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

View Article: PubMed Central - PubMed

ABSTRACT

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.

No MeSH data available.


The tendencies of the main variables in the system
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Fig6: The tendencies of the main variables in the system

Mentions: This paper adopts a long-term perspective (a short-term perspective is not possible) to investigate the dynamic nature of the new-product diffusion process (Cui et al. 2011b), and in particular, introducing PLC. In this study, a month is the simulation time unit, and the total simulation time consists of 240 months. The total simulation period is divided into four stages: introduction (from month 1 to month 60), growth (from month 61 to month 120), maturity (from month 121 to month 180), and decline (from month 181 to month 240). Figure 6 shows the simulation results for the six main variables under the different stages of PLC.Fig. 6


Absorptive capacity, technological innovation, and product life cycle: a system dynamics model
The tendencies of the main variables in the system
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5037108&req=5

Fig6: The tendencies of the main variables in the system
Mentions: This paper adopts a long-term perspective (a short-term perspective is not possible) to investigate the dynamic nature of the new-product diffusion process (Cui et al. 2011b), and in particular, introducing PLC. In this study, a month is the simulation time unit, and the total simulation time consists of 240 months. The total simulation period is divided into four stages: introduction (from month 1 to month 60), growth (from month 61 to month 120), maturity (from month 121 to month 180), and decline (from month 181 to month 240). Figure 6 shows the simulation results for the six main variables under the different stages of PLC.Fig. 6

View Article: PubMed Central - PubMed

ABSTRACT

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.

No MeSH data available.