JOURNAL ARTICLE

Exploring the Success Factors of Workplace Innovation: A Qualitative Study Based on Multiple Case Study

Abstract

This study investigates the paradox of workplace innovation, wherein two inherently competing objectives, productivity enhancement and quality of working life (QWL), must be pursued simultaneously. While prior research has largely focused on how workplace innovation can be designed and formulated, this study shifts attention to the organizational tensions that emerge during implementation. To elucidate these paradoxical dynamics, we conducted a qualitative multiple case study of 13 Korean firms across diverse industries. Data were gathered through semi-structured, in-depth interviews supplemented by written surveys, covering six thematic areas: innovation background and goals, specific initiatives, change management, impacts, feedback and refinement, and future plans. Comparative analysis of the cases revealed that prioritizing productivity alone often undermines employee well-being, satisfaction, and retention, whereas an exclusive emphasis on QWL can constrain productivity gains. Successful resolution of this paradox requires fostering employee autonomy and active participation. Key practices include establishing employee- led innovation mechanisms, granting discretion for problem-solving, ensuring visible leadership commitment, appointing dedicated change agents, and maintaining open, ongoing communication and feedback loops. By foregrounding the interplay between technological systems and human agency, this study advances understanding of sustainable workplace innovation. The findings provide practical guidance for developing integrated change strategies that reconcile competing organizational goals, bridge the gap between formal adoption and effective execution, and sustain meaningful innovation over the long term.

Keywords:
Qualitative research Business Case study research Knowledge management Marketing Process management Sociology Computer science Social science

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Topics

Organizational and Employee Performance
Physical Sciences →  Computer Science →  Artificial Intelligence
Technology Adoption and User Behaviour
Social Sciences →  Decision Sciences →  Information Systems and Management
Impact of AI and Big Data on Business and Society
Social Sciences →  Decision Sciences →  Management Science and Operations Research

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