TY - JOUR
T1 - How Do Innovation Types and Collaborative Modes Drive Firm Performance? An FsQCA Analysis Based on Evidence From Software Ecosystems
AU - Wei, Fenfen
AU - Feng, Nanping
AU - Evans, Richard David
AU - Zhao, Ruxiang
AU - Yang, Shanlin
N1 - Publisher Copyright:
© 1988-2012 IEEE.
PY - 2022/12/1
Y1 - 2022/12/1
N2 - Firm survival in dynamic software markets depends on collaboration with ecosystem complementors and exploitative and/or explorative innovation. Such activities have been widely accepted as crucial antecedents for collaboration success, but few studies have analyzed the configurational effects of collaborative modes effect on firm performance. Based on a sample of 224 software firms and fuzzy set qualitative comparative analysis, results show that four causal configurations equivalently explain high innovation performance, in which exploitation, exploration, and collaboration with academic and governmental institutes are proven to be core causal conditions. Theoretically, this article contributes to the literature on collaborative innovation in terms of causality research and collaborative pathways but also enriches the literature on software ecosystems regarding partnerships and collaborative processes. Practical guidance and flexible choices for software firms to achieve high innovation performance are provided by offering four equivalent configurations.
AB - Firm survival in dynamic software markets depends on collaboration with ecosystem complementors and exploitative and/or explorative innovation. Such activities have been widely accepted as crucial antecedents for collaboration success, but few studies have analyzed the configurational effects of collaborative modes effect on firm performance. Based on a sample of 224 software firms and fuzzy set qualitative comparative analysis, results show that four causal configurations equivalently explain high innovation performance, in which exploitation, exploration, and collaboration with academic and governmental institutes are proven to be core causal conditions. Theoretically, this article contributes to the literature on collaborative innovation in terms of causality research and collaborative pathways but also enriches the literature on software ecosystems regarding partnerships and collaborative processes. Practical guidance and flexible choices for software firms to achieve high innovation performance are provided by offering four equivalent configurations.
KW - Collaborative mode
KW - exploitation
KW - exploration
KW - fuzzy-set qualitative comparative analysis (fsQCA)
KW - innovation ambidexterity
KW - software ecosystems (SECOs)
UR - https://www.scopus.com/pages/publications/85115162927
U2 - 10.1109/TEM.2021.3102321
DO - 10.1109/TEM.2021.3102321
M3 - 文章
AN - SCOPUS:85115162927
SN - 0018-9391
VL - 69
SP - 3648
EP - 3659
JO - IEEE Transactions on Engineering Management
JF - IEEE Transactions on Engineering Management
IS - 6
ER -