Designing management & control systems for Performance, Well-Being & Ethics
Organizations rely on Management Control Systems (MCS) to drive performance, accountability, and strategic alignment. However, traditional control mechanisms often focus narrowly on financial outcomes, productivity, and efficiency, sometimes at the expense of employee well-being, ethical behavior, and workplace justice.
Recent studies highlight how performance-driven control systems can inadvertently lead to negative consequences such as increased stress, burnout, unethical behaviors (e.g., gaming the system, data manipulation), and workplace inequalities. There is a pressing need to rethink and redesign MCS to balance performance objectives with employee well-being and ethical considerations.
This research axis explores how responsible and ethical control systems can be developed to achieve organizational goals without causing harm to employees. We aim to study how MCS can foster a culture of well-being, inclusion, and ethical integrity while maintaining high performance.
How can we redefine performance to include well-being, fairness, and ethical behavior alongside productivity and financial outcomes?
What alternative performance indicators (e.g., psychological safety, ethical behavior metrics, employee engagement) can be integrated into MCS?
How do traditional KPIs (Key Performance Indicators) impact employee motivation, mental health, and ethical decision-making?
How do different control systems influence employee stress levels, motivation, and job satisfaction?
What are the unintended consequences of strict performance monitoring on employees' mental health, creativity, and long-term engagement?
Can flexible and participative control mechanisms mitigate stress and promote well-being?
What role do managerial trust and autonomy play in fostering a healthy performance culture?
How do performance management and control systems encourage (or discourage) ethical decision-making in organizations?
In what ways can algorithmic control (e.g., AI-driven performance monitoring) create ethical dilemmas or reinforce biases?
How can organizations embed ethical considerations into control systems to prevent gaming behaviors, manipulation, and unfair performance evaluations?
What are the best practices for ensuring ethical leadership and accountability within MCS frameworks?
How does the rise of remote and hybrid work challenge traditional performance monitoring and control systems?
What are the effects of algorithmic management, AI-driven surveillance, and digital tracking on employee autonomy, trust, and engagement?
How can organizations design fair, inclusive, and non-intrusive digital control mechanisms?
What role do data ethics play in AI-driven MCS, and how can we ensure transparency and accountability?