Businesses

In multinational fast-moving consumer goods firms, marketing, legal and IT depend on the same brand data but need it at different levels of detail. Marketing needs it by variant and territory to measure campaign returns; legal to prove valid territorial licensing; IT prefers aggregation to cut cost. No framework names an owner, so IT decides by default, on an objective that excludes the costs it imposes. We model the conflict as a non-cooperative game grounded in property rights and transaction cost economics, show that aggregation is strictly dominant, and explain why negotiation fails: the decisions are frequent and individually small, their value is privately known, and their cost is realised quarters later. A Monte Carlo simulation of 100,000 paired iterations, calibrated on public benchmarks and a listed multinational’s accounts, evaluates two archetypes differing 90-fold in size. Under the assumed parameters, reassigning the decision is worth about half a percent of sales almost regardless of size, of which 91–93 percent comes from restoring lost detail. Centralised governance outperforms negotiation, chargeback and veto rights, and that ordering survives removing the assumed revenue response to detail, which the magnitudes do not. We also supply a protocol for measuring detail already lost.

This paper examines how the Resource-Based View (RBV) and Dynamic Capabilities (DC) explain digital adaptation among rural entrepreneurs in South Africa. Although digital transformation is reshaping entrepreneurship globally, rural enterprises continue to face constraints related to infrastructure, digital skills, finance, and institutional support. This study explores how rural entrepreneurs in the Greater Tzaneen Municipality of Limpopo Province mobilise available resources and develop adaptive capabilities to engage with digital technologies. A qualitative interpretivist design was adopted, with data collected through semi-structured interviews with 22 rural entrepreneurs across multiple sectors. The findings show that digital adaptation in rural areas is incremental, socially embedded, and driven more by necessity than formal strategic planning. Entrepreneurs rely on social capital, informal learning, experimentation, and improvisation to sense opportunities, utilise digital tools, and respond to resource constraints. The study extends RBV by demonstrating how resource scarcity reshapes what entrepreneurs regard as strategically valuable resources. It also contributes to Dynamic Capabilities theory by showing how capabilities emerge informally through social learning, experimentation, and adaptive resource mobilisation. The proposed RBV-DC framework explains how rural entrepreneurs build adaptive resilience by mobilising, combining, and reconfiguring available resources under conditions of scarcity.

Artificial intelligence (AI) is reshaping organizational decision-making, managerial roles, and accountability, yet responsible AI governance remains dominated by principles, technical controls, and regulatory compliance. This conceptual paper develops a human leadership-based framework for translating responsible AI principles into organizational governance. In the framework, the responsible leader is an accountable human organizational actor—such as a board member, senior executive, business leader, or designated decision owner—while AI is treated as a governed technological system or operational agent rather than the accountable leader. Human responsible leadership influences responsible AI governance (RAIG) capability through six mediating organizational processes: stakeholder orientation, ethical sensemaking, accountability architecture, meaningful human oversight, inclusive participation, and long-term value orientation. RAIG capability is defined as the higher-order organizational capacity to institutionalize and adapt lifecycle governance arrangements; it enables observable governance practices, which are conceptually separated from downstream responsible AI outcomes. AI operational autonomy is proposed as a focal moderator: the leadership-to-RAIG pathway should become stronger when AI can initiate or execute consequential actions with limited contemporaneous human approval, particularly where actions are difficult to reverse, trace, or interrupt. The framework offers testable propositions and a research agenda, but it is conceptual and requires empirical validation; it is not a technical safety guarantee.