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Author(s): Ifekanandu Chukwudi Christian (Ph.D.).

Volume/Issue: Volume 5, Issue 2 (2025)

Page No: 1-18

Journal: British Journal of Library and Information Management (BJLIM)

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Abstract:

This study examined artificial intelligence adoption and operational efficiency of international shipping companies in South-West Nigeria. The study adopted the correlational research design, the quantitative research approach and the positivist research philosophy. The population of this study comprised 12 international shipping companies registered with the Nigerian Shipping Council in the South-West Geopolitical Zone of Nigeria. The census sampling technique was adopted where all the population was used as the sample size for the study. The unit of analysis consisted of managers of international shipping companies in South-West Nigeria. Data were collected from the respondents using a structured questionnaire. The data collected were analyzed statistically while the Pearson Product Moment Correlation Coefficient (PPMCC) and the SPSS version 24 were used to test the hypotheses. The findings revealed that machine learning adoption has significant relationship with cost efficiency and time efficiency of international shipping companies in South-West Nigeria. Robotic adoption was also found to have significant relationship with cost efficiency and time efficiency of international shipping companies in South-West Nigeria. The study equally revealed that predictive analytic adoption was significantly related to cost efficiency and time efficiency of international shipping companies in South-West Nigeria. From the findings, it was concluded that the adoption AI technologies such as machine learning, robotics and predictive analytic significantly enhance operational efficiency of international shipping companies in South-West Nigeria. Based on the findings and conclusion, it was recommended that shipping companies in Nigeria should adopt AI technologies such as machine learning, robotics and predictive analytic as it would enhance their operational efficiency.

Keywords:

Artificial intelligence, machine learning, robotics, predictive analytics, operational efficiency, cost efficiency and time efficiency.

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