The Influence of AI Governance Ethics and Algorithmic Transparency on Employee Trust and Responsible Use of AI-Powered Performance Management Systems: Evidence from Three Digital Loan Firms in Lagos, Nigeria

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This study examines how AI governance ethics and algorithmic transparency shape employee trust in, and responsible use of, AI-powered performance management systems (AI-PMS), and whether AI literacy strengthens the transparency–trust link. A convergent parallel mixed-methods design was applied across three CBN-licensed digital lenders in Lagos, Nigeria (Branch International Financial Services, FairMoney Microfinance Bank and Carbon Finance and Investments). From a population of 1,610 employees, 181 were drawn by proportionate stratified random sampling: 163 received a structured questionnaire and 18 took part in semi-structured interviews. Quantitative data were analysed with composite-based path modelling and 5,000-sample bootstrapping; interviews were analysed thematically. Governance ethics (β = .235) and algorithmic transparency (β = .394) both raised employee trust, and trust in turn predicted responsible use (β = .418). Transparency had no significant direct effect on responsible use once trust was modelled; its influence travelled through trust. AI literacy amplified the transparency–trust effect. The study proposes and tests the GET-RU model, which treats governance ethics and transparency as two justice mechanisms that build trust and, through trust, responsible use, in a Global South setting where formal AI regulation remains weak. Responsible use of AI performance systems depends on employee trust, which firms build through fair, accountable, privacy-protecting and explainable governance and through transparent scoring. Transparency works best when employees are AI-literate, so digital lenders should pair clear explanations of scores with AI literacy training. Keywords: AI governance ethics; algorithmic transparency; trust in technology; responsible use; AI literacy; performance management; digital lending; Nigeria