IJLTEMAS International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS) IJLTEMAS 2278-2540 IJLTEMAS 449 10.51583/IJLTEMAS.2026.150900100 Supply Chain Management Effect of Artificial Intelligence and Supply Chain Technology on Firm Value of Selected Consumer Goods Manufacturing Firms in Nigeria Abel Ishola OLADEJI, Bursary Department Oyo State College of Agriculture and Technology Igboora, Nigeria Nigeria Adebimpe Oke Margret Department of Economics, Ajayi Crowder University Oyo, Nigeria Nigeria Olaniyi DADA, Department of Business Administration and Management Oyo State College of Agriculture and Technology Igboora, Nigeria Nigeria 15 9 2277 2286 30 09 2026 05 10 2026 17 10 2026 Artificial intelligence; supply-chain technology; firm value; Tobin's Q; consumer goods; manufacturing firms; Nigeria; technology adoption; panel data. Abstract

The rapid diffusion of artificial intelligence (AI), digital analytics, automation and smart supply-chain technologies is transforming the manner in which manufacturing firms forecast demand, manage inventories, coordinate suppliers, optimise production and create shareholder value. Despite the increasing strategic importance of these technologies, empirical evidence on their relationship with firm value in Nigeria remains limited, particularly within the consumer-goods manufacturing sector. This study investigates the effect of artificial intelligence and supply-chain technology on the firm value of selected listed consumer-goods manufacturing firms in Nigeria over the period 2015–2024. The study adopts an ex-post facto research design and uses secondary data extracted from audited annual reports, corporate reports and Nigerian Exchange market information. Top Ten listed consumer-goods manufacturing firms constituted the sample. From correlation and regression anlysis, Supply Chain Technology was found to have a negative and statistically significant relationship with Tobin's Q (β = -0.101, p = 0.016), while Artificial Intelligence had a positive but statistically insignificant relationship with Tobin's Q (β = 0.651, p = 0.410). These findings suggest that the relationship between technological adoption and market valuation may be more complex than a simple assumption that greater technology adoption automatically results in higher firm value. It was therefore recommended that Consumer manufacturing firms should not view the acquisition of Supply Chain Technology or Artificial Intelligence as an end in itself. Rather, management should evaluate technology investments based on their expected contribution to operational efficiency, cost reduction, revenue growth, customer responsiveness, and ultimately firm value.

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