Short bio: Rita Bonvicini is a PhD fellow at the Department of Strategy and Innovation. She holds a Master of Science in “Economics and Management of Innovation and Technology” from Bocconi University. Before joining CBS in 2019, Rita worked in the private corporate and banking sector for almost 10 years.
Rita Bonvicini’ s PhD project focuses on the individual-level implications of open innovation. In her fist paper, she studies the effects of open innovation on the wages of R&D workers. On her second project, she is investigating the consequences of open innovation on employees’ well-being. For these studies, Rita mainly uses Danish Registry data from Denmark Statistics (e.g. demographic and labor market data, innovation data and data on prescriptions of stress-related medications). The first paper was presented at SEI (Strategy, Entrepreneurship and Innovation) consortium in Barcelona in 2021, as well as at CCC (Consortium on Competitiveness and Cooperation) in Toronto in 2022.
Areas of interest: Innovation, Diversity, Open Innovation, Collaboration, Employee Wellbeing, Strategic Human Capital, Organizational Change
Job market paper: "Sharing value: How R&D workers benefit from their employer’s open innovation activities"
Abstract: We use a stakeholder bargaining power lens to investigate the effect on employee wages of firm adoption of open innovation. The value derived from this change will differ depending on individual employee characteristics. We suggest that R&D workers are critical for integrating the knowledge gained from collaborative innovation activities, and that they will receive a higher premium compared to other workers. We hypothesize that this effect will vary with the firm’s replacement costs and the employee’s exit costs should an employee decide to leave the firm. We argue that R&D employees’ prior experience of working in “open” firms will increase their replacement costs while the number of alternative job opportunities will lower their exit costs. We use Danish employer-employee linked data to test our predictions.