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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 sensitivity simulation results of time spent in founding an external knowledge network
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Fig7: The sensitivity simulation results of time spent in founding an external knowledge network

Mentions: An organization needs external knowledge to arrive at the right time (Cui et al. 2011a). In this study, the time spent in founding an external knowledge network is defined as a time delay from the recognition of a technology gap to taking action in order to find a suitable external knowledge source. This parameter is measured at 6, 12, and 18 months, respectively, and the results are shown in Fig. 7.Fig. 7


Absorptive capacity, technological innovation, and product life cycle: a system dynamics model
The sensitivity simulation results of time spent in founding an external knowledge network
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig7: The sensitivity simulation results of time spent in founding an external knowledge network
Mentions: An organization needs external knowledge to arrive at the right time (Cui et al. 2011a). In this study, the time spent in founding an external knowledge network is defined as a time delay from the recognition of a technology gap to taking action in order to find a suitable external knowledge source. This parameter is measured at 6, 12, and 18 months, respectively, and the results are shown in Fig. 7.Fig. 7

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.