BUSINESS ETHICS, CORPORATE RESPONSIBILITY, AND STAKEHOLDER JUSTICE

The central difficulty of business ethics is not the absence of moral vocabulary but the exercise of judgment where incentives, rights, evidence and institutional power conflict. This chapter develops a critical, cross-functional analysis of business ethics, corporate responsibility and stakeholder justice for enterprises operating under heterogeneous economic and regulatory conditions. Through a transparently selective synthesis of seminal normative philosophy, stakeholder scholarship, organizational-behaviour research, international responsible-business standards and official Rwandan secondary sources, it examines twelve domains: moral concepts; rival ethical traditions; stakeholder obligations; corporate purpose and governance; decision processes; corruption; employment and market justice; data and artificial intelligence; intergenerational responsibility; ethical culture; cross-jurisdictional duties; and emerging dilemmas. A substantial hypothetical Rwandan procurement-and-labour case tests an issue-to-accountability protocol using explicit assumptions, numerical trade-offs, rights constraints, independent challenge and remediation. Rwanda’s 2024 enterprise survey, 2026 labour statistics, official anti-corruption and privacy instruments, and comparative international guidance provide institutional context without implying firm-level causal effects. Original educational instruments include a normative theory matrix, stakeholder-claim register, decision-flow diagram, misconduct indicator dictionary and analytical marking rubric. The chapter argues that defensible enterprise ethics requires neither uncritical shareholder primacy nor unspecified balancing of every preference. It requires justified priorities, non-negotiable protections, credible evidence, contestable decisions, proportionate remedies and organizational arrangements that make moral accountability operational. The synthesis is conceptual and pedagogical, not an empirically validated causal model.