Promoting a Sustainable Academic–Correctional Health Partnership: Lessons for Systemic Action Research

System Action Learning: Reorientating Practice for System Change in Preventive Health

It is now widely accepted that many of the problems we face in public health are complex, from chronic disease to COVID-19. To grapple with such complexity, researchers have turned to both complexity science and systems thinking to better understand the problems and their context. Less work, however, has focused on the nature of complex solutions, or intervention design, when tackling complex problems. This paper explores the nature of system intervention design through case illustrations of system action learning from a large systems level chronic disease prevention study in Australia. The research team worked with community partners in the design and implementation of a process of system action learning designed to reflect on existing initiatives and to reorient practice towards responses informed by system level insights and action. We were able to observe and document changes in the mental models and actions of practitioners and in doing so shine a light on what may be possible once we turn our attention to the nature and practice of system interventions.

Absorptive capacity, technological innovation, and product life cycle: a system dynamics model

Background

While past research has recognized the importance of the dynamic nature of absorptive capacity, there is limited knowledge on how to generate a fair and comprehensive analytical framework. Based on interviews with 24 Chinese firms, this study develops a system-dynamics model that incorporates an important feedback loop among absorptive capacity, technological innovation, and product life cycle (PLC).

Results

The simulation results reveal that (1) PLC affects the dynamic process of absorptive capacity; (2) the absorptive capacity of a firm peaks in the growth stage of PLC, and (3) the market demand at different PLC stages is the main driving force in firms’ technological innovations. This study also explores a sensitivity simulation using the variables of (1) time spent in founding an external knowledge network, (2) research and development period, and (3) knowledge diversity. The sensitivity simulation results show that the changes of these three variables have a greater impact on absorptive capacity and technological innovation during growth and maturity stages than in the introduction and declining stages of PLC.

Conclusions

We provide suggestions on how firms can adjust management policies to improve their absorptive capacity and technological innovation performance during different PLC stages.