The target population was 27,546 SMEs operating in Nigeria, which are registered and certified by CAC. Krejcie and Morgan's (1970) formula was used to determine the sample size and established that a sample size of 379 SMEs was required. Self-administered questionnaires were used as the main instrument for data collection; the questionnaires were subjected to content validity tests and inter-item reliability tests. Thus, the reliability coefficient of the instrument was found to be 0.86, which justified the use of the instrument in the study.Results: Descriptive statistics tools, such as simple regression analysis, were used to study the relationship between the variables. The result shows that machine learning has a positive influence on the performance of SMEs in south-east Nigeria.Limitations: One limitation of the study was the use of only inferential statistics, which excluded the use of other advanced statistical analytic approaches. In addition, the study involved the South-East region only; thus, it may not extend the results of the assessment to all SMEs in Nigeria.Contribution: Since AI's impact is highly related to improving SMES' performance, it is crucial to examine the importance of AI-related technologies and stresses on machine learning, especially for SMES' operation. It stated, in essence, the case for AI in the sector to be made much more extensive.Practical Implications: The study suggests that policymakers and industry players proactively encourage practices to ensure SMEs can integrate AI into their operations. This could be done by providing extra bonuses, expanding educational possibilities, and collaboratively creating effective legislation to stimulate innovations and support data protection and security.Novelty: Even though numerous studies have been conducted regarding the effect of AI on SMEs, little research has focused on the relationship between machine learning and the productivity of SMEs in the South-East of Nigeria, hence its significance

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Publication Details

Journal
Physical Education Health and Social Sciences
Published
2025-05-02
DOI
https://doi.org/10.63163/jpehss.v3i2.309
Primary Topic
Impact of AI and Big Data on Business and Society
Type
article
Field-Weighted Citation Impact
0.00
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article

The Role of AI and Machine Learning in Enhancing Decision-Making and Operational Efficiency in SMEs in Emerging Markets

Aliza Tabassam, Muhammad Waqas Qaisar, Rooha Shahid
Physical Education Health and Social Sciences
Impact of AI and Big Data on Business and Society
article

The Role of AI and Machine Learning in Enhancing Decision-Making and Operational Efficiency in SMEs in Emerging Markets

Aliza Tabassam, Muhammad Waqas Qaisar, Rooha Shahid
article en

Abstract

Purpose: Nevertheless, most SMEs in Nigeria still operate with analogue operations as they have not embraced technological innovations. The purpose of this study will be to examine the impact that AI has brought, specifically machine learning, on the sustainability and operations of SMEs operating in South-East Nigeria. In particular, the study aims to determine the impact that machine learning has on the performance of these enterprises.Research Methodology: The research methodology used in this study was the descriptive method. The target population was 27,546 SMEs operating in Nigeria, which are registered and certified by CAC. Krejcie and Morgan's (1970) formula was used to determine the sample size and established that a sample size of 379 SMEs was required. Self-administered questionnaires were used as the main instrument for data collection; the questionnaires were subjected to content validity tests and inter-item reliability tests. Thus, the reliability coefficient of the instrument was found to be 0.86, which justified the use of the instrument in the study.Results: Descriptive statistics tools, such as simple regression analysis, were used to study the relationship between the variables. The result shows that machine learning has a positive influence on the performance of SMEs in south-east Nigeria.Limitations: One limitation of the study was the use of only inferential statistics, which excluded the use of other advanced statistical analytic approaches. In addition, the study involved the South-East region only; thus, it may not extend the results of the assessment to all SMEs in Nigeria.Contribution: Since AI's impact is highly related to improving SMES' performance, it is crucial to examine the importance of AI-related technologies and stresses on machine learning, especially for SMES' operation. It stated, in essence, the case for AI in the sector to be made much more extensive.Practical Implications: The study suggests that policymakers and industry players proactively encourage practices to ensure SMEs can integrate AI into their operations. This could be done by providing extra bonuses, expanding educational possibilities, and collaboratively creating effective legislation to stimulate innovations and support data protection and security.Novelty: Even though numerous studies have been conducted regarding the effect of AI on SMEs, little research has focused on the relationship between machine learning and the productivity of SMEs in the South-East of Nigeria, hence its significance

Physical Education Health and Social SciencesVol. 3(2)
Allama Iqbal Open University (PK), Universitas Muhammadiyah Jember (ID), Department of Higher Education (IN)
Openalex Percentile: Top 88%
Impact of AI and Big Data on Business and Society
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