Human resource management and compliance are not peripheral administrative functions — they are strategic imperatives that shape organizational capability, culture, and risk profile. At the doctoral level, HR and compliance problems are examined through the lens of evidence-based management: not what has traditionally been done or what consultants recommend, but what the scholarly literature demonstrates about how people-related decisions affect organizational performance, legal liability, and ethical standing. DB8410 develops the capacity to analyze HR and compliance challenges with this combination of scholarly rigor and practical relevance.
Human resource management: scholarly frameworks
Evidence-based HR for doctoral-level analysis
- Strategic HRM and the HR-performance link: DB8410 examines the strategic HRM literature — the body of research examining how HR practices collectively influence organizational performance. The resource-based view of the firm (Barney, 1991) provides the theoretical foundation for strategic HRM: if human capital (the collective knowledge, skills, abilities, and experience of the workforce) is valuable, rare, inimitable, and non-substitutable, then effective HR practices that develop and retain this human capital can generate sustainable competitive advantage. Becker, Huselid, and Ulrich's (2001) HR Scorecard model connects HR architecture (HR practices, capabilities, and processes) to strategic execution and ultimately firm performance. The "high performance work systems" (HPWS) research stream identifies bundles of mutually reinforcing HR practices (rigorous selection, extensive training, performance-based pay, employee participation, information sharing, internal promotion) that collectively produce higher employee productivity, lower turnover, and better firm performance than the individual practices would separately
- Talent management in the digital economy: The course examines talent management as an increasingly critical leadership challenge in an economy where digital skills are scarce, half-life rapidly, and where AI tools are reshaping what human skills are valuable. Talent management encompasses the integrated HR processes of identifying high-potential employees, accelerating their development, managing their careers within the organization, and retaining them against external competition. The re-skilling and upskilling challenge — developing employees whose current skills are becoming obsolete while simultaneously developing the new skills that evolving organizational needs require — has emerged as one of the defining HR challenges of the 2020s, and DB8410 examines the evidence on reskilling program design and effectiveness
- Workforce analytics: The course examines the growing application of data analytics to human resource decisions — people analytics, HR analytics, or workforce analytics — the use of employee data to inform talent acquisition, development, retention, and performance management decisions. Research on workforce analytics examines both its potential (evidence-based hiring that reduces unconscious bias; early identification of flight risk enabling proactive retention interventions; learning analytics that identify which training interventions actually improve performance) and its risks (algorithmic bias that perpetuates or amplifies historical discrimination; privacy concerns about employee data collection; the ethical implications of using predictive analytics in employment decisions)
Business law for organizational leaders
DB8410 examines business law in the employment context — the legal frameworks within which HR decisions are made and the legal risks that HR problems can create. Employment discrimination law forms the foundation: Title VII of the Civil Rights Act (prohibiting discrimination based on race, color, religion, sex, and national origin), the Age Discrimination in Employment Act, the Americans with Disabilities Act, and their state-law equivalents create significant legal exposure for employment decisions that cannot be justified on legitimate business grounds. The course examines how discrimination law applies to the full range of HR decisions — hiring, promotion, compensation, performance management, and termination — and how evidence-based HR practices both improve organizational performance and reduce legal exposure by creating documented, consistent, objective processes for people decisions. The course also examines wage and hour law (FLSA minimum wage and overtime requirements, state wage laws, misclassification risks for gig workers and independent contractors), workplace safety law (OSHA requirements and their liability implications), employee privacy law (limits on employer monitoring of employee communications and activities), and the legal dimensions of cross-border HR in global organizations (employment law varies dramatically across jurisdictions, creating compliance complexity for multinational organizations).
Business ethics and value-based compliance
DB8410 examines business ethics and compliance through both rule-based and value-based frameworks — recognizing that compliance-as-minimum-standard and ethics-as-aspiration are different but complementary approaches to organizational integrity. Rule-based compliance (or "compliance by rule") focuses on adherence to external requirements — laws, regulations, standards, and contractual obligations. The compliance function in large organizations typically manages this: identifying applicable requirements, establishing internal policies and procedures that meet those requirements, training employees on compliance obligations, monitoring compliance behavior, and reporting compliance performance to governance bodies. Value-based compliance (or "ethics by values") focuses on embedding organizational values and ethical principles deeply enough that employees make the right decisions even when no specific rule applies — the behavioral ethics complement to rule-based compliance. DB8410 examines the scholarly and practitioner evidence on what factors predict organizational ethical failure: organizational culture characteristics (normalization of questionable practices, pressure to meet financial targets at any cost, leadership that signals that ethics is secondary to results), individual characteristics (moral disengagement, authority orientation, peer pressure), and structural factors (incentive systems that reward short-term results without regard to how they are achieved, inadequate ethics reporting channels). The course develops analytical competence to diagnose ethics and compliance vulnerabilities in organizations and to design interventions that address both the behavioral and structural dimensions of organizational integrity.
Secondary data analysis for HR and compliance problems
DB8410 develops competency in using secondary data — data collected for other purposes but reanalyzed to address specific research questions — to investigate HR and compliance problems. Secondary data sources for HR and compliance analysis include: publicly available government datasets (Bureau of Labor Statistics occupational employment and wage data; EEOC charge data; OSHA inspection and citation data); industry association workforce surveys; academic research datasets; and organizational data extracted from internal HRIS, ATS, LMS, and performance management systems. The course examines how to evaluate secondary data quality (coverage, accuracy, timeliness, consistency, and relevance to the specific research question), how to conduct secondary data analysis appropriately (without overinterpreting correlations as causal relationships, without generalizing findings beyond the scope of the data, and with appropriate acknowledgment of the data's limitations), and how to communicate secondary data findings clearly to organizational decision-makers who need actionable insights rather than statistical complexity.
DB8410 assignments include HR problem analyses, compliance assessments, ethics case analyses, and secondary data reports
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HR problem analyses, compliance assessments, ethics case studies, secondary data reports.
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Frequently asked questions
DB8410 develops the analytical frameworks needed to examine workforce planning in a period of rapid technological change — including the specific challenge of AI and automation's impact on job structures and skill requirements, which has emerged as one of the most pressing HR strategy questions of the current decade. The scholarly evidence on AI and automation's employment effects suggests a more nuanced picture than either the dystopian (mass technological unemployment) or optimistic (technology always creates more jobs than it destroys) narratives that dominate popular discourse. Automation and AI substitute for routine tasks — both cognitive (data processing, rule-based analysis, pattern recognition) and physical (repetitive assembly, material handling, inspection) — while complementing non-routine tasks that require judgment, creativity, emotional intelligence, and complex problem-solving. This task-level substitution is reshaping job structures across industries: some jobs are being eliminated; more jobs are being transformed (with routine components automated and human attention redirected to higher-value non-routine work); and some new jobs are being created (developing, deploying, maintaining, and governing AI systems). For workforce planning, this suggests a need for continuous skill demand forecasting, accelerated re-skilling investment, and workforce transition planning at the organizational level. The scholarly evidence on re-skilling program effectiveness is still developing, but suggests that successful re-skilling requires: sustained investment over a longer time horizon than most organizations commit to; competency-based learning that focuses on building transferable skills rather than technology-specific credentials; combination of formal learning with on-the-job application; and organizational culture change that normalizes continuous learning as a professional expectation rather than an episodic response to technological disruption. DB8410 develops the analytical capacity to apply this evidence to specific organizational workforce planning challenges.